diff --git a/01_materials/notebooks/06-01-2026.ipynb b/01_materials/notebooks/06-01-2026.ipynb
new file mode 100644
index 000000000..a440ecb8a
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+++ b/01_materials/notebooks/06-01-2026.ipynb
@@ -0,0 +1,784 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c3394c9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import set_config\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5ad6be76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bill_length_mm</th>\n",
+       "      <th>flipper_length_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>39.1</td>\n",
+       "      <td>181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39.5</td>\n",
+       "      <td>186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.3</td>\n",
+       "      <td>195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>36.7</td>\n",
+       "      <td>193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39.3</td>\n",
+       "      <td>190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>55.8</td>\n",
+       "      <td>207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>43.5</td>\n",
+       "      <td>202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>49.6</td>\n",
+       "      <td>193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>50.8</td>\n",
+       "      <td>210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>50.2</td>\n",
+       "      <td>198</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>342 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     bill_length_mm  flipper_length_mm\n",
+       "0              39.1                181\n",
+       "1              39.5                186\n",
+       "2              40.3                195\n",
+       "3              36.7                193\n",
+       "4              39.3                190\n",
+       "..              ...                ...\n",
+       "337            55.8                207\n",
+       "338            43.5                202\n",
+       "339            49.6                193\n",
+       "340            50.8                210\n",
+       "341            50.2                198\n",
+       "\n",
+       "[342 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Output dataframes instead of arrays\n",
+    "set_config(transform_output=\"pandas\")\n",
+    "\n",
+    "penguins = pd.read_csv(\"dataset/penguins.csv\")\n",
+    "penguins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "af9da9c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bill_length_mm</th>\n",
+       "      <th>flipper_length_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>32.1</td>\n",
+       "      <td>172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>59.6</td>\n",
+       "      <td>231</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     bill_length_mm  flipper_length_mm\n",
+       "min            32.1                172\n",
+       "max            59.6                231"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Grabbing the minimum and maximum values for our features \n",
+    "min_max_values = penguins.agg(['min', 'max'])\n",
+    "min_max_values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "94de6ab8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bill_length_mm</th>\n",
+       "      <th>flipper_length_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.884499</td>\n",
+       "      <td>-1.418347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.811126</td>\n",
+       "      <td>-1.062250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.664380</td>\n",
+       "      <td>-0.421277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.324737</td>\n",
+       "      <td>-0.563715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.847812</td>\n",
+       "      <td>-0.777373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>2.178824</td>\n",
+       "      <td>0.433355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>-0.077396</td>\n",
+       "      <td>0.077258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>1.041543</td>\n",
+       "      <td>-0.563715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>1.261662</td>\n",
+       "      <td>0.647013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>1.151602</td>\n",
+       "      <td>-0.207619</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>342 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     bill_length_mm  flipper_length_mm\n",
+       "0         -0.884499          -1.418347\n",
+       "1         -0.811126          -1.062250\n",
+       "2         -0.664380          -0.421277\n",
+       "3         -1.324737          -0.563715\n",
+       "4         -0.847812          -0.777373\n",
+       "..              ...                ...\n",
+       "337        2.178824           0.433355\n",
+       "338       -0.077396           0.077258\n",
+       "339        1.041543          -0.563715\n",
+       "340        1.261662           0.647013\n",
+       "341        1.151602          -0.207619\n",
+       "\n",
+       "[342 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First, lets make a copy of the original data so that we don’t accidentally overwrite it while pre-processing.\n",
+    "\n",
+    "penguins_copy = penguins.copy()\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "standardized_penguins = scaler.fit_transform(penguins_copy)\n",
+    "\n",
+    "# Convert the standardized data back to a DataFrame\n",
+    "standardized_penguins = pd.DataFrame(standardized_penguins, columns=standardized_penguins.columns)\n",
+    "\n",
+    "standardized_penguins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2f36e8ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot for bill_length_mm vs flipper_length_mm\n",
+    "plt.scatter(standardized_penguins['flipper_length_mm'], standardized_penguins['bill_length_mm'])\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.title('Penguin Flipper Length vs Bill Length', fontsize=14)\n",
+    "plt.ylabel('Bill Length (mm)', fontsize=12)\n",
+    "plt.xlabel('Flipper Length (mm)', fontsize=12)\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0d350bcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perform K means clustering\n",
+    "# Step 1. Initialized our model\n",
+    "kmeans = KMeans(n_clusters=5, random_state=123, n_init=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "76788def",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Step 2. Fit the model to our data\n",
+    "clusters = kmeans.fit(standardized_penguins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5088c998",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 4, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 4, 1, 4, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 4, 1,\n",
+       "       1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 4, 1, 4, 1, 1, 1, 4, 1, 4, 1, 4, 1,\n",
+       "       4, 1, 4, 1, 1, 1, 4, 1, 4, 4, 1, 1, 4, 1, 4, 1, 1, 1, 4, 1, 1, 1,\n",
+       "       1, 1, 4, 1, 1, 1, 2, 1, 4, 1, 4, 1, 4, 1, 1, 1, 1, 1, 1, 1, 4, 1,\n",
+       "       4, 1, 4, 1, 4, 1, 1, 1, 4, 1, 1, 1, 4, 1, 4, 1, 4, 1, 2, 1, 4, 1,\n",
+       "       1, 1, 4, 1, 4, 1, 1, 4, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 4, 2, 3, 2,\n",
+       "       3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 3, 3, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 3, 2, 3, 3, 2, 2,\n",
+       "       3, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2,\n",
+       "       3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2,\n",
+       "       3, 2, 3, 2, 3, 3, 3, 0, 3, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3, 3, 2,\n",
+       "       3, 2, 3, 2, 3, 2, 2, 3, 2, 3, 4, 0, 0, 4, 0, 4, 4, 0, 4, 0, 4, 0,\n",
+       "       4, 0, 4, 0, 0, 0, 4, 0, 4, 0, 4, 0, 4, 0, 0, 0, 4, 0, 1, 0, 4, 0,\n",
+       "       0, 0, 0, 0, 4, 0, 2, 4, 0, 4, 0, 0, 0, 2, 0, 0, 0, 0, 4, 0, 4, 0,\n",
+       "       4, 0, 0, 4, 0, 4, 4, 0, 4, 0, 0, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Create scatter plot to show how the data is grouped.\n",
+    "\n",
+    "clusters.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a4479c68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bill_length_mm</th>\n",
+       "      <th>flipper_length_mm</th>\n",
+       "      <th>Cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.884499</td>\n",
+       "      <td>-1.418347</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.811126</td>\n",
+       "      <td>-1.062250</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.664380</td>\n",
+       "      <td>-0.421277</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1.324737</td>\n",
+       "      <td>-0.563715</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.847812</td>\n",
+       "      <td>-0.777373</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>337</th>\n",
+       "      <td>2.178824</td>\n",
+       "      <td>0.433355</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>338</th>\n",
+       "      <td>-0.077396</td>\n",
+       "      <td>0.077258</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>1.041543</td>\n",
+       "      <td>-0.563715</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>1.261662</td>\n",
+       "      <td>0.647013</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>1.151602</td>\n",
+       "      <td>-0.207619</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>342 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     bill_length_mm  flipper_length_mm  Cluster\n",
+       "0         -0.884499          -1.418347        1\n",
+       "1         -0.811126          -1.062250        1\n",
+       "2         -0.664380          -0.421277        4\n",
+       "3         -1.324737          -0.563715        1\n",
+       "4         -0.847812          -0.777373        1\n",
+       "..              ...                ...      ...\n",
+       "337        2.178824           0.433355        0\n",
+       "338       -0.077396           0.077258        4\n",
+       "339        1.041543          -0.563715        0\n",
+       "340        1.261662           0.647013        0\n",
+       "341        1.151602          -0.207619        0\n",
+       "\n",
+       "[342 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# add cluster labels back to our data set\n",
+    "standardized_penguins_with_clusters = standardized_penguins.copy()\n",
+    "standardized_penguins_with_clusters[\"Cluster\"] = clusters.labels_\n",
+    "standardized_penguins_with_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c4329cde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the clusters\n",
+    "plt.scatter(standardized_penguins_with_clusters['flipper_length_mm'], standardized_penguins_with_clusters['bill_length_mm'], c=standardized_penguins_with_clusters['Cluster'])\n",
+    "plt.xlabel('Flipper Length (mm)')\n",
+    "plt.ylabel('Bill Length (mm)')\n",
+    "plt.title('K-Means Clustering of Penguins')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "943f8770",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "91.47610853298526"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.inertia_ #total WSSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "658ba2a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#initialize an empty dataframe\n",
+    "penguin_clust_ks = pd.DataFrame(columns=[\"k\", \"total_wssd\"])\n",
+    "\n",
+    "for k in range (1,11):\n",
+    "    elbow_kmeans = KMeans(n_clusters=k, random_state=123, n_init=10)    #initialized model\n",
+    "    elbow_kmeans.fit(standardized_penguins) #fit model to data\n",
+    "    current_k = pd.DataFrame({\"k\": [k], \"total_wssd\": elbow_kmeans.inertia_})\n",
+    "    penguin_clust_ks = pd.concat([penguin_clst_ks, current_k], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "96b3da63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>k</th>\n",
+       "      <th>total_wssd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>684.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>247.016625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>157.815322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>118.766244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>91.476109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>78.716593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>66.722277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>57.914115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>52.458720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>48.642403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>48.642403</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     k  total_wssd\n",
+       "0    1  684.000000\n",
+       "1    2  247.016625\n",
+       "2    3  157.815322\n",
+       "3    4  118.766244\n",
+       "4    5   91.476109\n",
+       "5    6   78.716593\n",
+       "6    7   66.722277\n",
+       "7    8   57.914115\n",
+       "8    9   52.458720\n",
+       "9   10   48.642403\n",
+       "10  10   48.642403"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "penguin_clust_ks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "aab76bc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the elbow plot\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(penguin_clust_ks['k'], penguin_clust_ks['total_wssd'], marker='o')\n",
+    "plt.xlabel('Number of Clusters (k)')\n",
+    "plt.ylabel('Within-Cluster Sum of Squares (WSSD)')\n",
+    "plt.title('Elbow Plot for K-Means Clustering')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/07-01-2026.ipynb b/01_materials/notebooks/07-01-2026.ipynb
new file mode 100644
index 000000000..eb22f9ace
--- /dev/null
+++ b/01_materials/notebooks/07-01-2026.ipynb
@@ -0,0 +1,1575 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2e848621",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "335df5e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>neighbourhood</th>\n",
+       "      <th>room_type</th>\n",
+       "      <th>accommodates</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.318800e+04</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.335800e+04</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.349000e+04</td>\n",
+       "      <td>Kensington-Cedar Cottage</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.426700e+04</td>\n",
+       "      <td>Kensington-Cedar Cottage</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.625400e+04</td>\n",
+       "      <td>Hastings-Sunrise</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4981</th>\n",
+       "      <td>9.970000e+17</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4982</th>\n",
+       "      <td>9.970000e+17</td>\n",
+       "      <td>South Cambie</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4983</th>\n",
+       "      <td>9.970000e+17</td>\n",
+       "      <td>South Cambie</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4984</th>\n",
+       "      <td>9.970000e+17</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4985</th>\n",
+       "      <td>9.980000e+17</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4986 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                id             neighbourhood        room_type  accommodates  \\\n",
+       "0     1.318800e+04                Riley Park  Entire home/apt             4   \n",
+       "1     1.335800e+04                  Downtown  Entire home/apt             2   \n",
+       "2     1.349000e+04  Kensington-Cedar Cottage  Entire home/apt             2   \n",
+       "3     1.426700e+04  Kensington-Cedar Cottage  Entire home/apt             4   \n",
+       "4     1.625400e+04          Hastings-Sunrise  Entire home/apt             4   \n",
+       "...            ...                       ...              ...           ...   \n",
+       "4981  9.970000e+17                  Downtown  Entire home/apt             4   \n",
+       "4982  9.970000e+17              South Cambie  Entire home/apt             4   \n",
+       "4983  9.970000e+17              South Cambie     Private room             2   \n",
+       "4984  9.970000e+17                  Downtown  Entire home/apt             4   \n",
+       "4985  9.980000e+17         Downtown Eastside  Entire home/apt             2   \n",
+       "\n",
+       "      bathrooms  bedrooms  beds  price  \n",
+       "0           1.0       0.0   2.0     14  \n",
+       "1           1.0       1.0   1.0     20  \n",
+       "2           1.0       1.0   1.0     35  \n",
+       "3           1.0       1.0   2.0     35  \n",
+       "4           1.0       2.0   3.0     36  \n",
+       "...         ...       ...   ...    ...  \n",
+       "4981        1.0       1.0   2.0   3000  \n",
+       "4982        NaN       2.0   NaN   5714  \n",
+       "4983        1.0       1.0   0.0   8495  \n",
+       "4984        NaN       2.0   NaN   9600  \n",
+       "4985        1.0       1.0   0.0   9999  \n",
+       "\n",
+       "[4986 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the Airbnb listings dataset from a CSV file\n",
+    "airbnb = pd.read_csv(\"dataset/listings.csv\")\n",
+    "airbnb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "104cc533",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(249.16526273565984)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate and return the average price from the \"price\" column in the Airbnb dataset\n",
+    "airbnb[\"price\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "79afb59d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(243.025)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#pull out random data\n",
+    "#np.random.seed(100)     #to ensure the random data is reproducable\n",
+    "airbnb.sample(n=40)[\"price\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "596d4cd4",
+   "metadata": {},
+   "source": [
+    "If we comment the np.random seed and re run the sample over and over, the mean price keeps on changing. We are going to repeat this process 20000 times and store the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "25d86e3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_list = [] #create an empty list\n",
+    "for i in range(20_000):\n",
+    "    sample = airbnb.sample(n=40)\n",
+    "    sample = sample.assign(replicate = i)\n",
+    "    sample_list.append(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c6425144",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>neighbourhood</th>\n",
+       "      <th>room_type</th>\n",
+       "      <th>accommodates</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>price</th>\n",
+       "      <th>replicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>322</th>\n",
+       "      <td>7.583961e+06</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2636</th>\n",
+       "      <td>5.272124e+07</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>199</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1255</th>\n",
+       "      <td>2.804928e+07</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3238</th>\n",
+       "      <td>6.800000e+17</td>\n",
+       "      <td>West Point Grey</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>242</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4558</th>\n",
+       "      <td>9.320000e+17</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>467</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>3.812348e+06</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>64</td>\n",
+       "      <td>19999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2808</th>\n",
+       "      <td>5.490000e+17</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>206</td>\n",
+       "      <td>19999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2471</th>\n",
+       "      <td>5.059522e+07</td>\n",
+       "      <td>Grandview-Woodland</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>187</td>\n",
+       "      <td>19999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4261</th>\n",
+       "      <td>8.920000e+17</td>\n",
+       "      <td>Arbutus Ridge</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>374</td>\n",
+       "      <td>19999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>3.994011e+06</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>65</td>\n",
+       "      <td>19999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>800000 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                id       neighbourhood        room_type  accommodates  \\\n",
+       "322   7.583961e+06            Downtown     Private room             1   \n",
+       "2636  5.272124e+07            Downtown  Entire home/apt             5   \n",
+       "1255  2.804928e+07          Riley Park  Entire home/apt             6   \n",
+       "3238  6.800000e+17     West Point Grey  Entire home/apt             2   \n",
+       "4558  9.320000e+17   Downtown Eastside  Entire home/apt             4   \n",
+       "...            ...                 ...              ...           ...   \n",
+       "187   3.812348e+06          Riley Park     Private room             2   \n",
+       "2808  5.490000e+17          Riley Park  Entire home/apt             4   \n",
+       "2471  5.059522e+07  Grandview-Woodland  Entire home/apt             6   \n",
+       "4261  8.920000e+17       Arbutus Ridge     Private room             4   \n",
+       "194   3.994011e+06            West End  Entire home/apt             2   \n",
+       "\n",
+       "      bathrooms  bedrooms  beds  price  replicate  \n",
+       "322         1.0       2.0   0.0     75          0  \n",
+       "2636        NaN       1.0   NaN    199          0  \n",
+       "1255        NaN       3.0   NaN    128          0  \n",
+       "3238        1.0       1.0   1.0    242          0  \n",
+       "4558        1.0       1.0   0.0    467          0  \n",
+       "...         ...       ...   ...    ...        ...  \n",
+       "187         1.0       1.0   1.0     64      19999  \n",
+       "2808        1.0       2.0   0.0    206      19999  \n",
+       "2471        2.5       5.0   3.0    187      19999  \n",
+       "4261        1.0       2.0   3.0    374      19999  \n",
+       "194         NaN       NaN   NaN     65      19999  \n",
+       "\n",
+       "[800000 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "samples = pd.concat(sample_list)\n",
+    "samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "95a864a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_estimates = samples.groupby(\"replicate\")[\"price\"].mean().reset_index(name = \"sample_mean\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "cd64f45a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>sample_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>202.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>190.975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>204.375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>226.750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>237.300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19995</th>\n",
+       "      <td>19995</td>\n",
+       "      <td>226.475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19996</th>\n",
+       "      <td>19996</td>\n",
+       "      <td>267.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19997</th>\n",
+       "      <td>19997</td>\n",
+       "      <td>317.325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19998</th>\n",
+       "      <td>19998</td>\n",
+       "      <td>236.550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19999</th>\n",
+       "      <td>19999</td>\n",
+       "      <td>261.550</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       replicate  sample_mean\n",
+       "0              0      202.800\n",
+       "1              1      190.975\n",
+       "2              2      204.375\n",
+       "3              3      226.750\n",
+       "4              4      237.300\n",
+       "...          ...          ...\n",
+       "19995      19995      226.475\n",
+       "19996      19996      267.875\n",
+       "19997      19997      317.325\n",
+       "19998      19998      236.550\n",
+       "19999      19999      261.550\n",
+       "\n",
+       "[20000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9302e39c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the histogram of the sampling distribution\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(sample_estimates['sample_mean'], bins=30, edgecolor='black')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Sampling Distribution of Mean Price Listings')\n",
+    "plt.xlabel('Means')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbe872e0",
+   "metadata": {},
+   "source": [
+    "All of the above was done while we had the full dataset available. That is not usually the case so now we will use bootstrapping method assuming that we don't have the full dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1d06df96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>neighbourhood</th>\n",
+       "      <th>room_type</th>\n",
+       "      <th>accommodates</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2912</th>\n",
+       "      <td>5.970000e+17</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>1.986261e+07</td>\n",
+       "      <td>Mount Pleasant</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2696</th>\n",
+       "      <td>5.333958e+07</td>\n",
+       "      <td>Arbutus Ridge</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3017</th>\n",
+       "      <td>6.310000e+17</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1226</th>\n",
+       "      <td>2.745958e+07</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2620</th>\n",
+       "      <td>5.255417e+07</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4883</th>\n",
+       "      <td>9.790000e+17</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>608</th>\n",
+       "      <td>1.554257e+07</td>\n",
+       "      <td>Kitsilano</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1400</th>\n",
+       "      <td>3.124019e+07</td>\n",
+       "      <td>Arbutus Ridge</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>3.100878e+06</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3599</th>\n",
+       "      <td>7.590000e+17</td>\n",
+       "      <td>Kensington-Cedar Cottage</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1066</th>\n",
+       "      <td>2.475420e+07</td>\n",
+       "      <td>Kitsilano</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>8.429321e+06</td>\n",
+       "      <td>Fairview</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>984</th>\n",
+       "      <td>2.341930e+07</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3851</th>\n",
+       "      <td>8.160000e+17</td>\n",
+       "      <td>Hastings-Sunrise</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3560</th>\n",
+       "      <td>7.510000e+17</td>\n",
+       "      <td>Kitsilano</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2692</th>\n",
+       "      <td>5.331242e+07</td>\n",
+       "      <td>Strathcona</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>988</th>\n",
+       "      <td>2.348250e+07</td>\n",
+       "      <td>Fairview</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4777</th>\n",
+       "      <td>9.650000e+17</td>\n",
+       "      <td>Renfrew-Collingwood</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4419</th>\n",
+       "      <td>9.120000e+17</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1465</th>\n",
+       "      <td>3.287258e+07</td>\n",
+       "      <td>Kitsilano</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>1.455531e+07</td>\n",
+       "      <td>Grandview-Woodland</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1594</th>\n",
+       "      <td>3.490318e+07</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3076</th>\n",
+       "      <td>6.440000e+17</td>\n",
+       "      <td>Kensington-Cedar Cottage</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>273</th>\n",
+       "      <td>6.315732e+06</td>\n",
+       "      <td>Fairview</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>2.988185e+06</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4601</th>\n",
+       "      <td>9.360000e+17</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3795</th>\n",
+       "      <td>8.020000e+17</td>\n",
+       "      <td>Riley Park</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>7.183413e+06</td>\n",
+       "      <td>Grandview-Woodland</td>\n",
+       "      <td>Private room</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1799</th>\n",
+       "      <td>3.788796e+07</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1619</th>\n",
+       "      <td>3.519493e+07</td>\n",
+       "      <td>West End</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3300</th>\n",
+       "      <td>6.910000e+17</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>5.347437e+06</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>9.636681e+06</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4775</th>\n",
+       "      <td>9.650000e+17</td>\n",
+       "      <td>Hastings-Sunrise</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2162</th>\n",
+       "      <td>4.510529e+07</td>\n",
+       "      <td>Kensington-Cedar Cottage</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3115</th>\n",
+       "      <td>6.550000e+17</td>\n",
+       "      <td>Downtown</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4255</th>\n",
+       "      <td>8.910000e+17</td>\n",
+       "      <td>Downtown Eastside</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2480</th>\n",
+       "      <td>5.073003e+07</td>\n",
+       "      <td>Oakridge</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2549</th>\n",
+       "      <td>5.156516e+07</td>\n",
+       "      <td>Grandview-Woodland</td>\n",
+       "      <td>Entire home/apt</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>192</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                id             neighbourhood        room_type  accommodates  \\\n",
+       "2912  5.970000e+17                Riley Park  Entire home/apt             9   \n",
+       "787   1.986261e+07            Mount Pleasant  Entire home/apt             9   \n",
+       "2696  5.333958e+07             Arbutus Ridge  Entire home/apt             3   \n",
+       "3017  6.310000e+17                  West End  Entire home/apt             2   \n",
+       "1226  2.745958e+07                  Downtown  Entire home/apt             1   \n",
+       "2620  5.255417e+07         Downtown Eastside  Entire home/apt             4   \n",
+       "4883  9.790000e+17         Downtown Eastside  Entire home/apt             7   \n",
+       "608   1.554257e+07                 Kitsilano  Entire home/apt             5   \n",
+       "1400  3.124019e+07             Arbutus Ridge     Private room             2   \n",
+       "149   3.100878e+06                  Downtown  Entire home/apt             2   \n",
+       "3599  7.590000e+17  Kensington-Cedar Cottage  Entire home/apt             8   \n",
+       "1066  2.475420e+07                 Kitsilano  Entire home/apt             2   \n",
+       "361   8.429321e+06                  Fairview  Entire home/apt             4   \n",
+       "984   2.341930e+07                  Downtown  Entire home/apt             5   \n",
+       "3851  8.160000e+17          Hastings-Sunrise  Entire home/apt             4   \n",
+       "3560  7.510000e+17                 Kitsilano  Entire home/apt             4   \n",
+       "2692  5.331242e+07                Strathcona  Entire home/apt             4   \n",
+       "988   2.348250e+07                  Fairview  Entire home/apt             2   \n",
+       "4777  9.650000e+17       Renfrew-Collingwood     Private room             5   \n",
+       "4419  9.120000e+17                  Downtown  Entire home/apt             4   \n",
+       "1465  3.287258e+07                 Kitsilano  Entire home/apt             4   \n",
+       "548   1.455531e+07        Grandview-Woodland  Entire home/apt             2   \n",
+       "1594  3.490318e+07                  West End  Entire home/apt             6   \n",
+       "3076  6.440000e+17  Kensington-Cedar Cottage  Entire home/apt             2   \n",
+       "273   6.315732e+06                  Fairview  Entire home/apt             3   \n",
+       "142   2.988185e+06                  West End  Entire home/apt             4   \n",
+       "4601  9.360000e+17         Downtown Eastside  Entire home/apt             4   \n",
+       "3795  8.020000e+17                Riley Park  Entire home/apt             4   \n",
+       "310   7.183413e+06        Grandview-Woodland     Private room             2   \n",
+       "1799  3.788796e+07                  Downtown  Entire home/apt             6   \n",
+       "1619  3.519493e+07                  West End  Entire home/apt             3   \n",
+       "3300  6.910000e+17                  Downtown  Entire home/apt             4   \n",
+       "240   5.347437e+06                  Downtown  Entire home/apt             3   \n",
+       "391   9.636681e+06         Downtown Eastside  Entire home/apt             2   \n",
+       "4775  9.650000e+17          Hastings-Sunrise  Entire home/apt             4   \n",
+       "2162  4.510529e+07  Kensington-Cedar Cottage  Entire home/apt             4   \n",
+       "3115  6.550000e+17                  Downtown  Entire home/apt             5   \n",
+       "4255  8.910000e+17         Downtown Eastside  Entire home/apt             4   \n",
+       "2480  5.073003e+07                  Oakridge  Entire home/apt             3   \n",
+       "2549  5.156516e+07        Grandview-Woodland  Entire home/apt             4   \n",
+       "\n",
+       "      bathrooms  bedrooms  beds  price  \n",
+       "2912        3.0       4.0   5.0    214  \n",
+       "787         2.0       3.0   3.0    109  \n",
+       "2696        1.0       1.0   0.0    200  \n",
+       "3017        1.0       1.0   0.0    223  \n",
+       "1226        NaN       1.0   NaN    126  \n",
+       "2620        1.0       1.0   2.0    198  \n",
+       "4883        2.0       3.0   4.0    834  \n",
+       "608         1.0       2.0   3.0     99  \n",
+       "1400        1.0       1.0   2.0    133  \n",
+       "149         1.0       1.0   0.0     60  \n",
+       "3599        NaN       3.0   NaN    273  \n",
+       "1066        1.0       1.0   1.0    120  \n",
+       "361         1.0       1.0   1.0     79  \n",
+       "984         1.0       1.0   3.0    117  \n",
+       "3851        1.0       2.0   2.0    300  \n",
+       "3560        1.0       1.0   2.0    269  \n",
+       "2692        1.0       2.0   3.0    200  \n",
+       "988         1.0       1.0   1.0    118  \n",
+       "4777        1.0       2.0   3.0    640  \n",
+       "4419        1.5       2.0   2.0    409  \n",
+       "1465        2.0       2.0   2.0    136  \n",
+       "548         NaN       1.0   NaN     95  \n",
+       "1594        2.0       3.0   3.0    141  \n",
+       "3076        1.0       1.0   1.0    227  \n",
+       "273         1.0       1.0   2.0     70  \n",
+       "142         NaN       2.0   NaN     59  \n",
+       "4601        NaN       2.0   NaN    495  \n",
+       "3795        1.0       2.0   2.0    298  \n",
+       "310         1.0       1.0   1.0     75  \n",
+       "1799        2.0       2.0   3.0    150  \n",
+       "1619        1.0       1.0   2.0    143  \n",
+       "3300        1.0       1.0   1.0    249  \n",
+       "240         1.0       2.0   2.0     69  \n",
+       "391         NaN       1.0   NaN     80  \n",
+       "4775        1.0       2.0   4.0    635  \n",
+       "2162        1.0       2.0   3.0    169  \n",
+       "3115        2.0       3.0   3.0    230  \n",
+       "4255        1.0       0.0   0.0    372  \n",
+       "2480        NaN       2.0   NaN    188  \n",
+       "2549        1.0       2.0   2.0    192  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#bootstrapping\n",
+    "np.random.seed(1234)\n",
+    "one_sample=airbnb.sample(40)\n",
+    "one_sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f9192866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(219.85)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_sample[\"price\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e4c3983d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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noO2lSlt0t6iayqTKTXjPT+TvJOLG4+j758KyFnb7CqlCGkn3nahbVfT0cB9Ulxzdp6H7L+Klbm/qGqUKZ/S9btFibScFJdr26rakbTpgwAAXAOu4UtN6N+QZLOH+FmvdVfFUsBJ9oaG2+8s2Jk/rKn9hBTusAEdTt7uNPUY39rExer/XNtS+o+NVTZQPSkvYZSxadLrj3d7aLuqGrYCkvkOnb8z5T3QRRcfbDz74wN1PFLmu6gYafcyIZ13VdVMXQJQOdeerjYJfdVuNRcFwXRfMAIIgeE19vXUQ1/0QugKq+1jUb1k35idieNGGinXwVmVKJxvdc6T7j3T1UJUSnbhiDX8cqzWhphaG6IpoTVL90LxEndTC/FKff7XIxFJbkJrM36nvOm7sNt2Y39gYYaVJleV4KujJpqF1dR+Djgn/+te/XJCo1jIN9DFp0qRq88faTqrgKoBSpVQtwXrp+2o50P10kcIgIbrinQy6MT1ZoyfWVf7CfUD3BemiSTQFaBt7jG4Kx8a6KB0qH7p3JpaaAoamev7T/Wdq/dG9e9EPxNa6akCQ2267LeZ3NUhCbfdZath83Z8VHVSqNV3TlI+6gKN9VK2OCqoig0sFRgpedYENqA1BELyn7gXqUqSXnlqvE4NuGA1PAjVV/DUimG5W1YE5sjVII4GFn4d/dVJQF63IK4jRI3jVRhWmN954w13t1A3zoYZ2Y6ivcB30e2FLl+hmbHV12ZiH8WngieirmLp5VhryxPWwBUGjtNX0XJWwq48quRvz7JVY+a+0h+lO1O8kmraXupzoqn9ka1B9yuTGBshqsRDtF6owRZc1Vc4ir3yrYhRdaVTao6eH+6Aq/bVVtqKp+41aAxTARA4nriCmPtQ9R4NO6KX1UOuQKnRqCYwMeLXeCoAaUvkN9zete3QLi6Y1dH9MdJ5G7o+qpDZ0H6jtGJ2KY2P0shUoad+prQVF+aDzhQYyqO+FjsjtHWu7qBw19AG6dZ3/arpgoAs76qIaa6RBratGmtQAIfU9LmhABA1qpHyKFSDppYF41GUzDL7UPVSD64T0XvtedHAGROOeIHgtuquDriCqohLZFSY8uaiyH0kHXV2F0ug+kXQlTQd+dYmRsAVAV5cj1eep1uFVyuirknX1mU6U8AQT/Xvhlb7aRrqri7o/qJIYeRVP71UhU3/5+tJoY+qqpLRGb7Mw/1Qh02hX+h31t48Wa4jfWDTKUeQ9PRptTaOyhds+Ub+TaCqTqmjogYohVRrC4eAbIgymovO8Jtq2ChhUYYkU5t3tt99eZXp02dM+oe5muoodecVYgblabjTyWF1draKXp/02HPZbtNx4RrKq6XiioCGsGEcPDaz73fr162cNoe6/Klu6Yh+5XLU8qRtSQ/fHROdpWNb0HY1YFmvEzrr2gbqO0ak4NoajQoY0jLr277DsxqLWKpUtjXAZ6xhY236jFg9V6NWaGDmfLvSoBScyAEj0+S+a1lProrJwyy23xJxHn+u4GHl8Cek+ndpGvTzxxBNdkBP9Eq2n/h+OUKgLAAriNNJgJL3X8WhjzkvwAy1B8JpubFYlVRUyHUxVIdMJTUO8hsKKuPop64Suk64O1LrSe9BBB7lhbFVh2GmnndwJSRUIXSELr4Dq+3o4ok7KOumEQ2SHrR3xXClTJUJX6DT8sSoSut9Av6WryY1B66ZBCTTsrE7CuilcFX6dlHVFTvnQUOqyoCFplYfqm66hddWlSL/VkAfbquKpk6C2jyoOusKpSoSumOomYF3pF1X4dSJXK4T6tqvVRpU9PZ1cz6TRlcy6qMKgZeimc1UctI3VzUtDC4cS8TuJpm2me+k0sIWuYKtVRs8t0r0VDe32qKvb2p+0/bQdtT/pPoOa7s1Sq4sq3Lo6rnsTQtpmuoleFw10D4WGyNaV/litVLp/RzfeK3/V4qKuVQo4tS20r9SHKkwK6tWFSoMAqIuNtp22sZ5DEw9dPVceqnKmLn66l0MXO7ROkS2oWraWWdtAFLXRfqF9RmVb+6LyKxwiW62QesZSQyUyT8Njl/ZH3eOhCxQ6duoCh674awAHXfGPvpBUn2N0Ko6NSofyR/mvfNd+r3ISOdBING0nDfiiIal1fFPZ13ZUq5IGEtC20wALNVHAoSBLgbMGcAiHyNZ9S7U9j2tjz3/RdB5U4KpjnO7BiqSAXy9ta90bp8FU1OKsbawAUMdgTdcxONZ9vKJjUdhKHE0Xt3TsijzmKKjUfqSh7HV+ViuVuuhpGPPIQSSAmFI9PB3QULGGjo0efrmuIbI1vPWee+7phnDVcKjbbrttcMMNN1QOzSwa9vPCCy90Q25qGNXI3UbDv2pI2M6dOwc5OTluyONbbrmlypCxsnbtWjecZ7t27YJNNtnEDUM7e/Zst6zIYVnD4Ug13HA0DcV8zDHHuLRqSNLjjz/eDV1b0zDb0cuoaejqWPkUS1lZWTB69Gg3/LTWtWvXrsFVV11VZejn2n4nlvC3Z86cGfTr1y9o0aKF20Z33HFHlflqG/Y11vDH8t5777lhWzW0r9LTt2/f4J///GeVeTQMsIaX7dixo1snDX3729/+Nnj66afjGiJb21rDeysv8vLygv32288NWxstnt+pqzzXtN7ReRKmLXqY6+jhoFU+NOy08kflSUO2a+hafffxxx+vc3vGGjr3gw8+cMNma4jleIbLnjx5stunFi1aVGW6hn2+6KKLgs0228z99pFHHhksXrw45jI//fTTYODAgW6/ys/PDw466CCXjlh5VdcQ2ffdd5/bh7UtdSxQHsZaz1jD9Yq254ABA9xQxsqDbt26Beecc06wbNmyKvONHz/epTVymOWa1FYunnjiCTdUudKrY8vQoUOrDNle3/2xPnlaU7pq2h/1XstUWdN+3qtXL1fmtO/XJp5jdGMdG8N10/DbOvZpOytNGmZ64cKF1ZYZXb7k7rvvdvuIvqd9T8PnX3755S69dXn99deDffbZx31XQ7Frv/jmm2+qzFOfIbLjydvo8h8+1iDWKzKvtYybb77Z5Z/KZ9u2bd166xyyevXqoL5q2ufCPN1mm23cPqdy9Y9//KPaORiIJUP/xA6PACSTrgbusssu7qpVXU9wT1e6CqkR/NStozlRq5WuSurqbE1PM2+O1PVLQ95qdLFYffITTVeHdTVa3WdidRNKV9rvVfYjH6iMpu/tt992rd5quamt1QZA88A9QUAjiPW8AnWhUNet6KeRA6kokwpI1L1G3YvUbakxqGupusKp25luyvaBntGkLlBXXXVVqpMCAF7jniCgEai/um6E1lVE9bEPh8/VgwbrO9oSkAh6WK8CId1joPs9Jk+e7IYZ1g3sjfl8jRNOOMG9fKF7jnwJ+ACgKSMIAhqBbu7Wzcbq8qMKkB7Wp5tZNagCkAq6ef/WW291DwjVgwV1Y7dagmq7KRoAgHTBPUEAAAAAvMI9QQAAAAC8QhAEAAAAwCvN+p4gPeF86dKlVlBQ0KCH+wEAAABID3ryT3FxsXsQu0bgTdsgSAEQI2sBAAAACC1evNi6dOliaRsEqQUoXFE92wLVlZWV2WuvvWYDBgywnJwcsiiJyOvGQT43HvKavE5HlGvyOh1Rrn9VVFTkGkjCGCFtg6CwC5wCIIKgmneK/Px8lz8EQclFXjcO8rnxkNfkdTqiXJPX6YhyXVU8t8kwMAIAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAArxAEAQAAAPAKQRAAAAAAr6Q0COrRo4dlZGRUew0fPjyVyQIAAACQxrJT+eMzZsyw8vLyyvdfffWV9e/f344//vhUJgsAAABAGktpENS+ffsq78eMGWO9evWyAw44IGVpAgAAAJDeUhoERSotLbVHHnnERo4c6brExVJSUuJeoaKiIve3rKzMvVJt5cqVVlxcnLTlFxQUWGFhYb2+E+ZLU8ifdEdek8/phjJNXqcjyjV5nY4o11bv+m5GEASBNQFPPvmkDRkyxBYtWmSdO3eOOc+oUaNs9OjR1aZPmjTJ8vPzGyGVAAAAAJqidevWuXhi9erV1rp16+YRBA0cONByc3PtxRdfrHGeWC1BXbt2dS0wda1oss2fP99OG36xFe452Fq17ZDw5a9dtdxWfvy8TbxzrPXs2bNeEfHUqVPdvVY5OTkJTxfI68ZGmSav0xHlmrxOR5Rr8rqxKTZQr6l4gqAm0R1u4cKF9vrrr9vkyZNrnS8vL8+9oqlyn+oKflZWlpWWlllumw7WsrBLwpdfVqEug2Xudxqyrk0hj3xBXpPP6YYyTV6nI8o1eZ2OfC/XOfVY9ybxnKCJEydahw4d7Igjjkh1UgAAAACkuZQHQRUVFS4IGjZsmGVnN4mGKQAAAABpLOVBkLrBaTCE008/PdVJAQAAAOCBlDe9DBgwwJrI2AwAAAAAPJDyliAAAAAAaEwEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8QhAEAAAAwCsEQQAAAAC8kvIg6Pvvv7eTTz7ZNttsM2vZsqXtuOOONnPmzFQnCwAAAECayk7lj69atcr22WcfO+igg2zKlCnWvn17+/bbb61t27apTBYAAACANJbSIOjmm2+2rl272sSJEyun9ezZM5VJAgAAAJDmUhoEvfDCCzZw4EA7/vjjbdq0abbFFlvY+eefb2eddVbM+UtKStwrVFRU5P6WlZW5VyqVl5dbbm6O5WQqUysSvnwtV8vX79RnXcN56/rOypUrrbi42JKloKDACgsLLZ3Fm9cgn5sLyjR5nY4o1+R1OqJcW73rYBlBEASWIi1atHB/R44c6QKhGTNm2IgRI2zChAk2bNiwavOPGjXKRo8eXW36pEmTLD8/v1HSDAAAAKDpWbdunQ0ZMsRWr15trVu3brpBUG5uru2+++72wQcfVE676KKLXDD04YcfxtUSpO50asWoa0WTbf78+Xba8Iut+8CzrKCwc8KXX7xyqS189R6beOfYenUZVEQ8depU69+/v+Xk5NSa9sI9B1urth0s0dauWm4rP36+3mlvbuLJa5DPzQllmrxOR5Rr8jodUa7/LzZQz6N4gqCUdofr1KmTbb/99lWmbbfddvbMM8/EnD8vL8+9oqnCmepKZ1ZWlpWWlllZhdmGJAy6p+Vq+fqdhqxrbXkUpj23TQdrWdjFmlram5umUB59QD6T1+mIck1epyPKNXndWOpT/0rpENkaGW727NlVps2ZM8e6d++esjQBAAAASG8pDYIuueQSmz59ut144402d+5cd2/P3XffbcOHD09lsgAAAACksZQGQXvssYc9++yz9thjj1mfPn3suuuus7Fjx9rQoUNTmSwAAAAAaSyl9wTJb3/7W/cCAAAAgLRvCQIAAACAxkYQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArKQ2CRo0aZRkZGVVe2267bSqTBAAAACDNZac6ATvssIO9/vrrle+zs1OeJAAAAABpLOURh4Kejh07xjVvSUmJe4WKiorc37KyMvdKpfLycsvNzbGcTGVqRcKXr+Vq+fqd+qxrOG9t32mqaW9u4slrkM/NCWWavE5HlGvyOh1Rrq3edbCMIAgCS2F3uFtuucXatGljLVq0sH79+tlNN91k3bp1q3H+0aNHV5s+adIky8/Pb4QUAwAAAGiK1q1bZ0OGDLHVq1db69atm24QNGXKFFuzZo1ts802tmzZMhfgfP/99/bVV19ZQUFBXC1BXbt2tZUrV9a5osk2f/58O234xdZ94FlWUNg54csvXrnUFr56j028c6z17NmzXhHx1KlTrX///paTk9Os0t7cxJPXIJ+bE8o0eZ2OKNfkdTqiXP9fbFBYWBhXEJTS7nCHH3545f/79u1re+21l3Xv3t2efPJJO+OMM6rNn5eX517RVOFMdaUzKyvLSkvLrKzCbEMSxpvQcrV8/U5D1rW2PGrqaW9umkJ59AH5TF6nI8o1eZ2OKNfkdWOpT/2rSQ2Rvemmm9rWW29tc+fOTXVSAAAAAKSpJhUEqWvcvHnzrFOnTqlOCgAAAIA0ldIg6LLLLrNp06bZggUL7IMPPrBjjjnGdZk66aSTUpksAAAAAGkspfcELVmyxAU8P/74o7Vv39723Xdfmz59uvs/AAAAAKRdEPT444+n8ucBAAAAeKhJ3RMEAAAAAMlGEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALzSoCDou+++S3xKAAAAAKCpBkG9e/e2gw46yB555BFbv3594lMFAAAAAE0pCPr000+tb9++NnLkSOvYsaOdc8459vHHHyc+dQAAAADQFIKgnXfe2caNG2dLly61+++/35YtW2b77ruv9enTx2677TZbsWJFotMJAAAAAKkfGCE7O9uOPfZYe+qpp+zmm2+2uXPn2mWXXWZdu3a1U045xQVHAAAAAJA2QdDMmTPt/PPPt06dOrkWIAVA8+bNs6lTp7pWosGDBycupQAAAACQANkN+ZICnokTJ9rs2bNt0KBB9tBDD7m/mZm/xlQ9e/a0Bx54wHr06JGINAIAAABAaoOg8ePH2+mnn26nnnqqawWKpUOHDnbfffdtbPoAAAAAIPXd4b799lu76qqragyAJDc314YNGxb3MseMGWMZGRl28cUXNyRJAAAAAJC8IEhd4TQYQjRNe/DBB+u9vBkzZthdd93lht0GAAAAgCYXBN10001WWFgYswvcjTfeWK9lrVmzxoYOHWr33HOPtW3btiHJAQAAAIDk3hO0aNEiN/hBtO7du7vP6mP48OF2xBFH2KGHHmrXX399rfOWlJS4V6ioqMj9LSsrc69UKi8vt9zcHMvJVKZWJHz5Wq6Wr9+pz7qG89b2naaa9qZi5cqVVlxcXOd8Wj/RUPFZWVlxLbu0tNR1HU2WgoKCmBcsmrN4yjTI6+aGck1epyPKNXnd2OpTN8gIgiCo7w9069bN7rjjDjvqqKOqTH/++eddULNkyZK4lvP444/bDTfc4LrDtWjRwg488ED3INaxY8fGnH/UqFE2evToatMnTZpk+fn59V0NAAAAAGli3bp1NmTIEFu9erW1bt068S1BJ510kl100UXuKvP+++/vpk2bNs1GjBhhJ554YlzLWLx4sZtfzxRSABQPDcYwcuTIKi1BejDrgAED6lzRZJs/f76dNvxi6z7wLCso7Jzw5RevXGoLX73HJt45NmYrXG0RsfK4f//+lpOT06zS3hSEeVO452Br1bZDrfNmZ5od1yvTnplXYRviaFBbMf8bm/Xyw7brsedZu07dLNHWrlpuKz9+vlnm+8aWaZDXzQ3lmrxOR5Rr8rqxhb3E4tGgIOi6666zBQsW2CGHHGLZ2b8uoqKiwk455ZS47wn65JNPbPny5bbrrrtW6U70zjvvuFYmdXuL7lKUl5fnXtFUEUp1ZUhpLS0ts7IKsw0b9wzamLRcLV+/05B1rS2PmnraUynMm9w2HaxlYZda5/21K+EP1rJd57jyMXvFD7Z+fYllty6sc9m+5Xs8msJ+7wvymrxOR5Rr8jod+V6uc+qx7g0KgnQPwxNPPOGCoVmzZlnLli1txx13dPcExUsB1Jdfflll2mmnnWbbbrutXXHFFXHfUwEAAAAA9dGgICi09dZbu1dDqCtdnz59qkxr1aqVbbbZZtWmAwAAAEBKgyB1W3vggQfsjTfecF3a1BUu0ptvvpmo9AEAAABA6oMgDWigIEhDW6vVJiMjIyGJefvttxOyHAAAAABIaBCkoa2ffPJJGzRoUEO+DgAAAAApk9nQgRF69+6d+NQAAAAAQFMMgi699FIbN26cNeA5qwAAAADQ/LrDvffee/bWW2/ZlClTbIcddqg2JvfkyZMTlT4AAAAASH0QtOmmm9oxxxyT2JQAAAAAQFMNgiZOnJj4lAAAAABAU70nSDZs2GCvv/663XXXXVZcXOymLV261NasWZPI9AEAAABA6luCFi5caIcddpgtWrTISkpKrH///lZQUGA333yzez9hwoTEphIAAAAAUtkSpIel7r777rZq1Spr2bJl5XTdJ/TGG28kKm0AAAAA0DRagt5991374IMP3POCIvXo0cO+//77RKUNAAAAAJpGS1BFRYWVl5dXm75kyRLXLQ4AAAAA0ioIGjBggI0dO7byfUZGhhsQ4dprr7VBgwYlMn0AAAAAkPrucLfeeqsNHDjQtt9+e1u/fr0NGTLEvv32WyssLLTHHnsssSkEAAAAgFQHQV26dLFZs2bZ448/bl988YVrBTrjjDNs6NChVQZKAAAAAIC0CILcF7Oz7eSTT05sagAAAACgKQZBDz30UK2fn3LKKQ1NDwAAAAA0vSBIzwmKVFZWZuvWrXNDZufn5xMEAQAAAEiv0eH0kNTIl+4Jmj17tu27774MjAAAAAAg/YKgWLbaaisbM2ZMtVYiAAAAAEjLICgcLGHp0qWJXCQAAAAApP6eoBdeeKHK+yAIbNmyZXbHHXfYPvvsk6i0AQAAAEDTCIKOPvroKu8zMjKsffv2dvDBB7sHqQIAAABAWgVBFRUViU8JAAAAADS3e4IAAAAAIC1bgkaOHBn3vLfddltDfgIAAAAAmk4Q9Nlnn7mXHpK6zTbbuGlz5syxrKws23XXXavcKwQAAAAAzT4IOvLII62goMAefPBBa9u2rZumh6aedtpptt9++9mll16a6HQCAAAAQOruCdIIcDfddFNlACT6//XXX8/ocAAAAADSLwgqKiqyFStWVJuuacXFxYlIFwAAAAA0nSDomGOOcV3fJk+ebEuWLHGvZ555xs444ww79thjE59KAAAAAEjlPUETJkywyy67zIYMGeIGR3ALys52QdAtt9ySqLQBAAAAQNMIgvLz8+1f//qXC3jmzZvnpvXq1ctatWqV6PQBAAAAQNN5WOqyZcvca6uttnIBUBAEiUsZAAAAADSVIOjHH3+0Qw45xLbeemsbNGiQC4RE3eEYHhsAAABA2gVBl1xyieXk5NiiRYtc17jQCSecYK+88koi0wcAAAAAqb8n6LXXXrNXX33VunTpUmW6usUtXLgwUWkDAAAAgKbRErR27doqLUChn376yfLy8hKRLgAAAABoOkHQfvvtZw899FDl+4yMDKuoqLC//e1vdtBBByUyfQAAAACQ+u5wCnY0MMLMmTOttLTULr/8cvv6669dS9D777+f2BQCAAAAQKpbgvr06WNz5syxfffd1wYPHuy6xx177LH22WefuecFAQAAAEDatASVlZXZYYcdZhMmTLA///nPyUkVAAAAADSVliANjf3FF18kJzUAAAAA0BS7w5188sl23333JT41AAAAANAUB0bYsGGD3X///fb666/bbrvtZq1atary+W233Zao9AEAAABA6oKg7777znr06GFfffWV7brrrm6aBkiIpOGyAQAAACAtgqCtttrKli1bZm+99ZZ7f8IJJ9jtt99um2++ebLSBwAAAACpuycoCIIq76dMmeKGxwYAAACAtB4YoaagCAAAAADSKgjS/T7R9/xwDxAAAACAtL0nSC0/p556quXl5bn369evt3PPPbfa6HCTJ09ObCoBAAAAIBVB0LBhw6o9LwgAAAAA0jYImjhxYvJSAgAAAABNfWCEjTV+/Hjr27evtW7d2r369evnRpwDAAAAgLQMgrp06WJjxoyxTz75xGbOnGkHH3ywDR482L7++utUJgsAAABAGqtXd7hEO/LII6u8v+GGG1zr0PTp022HHXZIWboAAAAApK+UBkGRysvL7amnnnIPX1W3uFhKSkrcK1RUVOT+lpWVuVeq05+bm2M5mcrUioQvX8tVs92CBQvcb9UnXTJ37lzLysqKOc/ixYstKyOzyaU9XgUFBVZYWGip3q7h5/HmYU5WhrVokZfUfFfatQ6p3j8SKVyXdFqnpoq8Jq/TEeWavE5HlGurd90gI0jxE0+//PJLF/RouO1NNtnEJk2aZIMGDYo576hRo2z06NHVpus7+fn5jZBaAAAAAE3RunXrbMiQIbZ69Wo33kCTDoJKS0tt0aJFLrFPP/203XvvvTZt2jTbfvvt42oJ6tq1q61cubLOFU22+fPn22nDL7buA8+ygsLOCV/+sjmf2fTHxtqux55n7Tp1i/t72Zlmx/XKtGfmVdiGGhobVsz/xma9/LDtc/rV1qFrb2sqaY/H2lXLbeXHz9vEO8daz549LZXbVa05A9out9dWdbANcdxuF+ZLsvK9eOVSW/jqPUnLm1Re5Zk6dar179/fcnJyUp2ctEZek9fpiHJNXqcjyvX/xQbqHRRPEJTy7nC5ubnWu/evFcDddtvNZsyYYePGjbO77rqr2rx6SGv4oNZIqgilujKkrmalpWVWVmFxVYDrq6w8sPXrSyy7daG1LOwS9/d+7Wb1g7Vs17nGdGWv+MEtu6mlPa5lVyiQLnP5n4wy0JDtqvnimTfMl6Tle5LzJtWawn7vC/KavE5HlGvyOh35Xq5z6rHuKR0dLpaKiooqrT0AAAAAkEgpbQm66qqr7PDDD7du3bpZcXGxu7fn7bfftldffTWVyQIAAACQxlIaBC1fvtxOOeUUW7ZsmbVp08Y9OFUBkPr6AwAAAEDaBUH33XdfKn8eAAAAgIea3D1BAAAAAJBMBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvEIQBAAAAMArBEEAAAAAvJLSIOimm26yPfbYwwoKCqxDhw529NFH2+zZs1OZJAAAAABpLqVB0LRp02z48OE2ffp0mzp1qpWVldmAAQNs7dq1qUwWAAAAgDSWncoff+WVV6q8f+CBB1yL0CeffGL7779/ytIFAAAAIH2lNAiKtnr1ave3Xbt2MT8vKSlxr1BRUZH7qxYkvVKpvLzccnNzLCdTmVqR8OXnZGVYixZ59V5+OG9t32nosuOVzOVrmWrOXLBggdsGibZ48WLLysiMK+3x5HWj5numuTKpfEnG/rFy5UorLi62ZCktLbXc3Nxq08PtPHfuXMvKymrQstUFt7CwcKPTmO7CcpPq46sPyOv0y+tkHyObw3GMck1eN7b67NcZQRAE1gRUVFTYUUcdZT///LO99957MecZNWqUjR49utr0SZMmWX5+fiOkEgAAAEBTtG7dOhsyZIhrWGndunXzCILOO+88mzJliguAunTpEndLUNeuXd3VlrpWNNnmz59vpw2/2LoPPMsKCjsnfPnL5nxm0x8ba/ucfrV16No77u+phWFA2+X22qoOtqGGW8Aauux4JXP54bJ3PfY8a9epmyXaivnf2KyXH44r7fHkdWPme/HKpbbw1Xts4p1jrWfPnkkp74V7DrZWbTtYsvI91nbNzjQ7rlemPTOvwjY0oAFt7arltvLj55OSL+l4RU33a/bv399ycnJSnZy0Rl6nV14n+xjZXI5jlGvyurEpNlALaTxBUJPoDnfBBRfYSy+9ZO+8806NAZDk5eW5VzQdxFJ9gla3nNLSMiursLgqwPVVVh7Y+vUlDV6+vlPT9zZ22XVJ5vLDZWe3LrSWhTWXnYbKXvFDvdNeW143ar5XqEtZmSubid4/wvKe26ZDUvM91nb9tevgD9ayXecG5Vsy8yVdNYVjrC/I6/TI62QfI5vbcYxyTV43lvrsDykNgtQIdeGFF9qzzz5rb7/9dpO+mgEAAAAgPaQ0CNLw2Lqf5/nnn3c3+P3www9ueps2baxly5apTBoAAACANJXS5wSNHz/e9dk78MADrVOnTpWvJ554IpXJAgAAAJDGUt4dDgAAAAC8aQkCAAAAgMZGEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKwRBAAAAALxCEAQAAADAKykNgt555x078sgjrXPnzpaRkWHPPfdcKpMDAAAAwAMpDYLWrl1rO+20k915552pTAYAAAAAj2Sn8scPP/xw9wIAAAAAL4Kg+iopKXGvUFFRkftbVlbmXqlUXl5uubk5lpOpTK1I+PJzsjKsRYu8ei8/nLe27zR02fFK5vKbUtrjyeuGLrshtFw19S5YsMCVz0RavHixZWVkpiTf65vP1ZadaW5fVZ6k+rjRECtXrrTi4uKkLb+0tNRyc3Pd/8NyM3fuXMvKytroZRcUFFhhYaE1R8nO9/z8fPe3OZbJ5ibM42TmddLrBM3kONYYeY348jrZx7CCJnJ8r09ZywiCILAmQPcEPfvss3b00UfXOM+oUaNs9OjR1aZPmjSp8gQCAAAAwD/r1q2zIUOG2OrVq61169bpEwTFagnq2rWri27rWtFkmz9/vp02/GLrPvAsKyjsnPDlL5vzmU1/bKztc/rV1qFr77i/pytQA9out9dWdbANNdwC1tBlxyuZy29KaY8nrxu67IYIl7/rsedZu07dErrsFfO/sVkvP5ySfK9vPkcrXrnUFr56j028c6z17NnTmpPwOFO452Br1bZDwpcfbtewzGRnmh3XK9OemVdhGzbyYvbaVctt5cfPk+815E3RZy/bxeecbv3797ecnJyNy2zUeaV46tSpSc3rZNcJmstxrDHyGnXndbLPHWub0PFdsYFapOIJgppVd7i8vDz3iqaNneqdS11FSkvLrKzCGlQxq0tZeWDr15c0ePn6Tk3f29hl1yWZy2+Kaa8trzd22fURLj+7daG1LOyS0GVnr/gh5fkebz5XW3aFunyVuX021ceNhh5nctt0SPg2jdyuYZn5tRvPD9ayXeeN3s7kex158/+7cDSF85kvkpnXSa8TNLPjGOU6tXmd7HNHWRMqj/X5fZ4TBAAAAMArKW0JWrNmjbvhNrK57vPPP7d27dpZt26J7b4DAAAAACkPgmbOnGkHHXRQ5fuRI0e6v8OGDbMHHngghSkDAAAAkK5SGgQdeOCB1kTGZQAAAADgCe4JAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXmkSQdCdd95pPXr0sBYtWthee+1lH3/8caqTBAAAACBNpTwIeuKJJ2zkyJF27bXX2qeffmo77bSTDRw40JYvX57qpAEAAABIQykPgm677TY766yz7LTTTrPtt9/eJkyYYPn5+Xb//fenOmkAAAAA0lB2Kn+8tLTUPvnkE7vqqqsqp2VmZtqhhx5qH374YbX5S0pK3Cu0evVq9/enn36ysrIySyWlJTPTbO3yhWal6xK+/PU/LbXc3Gxbv2KJFWXF/72sTLN1eZm2eulcK69I7LLjlczlN6W0x5PXDV12Q6Rrvtc3n6Ot/XmFBeXl9vXXX1ceQ5qLJUuWWFBR0WjHmY3N60jke115s8HWrVtnn3/+uWVlJWGnQqXy8vKk53Wy99Xmsj81Rl6j7rxujPKYmflrXfjHH39M6SYpLi52f4MgqHPejCCeuZJk6dKltsUWW9gHH3xg/fr1q5x++eWX27Rp0+yjjz6qMv+oUaNs9OjRKUgpAAAAgOZg8eLF1qVLl6bbElRfajHS/UOhiooK1wq02WabWUZGRkrT1lQVFRVZ165dXWFo3bp1qpOT1shr8jndUKbJ63REuSav0xHl+ldq21FrUOfOna0uKQ2CCgsLXZPd//73vyrT9b5jx47V5s/Ly3OvSJtuumnS05kOFAARBJHX6YQyTV6nI8o1eZ2OKNfkdWNq06ZN0x8YITc313bbbTd74403qrTu6H1k9zgAAAAASJSUd4dT97Zhw4bZ7rvvbnvuuaeNHTvW1q5d60aLAwAAAIC0C4JOOOEEW7FihV1zzTX2ww8/2M4772yvvPKKbb755qlOWlpQ90E9gym6GyHI6+aKMk1epyPKNXmdjijX5HVTltLR4QAAAADAu4elAgAAAEBjIggCAAAA4BWCIAAAAABeIQgCAAAA4BWCoGbonXfesSOPPNI9DTcjI8Oee+65Kp9rrAuNttepUydr2bKlHXroofbtt99Wmeenn36yoUOHugeY6YGzZ5xxhq1Zs6aR16Rpu+mmm2yPPfawgoIC69Chgx199NE2e/bsKvOsX7/ehg8fbptttpltsskmdtxxx1V7+O+iRYvsiCOOsPz8fLecP/7xj7Zhw4ZGXpumbfz48da3b9/KB+rpOWFTpkyp/Jx8Tp4xY8a448jFF19MfifYqFGjXN5GvrbddlvyOUm+//57O/nkk93xWOe+HXfc0WbOnFn5OefGxOjRo0e1cq2XzoXC8TpxysvL7eqrr7aePXu6Mt2rVy+77rrrXFkOUa43gkaHQ/Py8ssvB3/+85+DyZMnay8Inn322SqfjxkzJmjTpk3w3HPPBbNmzQqOOuqooGfPnsEvv/xSOc9hhx0W7LTTTsH06dODd999N+jdu3dw0kknpWBtmq6BAwcGEydODL766qvg888/DwYNGhR069YtWLNmTeU85557btC1a9fgjTfeCGbOnBn85je/Cfbee+/Kzzds2BD06dMnOPTQQ4PPPvvMbbvCwsLgqquuStFaNU0vvPBC8O9//zuYM2dOMHv27OBPf/pTkJOT4/JeyOfk+Pjjj4MePXoEffv2DUaMGFE5nfxOjGuvvTbYYYcdgmXLllW+VqxYQT4nwU8//RR07949OPXUU4OPPvoo+O6774JXX301mDt3buU8nBsTY/ny5VXK9NSpU11d5K233nKfc/xInBtuuCHYbLPNgpdeeimYP39+8NRTTwWbbLJJMG7cuMp5KNcNRxDUzEUHQRUVFUHHjh2DW265pXLazz//HOTl5QWPPfaYe//NN9+4782YMaNynilTpgQZGRnB999/38hr0LwO/Mq3adOmVearKuo6KIX+85//uHk+/PBD915BT2ZmZvDDDz9UzjN+/PigdevWQUlJSQrWovlo27ZtcO+995LPSVJcXBxstdVWrgJzwAEHVAZBlOvEBkG62BQL+ZxYV1xxRbDvvvvW+DnnxuTRsaNXr14ujynXiXXEEUcEp59+epVpxx57bDB06FD3f8r1xqE7XJqZP3++e+isusCF2rRpY3vttZd9+OGH7r3+qgvc7rvvXjmP5s/MzLSPPvooJeluDlavXu3+tmvXzv395JNPrKysrEpeq6tLt27dquS1umREPvx34MCBVlRUZF9//XWjr0Nzaf5//PHHbe3ata5bHPmcHOq6om6akeVXyO/EUldkdV3ecsstXRdkdY8lnxPvhRdecOe0448/3nU73mWXXeyee+6p/JxzY3KUlpbaI488YqeffrrrEsfxI7H23ntve+ONN2zOnDnu/axZs+y9996zww8/3L2nXG+c7I38PpoYBUASWekO34ef6a9OEpGys7Nd5T6cB1VVVFS4eyb22Wcf69OnT2U+5ubmuoCytryOtS0itxV+9eWXX7qgR/3JdX/Vs88+a9tvv719/vnn5HOCKcj89NNPbcaMGdU+o1wnji4+PfDAA7bNNtvYsmXLbPTo0bbffvvZV199RT4n2HfffefuLRw5cqT96U9/cmX7oosucseOYcOGcW5MEt2T/PPPP9upp57q3nP8SKwrr7zSXTTVBdasrCx3kfCGG25wF1TC/BbqfA1DEATEedVcFRddgUFyqKKogEctbk8//bSruEybNo3sTrDFixfbiBEjbOrUqdaiRQvyN4nCq7WigT8UFHXv3t2efPJJd5MzEnuhSi1BN954o3uvliAdsydMmOCOJUiO++67z5VztXYi8XSsePTRR23SpEm2ww47uHOkLsgqvynXG4/ucGmmY8eO7m/0CGV6H36mv8uXL6/yuUYr04hx4Tz4PxdccIG99NJL9tZbb1mXLl2q5LW6AugqWG15HWtbRG4r/EpXbHv37m277babG5lvp512snHjxpHPCabuKtr/d911V9cCrJeCzdtvv939X1cUKdfJoVbjrbfe2ubOnUu5TjCNhqqW40jbbbddZfdDzo2Jt3DhQnv99dftzDPPrJzGeTGxNJqsWoNOPPFE17X+D3/4g11yySXuHBnmt1DnaxiCoDSjYRS1U6gPaUhNqbrXR12NRH9VcVdlKPTmm2+6K2m6UolfadwJBUDqlqX8Ud5GUmU9JyenSl5rCG2ddCPzWt28IoNOXYHXMNDRJ2xUpfJYUlJCPifYIYcc4sqkriiGL11BV/eK8P+U6+TQYwjmzZvnKuwcPxJLXZWjH2Gg+yjU8iacGxNv4sSJrmu97i0MUa4Ta926de5+7UjqFqfzo1CuN9JGDqyAFI3qpOGW9dImvO2229z/Fy5cWDlc4qabbho8//zzwRdffBEMHjw45hDZu+yyixtK9L333nOjRDFEdlXnnXeeG2r87bffrjIc6Lp16yrn0VCgGjb7zTffdENk9+vXz72ih8geMGCAG2b7lVdeCdq3b88Q2VGuvPJKN+qehgBVmdV7jVb42muvkc+NIHJ0OMp14lx66aXu+KFy/f7777uh8jVEvkaaJJ8TP9x7dna2G1L422+/DR599NEgPz8/eOSRRyrn4dyYOOXl5e7cp1H5onFeTJxhw4YFW2yxReUQ2Xo0io4hl19+eeU8lOuGIwhqhjQWv4Kf6Jd2lnDIxKuvvjrYfPPN3dDYhxxyiHv2SqQff/zRBT0ab17DNZ922mkuuML/iZXHeunZQSEFlueff74bzlkn3GOOOcYFSpEWLFgQHH744UHLli3dwUsVo7KyMrI6goYA1TM+cnNzXZCoMhsGQORz4wdBlOvEOOGEE4JOnTq5cq2KjN5HPreGfE6sF1980V100nlv2223De6+++4qn3NuTBw9g0nnw+i6hVCuE6eoqMgdmxVwtmjRIthyyy3dcyIjH7FBuW64DP2zsa1JAAAAANBccE8QAAAAAK8QBAEAAADwCkEQAAAAAK8QBAEAAADwCkEQAAAAAK8QBAEAAADwCkEQAAAAAK8QBAEAAADwCkEQAKBBevToYWPHjvUm995++23LyMiwn3/+Oe7vjBo1ynbeeeekpgsAUH8EQQDguVNPPdVV7vXKzc213r1721//+lfbsGFDrd+bMWOGnX322eaLvffe25YtW2Zt2rRJ6HIPPPBAu/jiixO6TABA7bLr+BwA4IHDDjvMJk6caCUlJfbyyy/b8OHDLScnx6666qpq85aWlrpgqX379tYclZWVuXWrL61zx44dk5ImAEDjoiUIAGB5eXmugt+9e3c777zz7NBDD7UXXnihsqXo6KOPthtuuME6d+5s22yzTczucOomds4559jmm29uLVq0sD59+thLL71U+fl7771n++23n7Vs2dK6du1qF110ka1du7bOrmR33XWXmz8/P99+//vf2+rVq6vMd++999p2223nfnPbbbe1f/3rX5WfLViwwLVwPfHEE3bAAQe4eR599NGYv6f5tKxjjjnG/dZWW21VmQc1dYe75557KtOm791222226aabVlv2ww8/7PJLrUgnnniiFRcXV+bttGnTbNy4cZWtcUozACC5CIIAANUoUFGLT+iNN96w2bNn29SpU6sENqGKigo7/PDD7f3337dHHnnEvvnmGxszZoxlZWW5z+fNm+dam4477jj74osvXFCioOiCCy6oNffnzp1rTz75pL344ov2yiuv2GeffWbnn39+5ecKaK655hoXoP3nP/+xG2+80a6++mp78MEHqyznyiuvtBEjRrh5Bg4cWOPvjR492gVaSuOgQYNs6NCh9tNPP8WcV+t67rnnuuV+/vnn1r9/f5eOaFr35557zuWbXgp6lDei4Kdfv3521llnua52eimoAgAkWQAA8NqwYcOCwYMHu/9XVFQEU6dODfLy8oLLLrus8vPNN988KCkpqfK97t27B//4xz/c/1999dUgMzMzmD17dszfOOOMM4Kzzz67yrR3333XfeeXX36J+Z1rr702yMrKCpYsWVI5bcqUKe47y5Ytc+979eoVTJo0qcr3rrvuuqBfv37u//Pnzw90qhs7dmyd+aD5/vKXv1S+X7NmjZum35S33nrLvV+1apV7f8IJJwRHHHFElWUMHTo0aNOmTZV1yM/PD4qKiiqn/fGPfwz22muvyvcHHHBAMGLEiDrTBwBIHO4JAgC4FopNNtnE3S+jVp0hQ4a47mihHXfc0d0TUxO1hHTp0sW23nrrmJ/PmjXLta5EdkVT3KHfmj9/vuvOFku3bt1siy22qHyvVhN9R61SBQUFrpXljDPOcC0pIQ3oED14we677x7XVu7bt2/l/1u1amWtW7e25cuXx5xXaVAXuEh77rlntZYydYNTWkOdOnWqcZkAgMZBEAQAsIMOOsjGjx/vAh3d95OdXfX0oICgru5ztVmzZo27X0j3AcUKdBpCywzvy9lrr72qfBZ2w4s3/aHoARN0j46Cro2RjGUCADYOQRAAwAUJGhq7odSCsmTJEpszZ07M1qBdd93V3SdU399YtGiRLV261AVmMn36dMvMzHSDM2gABk3/7rvv3L07jU1p0DDhkaLfx0OBZ3l5eQJTBgCoC0EQAGCjaeS1/fff3w18oBHSFOz897//da0eGhDhiiuusN/85jduIIQzzzzTBV0KijTQwh133FHjcjWa27Bhw+zvf/+7FRUVuZYkDVwQDlWtgQw0Td3f9Dsa4nvmzJm2atUqGzlyZFK37IUXXujWWet75JFH2ptvvmlTpkxx61wf6i730UcfuVHh1CWxXbt2LtADACQPR1kAQEI888wztscee9hJJ51k22+/vV1++eWVLRxqKdKoaGop0jDZu+yyixvVLWzhqYmCqWOPPdaN1DZgwAC3nMghsBVQaVhrPeNI9y0pGHvggQesZ8+eSd+q++yzj02YMMEFQTvttJMbve6SSy5xgVt9XHbZZa77nvJMz15S6xcAILkyNDpCkn8DAIB608AMGlpagy40FxqgQS1g7777bqqTAgCoBd3hAABoIHXT0/OB1L1PXeH0fKLIlioAQNNEEAQAQAN9/PHH9re//c2Ki4ttyy23tNtvv9110QMANG10hwMAAADgFQZGAAAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAAXiEIAgAAAOAVgiAAAAAA5pP/B0RUpbLc0sezAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Histogram of price per night (dollars) for one sample of size 40.\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(one_sample['price'], bins=30, edgecolor='black', alpha=0.7)\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Histogram of price per night (dollars) for one sample of size 40')\n",
+    "plt.xlabel('Price per night')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2c4d1614",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boot1=one_sample.sample(frac=1, replace=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "033f10e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the histogram of the bootstrap distribution\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(boot1['price'], bins=20, edgecolor='black', alpha=0.7)\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Bootstrap distribution')\n",
+    "plt.xlabel('Price per night ($USD)')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "fa767710",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bootstrap_samples = []  #initialize empty list\n",
+    "for i in range(20_000):\n",
+    "    sample=one_sample.sample(frac=1, replace=True)\n",
+    "    sample = sample.assign(replicate = i)\n",
+    "    bootstrap_samples.append(sample)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1c4e6ecc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boot20000 = pd.concat(bootstrap_samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "507218c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boot_means = boot20000.groupby(\"replicate\")[\"price\"].mean().reset_index(name=\"mean_price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6fab333f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>mean_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>208.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>205.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>183.925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>211.600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>239.475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19995</th>\n",
+       "      <td>19995</td>\n",
+       "      <td>176.725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19996</th>\n",
+       "      <td>19996</td>\n",
+       "      <td>217.450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19997</th>\n",
+       "      <td>19997</td>\n",
+       "      <td>204.600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19998</th>\n",
+       "      <td>19998</td>\n",
+       "      <td>216.900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19999</th>\n",
+       "      <td>19999</td>\n",
+       "      <td>198.375</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       replicate  mean_price\n",
+       "0              0     208.250\n",
+       "1              1     205.800\n",
+       "2              2     183.925\n",
+       "3              3     211.600\n",
+       "4              4     239.475\n",
+       "...          ...         ...\n",
+       "19995      19995     176.725\n",
+       "19996      19996     217.450\n",
+       "19997      19997     204.600\n",
+       "19998      19998     216.900\n",
+       "19999      19999     198.375\n",
+       "\n",
+       "[20000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boot_means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "80d1653a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the histogram of the sampling distribution\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(boot_means['mean_price'], bins=30, edgecolor='black')\n",
+    "\n",
+    "# Add titles and labels\n",
+    "plt.title('Distribution of the bootstrap sample mean')\n",
+    "plt.xlabel('Means')\n",
+    "plt.ylabel('Frequency')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a64e7b8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.025    171.248750\n",
+       "0.975    276.300625\n",
+       "Name: mean_price, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#confidence interval\n",
+    "boot_means[\"mean_price\"].quantile([0.025,0.975])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/08-01-2026.ipynb b/01_materials/notebooks/08-01-2026.ipynb
new file mode 100644
index 000000000..bcaf6306b
--- /dev/null
+++ b/01_materials/notebooks/08-01-2026.ipynb
@@ -0,0 +1,1148 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "67eed476",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import statsmodels.formula.api as smf\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bc985d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#null: no linear relationship between house size and house price; b1=0\n",
+    "#alternative: there is significant relationship between house size and price; b1 !=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "141b912b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "0    Residential  Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767  \n",
+       "1    Residential  Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916  \n",
+       "2    Residential  Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231  \n",
+       "3    Residential  Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522  \n",
+       "4    Residential  Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966  \n",
+       "..           ...                           ...     ...        ...         ...  \n",
+       "808  Residential  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043  \n",
+       "809  Residential  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352  \n",
+       "810  Residential  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224  \n",
+       "811  Residential  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010  \n",
+       "812  Residential  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948  \n",
+       "\n",
+       "[813 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacramento = pd.read_csv(\"../../01_materials/notebooks/dataset/sacramento.csv\")\n",
+    "sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1b928c40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2283d958ad0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between x and y\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento[\"price\"], label='House size vs House price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "020f1135",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#syntax: model (response variable ~ predictor, data), fit()\n",
+    "# Simple linear regression formula\n",
+    "model = smf.ols(\"price ~ sq__ft\", data=sacramento).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8e827edd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.531\n",
+      "Model:                            OLS   Adj. R-squared:                  0.530\n",
+      "Method:                 Least Squares   F-statistic:                     917.9\n",
+      "Date:                Thu, 08 Jan 2026   Prob (F-statistic):          1.88e-135\n",
+      "Time:                        19:17:28   Log-Likelihood:                -10351.\n",
+      "No. Observations:                 813   AIC:                         2.071e+04\n",
+      "Df Residuals:                     811   BIC:                         2.072e+04\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept    1.62e+04   7611.940      2.128      0.034    1254.119    3.11e+04\n",
+      "sq__ft       134.6408      4.444     30.297      0.000     125.918     143.364\n",
+      "==============================================================================\n",
+      "Omnibus:                      305.482   Durbin-Watson:                   1.939\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1623.560\n",
+      "Skew:                           1.628   Prob(JB):                         0.00\n",
+      "Kurtosis:                       9.109   Cond. No.                     4.53e+03\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 4.53e+03. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model.summary())"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "bcfd7f71",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "115dc661",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Residual plots\n",
+    "fitted = model.fittedvalues\n",
+    "residuals = model.resid\n",
+    "\n",
+    "\n",
+    "# Homoscedasticity: Residuals vs. Fitted\n",
+    "plt.scatter(fitted, residuals)\n",
+    "plt.axhline(0, color='red', linestyle='--')\n",
+    "plt.xlabel('Fitted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.title('Residuals vs Fitted')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b370168b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sm.qqplot(residuals, line = 'q')\n",
+    "plt.title('Q-Q Plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d16ef605",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#multivariable linear regression\n",
+    "multi_model = smf.ols(\"price ~ sq__ft + beds\", data=sacramento).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "92af5ea4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.544\n",
+      "Model:                            OLS   Adj. R-squared:                  0.543\n",
+      "Method:                 Least Squares   F-statistic:                     482.9\n",
+      "Date:                Thu, 08 Jan 2026   Prob (F-statistic):          8.39e-139\n",
+      "Time:                        19:24:14   Log-Likelihood:                -10340.\n",
+      "No. Observations:                 813   AIC:                         2.069e+04\n",
+      "Df Residuals:                     810   BIC:                         2.070e+04\n",
+      "Df Model:                           2                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept   5.624e+04   1.12e+04      5.010      0.000    3.42e+04    7.83e+04\n",
+      "sq__ft       156.2329      6.282     24.868      0.000     143.901     168.565\n",
+      "beds       -2.289e+04   4770.454     -4.799      0.000   -3.23e+04   -1.35e+04\n",
+      "==============================================================================\n",
+      "Omnibus:                      315.937   Durbin-Watson:                   1.928\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1853.999\n",
+      "Skew:                           1.656   Prob(JB):                         0.00\n",
+      "Kurtosis:                       9.615   Cond. No.                     7.13e+03\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 7.13e+03. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (multi_model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4c8d488c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Residential', 'Multi-Family', 'Condo'], dtype=object)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacramento[\"type\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "12618ce9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>type_Multi-Family</th>\n",
+       "      <th>type_Residential</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "                        sale_date   price   latitude   longitude  \\\n",
+       "0    Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767   \n",
+       "1    Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916   \n",
+       "2    Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231   \n",
+       "3    Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522   \n",
+       "4    Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966   \n",
+       "..                            ...     ...        ...         ...   \n",
+       "808  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043   \n",
+       "809  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352   \n",
+       "810  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224   \n",
+       "811  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010   \n",
+       "812  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948   \n",
+       "\n",
+       "     type_Multi-Family  type_Residential  \n",
+       "0                False              True  \n",
+       "1                False              True  \n",
+       "2                False              True  \n",
+       "3                False              True  \n",
+       "4                False              True  \n",
+       "..                 ...               ...  \n",
+       "808              False              True  \n",
+       "809              False              True  \n",
+       "810              False              True  \n",
+       "811              False              True  \n",
+       "812              False              True  \n",
+       "\n",
+       "[813 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacramento_encoded = pd.get_dummies(sacramento, columns=[\"type\"],drop_first=True)\n",
+    "sacramento_encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a27b03ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>type_Multi_Family</th>\n",
+       "      <th>type_Residential</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "                        sale_date   price   latitude   longitude  \\\n",
+       "0    Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767   \n",
+       "1    Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916   \n",
+       "2    Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231   \n",
+       "3    Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522   \n",
+       "4    Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966   \n",
+       "..                            ...     ...        ...         ...   \n",
+       "808  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043   \n",
+       "809  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352   \n",
+       "810  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224   \n",
+       "811  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010   \n",
+       "812  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948   \n",
+       "\n",
+       "     type_Multi_Family  type_Residential  \n",
+       "0                False              True  \n",
+       "1                False              True  \n",
+       "2                False              True  \n",
+       "3                False              True  \n",
+       "4                False              True  \n",
+       "..                 ...               ...  \n",
+       "808              False              True  \n",
+       "809              False              True  \n",
+       "810              False              True  \n",
+       "811              False              True  \n",
+       "812              False              True  \n",
+       "\n",
+       "[813 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#rename columns replace hyphens with underscores\n",
+    "sacramento_encoded.columns=sacramento_encoded.columns.str.replace(\"-\",\"_\")\n",
+    "sacramento_encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a2ea3d12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now fit the model using the new column names\n",
+    "multi_cat_model = smf.ols(\"price ~ sq__ft + beds + type_Multi_Family + type_Residential\", data=sacramento_encoded).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "31347139",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.551\n",
+      "Model:                            OLS   Adj. R-squared:                  0.549\n",
+      "Method:                 Least Squares   F-statistic:                     247.6\n",
+      "Date:                Thu, 08 Jan 2026   Prob (F-statistic):          8.92e-139\n",
+      "Time:                        19:34:01   Log-Likelihood:                -10334.\n",
+      "No. Observations:                 813   AIC:                         2.068e+04\n",
+      "Df Residuals:                     808   BIC:                         2.070e+04\n",
+      "Df Model:                           4                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "=============================================================================================\n",
+      "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "---------------------------------------------------------------------------------------------\n",
+      "Intercept                  2.927e+04   1.42e+04      2.061      0.040    1387.739    5.71e+04\n",
+      "type_Multi_Family[T.True] -1.248e+04   2.72e+04     -0.458      0.647   -6.59e+04     4.1e+04\n",
+      "type_Residential[T.True]   3.683e+04   1.37e+04      2.681      0.007    9869.779    6.38e+04\n",
+      "sq__ft                      156.5807      6.268     24.980      0.000     144.277     168.884\n",
+      "beds                      -2.527e+04   5059.791     -4.995      0.000   -3.52e+04   -1.53e+04\n",
+      "==============================================================================\n",
+      "Omnibus:                      321.334   Durbin-Watson:                   1.949\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1932.454\n",
+      "Skew:                           1.680   Prob(JB):                         0.00\n",
+      "Kurtosis:                       9.764   Cond. No.                     1.75e+04\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 1.75e+04. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(multi_cat_model.summary())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/09-12-2025.ipynb b/01_materials/notebooks/09-12-2025.ipynb
new file mode 100644
index 000000000..51fc53015
--- /dev/null
+++ b/01_materials/notebooks/09-12-2025.ipynb
@@ -0,0 +1,2559 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93d84571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load libraries\n",
+    "import pandas as pd     #advanced version of excel. Useful for complex analysis and transformations with just a few lines of code\n",
+    "import matplotlib.pyplot as plt         \n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6864eb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load in our data set. \n",
+    "cancer = pd.read_csv(\"dataset/wdbc.csv\")\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "945d91da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 569 entries, 0 to 568\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       569 non-null    int64  \n",
+      " 1   diagnosis                569 non-null    object \n",
+      " 2   radius_mean              569 non-null    float64\n",
+      " 3   texture_mean             569 non-null    float64\n",
+      " 4   perimeter_mean           569 non-null    float64\n",
+      " 5   area_mean                569 non-null    float64\n",
+      " 6   smoothness_mean          569 non-null    float64\n",
+      " 7   compactness_mean         569 non-null    float64\n",
+      " 8   concavity_mean           569 non-null    float64\n",
+      " 9   concave points_mean      569 non-null    float64\n",
+      " 10  symmetry_mean            569 non-null    float64\n",
+      " 11  fractal_dimension_mean   569 non-null    float64\n",
+      " 12  radius_se                569 non-null    float64\n",
+      " 13  texture_se               569 non-null    float64\n",
+      " 14  perimeter_se             569 non-null    float64\n",
+      " 15  area_se                  569 non-null    float64\n",
+      " 16  smoothness_se            569 non-null    float64\n",
+      " 17  compactness_se           569 non-null    float64\n",
+      " 18  concavity_se             569 non-null    float64\n",
+      " 19  concave points_se        569 non-null    float64\n",
+      " 20  symmetry_se              569 non-null    float64\n",
+      " 21  fractal_dimension_se     569 non-null    float64\n",
+      " 22  radius_worst             569 non-null    float64\n",
+      " 23  texture_worst            569 non-null    float64\n",
+      " 24  perimeter_worst          569 non-null    float64\n",
+      " 25  area_worst               569 non-null    float64\n",
+      " 26  smoothness_worst         569 non-null    float64\n",
+      " 27  compactness_worst        569 non-null    float64\n",
+      " 28  concavity_worst          569 non-null    float64\n",
+      " 29  concave points_worst     569 non-null    float64\n",
+      " 30  symmetry_worst           569 non-null    float64\n",
+      " 31  fractal_dimension_worst  569 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 142.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer.info()   #give us a summary of our dataset. Various columns, non-null counts, data type etc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d8cebc37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['M', 'B'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].unique()    #what categories we have"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "27c96c16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer[\"diagnosis\"] = cancer[\"diagnosis\"].replace({\n",
+    "    \"M\" : \"Malignant\",\n",
+    "    \"B\": \"Benign\"\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "344fcd7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant', 'Benign'], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "13abab27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       357\n",
+       "Malignant    212\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "768bf99f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diagnosis\n",
+       "Benign       0.627417\n",
+       "Malignant    0.372583\n",
+       "Name: proportion, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"].value_counts(normalize=True)        #to make it in percentage by adding normalize = true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9778563a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Plot\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "db86da9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot existing data\n",
+    "plt.scatter(cancer[\"perimeter_mean\"], cancer['concavity_mean'], \n",
+    "            color=cancer[\"diagnosis\"].map(color_map))\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add new observation\n",
+    "new_observation = {'perimeter_mean': 97, 'concavity_mean': 0.20}\n",
+    "plt.scatter(new_observation['perimeter_mean'], new_observation['concavity_mean'],\n",
+    "            color='red', edgecolor='black', s=100, label='New Observation')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Perimeter Mean')\n",
+    "plt.ylabel('Concavity Mean')\n",
+    "plt.title('Scatter Plot of Perimeter Mean vs Concavity Mean')\n",
+    "plt.legend(handles=handles + [plt.Line2D([0], [0], marker='o', color='w', \n",
+    "                                          markerfacecolor='red', markeredgecolor='black', \n",
+    "                                          markersize=10, label='New Observation')], \n",
+    "           title='Diagnosis')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c8f005c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# perimeter mean = 97\n",
+    "# concavity mean = 0.20\n",
+    "\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.2\n",
+    "\n",
+    "cancer[\"dist_from_new\"] = ((cancer[\"perimeter_mean\"] - new_obs_Perimeter)**2 +\n",
+    "(cancer[\"concavity_mean\"] - new_obs_Concavity)**2)**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "60317ac6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>25.800194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>35.900178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>33.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>19.420044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>38.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>45.000021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>34.200046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>11.300511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>43.100266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>49.080407</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0                0.14710  ...          17.33           184.60      2019.0   \n",
+       "1                0.07017  ...          23.41           158.80      1956.0   \n",
+       "2                0.12790  ...          25.53           152.50      1709.0   \n",
+       "3                0.10520  ...          26.50            98.87       567.7   \n",
+       "4                0.10430  ...          16.67           152.20      1575.0   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "564              0.13890  ...          26.40           166.10      2027.0   \n",
+       "565              0.09791  ...          38.25           155.00      1731.0   \n",
+       "566              0.05302  ...          34.12           126.70      1124.0   \n",
+       "567              0.15200  ...          39.42           184.60      1821.0   \n",
+       "568              0.00000  ...          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "0                  0.2654          0.4601                  0.11890   \n",
+       "1                  0.1860          0.2750                  0.08902   \n",
+       "2                  0.2430          0.3613                  0.08758   \n",
+       "3                  0.2575          0.6638                  0.17300   \n",
+       "4                  0.1625          0.2364                  0.07678   \n",
+       "..                    ...             ...                      ...   \n",
+       "564                0.2216          0.2060                  0.07115   \n",
+       "565                0.1628          0.2572                  0.06637   \n",
+       "566                0.1418          0.2218                  0.07820   \n",
+       "567                0.2650          0.4087                  0.12400   \n",
+       "568                0.0000          0.2871                  0.07039   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "0        25.800194  \n",
+       "1        35.900178  \n",
+       "2        33.000000  \n",
+       "3        19.420044  \n",
+       "4        38.100000  \n",
+       "..             ...  \n",
+       "564      45.000021  \n",
+       "565      34.200046  \n",
+       "566      11.300511  \n",
+       "567      43.100266  \n",
+       "568      49.080407  \n",
+       "\n",
+       "[569 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c823af69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>8915</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>19.10</td>\n",
+       "      <td>97.03</td>\n",
+       "      <td>687.3</td>\n",
+       "      <td>0.08992</td>\n",
+       "      <td>0.09823</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.04819</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>109.1</td>\n",
+       "      <td>809.8</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>0.3030</td>\n",
+       "      <td>0.1804</td>\n",
+       "      <td>0.1489</td>\n",
+       "      <td>0.2962</td>\n",
+       "      <td>0.08472</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.43</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.2164</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.1667</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.13</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.6577</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.1712</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.06</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.2101</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.1120</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.69</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.2057</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.1530</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "291      8915     Benign        14.96         19.10           97.03   \n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "291      687.3          0.08992           0.09823         0.05940   \n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "291              0.04819  ...          26.19            109.1       809.8   \n",
+       "138              0.08624  ...          21.43            121.4       971.4   \n",
+       "15               0.07364  ...          37.13            124.1       943.2   \n",
+       "514              0.04335  ...          28.06            113.8       967.0   \n",
+       "54               0.03334  ...          31.69            117.7      1030.0   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "291            0.1313             0.3030           0.1804   \n",
+       "138            0.1411             0.2164           0.3355   \n",
+       "15             0.1678             0.6577           0.7026   \n",
+       "514            0.1246             0.2101           0.2866   \n",
+       "54             0.1389             0.2057           0.2712   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "291                0.1489          0.2962                  0.08472   \n",
+       "138                0.1667          0.3414                  0.07147   \n",
+       "15                 0.1712          0.4218                  0.13410   \n",
+       "514                0.1120          0.2282                  0.06954   \n",
+       "54                 0.1530          0.2675                  0.07873   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "291       0.143765  \n",
+       "138       0.156924  \n",
+       "15        0.272403  \n",
+       "514       0.288548  \n",
+       "54        0.298910  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#k = 5. To see the five closest to the new observation\n",
+    "\n",
+    "cancer.nsmallest(5, \"dist_from_new\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "47a5df0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create mapping between values and colors\n",
+    "labels = cancer[\"diagnosis\"].unique().tolist()\n",
+    "colors = list(mcolors.TABLEAU_COLORS.keys())\n",
+    "color_map = {l: colors[i % len(colors)] for i, l in enumerate(labels)}\n",
+    "\n",
+    "# Create a 3D plot\n",
+    "ax = plt.axes(projection=\"3d\")\n",
+    "\n",
+    "# Plot data points with color corresponding to diagnosis\n",
+    "sc = ax.scatter3D(cancer['perimeter_mean'], cancer['concavity_mean'], cancer['symmetry_mean'], \n",
+    "                  c=cancer['diagnosis'].map(color_map), marker='o')\n",
+    "\n",
+    "# Add axis labels\n",
+    "ax.set_xlabel('Perimeter Mean')\n",
+    "ax.set_ylabel('Concavity Mean')\n",
+    "ax.set_zlabel('Symmetry Mean')\n",
+    "ax.set_title('3D Scatter Plot of Perimeter Mean, Concavity Mean, and Symmetry Mean')\n",
+    "\n",
+    "# Create custom legend handles\n",
+    "handles = [plt.Line2D([0], [0], marker='o', color='w', label=label,\n",
+    "                      markersize=10, markerfacecolor=color_map[label])\n",
+    "           for label in labels]\n",
+    "\n",
+    "# Add legend\n",
+    "plt.legend(handles=handles, title='Diagnosis')\n",
+    "\n",
+    "# Show plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a3885e72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#we want to include a third feature\n",
+    "\n",
+    "new_obs_Perimeter = 97\n",
+    "new_obs_Concavity = 0.20\n",
+    "new_obs_Symmetry = 0.22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2834a52f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       332.825250\n",
+       "1       644.412149\n",
+       "2       544.500089\n",
+       "3       188.569845\n",
+       "4       725.805766\n",
+       "          ...     \n",
+       "564    1012.502087\n",
+       "565     584.822572\n",
+       "566      63.852638\n",
+       "567     928.816655\n",
+       "568    1204.445079\n",
+       "Length: 569, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((cancer[\"perimeter_mean\"] - new_obs_Perimeter)**2 + \n",
+    " (cancer[\"concavity_mean\"] - new_obs_Concavity)**2 +\n",
+    " (cancer[\"symmetry_mean\"] - new_obs_Symmetry)**2)**1/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c980a649",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>dist_from_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>8915</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>19.10</td>\n",
+       "      <td>97.03</td>\n",
+       "      <td>687.3</td>\n",
+       "      <td>0.08992</td>\n",
+       "      <td>0.09823</td>\n",
+       "      <td>0.05940</td>\n",
+       "      <td>0.04819</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.19</td>\n",
+       "      <td>109.1</td>\n",
+       "      <td>809.8</td>\n",
+       "      <td>0.1313</td>\n",
+       "      <td>0.3030</td>\n",
+       "      <td>0.1804</td>\n",
+       "      <td>0.1489</td>\n",
+       "      <td>0.2962</td>\n",
+       "      <td>0.08472</td>\n",
+       "      <td>0.143765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>868826</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.95</td>\n",
+       "      <td>17.57</td>\n",
+       "      <td>96.85</td>\n",
+       "      <td>678.1</td>\n",
+       "      <td>0.11670</td>\n",
+       "      <td>0.13050</td>\n",
+       "      <td>0.15390</td>\n",
+       "      <td>0.08624</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.43</td>\n",
+       "      <td>121.4</td>\n",
+       "      <td>971.4</td>\n",
+       "      <td>0.1411</td>\n",
+       "      <td>0.2164</td>\n",
+       "      <td>0.3355</td>\n",
+       "      <td>0.1667</td>\n",
+       "      <td>0.3414</td>\n",
+       "      <td>0.07147</td>\n",
+       "      <td>0.156924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>84799002</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>14.54</td>\n",
+       "      <td>27.54</td>\n",
+       "      <td>96.73</td>\n",
+       "      <td>658.8</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.15950</td>\n",
+       "      <td>0.16390</td>\n",
+       "      <td>0.07364</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.13</td>\n",
+       "      <td>124.1</td>\n",
+       "      <td>943.2</td>\n",
+       "      <td>0.1678</td>\n",
+       "      <td>0.6577</td>\n",
+       "      <td>0.7026</td>\n",
+       "      <td>0.1712</td>\n",
+       "      <td>0.4218</td>\n",
+       "      <td>0.13410</td>\n",
+       "      <td>0.272403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>514</th>\n",
+       "      <td>91594602</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.05</td>\n",
+       "      <td>19.07</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>701.9</td>\n",
+       "      <td>0.09215</td>\n",
+       "      <td>0.08597</td>\n",
+       "      <td>0.07486</td>\n",
+       "      <td>0.04335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.06</td>\n",
+       "      <td>113.8</td>\n",
+       "      <td>967.0</td>\n",
+       "      <td>0.1246</td>\n",
+       "      <td>0.2101</td>\n",
+       "      <td>0.2866</td>\n",
+       "      <td>0.1120</td>\n",
+       "      <td>0.2282</td>\n",
+       "      <td>0.06954</td>\n",
+       "      <td>0.288548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>857438</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>15.10</td>\n",
+       "      <td>22.02</td>\n",
+       "      <td>97.26</td>\n",
+       "      <td>712.8</td>\n",
+       "      <td>0.09056</td>\n",
+       "      <td>0.07081</td>\n",
+       "      <td>0.05253</td>\n",
+       "      <td>0.03334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.69</td>\n",
+       "      <td>117.7</td>\n",
+       "      <td>1030.0</td>\n",
+       "      <td>0.1389</td>\n",
+       "      <td>0.2057</td>\n",
+       "      <td>0.2712</td>\n",
+       "      <td>0.1530</td>\n",
+       "      <td>0.2675</td>\n",
+       "      <td>0.07873</td>\n",
+       "      <td>0.298910</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "291      8915     Benign        14.96         19.10           97.03   \n",
+       "138    868826  Malignant        14.95         17.57           96.85   \n",
+       "15   84799002  Malignant        14.54         27.54           96.73   \n",
+       "514  91594602  Malignant        15.05         19.07           97.26   \n",
+       "54     857438  Malignant        15.10         22.02           97.26   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "291      687.3          0.08992           0.09823         0.05940   \n",
+       "138      678.1          0.11670           0.13050         0.15390   \n",
+       "15       658.8          0.11390           0.15950         0.16390   \n",
+       "514      701.9          0.09215           0.08597         0.07486   \n",
+       "54       712.8          0.09056           0.07081         0.05253   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "291              0.04819  ...          26.19            109.1       809.8   \n",
+       "138              0.08624  ...          21.43            121.4       971.4   \n",
+       "15               0.07364  ...          37.13            124.1       943.2   \n",
+       "514              0.04335  ...          28.06            113.8       967.0   \n",
+       "54               0.03334  ...          31.69            117.7      1030.0   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "291            0.1313             0.3030           0.1804   \n",
+       "138            0.1411             0.2164           0.3355   \n",
+       "15             0.1678             0.6577           0.7026   \n",
+       "514            0.1246             0.2101           0.2866   \n",
+       "54             0.1389             0.2057           0.2712   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
+       "291                0.1489          0.2962                  0.08472   \n",
+       "138                0.1667          0.3414                  0.07147   \n",
+       "15                 0.1712          0.4218                  0.13410   \n",
+       "514                0.1120          0.2282                  0.06954   \n",
+       "54                 0.1530          0.2675                  0.07873   \n",
+       "\n",
+       "     dist_from_new  \n",
+       "291       0.143765  \n",
+       "138       0.156924  \n",
+       "15        0.272403  \n",
+       "514       0.288548  \n",
+       "54        0.298910  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# k = 5\n",
+    "cancer.nsmallest(5,\"dist_from_new\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1273a076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import set_config\n",
+    "#output data frames instead of arrays\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9051cb65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "211ce403",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>0.30010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>0.08690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>0.19740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>0.24140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>0.19800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>0.24390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>0.14400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>0.09251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>0.35140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>Benign</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     diagnosis  perimeter_mean  concavity_mean\n",
+       "0    Malignant          122.80         0.30010\n",
+       "1    Malignant          132.90         0.08690\n",
+       "2    Malignant          130.00         0.19740\n",
+       "3    Malignant           77.58         0.24140\n",
+       "4    Malignant          135.10         0.19800\n",
+       "..         ...             ...             ...\n",
+       "564  Malignant          142.00         0.24390\n",
+       "565  Malignant          131.20         0.14400\n",
+       "566  Malignant          108.30         0.09251\n",
+       "567  Malignant          140.10         0.35140\n",
+       "568     Benign           47.92         0.00000\n",
+       "\n",
+       "[569 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_train = cancer[[\"diagnosis\", \"perimeter_mean\", \"concavity_mean\"]]\n",
+    "cancer_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "948e327f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1. Initialize our model\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "a3657b73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 2. Define our predictors and response variable\n",
+    "\n",
+    "X = cancer_train[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "c42ce8af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 3. Fit our model to our data\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8bcc78be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>97</td>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   perimeter_mean  concavity_mean\n",
+       "0              97             0.2"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_obs = pd.DataFrame({\"perimeter_mean\": [97], \"concavity_mean\": [0.2]})\n",
+    "new_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "242a9f44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Malignant'], dtype=object)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use the predict method on our trained model to predict whether the new observation is M or B\n",
+    "knn.predict(new_obs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/10-12-2025.ipynb b/01_materials/notebooks/10-12-2025.ipynb
new file mode 100644
index 000000000..6abe8b626
--- /dev/null
+++ b/01_materials/notebooks/10-12-2025.ipynb
@@ -0,0 +1,4919 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0075424e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load all libraries\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5333fa84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>M</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>M</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>M</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>M</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>M</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>B</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
+       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
+       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
+       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
+       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
+       "..        ...       ...          ...           ...             ...        ...   \n",
+       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
+       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
+       "566    926954         M        16.60         28.08          108.30      858.1   \n",
+       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
+       "568     92751         B         7.76         24.54           47.92      181.0   \n",
+       "\n",
+       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+       "0            0.11840           0.27760         0.30010              0.14710   \n",
+       "1            0.08474           0.07864         0.08690              0.07017   \n",
+       "2            0.10960           0.15990         0.19740              0.12790   \n",
+       "3            0.14250           0.28390         0.24140              0.10520   \n",
+       "4            0.10030           0.13280         0.19800              0.10430   \n",
+       "..               ...               ...             ...                  ...   \n",
+       "564          0.11100           0.11590         0.24390              0.13890   \n",
+       "565          0.09780           0.10340         0.14400              0.09791   \n",
+       "566          0.08455           0.10230         0.09251              0.05302   \n",
+       "567          0.11780           0.27700         0.35140              0.15200   \n",
+       "568          0.05263           0.04362         0.00000              0.00000   \n",
+       "\n",
+       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
+       "0    ...        25.380          17.33           184.60      2019.0   \n",
+       "1    ...        24.990          23.41           158.80      1956.0   \n",
+       "2    ...        23.570          25.53           152.50      1709.0   \n",
+       "3    ...        14.910          26.50            98.87       567.7   \n",
+       "4    ...        22.540          16.67           152.20      1575.0   \n",
+       "..   ...           ...            ...              ...         ...   \n",
+       "564  ...        25.450          26.40           166.10      2027.0   \n",
+       "565  ...        23.690          38.25           155.00      1731.0   \n",
+       "566  ...        18.980          34.12           126.70      1124.0   \n",
+       "567  ...        25.740          39.42           184.60      1821.0   \n",
+       "568  ...         9.456          30.37            59.16       268.6   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer = pd.read_csv('dataset/wdbc.csv')\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "00c67dc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.380</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.990</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.570</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.910</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.540</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.450</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.690</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.980</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.740</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.456</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant        17.99         10.38          122.80   \n",
+       "1      842517  Malignant        20.57         17.77          132.90   \n",
+       "2    84300903  Malignant        19.69         21.25          130.00   \n",
+       "3    84348301  Malignant        11.42         20.38           77.58   \n",
+       "4    84358402  Malignant        20.29         14.34          135.10   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant        21.56         22.39          142.00   \n",
+       "565    926682  Malignant        20.13         28.25          131.20   \n",
+       "566    926954  Malignant        16.60         28.08          108.30   \n",
+       "567    927241  Malignant        20.60         29.33          140.10   \n",
+       "568     92751     Benign         7.76         24.54           47.92   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0       1001.0          0.11840           0.27760         0.30010   \n",
+       "1       1326.0          0.08474           0.07864         0.08690   \n",
+       "2       1203.0          0.10960           0.15990         0.19740   \n",
+       "3        386.1          0.14250           0.28390         0.24140   \n",
+       "4       1297.0          0.10030           0.13280         0.19800   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564     1479.0          0.11100           0.11590         0.24390   \n",
+       "565     1261.0          0.09780           0.10340         0.14400   \n",
+       "566      858.1          0.08455           0.10230         0.09251   \n",
+       "567     1265.0          0.11780           0.27700         0.35140   \n",
+       "568      181.0          0.05263           0.04362         0.00000   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0                0.14710  ...        25.380          17.33           184.60   \n",
+       "1                0.07017  ...        24.990          23.41           158.80   \n",
+       "2                0.12790  ...        23.570          25.53           152.50   \n",
+       "3                0.10520  ...        14.910          26.50            98.87   \n",
+       "4                0.10430  ...        22.540          16.67           152.20   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564              0.13890  ...        25.450          26.40           166.10   \n",
+       "565              0.09791  ...        23.690          38.25           155.00   \n",
+       "566              0.05302  ...        18.980          34.12           126.70   \n",
+       "567              0.15200  ...        25.740          39.42           184.60   \n",
+       "568              0.00000  ...         9.456          30.37            59.16   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0        2019.0           0.16220            0.66560           0.7119   \n",
+       "1        1956.0           0.12380            0.18660           0.2416   \n",
+       "2        1709.0           0.14440            0.42450           0.4504   \n",
+       "3         567.7           0.20980            0.86630           0.6869   \n",
+       "4        1575.0           0.13740            0.20500           0.4000   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564      2027.0           0.14100            0.21130           0.4107   \n",
+       "565      1731.0           0.11660            0.19220           0.3215   \n",
+       "566      1124.0           0.11390            0.30940           0.3403   \n",
+       "567      1821.0           0.16500            0.86810           0.9387   \n",
+       "568       268.6           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                  0.2654          0.4601                  0.11890  \n",
+       "1                  0.1860          0.2750                  0.08902  \n",
+       "2                  0.2430          0.3613                  0.08758  \n",
+       "3                  0.2575          0.6638                  0.17300  \n",
+       "4                  0.1625          0.2364                  0.07678  \n",
+       "..                    ...             ...                      ...  \n",
+       "564                0.2216          0.2060                  0.07115  \n",
+       "565                0.1628          0.2572                  0.06637  \n",
+       "566                0.1418          0.2218                  0.07820  \n",
+       "567                0.2650          0.4087                  0.12400  \n",
+       "568                0.0000          0.2871                  0.07039  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer[\"diagnosis\"]= cancer[\"diagnosis\"].replace({\n",
+    "       \"M\" : \"Malignant\",\n",
+    "       \"B\" : \"Benign\"})\n",
+    "cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bbb7603e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardized_cancer = cancer.copy()   #make a copy of the data\n",
+    "columns_to_exclude = ['id','diagnosis'] #columns we don't want to scale\n",
+    "columns_to_scale = standardized_cancer.columns.difference(columns_to_exclude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "88ac9d91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()   #initialize scaler\n",
+    "standardized_cancer[columns_to_scale] = scaler.fit_transform(standardized_cancer[columns_to_scale])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "42c3090b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>radius_worst</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>842302</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.097064</td>\n",
+       "      <td>-2.073335</td>\n",
+       "      <td>1.269934</td>\n",
+       "      <td>0.984375</td>\n",
+       "      <td>1.568466</td>\n",
+       "      <td>3.283515</td>\n",
+       "      <td>2.652874</td>\n",
+       "      <td>2.532475</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.886690</td>\n",
+       "      <td>-1.359293</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>2.001237</td>\n",
+       "      <td>1.307686</td>\n",
+       "      <td>2.616665</td>\n",
+       "      <td>2.109526</td>\n",
+       "      <td>2.296076</td>\n",
+       "      <td>2.750622</td>\n",
+       "      <td>1.937015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>842517</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.829821</td>\n",
+       "      <td>-0.353632</td>\n",
+       "      <td>1.685955</td>\n",
+       "      <td>1.908708</td>\n",
+       "      <td>-0.826962</td>\n",
+       "      <td>-0.487072</td>\n",
+       "      <td>-0.023846</td>\n",
+       "      <td>0.548144</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.805927</td>\n",
+       "      <td>-0.369203</td>\n",
+       "      <td>1.535126</td>\n",
+       "      <td>1.890489</td>\n",
+       "      <td>-0.375612</td>\n",
+       "      <td>-0.430444</td>\n",
+       "      <td>-0.146749</td>\n",
+       "      <td>1.087084</td>\n",
+       "      <td>-0.243890</td>\n",
+       "      <td>0.281190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>84300903</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.579888</td>\n",
+       "      <td>0.456187</td>\n",
+       "      <td>1.566503</td>\n",
+       "      <td>1.558884</td>\n",
+       "      <td>0.942210</td>\n",
+       "      <td>1.052926</td>\n",
+       "      <td>1.363478</td>\n",
+       "      <td>2.037231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.511870</td>\n",
+       "      <td>-0.023974</td>\n",
+       "      <td>1.347475</td>\n",
+       "      <td>1.456285</td>\n",
+       "      <td>0.527407</td>\n",
+       "      <td>1.082932</td>\n",
+       "      <td>0.854974</td>\n",
+       "      <td>1.955000</td>\n",
+       "      <td>1.152255</td>\n",
+       "      <td>0.201391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84348301</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>-0.768909</td>\n",
+       "      <td>0.253732</td>\n",
+       "      <td>-0.592687</td>\n",
+       "      <td>-0.764464</td>\n",
+       "      <td>3.283553</td>\n",
+       "      <td>3.402909</td>\n",
+       "      <td>1.915897</td>\n",
+       "      <td>1.451707</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.281464</td>\n",
+       "      <td>0.133984</td>\n",
+       "      <td>-0.249939</td>\n",
+       "      <td>-0.550021</td>\n",
+       "      <td>3.394275</td>\n",
+       "      <td>3.893397</td>\n",
+       "      <td>1.989588</td>\n",
+       "      <td>2.175786</td>\n",
+       "      <td>6.046041</td>\n",
+       "      <td>4.935010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84358402</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.750297</td>\n",
+       "      <td>-1.151816</td>\n",
+       "      <td>1.776573</td>\n",
+       "      <td>1.826229</td>\n",
+       "      <td>0.280372</td>\n",
+       "      <td>0.539340</td>\n",
+       "      <td>1.371011</td>\n",
+       "      <td>1.428493</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.298575</td>\n",
+       "      <td>-1.466770</td>\n",
+       "      <td>1.338539</td>\n",
+       "      <td>1.220724</td>\n",
+       "      <td>0.220556</td>\n",
+       "      <td>-0.313395</td>\n",
+       "      <td>0.613179</td>\n",
+       "      <td>0.729259</td>\n",
+       "      <td>-0.868353</td>\n",
+       "      <td>-0.397100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>926424</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>2.110995</td>\n",
+       "      <td>0.721473</td>\n",
+       "      <td>2.060786</td>\n",
+       "      <td>2.343856</td>\n",
+       "      <td>1.041842</td>\n",
+       "      <td>0.219060</td>\n",
+       "      <td>1.947285</td>\n",
+       "      <td>2.320965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.901185</td>\n",
+       "      <td>0.117700</td>\n",
+       "      <td>1.752563</td>\n",
+       "      <td>2.015301</td>\n",
+       "      <td>0.378365</td>\n",
+       "      <td>-0.273318</td>\n",
+       "      <td>0.664512</td>\n",
+       "      <td>1.629151</td>\n",
+       "      <td>-1.360158</td>\n",
+       "      <td>-0.709091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>926682</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.704854</td>\n",
+       "      <td>2.085134</td>\n",
+       "      <td>1.615931</td>\n",
+       "      <td>1.723842</td>\n",
+       "      <td>0.102458</td>\n",
+       "      <td>-0.017833</td>\n",
+       "      <td>0.693043</td>\n",
+       "      <td>1.263669</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.536720</td>\n",
+       "      <td>2.047399</td>\n",
+       "      <td>1.421940</td>\n",
+       "      <td>1.494959</td>\n",
+       "      <td>-0.691230</td>\n",
+       "      <td>-0.394820</td>\n",
+       "      <td>0.236573</td>\n",
+       "      <td>0.733827</td>\n",
+       "      <td>-0.531855</td>\n",
+       "      <td>-0.973978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>926954</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>0.702284</td>\n",
+       "      <td>2.045574</td>\n",
+       "      <td>0.672676</td>\n",
+       "      <td>0.577953</td>\n",
+       "      <td>-0.840484</td>\n",
+       "      <td>-0.038680</td>\n",
+       "      <td>0.046588</td>\n",
+       "      <td>0.105777</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.561361</td>\n",
+       "      <td>1.374854</td>\n",
+       "      <td>0.579001</td>\n",
+       "      <td>0.427906</td>\n",
+       "      <td>-0.809587</td>\n",
+       "      <td>0.350735</td>\n",
+       "      <td>0.326767</td>\n",
+       "      <td>0.414069</td>\n",
+       "      <td>-1.104549</td>\n",
+       "      <td>-0.318409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>927241</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>1.838341</td>\n",
+       "      <td>2.336457</td>\n",
+       "      <td>1.982524</td>\n",
+       "      <td>1.735218</td>\n",
+       "      <td>1.525767</td>\n",
+       "      <td>3.272144</td>\n",
+       "      <td>3.296944</td>\n",
+       "      <td>2.658866</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.961239</td>\n",
+       "      <td>2.237926</td>\n",
+       "      <td>2.303601</td>\n",
+       "      <td>1.653171</td>\n",
+       "      <td>1.430427</td>\n",
+       "      <td>3.904848</td>\n",
+       "      <td>3.197605</td>\n",
+       "      <td>2.289985</td>\n",
+       "      <td>1.919083</td>\n",
+       "      <td>2.219635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>92751</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.808401</td>\n",
+       "      <td>1.221792</td>\n",
+       "      <td>-1.814389</td>\n",
+       "      <td>-1.347789</td>\n",
+       "      <td>-3.112085</td>\n",
+       "      <td>-1.150752</td>\n",
+       "      <td>-1.114873</td>\n",
+       "      <td>-1.261820</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.410893</td>\n",
+       "      <td>0.764190</td>\n",
+       "      <td>-1.432735</td>\n",
+       "      <td>-1.075813</td>\n",
+       "      <td>-1.859019</td>\n",
+       "      <td>-1.207552</td>\n",
+       "      <td>-1.305831</td>\n",
+       "      <td>-1.745063</td>\n",
+       "      <td>-0.048138</td>\n",
+       "      <td>-0.751207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "0      842302  Malignant     1.097064     -2.073335        1.269934   \n",
+       "1      842517  Malignant     1.829821     -0.353632        1.685955   \n",
+       "2    84300903  Malignant     1.579888      0.456187        1.566503   \n",
+       "3    84348301  Malignant    -0.768909      0.253732       -0.592687   \n",
+       "4    84358402  Malignant     1.750297     -1.151816        1.776573   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "564    926424  Malignant     2.110995      0.721473        2.060786   \n",
+       "565    926682  Malignant     1.704854      2.085134        1.615931   \n",
+       "566    926954  Malignant     0.702284      2.045574        0.672676   \n",
+       "567    927241  Malignant     1.838341      2.336457        1.982524   \n",
+       "568     92751     Benign    -1.808401      1.221792       -1.814389   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "0     0.984375         1.568466          3.283515        2.652874   \n",
+       "1     1.908708        -0.826962         -0.487072       -0.023846   \n",
+       "2     1.558884         0.942210          1.052926        1.363478   \n",
+       "3    -0.764464         3.283553          3.402909        1.915897   \n",
+       "4     1.826229         0.280372          0.539340        1.371011   \n",
+       "..         ...              ...               ...             ...   \n",
+       "564   2.343856         1.041842          0.219060        1.947285   \n",
+       "565   1.723842         0.102458         -0.017833        0.693043   \n",
+       "566   0.577953        -0.840484         -0.038680        0.046588   \n",
+       "567   1.735218         1.525767          3.272144        3.296944   \n",
+       "568  -1.347789        -3.112085         -1.150752       -1.114873   \n",
+       "\n",
+       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
+       "0               2.532475  ...      1.886690      -1.359293         2.303601   \n",
+       "1               0.548144  ...      1.805927      -0.369203         1.535126   \n",
+       "2               2.037231  ...      1.511870      -0.023974         1.347475   \n",
+       "3               1.451707  ...     -0.281464       0.133984        -0.249939   \n",
+       "4               1.428493  ...      1.298575      -1.466770         1.338539   \n",
+       "..                   ...  ...           ...            ...              ...   \n",
+       "564             2.320965  ...      1.901185       0.117700         1.752563   \n",
+       "565             1.263669  ...      1.536720       2.047399         1.421940   \n",
+       "566             0.105777  ...      0.561361       1.374854         0.579001   \n",
+       "567             2.658866  ...      1.961239       2.237926         2.303601   \n",
+       "568            -1.261820  ...     -1.410893       0.764190        -1.432735   \n",
+       "\n",
+       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "0      2.001237          1.307686           2.616665         2.109526   \n",
+       "1      1.890489         -0.375612          -0.430444        -0.146749   \n",
+       "2      1.456285          0.527407           1.082932         0.854974   \n",
+       "3     -0.550021          3.394275           3.893397         1.989588   \n",
+       "4      1.220724          0.220556          -0.313395         0.613179   \n",
+       "..          ...               ...                ...              ...   \n",
+       "564    2.015301          0.378365          -0.273318         0.664512   \n",
+       "565    1.494959         -0.691230          -0.394820         0.236573   \n",
+       "566    0.427906         -0.809587           0.350735         0.326767   \n",
+       "567    1.653171          1.430427           3.904848         3.197605   \n",
+       "568   -1.075813         -1.859019          -1.207552        -1.305831   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
+       "0                2.296076        2.750622                 1.937015  \n",
+       "1                1.087084       -0.243890                 0.281190  \n",
+       "2                1.955000        1.152255                 0.201391  \n",
+       "3                2.175786        6.046041                 4.935010  \n",
+       "4                0.729259       -0.868353                -0.397100  \n",
+       "..                    ...             ...                      ...  \n",
+       "564              1.629151       -1.360158                -0.709091  \n",
+       "565              0.733827       -0.531855                -0.973978  \n",
+       "566              0.414069       -1.104549                -0.318409  \n",
+       "567              2.289985        1.919083                 2.219635  \n",
+       "568             -1.745063       -0.048138                -0.751207  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "standardized_cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8175354c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#set the seed\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "#split the data\n",
+    "cancer_train, cancer_test = train_test_split(standardized_cancer,train_size=0.75, shuffle=True, stratify=standardized_cancer[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1b958728",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 426 entries, 164 to 284\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       426 non-null    int64  \n",
+      " 1   diagnosis                426 non-null    object \n",
+      " 2   radius_mean              426 non-null    float64\n",
+      " 3   texture_mean             426 non-null    float64\n",
+      " 4   perimeter_mean           426 non-null    float64\n",
+      " 5   area_mean                426 non-null    float64\n",
+      " 6   smoothness_mean          426 non-null    float64\n",
+      " 7   compactness_mean         426 non-null    float64\n",
+      " 8   concavity_mean           426 non-null    float64\n",
+      " 9   concave points_mean      426 non-null    float64\n",
+      " 10  symmetry_mean            426 non-null    float64\n",
+      " 11  fractal_dimension_mean   426 non-null    float64\n",
+      " 12  radius_se                426 non-null    float64\n",
+      " 13  texture_se               426 non-null    float64\n",
+      " 14  perimeter_se             426 non-null    float64\n",
+      " 15  area_se                  426 non-null    float64\n",
+      " 16  smoothness_se            426 non-null    float64\n",
+      " 17  compactness_se           426 non-null    float64\n",
+      " 18  concavity_se             426 non-null    float64\n",
+      " 19  concave points_se        426 non-null    float64\n",
+      " 20  symmetry_se              426 non-null    float64\n",
+      " 21  fractal_dimension_se     426 non-null    float64\n",
+      " 22  radius_worst             426 non-null    float64\n",
+      " 23  texture_worst            426 non-null    float64\n",
+      " 24  perimeter_worst          426 non-null    float64\n",
+      " 25  area_worst               426 non-null    float64\n",
+      " 26  smoothness_worst         426 non-null    float64\n",
+      " 27  compactness_worst        426 non-null    float64\n",
+      " 28  concavity_worst          426 non-null    float64\n",
+      " 29  concave points_worst     426 non-null    float64\n",
+      " 30  symmetry_worst           426 non-null    float64\n",
+      " 31  fractal_dimension_worst  426 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 109.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d74e3d9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 143 entries, 357 to 332\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   id                       143 non-null    int64  \n",
+      " 1   diagnosis                143 non-null    object \n",
+      " 2   radius_mean              143 non-null    float64\n",
+      " 3   texture_mean             143 non-null    float64\n",
+      " 4   perimeter_mean           143 non-null    float64\n",
+      " 5   area_mean                143 non-null    float64\n",
+      " 6   smoothness_mean          143 non-null    float64\n",
+      " 7   compactness_mean         143 non-null    float64\n",
+      " 8   concavity_mean           143 non-null    float64\n",
+      " 9   concave points_mean      143 non-null    float64\n",
+      " 10  symmetry_mean            143 non-null    float64\n",
+      " 11  fractal_dimension_mean   143 non-null    float64\n",
+      " 12  radius_se                143 non-null    float64\n",
+      " 13  texture_se               143 non-null    float64\n",
+      " 14  perimeter_se             143 non-null    float64\n",
+      " 15  area_se                  143 non-null    float64\n",
+      " 16  smoothness_se            143 non-null    float64\n",
+      " 17  compactness_se           143 non-null    float64\n",
+      " 18  concavity_se             143 non-null    float64\n",
+      " 19  concave points_se        143 non-null    float64\n",
+      " 20  symmetry_se              143 non-null    float64\n",
+      " 21  fractal_dimension_se     143 non-null    float64\n",
+      " 22  radius_worst             143 non-null    float64\n",
+      " 23  texture_worst            143 non-null    float64\n",
+      " 24  perimeter_worst          143 non-null    float64\n",
+      " 25  area_worst               143 non-null    float64\n",
+      " 26  smoothness_worst         143 non-null    float64\n",
+      " 27  compactness_worst        143 non-null    float64\n",
+      " 28  concavity_worst          143 non-null    float64\n",
+      " 29  concave points_worst     143 non-null    float64\n",
+      " 30  symmetry_worst           143 non-null    float64\n",
+      " 31  fractal_dimension_worst  143 non-null    float64\n",
+      "dtypes: float64(30), int64(1), object(1)\n",
+      "memory usage: 36.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "cancer_test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f13959a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# k = 5\n",
+    "#Step 1. Initialize our model\n",
+    "knn = KNeighborsClassifier(n_neighbors=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2bccd77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 2. define our X and y variables (Predictor(X) and response(y))\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "34230104",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Step 3. fit model to train data\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5ab4ae95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th>radius_mean</th>\n",
+       "      <th>texture_mean</th>\n",
+       "      <th>perimeter_mean</th>\n",
+       "      <th>area_mean</th>\n",
+       "      <th>smoothness_mean</th>\n",
+       "      <th>compactness_mean</th>\n",
+       "      <th>concavity_mean</th>\n",
+       "      <th>concave points_mean</th>\n",
+       "      <th>...</th>\n",
+       "      <th>texture_worst</th>\n",
+       "      <th>perimeter_worst</th>\n",
+       "      <th>area_worst</th>\n",
+       "      <th>smoothness_worst</th>\n",
+       "      <th>compactness_worst</th>\n",
+       "      <th>concavity_worst</th>\n",
+       "      <th>concave points_worst</th>\n",
+       "      <th>symmetry_worst</th>\n",
+       "      <th>fractal_dimension_worst</th>\n",
+       "      <th>predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>901028</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.073075</td>\n",
+       "      <td>-0.716655</td>\n",
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+       "      <td>-0.174028</td>\n",
+       "      <td>-0.635527</td>\n",
+       "      <td>-0.936601</td>\n",
+       "      <td>-0.926297</td>\n",
+       "      <td>-0.723241</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.015832</td>\n",
+       "      <td>-0.313383</td>\n",
+       "      <td>-0.327294</td>\n",
+       "      <td>-0.748217</td>\n",
+       "      <td>-0.976252</td>\n",
+       "      <td>-1.052282</td>\n",
+       "      <td>-0.899073</td>\n",
+       "      <td>-0.871589</td>\n",
+       "      <td>-0.710199</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>901041</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.234963</td>\n",
+       "      <td>0.530653</td>\n",
+       "      <td>-0.277170</td>\n",
+       "      <td>-0.309407</td>\n",
+       "      <td>-0.750104</td>\n",
+       "      <td>-0.769638</td>\n",
+       "      <td>-0.695035</td>\n",
+       "      <td>-0.636573</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.573662</td>\n",
+       "      <td>-0.426569</td>\n",
+       "      <td>-0.455973</td>\n",
+       "      <td>-0.805204</td>\n",
+       "      <td>-0.557036</td>\n",
+       "      <td>-0.724371</td>\n",
+       "      <td>-0.890242</td>\n",
+       "      <td>-0.426699</td>\n",
+       "      <td>-0.962341</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>212</th>\n",
+       "      <td>8810703</td>\n",
+       "      <td>Malignant</td>\n",
+       "      <td>3.971288</td>\n",
+       "      <td>-0.190738</td>\n",
+       "      <td>3.976130</td>\n",
+       "      <td>5.244841</td>\n",
+       "      <td>1.269571</td>\n",
+       "      <td>0.895628</td>\n",
+       "      <td>2.903973</td>\n",
+       "      <td>2.852321</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.173652</td>\n",
+       "      <td>2.419765</td>\n",
+       "      <td>2.845036</td>\n",
+       "      <td>-0.796437</td>\n",
+       "      <td>-0.653093</td>\n",
+       "      <td>0.229857</td>\n",
+       "      <td>0.683579</td>\n",
+       "      <td>-2.026684</td>\n",
+       "      <td>-1.590202</td>\n",
+       "      <td>Malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>91813702</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.507616</td>\n",
+       "      <td>-1.633519</td>\n",
+       "      <td>-0.536668</td>\n",
+       "      <td>-0.530110</td>\n",
+       "      <td>-0.450497</td>\n",
+       "      <td>-0.782146</td>\n",
+       "      <td>-0.743497</td>\n",
+       "      <td>-0.579053</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.043377</td>\n",
+       "      <td>-0.596944</td>\n",
+       "      <td>-0.554943</td>\n",
+       "      <td>-0.138898</td>\n",
+       "      <td>-0.298127</td>\n",
+       "      <td>-0.446594</td>\n",
+       "      <td>-0.115817</td>\n",
+       "      <td>0.338511</td>\n",
+       "      <td>-0.444757</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
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+       "      <td>Benign</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>-1.631243</td>\n",
+       "      <td>-1.254913</td>\n",
+       "      <td>-0.994422</td>\n",
+       "      <td>0.001377</td>\n",
+       "      <td>-0.887193</td>\n",
+       "      <td>-0.880434</td>\n",
+       "      <td>-0.796903</td>\n",
+       "      <td>-0.729224</td>\n",
+       "      <td>-0.344455</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>9010877</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.206561</td>\n",
+       "      <td>-0.544452</td>\n",
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+       "      <td>-0.291490</td>\n",
+       "      <td>-1.209121</td>\n",
+       "      <td>-0.897940</td>\n",
+       "      <td>-0.841049</td>\n",
+       "      <td>-0.881874</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.647666</td>\n",
+       "      <td>-0.402145</td>\n",
+       "      <td>-0.381613</td>\n",
+       "      <td>-0.485202</td>\n",
+       "      <td>-0.551311</td>\n",
+       "      <td>-0.651448</td>\n",
+       "      <td>-0.681180</td>\n",
+       "      <td>-0.258450</td>\n",
+       "      <td>-0.450299</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>908469</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>0.208100</td>\n",
+       "      <td>-0.546779</td>\n",
+       "      <td>0.120315</td>\n",
+       "      <td>0.053500</td>\n",
+       "      <td>-0.506718</td>\n",
+       "      <td>-0.636788</td>\n",
+       "      <td>-0.694784</td>\n",
+       "      <td>-0.519727</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.836565</td>\n",
+       "      <td>-0.147774</td>\n",
+       "      <td>-0.181211</td>\n",
+       "      <td>-0.463284</td>\n",
+       "      <td>-0.631464</td>\n",
+       "      <td>-0.720533</td>\n",
+       "      <td>-0.531351</td>\n",
+       "      <td>-0.607890</td>\n",
+       "      <td>-0.868688</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>892399</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-1.027362</td>\n",
+       "      <td>0.884367</td>\n",
+       "      <td>-1.034658</td>\n",
+       "      <td>-0.912073</td>\n",
+       "      <td>0.365770</td>\n",
+       "      <td>-0.689284</td>\n",
+       "      <td>-0.801626</td>\n",
+       "      <td>-0.778182</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.237300</td>\n",
+       "      <td>-1.106878</td>\n",
+       "      <td>-0.910393</td>\n",
+       "      <td>-0.792053</td>\n",
+       "      <td>-1.069510</td>\n",
+       "      <td>-1.106350</td>\n",
+       "      <td>-1.269232</td>\n",
+       "      <td>-1.089989</td>\n",
+       "      <td>-0.896396</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>488</th>\n",
+       "      <td>913512</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.695066</td>\n",
+       "      <td>-0.725963</td>\n",
+       "      <td>-0.678775</td>\n",
+       "      <td>-0.666627</td>\n",
+       "      <td>1.169940</td>\n",
+       "      <td>-0.221940</td>\n",
+       "      <td>-0.577646</td>\n",
+       "      <td>-0.453952</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.665579</td>\n",
+       "      <td>-0.616305</td>\n",
+       "      <td>-0.581488</td>\n",
+       "      <td>0.886862</td>\n",
+       "      <td>-0.677903</td>\n",
+       "      <td>-0.591000</td>\n",
+       "      <td>-0.250572</td>\n",
+       "      <td>-0.156530</td>\n",
+       "      <td>-0.205361</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>897132</td>\n",
+       "      <td>Benign</td>\n",
+       "      <td>-0.825712</td>\n",
+       "      <td>0.132725</td>\n",
+       "      <td>-0.825000</td>\n",
+       "      <td>-0.761051</td>\n",
+       "      <td>0.643316</td>\n",
+       "      <td>-0.692695</td>\n",
+       "      <td>-1.052023</td>\n",
+       "      <td>-1.066224</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.016737</td>\n",
+       "      <td>-0.904036</td>\n",
+       "      <td>-0.781363</td>\n",
+       "      <td>0.439736</td>\n",
+       "      <td>-1.002397</td>\n",
+       "      <td>-1.241784</td>\n",
+       "      <td>-1.437181</td>\n",
+       "      <td>0.632947</td>\n",
+       "      <td>-1.037706</td>\n",
+       "      <td>Benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
+       "357    901028     Benign    -0.073075     -0.716655       -0.142066   \n",
+       "361    901041     Benign    -0.234963      0.530653       -0.277170   \n",
+       "212   8810703  Malignant     3.971288     -0.190738        3.976130   \n",
+       "527  91813702     Benign    -0.507616     -1.633519       -0.536668   \n",
+       "21    8510824     Benign    -1.313080     -1.593959       -1.302806   \n",
+       "..        ...        ...          ...           ...             ...   \n",
+       "364   9010877     Benign    -0.206561     -0.544452       -0.267285   \n",
+       "434    908469     Benign     0.208100     -0.546779        0.120315   \n",
+       "299    892399     Benign    -1.027362      0.884367       -1.034658   \n",
+       "488    913512     Benign    -0.695066     -0.725963       -0.678775   \n",
+       "332    897132     Benign    -0.825712      0.132725       -0.825000   \n",
+       "\n",
+       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
+       "357  -0.174028        -0.635527         -0.936601       -0.926297   \n",
+       "361  -0.309407        -0.750104         -0.769638       -0.695035   \n",
+       "212   5.244841         1.269571          0.895628        2.903973   \n",
+       "527  -0.530110        -0.450497         -0.782146       -0.743497   \n",
+       "21   -1.083572         0.429819         -0.747086       -0.743748   \n",
+       "..         ...              ...               ...             ...   \n",
+       "364  -0.291490        -1.209121         -0.897940       -0.841049   \n",
+       "434   0.053500        -0.506718         -0.636788       -0.694784   \n",
+       "299  -0.912073         0.365770         -0.689284       -0.801626   \n",
+       "488  -0.666627         1.169940         -0.221940       -0.577646   \n",
+       "332  -0.761051         0.643316         -0.692695       -1.052023   \n",
+       "\n",
+       "     concave points_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+       "357            -0.723241  ...      -0.015832        -0.313383   -0.327294   \n",
+       "361            -0.636573  ...       0.573662        -0.426569   -0.455973   \n",
+       "212             2.852321  ...      -1.173652         2.419765    2.845036   \n",
+       "527            -0.579053  ...      -1.043377        -0.596944   -0.554943   \n",
+       "21             -0.726337  ...      -1.631243        -1.254913   -0.994422   \n",
+       "..                   ...  ...            ...              ...         ...   \n",
+       "364            -0.881874  ...      -0.647666        -0.402145   -0.381613   \n",
+       "434            -0.519727  ...      -0.836565        -0.147774   -0.181211   \n",
+       "299            -0.778182  ...      -0.237300        -1.106878   -0.910393   \n",
+       "488            -0.453952  ...      -0.665579        -0.616305   -0.581488   \n",
+       "332            -1.066224  ...       0.016737        -0.904036   -0.781363   \n",
+       "\n",
+       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
+       "357         -0.748217          -0.976252        -1.052282   \n",
+       "361         -0.805204          -0.557036        -0.724371   \n",
+       "212         -0.796437          -0.653093         0.229857   \n",
+       "527         -0.138898          -0.298127        -0.446594   \n",
+       "21           0.001377          -0.887193        -0.880434   \n",
+       "..                ...                ...              ...   \n",
+       "364         -0.485202          -0.551311        -0.651448   \n",
+       "434         -0.463284          -0.631464        -0.720533   \n",
+       "299         -0.792053          -1.069510        -1.106350   \n",
+       "488          0.886862          -0.677903        -0.591000   \n",
+       "332          0.439736          -1.002397        -1.241784   \n",
+       "\n",
+       "     concave points_worst  symmetry_worst  fractal_dimension_worst  predicted  \n",
+       "357             -0.899073       -0.871589                -0.710199     Benign  \n",
+       "361             -0.890242       -0.426699                -0.962341     Benign  \n",
+       "212              0.683579       -2.026684                -1.590202  Malignant  \n",
+       "527             -0.115817        0.338511                -0.444757     Benign  \n",
+       "21              -0.796903       -0.729224                -0.344455     Benign  \n",
+       "..                    ...             ...                      ...        ...  \n",
+       "364             -0.681180       -0.258450                -0.450299     Benign  \n",
+       "434             -0.531351       -0.607890                -0.868688     Benign  \n",
+       "299             -1.269232       -1.089989                -0.896396     Benign  \n",
+       "488             -0.250572       -0.156530                -0.205361     Benign  \n",
+       "332             -1.437181        0.632947                -1.037706     Benign  \n",
+       "\n",
+       "[143 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_test[\"predicted\"] = knn.predict(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]])\n",
+    "cancer_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5fd3d768",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9230769230769231"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(cancer_test[[\"perimeter_mean\",\"concavity_mean\"]], cancer_test[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "459d4560",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>predicted</th>\n",
+       "      <th>Benign</th>\n",
+       "      <th>Malignant</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Benign</th>\n",
+       "      <td>88</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Malignant</th>\n",
+       "      <td>9</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "predicted  Benign  Malignant\n",
+       "diagnosis                   \n",
+       "Benign         88          2\n",
+       "Malignant       9         44"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#confusion matrix\n",
+    "pd.crosstab(\n",
+    "    cancer_test[\"diagnosis\"],\n",
+    "    cancer_test[\"predicted\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4d9853e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9565217391304348"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(\n",
+    "    y_true=cancer_test[\"diagnosis\"],\n",
+    "    y_pred= cancer_test[\"predicted\"],\n",
+    "    pos_label=\"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a20d6647",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8301886792452831"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recall_score(\n",
+    "    y_true=cancer_test[\"diagnosis\"],\n",
+    "    y_pred= cancer_test[\"predicted\"],\n",
+    "    pos_label=\"Malignant\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70a2dc71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating a new test data\n",
+    "cancer_subtrain, cancer_validation = train_test_split(cancer_train,train_size=0.75,shuffle=True,stratify=cancer_train[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "5e20c812",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#initialize our model\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_subtrain[[\"perimeter_mean\", \"concavity_mean\"]]\n",
+    "y = cancer_subtrain[\"diagnosis\"]\n",
+    "knn.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b24e81ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8785046728971962"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "knn.score(cancer_validation[[\"perimeter_mean\",\"concavity_mean\"]],cancer_validation[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "2b823eb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#perform 5-fold cross validation\n",
+    "knn = KNeighborsClassifier(n_neighbors=3)\n",
+    "X = cancer_train[[\"perimeter_mean\",\"concavity_mean\"]]\n",
+    "y = cancer_train[\"diagnosis\"]\n",
+    "\n",
+    "returned_dictionary = cross_validate(\n",
+    "    estimator=knn,\n",
+    "    cv=5,    # setting up the cross validation number\n",
+    "    X=X,\n",
+    "    y=y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0ffdcf6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'fit_time': array([0.00652242, 0.00210071, 0.00290155, 0.00212359, 0.00197005]),\n",
+       " 'score_time': array([0.00770831, 0.00274205, 0.00307512, 0.00303745, 0.00399065]),\n",
+       " 'test_score': array([0.93023256, 0.89411765, 0.87058824, 0.95294118, 0.91764706])}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "returned_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "6a961fca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.006522</td>\n",
+       "      <td>0.007708</td>\n",
+       "      <td>0.930233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002101</td>\n",
+       "      <td>0.002742</td>\n",
+       "      <td>0.894118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002902</td>\n",
+       "      <td>0.003075</td>\n",
+       "      <td>0.870588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002124</td>\n",
+       "      <td>0.003037</td>\n",
+       "      <td>0.952941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001970</td>\n",
+       "      <td>0.003991</td>\n",
+       "      <td>0.917647</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time  test_score\n",
+       "0  0.006522    0.007708    0.930233\n",
+       "1  0.002101    0.002742    0.894118\n",
+       "2  0.002902    0.003075    0.870588\n",
+       "3  0.002124    0.003037    0.952941\n",
+       "4  0.001970    0.003991    0.917647"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8f5970d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.003124</td>\n",
+       "      <td>0.004111</td>\n",
+       "      <td>0.913105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000865</td>\n",
+       "      <td>0.000923</td>\n",
+       "      <td>0.014264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.003124    0.004111    0.913105\n",
+       "sem   0.000865    0.000923    0.014264"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df.agg([\"mean\",\"sem\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "12afda62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 1: define our perimeter grid i.e. the values of K and by how it shall jump and the maximum value which is 100 -1 = 99\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1,100,5)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a34e2fec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 2. initialize our grid search CV object\n",
+    "\n",
+    "cancer_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "47ddf522",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 100, 5)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">KNeighborsCla...n_neighbors=3)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 100, 5)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=16)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">16</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=3),\n",
+       "             param_grid={'n_neighbors': range(1, 100, 5)})"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#step 3. Fit our grid search object to our training data\n",
+    "\n",
+    "cancer_tune_grid.fit(cancer_train[[\"perimeter_mean\",\"concavity_mean\"]],cancer_train[\"diagnosis\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e948188e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>split5_test_score</th>\n",
+       "      <th>split6_test_score</th>\n",
+       "      <th>split7_test_score</th>\n",
+       "      <th>split8_test_score</th>\n",
+       "      <th>split9_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.004022</td>\n",
+       "      <td>0.001627</td>\n",
+       "      <td>0.004648</td>\n",
+       "      <td>0.001257</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.875692</td>\n",
+       "      <td>0.038684</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.003000</td>\n",
+       "      <td>0.000843</td>\n",
+       "      <td>0.003607</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{'n_neighbors': 6}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.908638</td>\n",
+       "      <td>0.043373</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.002526</td>\n",
+       "      <td>0.000480</td>\n",
+       "      <td>0.003696</td>\n",
+       "      <td>0.000719</td>\n",
+       "      <td>11</td>\n",
+       "      <td>{'n_neighbors': 11}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904042</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.002469</td>\n",
+       "      <td>0.000644</td>\n",
+       "      <td>0.004114</td>\n",
+       "      <td>0.001528</td>\n",
+       "      <td>16</td>\n",
+       "      <td>{'n_neighbors': 16}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920487</td>\n",
+       "      <td>0.045315</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005070</td>\n",
+       "      <td>0.001692</td>\n",
+       "      <td>0.008402</td>\n",
+       "      <td>0.001946</td>\n",
+       "      <td>21</td>\n",
+       "      <td>{'n_neighbors': 21}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.880952</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.046495</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.004117</td>\n",
+       "      <td>0.001096</td>\n",
+       "      <td>0.005850</td>\n",
+       "      <td>0.002055</td>\n",
+       "      <td>26</td>\n",
+       "      <td>{'n_neighbors': 26}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.002597</td>\n",
+       "      <td>0.000676</td>\n",
+       "      <td>0.004119</td>\n",
+       "      <td>0.000886</td>\n",
+       "      <td>31</td>\n",
+       "      <td>{'n_neighbors': 31}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913400</td>\n",
+       "      <td>0.043989</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.002851</td>\n",
+       "      <td>0.000623</td>\n",
+       "      <td>0.004173</td>\n",
+       "      <td>0.000591</td>\n",
+       "      <td>36</td>\n",
+       "      <td>{'n_neighbors': 36}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.002462</td>\n",
+       "      <td>0.000568</td>\n",
+       "      <td>0.003884</td>\n",
+       "      <td>0.000581</td>\n",
+       "      <td>41</td>\n",
+       "      <td>{'n_neighbors': 41}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.911074</td>\n",
+       "      <td>0.044873</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.002515</td>\n",
+       "      <td>0.000504</td>\n",
+       "      <td>0.004125</td>\n",
+       "      <td>0.000628</td>\n",
+       "      <td>46</td>\n",
+       "      <td>{'n_neighbors': 46}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915781</td>\n",
+       "      <td>0.045368</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.002479</td>\n",
+       "      <td>0.000352</td>\n",
+       "      <td>0.003724</td>\n",
+       "      <td>0.000614</td>\n",
+       "      <td>51</td>\n",
+       "      <td>{'n_neighbors': 51}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.883721</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913455</td>\n",
+       "      <td>0.046345</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.002602</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.003921</td>\n",
+       "      <td>0.000585</td>\n",
+       "      <td>56</td>\n",
+       "      <td>{'n_neighbors': 56}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.044212</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.003282</td>\n",
+       "      <td>0.000709</td>\n",
+       "      <td>0.003869</td>\n",
+       "      <td>0.000624</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{'n_neighbors': 61}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.918106</td>\n",
+       "      <td>0.041731</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.003108</td>\n",
+       "      <td>0.001201</td>\n",
+       "      <td>0.004267</td>\n",
+       "      <td>0.001060</td>\n",
+       "      <td>66</td>\n",
+       "      <td>{'n_neighbors': 66}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.002786</td>\n",
+       "      <td>0.000635</td>\n",
+       "      <td>0.003731</td>\n",
+       "      <td>0.000632</td>\n",
+       "      <td>71</td>\n",
+       "      <td>{'n_neighbors': 71}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.953488</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.837209</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.041694</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.002590</td>\n",
+       "      <td>0.000899</td>\n",
+       "      <td>0.004029</td>\n",
+       "      <td>0.000587</td>\n",
+       "      <td>76</td>\n",
+       "      <td>{'n_neighbors': 76}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.002770</td>\n",
+       "      <td>0.001069</td>\n",
+       "      <td>0.004509</td>\n",
+       "      <td>0.000529</td>\n",
+       "      <td>81</td>\n",
+       "      <td>{'n_neighbors': 81}</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.920432</td>\n",
+       "      <td>0.048861</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.002745</td>\n",
+       "      <td>0.000559</td>\n",
+       "      <td>0.004672</td>\n",
+       "      <td>0.000771</td>\n",
+       "      <td>86</td>\n",
+       "      <td>{'n_neighbors': 86}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.928571</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.915725</td>\n",
+       "      <td>0.047731</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.002459</td>\n",
+       "      <td>0.000731</td>\n",
+       "      <td>0.003993</td>\n",
+       "      <td>0.000554</td>\n",
+       "      <td>91</td>\n",
+       "      <td>{'n_neighbors': 91}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.002426</td>\n",
+       "      <td>0.000355</td>\n",
+       "      <td>0.003997</td>\n",
+       "      <td>0.000435</td>\n",
+       "      <td>96</td>\n",
+       "      <td>{'n_neighbors': 96}</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.976744</td>\n",
+       "      <td>0.930233</td>\n",
+       "      <td>0.860465</td>\n",
+       "      <td>0.813953</td>\n",
+       "      <td>0.906977</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.952381</td>\n",
+       "      <td>0.904762</td>\n",
+       "      <td>0.976190</td>\n",
+       "      <td>0.913344</td>\n",
+       "      <td>0.047625</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.004022      0.001627         0.004648        0.001257   \n",
+       "1        0.003000      0.000843         0.003607        0.000635   \n",
+       "2        0.002526      0.000480         0.003696        0.000719   \n",
+       "3        0.002469      0.000644         0.004114        0.001528   \n",
+       "4        0.005070      0.001692         0.008402        0.001946   \n",
+       "5        0.004117      0.001096         0.005850        0.002055   \n",
+       "6        0.002597      0.000676         0.004119        0.000886   \n",
+       "7        0.002851      0.000623         0.004173        0.000591   \n",
+       "8        0.002462      0.000568         0.003884        0.000581   \n",
+       "9        0.002515      0.000504         0.004125        0.000628   \n",
+       "10       0.002479      0.000352         0.003724        0.000614   \n",
+       "11       0.002602      0.000447         0.003921        0.000585   \n",
+       "12       0.003282      0.000709         0.003869        0.000624   \n",
+       "13       0.003108      0.001201         0.004267        0.001060   \n",
+       "14       0.002786      0.000635         0.003731        0.000632   \n",
+       "15       0.002590      0.000899         0.004029        0.000587   \n",
+       "16       0.002770      0.001069         0.004509        0.000529   \n",
+       "17       0.002745      0.000559         0.004672        0.000771   \n",
+       "18       0.002459      0.000731         0.003993        0.000554   \n",
+       "19       0.002426      0.000355         0.003997        0.000435   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           0.953488   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.906977   \n",
+       "3                  16  {'n_neighbors': 16}           0.906977   \n",
+       "4                  21  {'n_neighbors': 21}           0.906977   \n",
+       "5                  26  {'n_neighbors': 26}           0.906977   \n",
+       "6                  31  {'n_neighbors': 31}           0.906977   \n",
+       "7                  36  {'n_neighbors': 36}           0.906977   \n",
+       "8                  41  {'n_neighbors': 41}           0.906977   \n",
+       "9                  46  {'n_neighbors': 46}           0.906977   \n",
+       "10                 51  {'n_neighbors': 51}           0.906977   \n",
+       "11                 56  {'n_neighbors': 56}           0.906977   \n",
+       "12                 61  {'n_neighbors': 61}           0.906977   \n",
+       "13                 66  {'n_neighbors': 66}           0.930233   \n",
+       "14                 71  {'n_neighbors': 71}           0.930233   \n",
+       "15                 76  {'n_neighbors': 76}           0.930233   \n",
+       "16                 81  {'n_neighbors': 81}           0.930233   \n",
+       "17                 86  {'n_neighbors': 86}           0.906977   \n",
+       "18                 91  {'n_neighbors': 91}           0.906977   \n",
+       "19                 96  {'n_neighbors': 96}           0.906977   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0            0.837209           0.906977           0.860465   \n",
+       "1            0.953488           0.906977           0.837209   \n",
+       "2            0.930233           0.906977           0.837209   \n",
+       "3            0.953488           0.930233           0.837209   \n",
+       "4            0.953488           0.930233           0.837209   \n",
+       "5            0.953488           0.930233           0.837209   \n",
+       "6            0.953488           0.930233           0.837209   \n",
+       "7            0.953488           0.930233           0.837209   \n",
+       "8            0.953488           0.930233           0.837209   \n",
+       "9            0.953488           0.930233           0.837209   \n",
+       "10           0.953488           0.930233           0.837209   \n",
+       "11           0.976744           0.930233           0.860465   \n",
+       "12           0.953488           0.930233           0.860465   \n",
+       "13           0.953488           0.930233           0.860465   \n",
+       "14           0.953488           0.930233           0.860465   \n",
+       "15           0.976744           0.930233           0.860465   \n",
+       "16           0.976744           0.930233           0.860465   \n",
+       "17           0.976744           0.930233           0.860465   \n",
+       "18           0.976744           0.930233           0.860465   \n",
+       "19           0.976744           0.930233           0.860465   \n",
+       "\n",
+       "    split4_test_score  split5_test_score  split6_test_score  \\\n",
+       "0            0.813953           0.837209           0.880952   \n",
+       "1            0.837209           0.906977           0.928571   \n",
+       "2            0.837209           0.883721           0.952381   \n",
+       "3            0.837209           0.930233           0.952381   \n",
+       "4            0.837209           0.906977           0.952381   \n",
+       "5            0.837209           0.906977           0.952381   \n",
+       "6            0.837209           0.906977           0.952381   \n",
+       "7            0.837209           0.883721           0.928571   \n",
+       "8            0.837209           0.883721           0.952381   \n",
+       "9            0.837209           0.906977           0.952381   \n",
+       "10           0.837209           0.883721           0.952381   \n",
+       "11           0.837209           0.906977           0.952381   \n",
+       "12           0.837209           0.906977           0.952381   \n",
+       "13           0.837209           0.906977           0.952381   \n",
+       "14           0.837209           0.906977           0.952381   \n",
+       "15           0.813953           0.906977           0.952381   \n",
+       "16           0.813953           0.906977           0.952381   \n",
+       "17           0.813953           0.906977           0.928571   \n",
+       "18           0.813953           0.906977           0.904762   \n",
+       "19           0.813953           0.906977           0.904762   \n",
+       "\n",
+       "    split7_test_score  split8_test_score  split9_test_score  mean_test_score  \\\n",
+       "0            0.904762           0.880952           0.880952         0.875692   \n",
+       "1            0.928571           0.880952           0.976190         0.908638   \n",
+       "2            0.904762           0.928571           0.952381         0.904042   \n",
+       "3            0.952381           0.928571           0.976190         0.920487   \n",
+       "4            0.952381           0.880952           0.976190         0.913400   \n",
+       "5            0.928571           0.904762           0.976190         0.913400   \n",
+       "6            0.928571           0.904762           0.976190         0.913400   \n",
+       "7            0.952381           0.904762           0.976190         0.911074   \n",
+       "8            0.928571           0.904762           0.976190         0.911074   \n",
+       "9            0.952381           0.904762           0.976190         0.915781   \n",
+       "10           0.952381           0.904762           0.976190         0.913455   \n",
+       "11           0.952381           0.904762           0.976190         0.920432   \n",
+       "12           0.952381           0.904762           0.976190         0.918106   \n",
+       "13           0.952381           0.904762           0.976190         0.920432   \n",
+       "14           0.952381           0.904762           0.976190         0.920432   \n",
+       "15           0.952381           0.904762           0.976190         0.920432   \n",
+       "16           0.952381           0.904762           0.976190         0.920432   \n",
+       "17           0.952381           0.904762           0.976190         0.915725   \n",
+       "18           0.952381           0.904762           0.976190         0.913344   \n",
+       "19           0.952381           0.904762           0.976190         0.913344   \n",
+       "\n",
+       "    std_test_score  rank_test_score  \n",
+       "0         0.038684               20  \n",
+       "1         0.043373               18  \n",
+       "2         0.039147               19  \n",
+       "3         0.045315                1  \n",
+       "4         0.046495               11  \n",
+       "5         0.043989               11  \n",
+       "6         0.043989               11  \n",
+       "7         0.044873               16  \n",
+       "8         0.044873               16  \n",
+       "9         0.045368                8  \n",
+       "10        0.046345               10  \n",
+       "11        0.044212                2  \n",
+       "12        0.041731                7  \n",
+       "13        0.041694                2  \n",
+       "14        0.041694                2  \n",
+       "15        0.048861                2  \n",
+       "16        0.048861                2  \n",
+       "17        0.047731                9  \n",
+       "18        0.047625               14  \n",
+       "19        0.047625               14  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_grid = pd.DataFrame(cancer_tune_grid.cv_results_)\n",
+    "accuracy_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "15c9b4f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b8b69b58",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 16}"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer_tune_grid.best_params_  #keeps the best value of k"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/16-12-2025.ipynb b/01_materials/notebooks/16-12-2025.ipynb
new file mode 100644
index 000000000..8f64d5c5a
--- /dev/null
+++ b/01_materials/notebooks/16-12-2025.ipynb
@@ -0,0 +1,2616 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0382ba25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+    "from sklearn.compose import make_column_transformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import set_config\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "e101acbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#output data frames instead of arrays\n",
+    "\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "00f31fe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "0    Residential  Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767  \n",
+       "1    Residential  Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916  \n",
+       "2    Residential  Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231  \n",
+       "3    Residential  Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522  \n",
+       "4    Residential  Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966  \n",
+       "..           ...                           ...     ...        ...         ...  \n",
+       "808  Residential  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043  \n",
+       "809  Residential  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352  \n",
+       "810  Residential  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224  \n",
+       "811  Residential  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010  \n",
+       "812  Residential  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948  \n",
+       "\n",
+       "[813 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load in our dataset\n",
+    "sacramento = pd.read_csv(\"dataset/sacramento.csv\")\n",
+    "sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "672a0671",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento['price'])\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "cd889140",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>486</th>\n",
+       "      <td>7540 HICKORY AVE</td>\n",
+       "      <td>ORANGEVALE</td>\n",
+       "      <td>95662</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1456</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>225000</td>\n",
+       "      <td>38.703056</td>\n",
+       "      <td>-121.235221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>9013 CASALS ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95826</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>795</td>\n",
+       "      <td>Condo</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>126960</td>\n",
+       "      <td>38.557045</td>\n",
+       "      <td>-121.371670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>12901 FURLONG DR</td>\n",
+       "      <td>WILTON</td>\n",
+       "      <td>95693</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3788</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>691659</td>\n",
+       "      <td>38.413535</td>\n",
+       "      <td>-121.188211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>9474 VILLAGE TREE DR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>210000</td>\n",
+       "      <td>38.413947</td>\n",
+       "      <td>-121.408276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>549</th>\n",
+       "      <td>2901 PINTAIL WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3070</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>495000</td>\n",
+       "      <td>38.398488</td>\n",
+       "      <td>-121.473424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>191 BARNHART CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2605</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>257200</td>\n",
+       "      <td>38.675594</td>\n",
+       "      <td>-121.515878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>8316 NORTHAM DR</td>\n",
+       "      <td>ANTELOPE</td>\n",
+       "      <td>95843</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>246544</td>\n",
+       "      <td>38.720767</td>\n",
+       "      <td>-121.376678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>2130 CATHERWOOD WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1424</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>251000</td>\n",
+       "      <td>38.675506</td>\n",
+       "      <td>-121.510987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>620 KESWICK CT</td>\n",
+       "      <td>GRANITE BAY</td>\n",
+       "      <td>95746</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2356</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>600000</td>\n",
+       "      <td>38.732096</td>\n",
+       "      <td>-121.219142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>8593 DERLIN WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.447585</td>\n",
+       "      <td>-121.426627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>770</th>\n",
+       "      <td>7223 KALLIE KAY LN</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95823</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1463</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>174250</td>\n",
+       "      <td>38.477553</td>\n",
+       "      <td>-121.419463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>3228 I ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95816</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>215000</td>\n",
+       "      <td>38.573844</td>\n",
+       "      <td>-121.462839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>448</th>\n",
+       "      <td>5136 CABOT CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95820</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1462</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>121500</td>\n",
+       "      <td>38.528479</td>\n",
+       "      <td>-121.411806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>546</th>\n",
+       "      <td>241 LANFRANCO CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3397</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>465000</td>\n",
+       "      <td>38.665696</td>\n",
+       "      <td>-121.549437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1455 64TH AVE</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95822</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>122000</td>\n",
+       "      <td>38.492177</td>\n",
+       "      <td>-121.503392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>2981 WRINGER DR</td>\n",
+       "      <td>ROSEVILLE</td>\n",
+       "      <td>95661</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3838</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>613401</td>\n",
+       "      <td>38.735373</td>\n",
+       "      <td>-121.227072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>628</th>\n",
+       "      <td>7140 BLUE SPRINGS WAY</td>\n",
+       "      <td>CITRUS HEIGHTS</td>\n",
+       "      <td>95621</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1156</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>161653</td>\n",
+       "      <td>38.720653</td>\n",
+       "      <td>-121.302241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>8986 HAFLINGER WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1857</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.397923</td>\n",
+       "      <td>-121.450219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>7809 VALLECITOS WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95828</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>888</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>216021</td>\n",
+       "      <td>38.508217</td>\n",
+       "      <td>-121.411207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>8025 PEERLESS AVE</td>\n",
+       "      <td>ORANGEVALE</td>\n",
+       "      <td>95662</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>334150</td>\n",
+       "      <td>38.711470</td>\n",
+       "      <td>-121.216214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>743</th>\n",
+       "      <td>5579 JERRY LITELL WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3599</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>381300</td>\n",
+       "      <td>38.677126</td>\n",
+       "      <td>-121.500519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>511</th>\n",
+       "      <td>87 LACAM CIR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95820</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1188</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>123675</td>\n",
+       "      <td>38.532359</td>\n",
+       "      <td>-121.411670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>7577 EDDYLEE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95822</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1436</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>185074</td>\n",
+       "      <td>38.482910</td>\n",
+       "      <td>-121.491509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>5201 BLOSSOM RANCH DR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4303</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>450000</td>\n",
+       "      <td>38.399436</td>\n",
+       "      <td>-121.444041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>1140 EDMONTON DR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95833</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1204</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Thu May 15 00:00:00 EDT 2008</td>\n",
+       "      <td>174250</td>\n",
+       "      <td>38.624570</td>\n",
+       "      <td>-121.486913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>5340 BIRK WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>234000</td>\n",
+       "      <td>38.672495</td>\n",
+       "      <td>-121.515251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>5805 DOTMAR WAY</td>\n",
+       "      <td>NORTH HIGHLANDS</td>\n",
+       "      <td>95660</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>904</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>63000</td>\n",
+       "      <td>38.672642</td>\n",
+       "      <td>-121.380343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>3662 RIVER DR</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95833</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2447</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>246000</td>\n",
+       "      <td>38.604969</td>\n",
+       "      <td>-121.542550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>2327 32ND ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95817</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>220000</td>\n",
+       "      <td>38.557433</td>\n",
+       "      <td>-121.470340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>9629 CEDAR OAK WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3260</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>385000</td>\n",
+       "      <td>38.405527</td>\n",
+       "      <td>-121.431746</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street             city    zip state  beds  baths  sq__ft  \\\n",
+       "486       7540 HICKORY AVE       ORANGEVALE  95662    CA     3      1    1456   \n",
+       "399         9013 CASALS ST       SACRAMENTO  95826    CA     2      1     795   \n",
+       "233       12901 FURLONG DR           WILTON  95693    CA     5      3    3788   \n",
+       "408   9474 VILLAGE TREE DR        ELK GROVE  95758    CA     4      2    1776   \n",
+       "549       2901 PINTAIL WAY        ELK GROVE  95757    CA     4      3    3070   \n",
+       "43        191 BARNHART CIR       SACRAMENTO  95835    CA     4      2    2605   \n",
+       "181        8316 NORTHAM DR         ANTELOPE  95843    CA     3      2    1235   \n",
+       "250    2130 CATHERWOOD WAY       SACRAMENTO  95835    CA     3      2    1424   \n",
+       "140         620 KESWICK CT      GRANITE BAY  95746    CA     4      3    2356   \n",
+       "392        8593 DERLIN WAY       SACRAMENTO  95823    CA     3      2    1436   \n",
+       "770     7223 KALLIE KAY LN       SACRAMENTO  95823    CA     3      2    1463   \n",
+       "280              3228 I ST       SACRAMENTO  95816    CA     4      3    1939   \n",
+       "448         5136 CABOT CIR       SACRAMENTO  95820    CA     4      2    1462   \n",
+       "546      241 LANFRANCO CIR       SACRAMENTO  95835    CA     4      4    3397   \n",
+       "27           1455 64TH AVE       SACRAMENTO  95822    CA     3      2    1169   \n",
+       "550        2981 WRINGER DR        ROSEVILLE  95661    CA     4      3    3838   \n",
+       "628  7140 BLUE SPRINGS WAY   CITRUS HEIGHTS  95621    CA     3      2    1156   \n",
+       "197     8986 HAFLINGER WAY        ELK GROVE  95757    CA     3      2    1857   \n",
+       "491    7809 VALLECITOS WAY       SACRAMENTO  95828    CA     3      1     888   \n",
+       "786      8025 PEERLESS AVE       ORANGEVALE  95662    CA     2      1    1690   \n",
+       "743  5579 JERRY LITELL WAY       SACRAMENTO  95835    CA     5      3    3599   \n",
+       "511           87 LACAM CIR       SACRAMENTO  95820    CA     2      2    1188   \n",
+       "163       7577 EDDYLEE WAY       SACRAMENTO  95822    CA     4      2    1436   \n",
+       "325  5201 BLOSSOM RANCH DR        ELK GROVE  95757    CA     4      4    4303   \n",
+       "415       1140 EDMONTON DR       SACRAMENTO  95833    CA     3      2    1204   \n",
+       "599          5340 BIRK WAY       SACRAMENTO  95835    CA     3      2    1776   \n",
+       "131        5805 DOTMAR WAY  NORTH HIGHLANDS  95660    CA     2      1     904   \n",
+       "293          3662 RIVER DR       SACRAMENTO  95833    CA     4      3    2447   \n",
+       "52            2327 32ND ST       SACRAMENTO  95817    CA     2      1    1115   \n",
+       "661     9629 CEDAR OAK WAY        ELK GROVE  95757    CA     5      4    3260   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "486  Residential  Thu May 15 00:00:00 EDT 2008  225000  38.703056 -121.235221  \n",
+       "399        Condo  Mon May 19 00:00:00 EDT 2008  126960  38.557045 -121.371670  \n",
+       "233  Residential  Mon May 19 00:00:00 EDT 2008  691659  38.413535 -121.188211  \n",
+       "408  Residential  Mon May 19 00:00:00 EDT 2008  210000  38.413947 -121.408276  \n",
+       "549  Residential  Tue May 20 00:00:00 EDT 2008  495000  38.398488 -121.473424  \n",
+       "43   Residential  Fri May 16 00:00:00 EDT 2008  257200  38.675594 -121.515878  \n",
+       "181  Residential  Fri May 16 00:00:00 EDT 2008  246544  38.720767 -121.376678  \n",
+       "250  Residential  Mon May 19 00:00:00 EDT 2008  251000  38.675506 -121.510987  \n",
+       "140  Residential  Fri May 16 00:00:00 EDT 2008  600000  38.732096 -121.219142  \n",
+       "392  Residential  Mon May 19 00:00:00 EDT 2008  180000  38.447585 -121.426627  \n",
+       "770  Residential  Wed May 21 00:00:00 EDT 2008  174250  38.477553 -121.419463  \n",
+       "280  Residential  Mon May 19 00:00:00 EDT 2008  215000  38.573844 -121.462839  \n",
+       "448  Residential  Thu May 15 00:00:00 EDT 2008  121500  38.528479 -121.411806  \n",
+       "546  Residential  Tue May 20 00:00:00 EDT 2008  465000  38.665696 -121.549437  \n",
+       "27   Residential  Fri May 16 00:00:00 EDT 2008  122000  38.492177 -121.503392  \n",
+       "550  Residential  Tue May 20 00:00:00 EDT 2008  613401  38.735373 -121.227072  \n",
+       "628  Residential  Tue May 20 00:00:00 EDT 2008  161653  38.720653 -121.302241  \n",
+       "197  Residential  Fri May 16 00:00:00 EDT 2008  285000  38.397923 -121.450219  \n",
+       "491  Residential  Thu May 15 00:00:00 EDT 2008  216021  38.508217 -121.411207  \n",
+       "786  Residential  Wed May 21 00:00:00 EDT 2008  334150  38.711470 -121.216214  \n",
+       "743  Residential  Wed May 21 00:00:00 EDT 2008  381300  38.677126 -121.500519  \n",
+       "511  Residential  Thu May 15 00:00:00 EDT 2008  123675  38.532359 -121.411670  \n",
+       "163  Residential  Fri May 16 00:00:00 EDT 2008  185074  38.482910 -121.491509  \n",
+       "325  Residential  Mon May 19 00:00:00 EDT 2008  450000  38.399436 -121.444041  \n",
+       "415  Residential  Thu May 15 00:00:00 EDT 2008  174250  38.624570 -121.486913  \n",
+       "599  Residential  Tue May 20 00:00:00 EDT 2008  234000  38.672495 -121.515251  \n",
+       "131  Residential  Fri May 16 00:00:00 EDT 2008   63000  38.672642 -121.380343  \n",
+       "293  Residential  Mon May 19 00:00:00 EDT 2008  246000  38.604969 -121.542550  \n",
+       "52   Residential  Fri May 16 00:00:00 EDT 2008  220000  38.557433 -121.470340  \n",
+       "661  Residential  Tue May 20 00:00:00 EDT 2008  385000  38.405527 -121.431746  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(10)\n",
+    "small_sacramento = sacramento.sample(n=30)\n",
+    "small_sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "6f8fae0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'])\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House size vs Price')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "91d6a129",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculate the absolute difference between 2000 and each house in our data\n",
+    "small_sacramento[\"dist\"] = (2000 - small_sacramento[\"sq__ft\"]).abs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "2e777880",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>3228 I ST</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95816</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>215000</td>\n",
+       "      <td>38.573844</td>\n",
+       "      <td>-121.462839</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>8986 HAFLINGER WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1857</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.397923</td>\n",
+       "      <td>-121.450219</td>\n",
+       "      <td>143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>9474 VILLAGE TREE DR</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Mon May 19 00:00:00 EDT 2008</td>\n",
+       "      <td>210000</td>\n",
+       "      <td>38.413947</td>\n",
+       "      <td>-121.408276</td>\n",
+       "      <td>224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>5340 BIRK WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95835</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1776</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Tue May 20 00:00:00 EDT 2008</td>\n",
+       "      <td>234000</td>\n",
+       "      <td>38.672495</td>\n",
+       "      <td>-121.515251</td>\n",
+       "      <td>224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>786</th>\n",
+       "      <td>8025 PEERLESS AVE</td>\n",
+       "      <td>ORANGEVALE</td>\n",
+       "      <td>95662</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>334150</td>\n",
+       "      <td>38.711470</td>\n",
+       "      <td>-121.216214</td>\n",
+       "      <td>310</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   street        city    zip state  beds  baths  sq__ft  \\\n",
+       "280             3228 I ST  SACRAMENTO  95816    CA     4      3    1939   \n",
+       "197    8986 HAFLINGER WAY   ELK GROVE  95757    CA     3      2    1857   \n",
+       "408  9474 VILLAGE TREE DR   ELK GROVE  95758    CA     4      2    1776   \n",
+       "599         5340 BIRK WAY  SACRAMENTO  95835    CA     3      2    1776   \n",
+       "786     8025 PEERLESS AVE  ORANGEVALE  95662    CA     2      1    1690   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \\\n",
+       "280  Residential  Mon May 19 00:00:00 EDT 2008  215000  38.573844 -121.462839   \n",
+       "197  Residential  Fri May 16 00:00:00 EDT 2008  285000  38.397923 -121.450219   \n",
+       "408  Residential  Mon May 19 00:00:00 EDT 2008  210000  38.413947 -121.408276   \n",
+       "599  Residential  Tue May 20 00:00:00 EDT 2008  234000  38.672495 -121.515251   \n",
+       "786  Residential  Wed May 21 00:00:00 EDT 2008  334150  38.711470 -121.216214   \n",
+       "\n",
+       "     dist  \n",
+       "280    61  \n",
+       "197   143  \n",
+       "408   224  \n",
+       "599   224  \n",
+       "786   310  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#find the rows with the smallest difference\n",
+    "nearest_neighbors = small_sacramento.nsmallest(5, \"dist\")\n",
+    "nearest_neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "1e0a15dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x29b499225d0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot\n",
+    "plt.scatter(small_sacramento[\"sq__ft\"], small_sacramento['price'], label='All houses')\n",
+    "\n",
+    "# Plot nearest neighbors in orange\n",
+    "plt.scatter(nearest_neighbors[\"sq__ft\"], nearest_neighbors['price'], color='orange', label='Nearest neighbors', edgecolor='black')\n",
+    "\n",
+    "# Add a vertical line at 2,000 square feet\n",
+    "plt.axvline(x=2000, color='red', linestyle='--', label='2000 sqft')\n",
+    "\n",
+    "# Add labels, title, and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House Size vs Price')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "014f357e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(255630.0)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nearest_neighbors[\"price\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "dc48d8ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#now we will redo the analysis with the entire dataset instead of a subset as we did above.\n",
+    "# First, we create a train and test data with 75/25 split.\n",
+    "\n",
+    "sacramento_train, sacramento_test = train_test_split(sacramento, train_size=0.75, random_state=42)   #random state can be any integer instead of 42."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0b6bc12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 1. define our X and y. For multiple predictor variables, we can add to X_train separated by a comma, within the paranthesis.\n",
+    "X_train = sacramento_train[[\"sq__ft\"]]\n",
+    "y_train = sacramento_train[\"price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "44ce17c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 2. Initialize our KNNregressor\n",
+    "knn_regressor = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6e5cd55c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 3. define our parameter grid\n",
+    "param_grid = {\n",
+    "    \"n_neighbors\" : range(1,201,3)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "7e4f209b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 4. initialize our grid search\n",
+    "sacr_gridsearch = GridSearchCV(\n",
+    "    estimator=knn_regressor,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=5,\n",
+    "    scoring=\"neg_root_mean_squared_error\"       #r2 can also be put here instead of neg_root_means_squared\n",
+    ")   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "a4c0d4b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 201, 3)},\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">KNeighborsRegressor()</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 201, 3)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;neg_root_mean_squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsRegressor</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsRegressor(n_neighbors=19)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">19</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
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+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
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+       "        })\n",
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+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
+       "             param_grid={'n_neighbors': range(1, 201, 3)},\n",
+       "             scoring='neg_root_mean_squared_error')"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#step 5. Fit our grid search to training data\n",
+    "sacr_gridsearch.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "0a66dd90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001507</td>\n",
+       "      <td>0.000641</td>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>0.000398</td>\n",
+       "      <td>1</td>\n",
+       "      <td>{'n_neighbors': 1}</td>\n",
+       "      <td>-111432.646994</td>\n",
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+       "      <td>-111553.640165</td>\n",
+       "      <td>-121189.571040</td>\n",
+       "      <td>-110534.126317</td>\n",
+       "      <td>-115928.072257</td>\n",
+       "      <td>5952.545896</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.001203</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.000632</td>\n",
+       "      <td>4</td>\n",
+       "      <td>{'n_neighbors': 4}</td>\n",
+       "      <td>-84191.452465</td>\n",
+       "      <td>-96768.557437</td>\n",
+       "      <td>-99395.447194</td>\n",
+       "      <td>-91776.634858</td>\n",
+       "      <td>-96991.842965</td>\n",
+       "      <td>-93824.786984</td>\n",
+       "      <td>5417.090443</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.001202</td>\n",
+       "      <td>0.000399</td>\n",
+       "      <td>0.000703</td>\n",
+       "      <td>0.000604</td>\n",
+       "      <td>7</td>\n",
+       "      <td>{'n_neighbors': 7}</td>\n",
+       "      <td>-82055.054082</td>\n",
+       "      <td>-94969.980001</td>\n",
+       "      <td>-97268.515757</td>\n",
+       "      <td>-88221.977923</td>\n",
+       "      <td>-90245.964227</td>\n",
+       "      <td>-90552.298398</td>\n",
+       "      <td>5335.745149</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000928</td>\n",
+       "      <td>0.000504</td>\n",
+       "      <td>0.000982</td>\n",
+       "      <td>0.000035</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'n_neighbors': 10}</td>\n",
+       "      <td>-79700.428498</td>\n",
+       "      <td>-94364.712988</td>\n",
+       "      <td>-95672.079987</td>\n",
+       "      <td>-84119.947681</td>\n",
+       "      <td>-88172.348589</td>\n",
+       "      <td>-88405.903549</td>\n",
+       "      <td>6041.754570</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001040</td>\n",
+       "      <td>0.000698</td>\n",
+       "      <td>0.001295</td>\n",
+       "      <td>0.000408</td>\n",
+       "      <td>13</td>\n",
+       "      <td>{'n_neighbors': 13}</td>\n",
+       "      <td>-77034.701093</td>\n",
+       "      <td>-92991.526184</td>\n",
+       "      <td>-96360.545374</td>\n",
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+       "      <td>-87199.791789</td>\n",
+       "      <td>6815.832448</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.001010</td>\n",
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+       "      <td>0.002248</td>\n",
+       "      <td>0.000702</td>\n",
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+       "      <td>{'n_neighbors': 187}</td>\n",
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+       "      <td>-103880.184049</td>\n",
+       "      <td>-110873.710254</td>\n",
+       "      <td>-98278.819705</td>\n",
+       "      <td>-78553.282689</td>\n",
+       "      <td>-96198.137807</td>\n",
+       "      <td>11280.135742</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>0.000603</td>\n",
+       "      <td>0.000492</td>\n",
+       "      <td>0.002202</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>190</td>\n",
+       "      <td>{'n_neighbors': 190}</td>\n",
+       "      <td>-89807.967415</td>\n",
+       "      <td>-104221.691818</td>\n",
+       "      <td>-111088.875603</td>\n",
+       "      <td>-98698.022523</td>\n",
+       "      <td>-78654.732762</td>\n",
+       "      <td>-96494.258024</td>\n",
+       "      <td>11318.372825</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0.000996</td>\n",
+       "      <td>0.000096</td>\n",
+       "      <td>0.002501</td>\n",
+       "      <td>0.000613</td>\n",
+       "      <td>193</td>\n",
+       "      <td>{'n_neighbors': 193}</td>\n",
+       "      <td>-90255.812044</td>\n",
+       "      <td>-104469.469062</td>\n",
+       "      <td>-111371.637819</td>\n",
+       "      <td>-99100.345423</td>\n",
+       "      <td>-78654.579110</td>\n",
+       "      <td>-96770.368691</td>\n",
+       "      <td>11388.795184</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.001123</td>\n",
+       "      <td>0.000231</td>\n",
+       "      <td>0.002353</td>\n",
+       "      <td>0.000443</td>\n",
+       "      <td>196</td>\n",
+       "      <td>{'n_neighbors': 196}</td>\n",
+       "      <td>-90612.174931</td>\n",
+       "      <td>-104531.594900</td>\n",
+       "      <td>-111725.067577</td>\n",
+       "      <td>-99524.332322</td>\n",
+       "      <td>-78755.965603</td>\n",
+       "      <td>-97029.827067</td>\n",
+       "      <td>11433.017471</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>0.000822</td>\n",
+       "      <td>0.000411</td>\n",
+       "      <td>0.002524</td>\n",
+       "      <td>0.000679</td>\n",
+       "      <td>199</td>\n",
+       "      <td>{'n_neighbors': 199}</td>\n",
+       "      <td>-90882.885063</td>\n",
+       "      <td>-104723.464423</td>\n",
+       "      <td>-111923.255417</td>\n",
+       "      <td>-99830.027818</td>\n",
+       "      <td>-78899.733892</td>\n",
+       "      <td>-97251.873323</td>\n",
+       "      <td>11446.274851</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0        0.001507      0.000641         0.001203        0.000398   \n",
+       "1        0.001203      0.000400         0.001000        0.000632   \n",
+       "2        0.001202      0.000399         0.000703        0.000604   \n",
+       "3        0.000928      0.000504         0.000982        0.000035   \n",
+       "4        0.001040      0.000698         0.001295        0.000408   \n",
+       "..            ...           ...              ...             ...   \n",
+       "62       0.001010      0.000557         0.002248        0.000702   \n",
+       "63       0.000603      0.000492         0.002202        0.000400   \n",
+       "64       0.000996      0.000096         0.002501        0.000613   \n",
+       "65       0.001123      0.000231         0.002353        0.000443   \n",
+       "66       0.000822      0.000411         0.002524        0.000679   \n",
+       "\n",
+       "    param_n_neighbors                params  split0_test_score  \\\n",
+       "0                   1    {'n_neighbors': 1}     -111432.646994   \n",
+       "1                   4    {'n_neighbors': 4}      -84191.452465   \n",
+       "2                   7    {'n_neighbors': 7}      -82055.054082   \n",
+       "3                  10   {'n_neighbors': 10}      -79700.428498   \n",
+       "4                  13   {'n_neighbors': 13}      -77034.701093   \n",
+       "..                ...                   ...                ...   \n",
+       "62                187  {'n_neighbors': 187}      -89404.692337   \n",
+       "63                190  {'n_neighbors': 190}      -89807.967415   \n",
+       "64                193  {'n_neighbors': 193}      -90255.812044   \n",
+       "65                196  {'n_neighbors': 196}      -90612.174931   \n",
+       "66                199  {'n_neighbors': 199}      -90882.885063   \n",
+       "\n",
+       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0      -124930.376771     -111553.640165     -121189.571040   \n",
+       "1       -96768.557437      -99395.447194      -91776.634858   \n",
+       "2       -94969.980001      -97268.515757      -88221.977923   \n",
+       "3       -94364.712988      -95672.079987      -84119.947681   \n",
+       "4       -92991.526184      -96360.545374      -84692.931752   \n",
+       "..                ...                ...                ...   \n",
+       "62     -103880.184049     -110873.710254      -98278.819705   \n",
+       "63     -104221.691818     -111088.875603      -98698.022523   \n",
+       "64     -104469.469062     -111371.637819      -99100.345423   \n",
+       "65     -104531.594900     -111725.067577      -99524.332322   \n",
+       "66     -104723.464423     -111923.255417      -99830.027818   \n",
+       "\n",
+       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0      -110534.126317   -115928.072257     5952.545896               67  \n",
+       "1       -96991.842965    -93824.786984     5417.090443               50  \n",
+       "2       -90245.964227    -90552.298398     5335.745149               32  \n",
+       "3       -88172.348589    -88405.903549     6041.754570               19  \n",
+       "4       -84919.254540    -87199.791789     6815.832448                6  \n",
+       "..                ...              ...             ...              ...  \n",
+       "62      -78553.282689    -96198.137807    11280.135742               62  \n",
+       "63      -78654.732762    -96494.258024    11318.372825               63  \n",
+       "64      -78654.579110    -96770.368691    11388.795184               64  \n",
+       "65      -78755.965603    -97029.827067    11433.017471               65  \n",
+       "66      -78899.733892    -97251.873323    11446.274851               66  \n",
+       "\n",
+       "[67 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame(sacr_gridsearch.cv_results_)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "4ea9f25c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    n_neighbors  mean_test_score\n",
+      "0             1   -115928.072257\n",
+      "1             4    -93824.786984\n",
+      "2             7    -90552.298398\n",
+      "3            10    -88405.903549\n",
+      "4            13    -87199.791789\n",
+      "..          ...              ...\n",
+      "62          187    -96198.137807\n",
+      "63          190    -96494.258024\n",
+      "64          193    -96770.368691\n",
+      "65          196    -97029.827067\n",
+      "66          199    -97251.873323\n",
+      "\n",
+      "[67 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame(sacr_gridsearch.cv_results_)     # After fitting the model, we extract the cross-validation results using `cv_results_`. This output includes various metrics and parameters tested during the cross-validation process.\n",
+    "results = (\n",
+    "    results[[\n",
+    "        \"param_n_neighbors\",\n",
+    "        \"mean_test_score\"\n",
+    "    ]]\n",
+    "    .rename(columns={\"param_n_neighbors\": \"n_neighbors\"})\n",
+    ") # we specify the scoring metric as \"neg_root_mean_squared_error\" to evaluate the model performance based on RMSPE.\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "62868137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_neighbors</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>115928.072257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>93824.786984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>90552.298398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>88405.903549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>87199.791789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>187</td>\n",
+       "      <td>96198.137807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>190</td>\n",
+       "      <td>96494.258024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>193</td>\n",
+       "      <td>96770.368691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>196</td>\n",
+       "      <td>97029.827067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>199</td>\n",
+       "      <td>97251.873323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    n_neighbors  mean_test_score\n",
+       "0             1    115928.072257\n",
+       "1             4     93824.786984\n",
+       "2             7     90552.298398\n",
+       "3            10     88405.903549\n",
+       "4            13     87199.791789\n",
+       "..          ...              ...\n",
+       "62          187     96198.137807\n",
+       "63          190     96494.258024\n",
+       "64          193     96770.368691\n",
+       "65          196     97029.827067\n",
+       "66          199     97251.873323\n",
+       "\n",
+       "[67 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[\"mean_test_score\"] = -results[\"mean_test_score\"]\n",
+    "# could also code this as results[\"mean_test_score\"] = results[\"mean_test_score\"].abs()\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1421c4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Plot mean test scores with error bars\n",
+    "plt.plot(results['n_neighbors'], results['mean_test_score'], '-o', color='blue')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Regression Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3aac2fba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 19}"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacr_gridsearch.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "8172c473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make predictions on the test set\n",
+    "sacramento_test[\"predicted\"] = sacr_gridsearch.predict(sacramento_test[[\"sq__ft\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "9acda616",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74244.70520282978"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Now we will make predictions with test data set. \n",
+    "#calculate RMSPE\n",
+    "\n",
+    "mean_squared_error(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d95a61e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49520697046011675"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#calculate r2\n",
+    "r2_score(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "8c7c707a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\junai\\Documents\\DSI Certificate\\Learning\\4. LCR\\LCR\\lcr-env\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate a range of house sizes for prediction\n",
+    "sizes = np.linspace(sacramento[\"sq__ft\"].min(), sacramento[\"sq__ft\"].max(), 100).reshape(-1, 1)\n",
+    "\n",
+    "# Predict house prices for these sizes using the best model from GridSearchCV\n",
+    "predicted_prices = sacr_gridsearch.predict(sizes)\n",
+    "\n",
+    "# Plot the original data\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento[\"price\"], label=\"Actual Prices\")\n",
+    "\n",
+    "# Plot the model predictions as a line\n",
+    "plt.plot(sizes, predicted_prices, color='red', label=\"Model Predictions\")\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel(\"House size (square feet)\")\n",
+    "plt.ylabel(\"Price (USD)\")\n",
+    "plt.title(\"Scatter Plot of House Size vs Price with Model Predictions\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/17-12-2025.ipynb b/01_materials/notebooks/17-12-2025.ipynb
new file mode 100644
index 000000000..a82b4eddf
--- /dev/null
+++ b/01_materials/notebooks/17-12-2025.ipynb
@@ -0,0 +1,2217 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "305faa01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error , r2_score\n",
+    "from sklearn import set_config\n",
+    "\n",
+    "# Output dataframes instead of arrays\n",
+    "set_config(transform_output=\"pandas\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5ba5dfd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>street</th>\n",
+       "      <th>city</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>baths</th>\n",
+       "      <th>sq__ft</th>\n",
+       "      <th>type</th>\n",
+       "      <th>sale_date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1005 MORENO WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95838</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>38.646206</td>\n",
+       "      <td>-121.442767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10105 MONTE VALLO CT</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1578</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>190000</td>\n",
+       "      <td>38.573917</td>\n",
+       "      <td>-121.316916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10133 NEBBIOLO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2096</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>289000</td>\n",
+       "      <td>38.391085</td>\n",
+       "      <td>-121.347231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10165 LOFTON WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>266510</td>\n",
+       "      <td>38.387708</td>\n",
+       "      <td>-121.436522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10254 JULIANA WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Fri May 16 00:00:00 EDT 2008</td>\n",
+       "      <td>331200</td>\n",
+       "      <td>38.568030</td>\n",
+       "      <td>-121.309966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>9507 SEA CLIFF WAY</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95758</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>285000</td>\n",
+       "      <td>38.410992</td>\n",
+       "      <td>-121.479043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>9570 HARVEST ROSE WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2367</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>315537</td>\n",
+       "      <td>38.555993</td>\n",
+       "      <td>-121.340352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>9723 TERRAPIN CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95757</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2354</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>335750</td>\n",
+       "      <td>38.403492</td>\n",
+       "      <td>-121.430224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>9837 CORTE DORADO CT</td>\n",
+       "      <td>ELK GROVE</td>\n",
+       "      <td>95624</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1616</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>227887</td>\n",
+       "      <td>38.400676</td>\n",
+       "      <td>-121.381010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>9861 CULP WAY</td>\n",
+       "      <td>SACRAMENTO</td>\n",
+       "      <td>95827</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>Residential</td>\n",
+       "      <td>Wed May 21 00:00:00 EDT 2008</td>\n",
+       "      <td>131200</td>\n",
+       "      <td>38.558423</td>\n",
+       "      <td>-121.327948</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    street        city    zip state  beds  baths  sq__ft  \\\n",
+       "0          1005 MORENO WAY  SACRAMENTO  95838    CA     3      2    1410   \n",
+       "1     10105 MONTE VALLO CT  SACRAMENTO  95827    CA     4      2    1578   \n",
+       "2        10133 NEBBIOLO CT   ELK GROVE  95624    CA     4      3    2096   \n",
+       "3         10165 LOFTON WAY   ELK GROVE  95757    CA     3      2    1540   \n",
+       "4        10254 JULIANA WAY  SACRAMENTO  95827    CA     4      2    2484   \n",
+       "..                     ...         ...    ...   ...   ...    ...     ...   \n",
+       "808     9507 SEA CLIFF WAY   ELK GROVE  95758    CA     4      2    2056   \n",
+       "809  9570 HARVEST ROSE WAY  SACRAMENTO  95827    CA     5      3    2367   \n",
+       "810       9723 TERRAPIN CT   ELK GROVE  95757    CA     4      3    2354   \n",
+       "811   9837 CORTE DORADO CT   ELK GROVE  95624    CA     4      2    1616   \n",
+       "812          9861 CULP WAY  SACRAMENTO  95827    CA     4      2    1380   \n",
+       "\n",
+       "            type                     sale_date   price   latitude   longitude  \n",
+       "0    Residential  Fri May 16 00:00:00 EDT 2008  180000  38.646206 -121.442767  \n",
+       "1    Residential  Fri May 16 00:00:00 EDT 2008  190000  38.573917 -121.316916  \n",
+       "2    Residential  Fri May 16 00:00:00 EDT 2008  289000  38.391085 -121.347231  \n",
+       "3    Residential  Fri May 16 00:00:00 EDT 2008  266510  38.387708 -121.436522  \n",
+       "4    Residential  Fri May 16 00:00:00 EDT 2008  331200  38.568030 -121.309966  \n",
+       "..           ...                           ...     ...        ...         ...  \n",
+       "808  Residential  Wed May 21 00:00:00 EDT 2008  285000  38.410992 -121.479043  \n",
+       "809  Residential  Wed May 21 00:00:00 EDT 2008  315537  38.555993 -121.340352  \n",
+       "810  Residential  Wed May 21 00:00:00 EDT 2008  335750  38.403492 -121.430224  \n",
+       "811  Residential  Wed May 21 00:00:00 EDT 2008  227887  38.400676 -121.381010  \n",
+       "812  Residential  Wed May 21 00:00:00 EDT 2008  131200  38.558423 -121.327948  \n",
+       "\n",
+       "[813 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sacramento = pd.read_csv(\"dataset/sacramento.csv\")\n",
+    "sacramento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "40f628bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#split the data into train and test\n",
+    "\n",
+    "sacramento_train, sacramento_test = train_test_split(sacramento, train_size=0.75, random_state=123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ad77545c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 1. define our predictor and response variables\n",
+    "X_train = sacramento[[\"sq__ft\"]]\n",
+    "y_train = sacramento[\"price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "701452ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sq__ft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>2056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809</th>\n",
+       "      <td>2367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>810</th>\n",
+       "      <td>2354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>811</th>\n",
+       "      <td>1616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>1380</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>813 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sq__ft\n",
+       "0      1410\n",
+       "1      1578\n",
+       "2      2096\n",
+       "3      1540\n",
+       "4      2484\n",
+       "..      ...\n",
+       "808    2056\n",
+       "809    2367\n",
+       "810    2354\n",
+       "811    1616\n",
+       "812    1380\n",
+       "\n",
+       "[813 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7661433c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      180000\n",
+       "1      190000\n",
+       "2      289000\n",
+       "3      266510\n",
+       "4      331200\n",
+       "        ...  \n",
+       "808    285000\n",
+       "809    315537\n",
+       "810    335750\n",
+       "811    227887\n",
+       "812    131200\n",
+       "Name: price, Length: 813, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "370b9cda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#step 2. initialize a linear regression\n",
+    "lm = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7fd3a6ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#step 3. Fit model to our data using the fit method\n",
+    "lm.fit(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e6c30181",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([134.64083994])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.coef_        #slope of the line; b1. Means the price of house increase by 134.6 for every sq.ft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5b6bb1b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(16195.545596351672)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.intercept_   #intercept of the line; b0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8c9c9e0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# y = 139.6(x) + 16195"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b8fad1b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make predictions on the test set\n",
+    "sacramento_test[\"predicted\"] = lm.predict(sacramento_test[[\"sq__ft\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4082445a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "84669.5499485594"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#calculate RMSPE\n",
+    "mean_squared_error(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")**(1/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "70519272",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5095174174637644"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#calculate r2\n",
+    "r2_score(\n",
+    "    y_true=sacramento_test[\"price\"],\n",
+    "    y_pred=sacramento_test[\"predicted\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "38fc7064",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This line calculates the minimum and maximum values of the \"sq__ft\" column in the sacramento dataset.\n",
+    "sqft_prediction_grid = sacramento[[\"sq__ft\"]].agg([\"min\", \"max\"])\n",
+    "\n",
+    "# Uses the linear model to predict prices for the min and max square footage values.\n",
+    "sqft_prediction_grid[\"predicted\"] = lm.predict(sqft_prediction_grid)\n",
+    "\n",
+    "# Plot the original data\n",
+    "plt.scatter(sacramento[\"sq__ft\"], sacramento[\"price\"], label='Original Data')\n",
+    "\n",
+    "# Plot the model predictions as a line\n",
+    "plt.plot(sqft_prediction_grid[\"sq__ft\"], sqft_prediction_grid[\"predicted\"], color='red', label='Model Predictions')\n",
+    "\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('House size (square feet)')\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.title('Scatter Plot of House Size vs Price')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "baee374f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "returned_dictionary = cross_validate(\n",
+    "    estimator=lm,\n",
+    "    cv=5,\n",
+    "    X=sacramento[[\"sq__ft\"]],\n",
+    "    y=sacramento[\"price\"],\n",
+    "    scoring=\"neg_root_mean_squared_error\" #r2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ba43683b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001415</td>\n",
+       "      <td>0.002023</td>\n",
+       "      <td>-81369.919847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002997</td>\n",
+       "      <td>0.001004</td>\n",
+       "      <td>-97590.236340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000999</td>\n",
+       "      <td>0.001005</td>\n",
+       "      <td>-61790.733828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000997</td>\n",
+       "      <td>0.000993</td>\n",
+       "      <td>-92026.283010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001002</td>\n",
+       "      <td>0.001076</td>\n",
+       "      <td>-75474.947490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time    test_score\n",
+       "0  0.001415    0.002023 -81369.919847\n",
+       "1  0.002997    0.001004 -97590.236340\n",
+       "2  0.000999    0.001005 -61790.733828\n",
+       "3  0.000997    0.000993 -92026.283010\n",
+       "4  0.001002    0.001076 -75474.947490"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df = pd.DataFrame(returned_dictionary)\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ddc10327",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.001415</td>\n",
+       "      <td>0.002023</td>\n",
+       "      <td>81369.919847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.002997</td>\n",
+       "      <td>0.001004</td>\n",
+       "      <td>97590.236340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000999</td>\n",
+       "      <td>0.001005</td>\n",
+       "      <td>61790.733828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000997</td>\n",
+       "      <td>0.000993</td>\n",
+       "      <td>92026.283010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.001002</td>\n",
+       "      <td>0.001076</td>\n",
+       "      <td>75474.947490</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fit_time  score_time    test_score\n",
+       "0  0.001415    0.002023  81369.919847\n",
+       "1  0.002997    0.001004  97590.236340\n",
+       "2  0.000999    0.001005  61790.733828\n",
+       "3  0.000997    0.000993  92026.283010\n",
+       "4  0.001002    0.001076  75474.947490"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df[\"test_score\"] = cv_5_df[\"test_score\"].abs()\n",
+    "cv_5_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "c918df05",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.001482</td>\n",
+       "      <td>0.001220</td>\n",
+       "      <td>81650.424103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000387</td>\n",
+       "      <td>0.000201</td>\n",
+       "      <td>6302.216095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time    test_score\n",
+       "mean  0.001482    0.001220  81650.424103\n",
+       "sem   0.000387    0.000201   6302.216095"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df.agg([\"mean\",\"sem\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9a55a6b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#multivariable linear regression using both sq__ft and no. of bedrooms\n",
+    "\n",
+    "mlm=LinearRegression()\n",
+    "\n",
+    "returned_dictionary_2 = cross_validate(\n",
+    "    estimator=mlm,\n",
+    "    cv=5,\n",
+    "    X=sacramento[[\"sq__ft\",\"beds\"]],\n",
+    "    y=sacramento[\"price\"],\n",
+    "    scoring=\"r2\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "299ac79c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.001709</td>\n",
+       "      <td>0.001625</td>\n",
+       "      <td>0.51978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sem</th>\n",
+       "      <td>0.000312</td>\n",
+       "      <td>0.000246</td>\n",
+       "      <td>0.02097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fit_time  score_time  test_score\n",
+       "mean  0.001709    0.001625     0.51978\n",
+       "sem   0.000312    0.000246     0.02097"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_5_df_mlm = pd.DataFrame(returned_dictionary_2)\n",
+    "cv_5_df_mlm.agg([\"mean\",\"sem\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "04037fb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mlm.fit(sacramento[[\"sq__ft\",\"beds\"]],sacramento[\"price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3eb28c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   156.23291179, -22894.49430778])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mlm.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "cd87359e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(56239.192229688924)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mlm.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ca981a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/01_materials/notebooks/Classification-1.ipynb b/01_materials/notebooks/Classification-1.ipynb
index 7b6959a7a..ef4a0d169 100644
--- a/01_materials/notebooks/Classification-1.ipynb
+++ b/01_materials/notebooks/Classification-1.ipynb
@@ -23,7 +23,19 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcolors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmcolors\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -2326,7 +2338,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -2340,7 +2352,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/01_materials/notebooks/Classification-2.ipynb b/01_materials/notebooks/Classification-2.ipynb
index 96db650b8..b584f6200 100644
--- a/01_materials/notebooks/Classification-2.ipynb
+++ b/01_materials/notebooks/Classification-2.ipynb
@@ -1671,7 +1671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/01_materials/notebooks/Clustering.ipynb b/01_materials/notebooks/Clustering.ipynb
index 1d1ba49f1..5db53a839 100644
--- a/01_materials/notebooks/Clustering.ipynb
+++ b/01_materials/notebooks/Clustering.ipynb
@@ -875,7 +875,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -889,7 +889,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/01_materials/notebooks/Mylivecode/09-12-2025.ipynb b/01_materials/notebooks/Mylivecode/09-12-2025.ipynb
new file mode 100644
index 000000000..32441e7cd
--- /dev/null
+++ b/01_materials/notebooks/Mylivecode/09-12-2025.ipynb
@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93d84571",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m#load libraries\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m     \u001b[38;5;66;03m#advanced version of excel. Useful for complex analysis and transformations with just a few lines of code\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m         \n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcolors\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmcolors\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
+     ]
+    }
+   ],
+   "source": [
+    "#load libraries\n",
+    "import pandas as pd     #advanced version of excel. Useful for complex analysis and transformations with just a few lines of code\n",
+    "import matplotlib.pyplot as plt         \n",
+    "import matplotlib.colors as mcolors\n",
+    "from mpl_toolkits import mplot3d"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lcr-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..211a23175 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "len(wine_df)"
    ]
   },
   {
@@ -109,12 +399,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "len(wine_df.columns)"
    ]
   },
   {
@@ -127,12 +429,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(dtype('int64'), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# The response variable is categorical with 3 discrete class labels.\n",
+    "wine_df[\"class\"].dtype, wine_df[\"class\"].unique()\n",
+    "\n"
    ]
   },
   {
@@ -146,12 +462,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 219,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "len(wine_df.drop(columns=[\"class\"]).columns)"
    ]
   },
   {
@@ -175,10 +503,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -204,7 +559,8 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "\"It is important to standardize the predictor variables to put them on a common scale. This prevents variables with significantly larger values to skew the analysis, providing a more reliable model with results that give fairly similar weightage to all variables."
    ]
   },
   {
@@ -220,7 +576,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...Because the response variable Class contains only three distinct classes or categories represented as nominal integers (0,1,2) instead of a continuous quantity that may need scaling."
    ]
   },
   {
@@ -236,7 +592,8 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "Using a seed is important for the benfits of randomness to ensure fairness and unbiasness while ensuring our results are replicable. Setting a seed is important for reproducability and consistency to keep the analysis reliable and consistent.\n"
    ]
   },
   {
@@ -251,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -261,7 +618,46 @@
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "wine_train, wine_test = train_test_split(wine_df, train_size=0.75, shuffle=True, stratify=wine_df[\"class\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d624f5d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 133 entries, 78 to 66\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       133 non-null    float64\n",
+      " 1   malic_acid                    133 non-null    float64\n",
+      " 2   ash                           133 non-null    float64\n",
+      " 3   alcalinity_of_ash             133 non-null    float64\n",
+      " 4   magnesium                     133 non-null    float64\n",
+      " 5   total_phenols                 133 non-null    float64\n",
+      " 6   flavanoids                    133 non-null    float64\n",
+      " 7   nonflavanoid_phenols          133 non-null    float64\n",
+      " 8   proanthocyanins               133 non-null    float64\n",
+      " 9   color_intensity               133 non-null    float64\n",
+      " 10  hue                           133 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  133 non-null    float64\n",
+      " 12  proline                       133 non-null    float64\n",
+      " 13  class                         133 non-null    int64  \n",
+      "dtypes: float64(13), int64(1)\n",
+      "memory usage: 15.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "wine_train.info()"
    ]
   },
   {
@@ -284,12 +680,823 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "08818c64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "#Step 1. Initializing the KNN classifier\n",
+    "knn = KNeighborsClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "46de8b7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define our X and y. X having all the predictors.\n",
+    "X = predictors_standardized\n",
+    "y = wine_df[\"class\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "238896ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Define parameter grid for n_neighbours from 1 to 50\n",
+    "param_grid = {\n",
+    "    \"n_neighbors\" : range(1,51)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "25b01bb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Implement a GridSearchCV with 10-fold cross-validation\n",
+    "grid_search = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=10,\n",
+    "    scoring='accuracy'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "7062cfe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=10),\n",
+       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 51)}, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('estimator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">estimator&nbsp;</td>\n",
+       "            <td class=\"value\">KNeighborsCla..._neighbors=10)</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('param_grid',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">param_grid&nbsp;</td>\n",
+       "            <td class=\"value\">{&#x27;n_neighbors&#x27;: range(1, 51)}</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('scoring',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">scoring&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;accuracy&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('refit',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">refit&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('cv',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">cv&nbsp;</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('pre_dispatch',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">pre_dispatch&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;2*n_jobs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('error_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">error_score&nbsp;</td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('return_train_score',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">return_train_score&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: KNeighborsClassifier</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\"><pre>KNeighborsClassifier(n_neighbors=23)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"best_estimator___\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">23</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=10),\n",
+       "             param_grid={'n_neighbors': range(1, 51)}, scoring='accuracy')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fit model to train data\n",
+    "grid_search.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "675df57b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#identify the best value for n_neighbors\n",
+    "grid_search.best_params_[\"n_neighbors\"]"
    ]
   },
   {
@@ -305,12 +1512,682 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "ffefa9f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "best_n_neighbors = grid_search.best_params_[\"n_neighbors\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "87ec996c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=23)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">23</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=23)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Fit KNN on training data\n",
+    "knn_best = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
+    "knn_best.fit(predictors_standardized.loc[wine_train.index],wine_train[\"class\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9de31882",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predict on test set\n",
+    "y_pred = knn_best.predict(predictors_standardized.loc[wine_test.index])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "524101f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9777777777777777"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#calculate accuracy\n",
+    "accuracy = accuracy_score(wine_test[\"class\"],y_pred)\n",
+    "accuracy"
    ]
   },
   {
@@ -365,7 +2242,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +2256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index a05da5cd3..7e0016dcd 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -50,10 +50,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "\n",
@@ -82,12 +200,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5d79f1cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "len(mpg_data), len(mpg_data.columns)"
    ]
   },
   {
@@ -100,12 +230,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "ac306190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "mpg_data[\"mpg\"].dtype"
    ]
   },
   {
@@ -113,7 +255,8 @@
    "id": "6d759089",
    "metadata": {},
    "source": [
-    "Your explanation... \n"
+    "Your explanation... \n",
+    "The response variable mpg is a floating point numerical continuous variable. It measures miles per gallon (mpg) and can take decimal values over a continuous range makingit a continuous numeric variable. \n"
    ]
   },
   {
@@ -126,12 +269,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "9f034a5d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>4278</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>73</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>pontiac grand prix</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>4425</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>pontiac catalina</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>3086</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick estate wagon (sw)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>455.0</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>4951</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick electra 225 custom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>454.0</td>\n",
+       "      <td>220.0</td>\n",
+       "      <td>4354</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet impala</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "116  16.0          8         400.0       230.0    4278           9.5   \n",
+       "8    14.0          8         455.0       225.0    4425          10.0   \n",
+       "13   14.0          8         455.0       225.0    3086          10.0   \n",
+       "95   12.0          8         455.0       225.0    4951          11.0   \n",
+       "6    14.0          8         454.0       220.0    4354           9.0   \n",
+       "\n",
+       "     model_year origin                      name  \n",
+       "116          73    usa        pontiac grand prix  \n",
+       "8            70    usa          pontiac catalina  \n",
+       "13           70    usa   buick estate wagon (sw)  \n",
+       "95           73    usa  buick electra 225 custom  \n",
+       "6            70    usa          chevrolet impala  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here... "
+    "# Your answer here... \n",
+    "mpg_data.nlargest(5,'horsepower')"
    ]
   },
   {
@@ -144,12 +406,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "1b91233e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "len(mpg_data.drop(columns=['mpg']).columns)"
    ]
   },
   {
@@ -173,10 +447,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "732784d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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tCSpIXH+9vyABtm71rcd2QgghhMIECWragEZCqZzbjHV9+vj2I4QQQqiZIDmAj0SgRiJQoNiyxbcfIYQQQmGC5ADOll7uRwhJbe644w4deYIFznxly5aVK6+8Ut555x3tVGqXcePGSfHixaPaVxIdKEyQHCBqw8v9CCGxA+bHuXNFPvnE9xkrc2SbNm10joyNGzfK9OnTpUWLFvLggw9K27ZtdRIuktpQmCA5QPgnojaCJUTD+kqVfPsRQhIHOEZXrSrSooXIzTf7PvE9Fg7T+fLlk3LlyknFihWlYcOGMmjQIJk6daoWLKBxAKNGjZJ69epJoUKFdAhsz5495dChQ3rb3LlzpVu3brJ///5sLcewYcP0tg8++EAaNWqky2DjHDfffLPs3Lkz+oMitqEwQXKAPBII/wSBAoXxfcwY5psgJJFIxAisK664Qho0aCCT/3dyhCC+9NJL8ssvv8h7770n33zzjTzyyCN6W9OmTWXMmDFStGhRreHA0q9fP73tn3/+kSeeeEKWL18un332mdZ+wLRCEgfmmSCWII/ExInWeSYgSDDPBCHJE4GFlwBEYLVrF/uXgLPOOktWrFih/+6DTvyPqlWrypNPPindu3eXsWPH6oyfxYoV0xoJaB/M3Hnnndl/V69eXQskF154odZqFC5cOIajIcGgMEGCAoEBPz7MgElI6kRgXX557FM2G2nBZ82aJSNGjJDff/9dDhw4oH0pjh07pmuRhMr6+dNPP2mTBzQTe/fuzXbq3Lx5s5xzzjkxGwsJDs0cJCR4i8GPT5cuvk+m0iYk8UjkCKzffvtNqlWrpk0TcMasX7++TJo0SQsIr7zyit4HaZyDcfjwYWndurU2f3z00Ufy448/ypQpU8IeR9JUmHjmmWe09GpWg11++eXZjjjGApUYIYSQxI/Agk/EypUrpVOnTlp4gEZh5MiRcvHFF0vt2rXlr7/+8tsfpo7A0uvQYuzevVvPEZdddpk2m9D5MvFICDMHJM3XX39dS6yB3HPPPfL4449nf2cBHEIIsY7AgrOlld8ErAzYHs0IrOPHj8v27du1MLBjxw6ZMWOGNmlAG3H77bfLqlWrtCPlyy+/LP/6179kwYIF8tprr/m1AT8K+EHMnj1bO27i975y5cpayMBxeJlEO3DGJIlF3DUTeHBuueUWefPNN6VEiRI5tuNhgjOOsUDVlU7EK2acEJI8JEIEFoSH8uXLa4EAOSfmzJmjHSURHoqy6hAOEBr67LPPSt26dbXJAsKGGUR0QGDo3LmzlClTRp577jn9idDSCRMmaP8IaCheeOGF6A2EuCJDwTsmjnTt2lVKliwpo0eP1maN8847T4cHAXxHCBG6CEEC0uz//d//hdROQDrGYgAnH8QzI3Y52QQRVu0kJH2AI+KGDRu0f4HbMtBWvxnICcMILOL22cMciiibcHNoXM0c48ePl6VLl2ozhxVITFKlShWpUKGCDi0aMGCArF69Ojtm2QpIusOHD5dUiRkPFPWMmHGEbUYrPBPaD0ZwEJJ8MAKLpJ1mYsuWLTqj2cyZM7N9JQI1E1bOPC1btpR169ZJjRo1UlYzgckcWeuChXoZ9s8NG7xXW1IbQkjyaiYISTvNBDx74ZGLtKsGcNyZP3++/Pvf/9YCAexsZho3bqw/QwkTSOmKJZmJV8x4KG1Ip04iUPjUquXzCIcjF8NECSGExFWYgIYBIUNmkJcdYT8wZwQKEmDZsmX6E04+qUw8YsbDZdADQ4eeXgfNCBy+mAmTEEJI3IQJFGyBR68ZFH8pVaqUXv/HH3/Ixx9/LNdcc41eB5+Jhx56SJo1a2YZQppKxCNmPJw2JJBY+G4QQghJDuIeGhoMxBUj9epVV12ltRUPP/ywTnzy3//+V1KdeFTtdKrlMLQVyDHGcFVCCElvEiJplQFK0BrAaXLevHmSzjHjePOH4GA2PUQrZtyNlsNr3w1GkRBCSHKSsJqJdMeo2lmxov96aCyiYVoIpw2Jtu8GnD8RwdKiBUKCfZ/4Ho+yyYQQQpJYM0GiEzNu540/lDYk2r4b8cypQQghJHKomUjxqp1O3viDaUOC4YXvhp0oEvplEEJiDaqcorikEUUYL6pWrRo091IiQWEihTHe+AOjNIw3/mACxcaNInPmiHz8sS+3BISGaOX7d5JTgxCSeNxxxx3ZVZ3z5MmjEx898sgjOhFSMgO/vW3btuWIOvSaYcOG6WSNwUCG6HvvvVcSHZo5UpRwb/wQBvDGDzOKlcnD7FCJ/0uB+f7hX+FFvv945NQghHgLCnu9++67uiooEhKi5hKECxT1ihZIcohzZGZG550YuY5QEyrelClTRpIBaiZSFC/f+AO1FfhEKm8v/BjikVODkKQB/1EPH4794rDKArIOY+LF23z79u2lVatWulSCQVZWlq6bBK1FgQIFdAXRibCpmvj888+lVq1aOp1zixYt5L333tPCwr59+/R2VA4tXry43g/VQ3HOzZs362zJ/fr1k4oVK+pcRciUbI4M3LRpky4SiarU2H7uuefKtGnT9La9e/fqqtWYsNEvnB9CUTAzByIML7roIn1uJE989NFH5eTJk9nbURKid+/eWjODApa4JtA8eGnmQJ/eeust6dChgy56iT7jmphBmfarr75aChcuLGXLlpXbbrtN/v77b4km1EykKF6/8QdqK7yOIoHpxer3y6hD4mVODUKShiNHRAoXjv15Dx1CFkFXh2IiW7hwoS7SaABB4sMPP5TXXntNT34om3DrrbfqSbx58+a6LsT1118vDz74oNx9993y888/awEhkCNHjmhtByZTJDM844wz5P7775dff/1VF45EUcgpU6ZoTQkyLONcvXr1khMnTuhzQpjAvphkAapQ4/v06dOldOnSulTD0aNHLce1detWnUQRZp33339ffv/9d7nnnnu08DPMJDBACOrbt68sXrxYFi1apPe/5JJL5MorrxSvQDFLlGd//vnn5eWXX9YCEYQmCDAQvq644gp9HVGNG+NBVukbb7xR17eKGirF2b9/P6Yo/ZlOzJmDqTn8gv3izaRJSmVk+BZz34x12E5IqnP06FH166+/6s9sDh2y9x/Z6wXntUnXrl1Vrly5VKFChVS+fPn0721mZqaaOHGi3n7s2DFVsGBBtXDhQr/j7rrrLtWlSxf994ABA1TdunX9tg8ePFi3tXfvXv393Xff1d+XLVuWvc+mTZv0ubdu3ep3bMuWLdXAgQP13/Xq1VPDhg2z7Pu//vUv1a1bN8ttGzZs0Of7+eef9fdBgwapOnXqqKysrOx9XnnlFVW4cGF16tQp/b158+bq0ksv9Wvnwgsv1OMLxtChQ1WDBg2Cbq9SpYoaPXp09nf0aciQIdnfDx06pNdNnz5df3/iiSfUVVdd5dfGli1b9D6rV6+2/+w5nEOpmUhRkumN34giiZZfBiFJS8GCPi1BPM7rAJglXn31VTl8+LB+G86dO7fOWAzwtg+NQuCbObQF559/vv579erVcuGFF/pthznBKjOyuZwCtA/wnahdu7bffjB9QHMBYHbo0aOHfP3119r8gn4ZbWA9vi9dulRnW4aJpmnTppZj/O2336RJkybazGAAjcOhQ4fkzz//lMqVK+t1geUeYA5BUUsvMZ8D2hZU8zTOsXz5cpkzZ0629sUMylQEXiuvoDCRosQji2Yi5NQgJKXAf1aX5oZYggmtZs2a+u933nlH+0S8/fbbctddd+nJFnz55Zfar8GM0wrP8GswT+ZoG46ScPoMLA5pTKZQ97du3VqfHwIFTC4jR46UBx54QPsVwDwAHwr4eKAAJcwiL7zwgutrkSdPHr/v6C98Rrwk1DlwTeAjYuX8Gs0imXTATGFinUUz3jk1CCHxB9EVgwYNkiFDhmh7vdlZEgKHeYHDJqhTp44sWbIkR0hkOKDZgGYCb+WBbZsjMXCe7t27y+TJk3WdpzfffDN7G/w2EH0Cnw44Or7xxhuW5zr77LO1D4TP0uBjwYIFumjlmfhRTRAaNmwov/zyi3bcDLwmEPqiBYWJFCeakRiEEGLFDTfcoDUFr7zyip5s4UyJqs9wToSqHWYFOA7iO7jvvvu0QyMcBdesWSOffvqpjt4AZk1EIFDZw/nw9ttv14ICHDl/+OEHrX2AJgL06dNHvvrqK70N54UJAIIBeOyxx2Tq1KnaFIMJ+IsvvsjeFkjPnj1ly5YtWqOBvuK4oUOHamfLzAjDUyF0IWrEvOA6uQGalT179kiXLl20QIZ2MP5u3bppwSta0MyRBkQrEoMQQqyAzwSiLBBxAL+EJ554QmsAMMmvX79eh3jiDRoaDICQUYSKQmvw4osvat+EwYMH62PDmUIQyvnkk0/qYxFxgaiMiy++WNq2bau3YwLFBAu/BvgWINIDfh2GD8bAgQN1GChMKJdddpmOCrECJhqYQ/r376/NOIicgBkHGphIgQBl+I8YwOSCytlOQUQLNCYQzOAHAv8RRNZg3NHKyQEy/ucdmrIcOHBAihUrJvv379cPEokMVvYkJDogYyTenjGxItww3Xnqqad0KCm0ASR+z57dOZSaCWIbpN+2iriAoyfNJoSQSBg7dqyO6EAUBt6skUMB2g2SHFCYILZgZU9CSDRZu3atNlfA3o8wS5gtYIIgyQHNHMSWaQOVRoOl5zZyVsCxkxEYhLiDZg6SzGYORnOQsLCyJyGEkFBQmCBhtRKzZ9u7SKzsSUjkpLhPPEnRZ44+E8SRw2UoWNmTkMizGiL1NMIUCYkVeOasMms6gcIEceRwaUUi1fkgJFlBkifkXzBqLKC8dKiETYR4oZGAIIFnDs9eYEpyJ1CYIJamDWgk7AoSiVbng5BkxUgB7XVhKEJCAUHCnH7cDRQmiGOHSzOs7EmId0ATgWJMZ5xxhvzzzz+8tCTqwLQRiUbCgMIEce1IiSyyw4ZRI0GI1+DH3YsfeELSLprjmWee0VI5irKYY1+RUx0Z0VBOFnXnd+zYEdd+pgN2HSlbtqQgQQghJEGECVQ2e/3116V+/fp+61Fl7r///a9MmDBB5s2bJ3/99Zd0ZN7mqANHSpgvgvl+YT0qB9PhkhBCSEIIE4cOHdIlZFFfvkSJEtnrkW3r7bffllGjRskVV1whF1xwga4Ot3DhQvn+++/j2udUB9pV1NsAgQIFHS4JIYQknDABM8a1114rrVq18lv/008/aQck8/qzzjpL52xftGhR0PZQbhXpP80LcQ4UQBMnouyu/3poLLCeCiJCCCEJ4YCJuvFLly7VZo5Atm/frmvNI2TFTNmyZfW2YIwYMUKGDx8elf6mW3nxdu18i3kdTBv0CyOEEJIQwgRq1D/44IMyc+bMHIVFIgFV5vr27Zv9HZqJSjDwk5CwvDghhJCkEyZgxkBiloYNG2avO3XqlMyfP1/+/e9/y1dffSUnTpyQffv2+WknEM0RKrlGvnz59JJMGgC7b/tujwsHy4sTQghJSp+Jli1bysqVK2XZsmXZS6NGjbQzpvE3kmnMNlWZWr16tWzevFmaNGkiyQombpTzbtFC5OabfZ/4jvXROC6SbJfGOkTrYj9CCCEkoTQTRYoUkbp16/qtK1SokM4pYay/6667tMmiZMmSuo76Aw88oAWJiy++WJIRtxqAaGoOnJQXv/xyd+cghBCS2sQ9miMUo0ePlrZt2+pkVc2aNdPmjcmRvorHCbcagGhrDuxmu2R5cUIIIcHIUF4UMk9g4IBZrFgxnbcC2o14MXeuzzQRjjlz/DUAbo+Ldr8IIYSkPgdszqEJrZlIJdxqAKKtOWC2S0IIIZFCYSLB6l0E7uf2OLsw2yUhhJBIoZkjRsCnAdEXcJq0MiwhTTWyS27Y4B/uGclxMGFgAUYY6c6d1mGlVnkmkJ5jzBgmrkomohU+TAhJTw7YNHOwBHmMMDQAiL6AAGAWDELVu3BzHASDe+8V2b07eH8ggKBdIwoEn1bZLqdO9QkzZiEj8FiSGDDxGCEkbqgUZ//+/Zh+9WciMGmSUmeeCZHg9FKpkm+9F8fhu3mfYEtGhm8JdV5swz5ujiWxhfeKEBLPOZRmjhTKgGmYRELljTADzQYKeY0bl9P8Ea6tYOYVEnt4rwgh0YJmjgQGk6+bMMtwx4VLQBUI9AzY31yw1TBhlCzJZFbJAhOPEULiDaM5UggvEksZWTXhKxGrc5LIYOIxQki8oTCRQrgNDzVjOHh+9FHszkkiI9rhw4QQEg4KEymEkYDKC4Fi1y6RMmXC74v94oER+vrJJ77PdC5ExsRjhJB4Q2EihTAnoPICVCcNx8MPx34ij1YF1WSFiccIIfGGwkSKgdwP/ft70xYm6HAYFUVjhVFBNdDR1PD1SFeBAvcd1WMRnWMGmqpIqsoSQogdmLQqxYCWAKr/SDDCPu2YOWLphBmugir6jQqqSL6VjuGqwRKPpeO1IITEFgoTKYbT8NBgIKsmwkMTybGPIZDRCzsmhJBIoDCRYkSqJShSxJfECm+50ARAQxGuLgjefmMBQyBZe4MQkpjQZyLFiFRLMGnSaft6ojn2pXsIJB1PCSGJCoWJBOHECd/E/MADvk98j0aYYChKlRK54orEdexL5xBIOp4SQhIZ1uZIAB55RGTUKP8Qy8xMkWuu8YVeOnWiMyYeYGWesKOVMBNYzhw2efRp4UJ/Rz/gpfOfVS0SZOa0GpshYKRi5AJrbxBCEr02B6uGxpn+/cNX+ES1UKcVOrF/qVI52ypcOOf6UO1bVSvF8YFtWK1z0+9Q5zXac1t5NVmZM8deJVjsRwgh8agaSgfMOAJTBjQS4UB0Bt7Grd66Q1US3bMnZ1uHD4scOiQyfLhIrVqhNQiGhiNQu7F7d859rdYZuR+caguCndfc3saN6RMCScdTQkiiQzNHHIFvxEMP2dvXquQ3Jl3kXTCHgmIfCCh9+0ZWPtxpOXMn/Q4FVfo5gXkJWT7DMWcOw0IJIfExc9ABM4788Yf9ffGWbs42Gcoh78Yb7ZcPj3a+CjvncnJep+2lAunseEoISQ4oTMSRGjXcqbzDZYJ00pabbYmgqk+n0ueJFqJLCCGBUJiIExAIzj3XeQgn/AO80hqEysfgda4Gr3NEpGouiWAkUoguIYQEQp8Jh4RyeLSLla+DE9+DTz+1V9HTTlvhfCaCZb/08lxOzmu3PS/uUyKSquMihCQmSeEz8eqrr0r9+vV1B7E0adJEpk+fnr398ssvl4yMDL+le/fuSZ2BMJivQygCVdlO3srdqsVDqdadntuJCt4LlX4qZ4o0am906eL7pCBBCEkIVBz5/PPP1ZdffqnWrFmjVq9erQYNGqTy5MmjVq1apbc3b95c3XPPPWrbtm3ZS7hYV7cxsuFADoOMjJyx/ViHxU6Og5Mnc+ZHCFzy51eqSJHQORSMdqz6Y/QJx0yYEHk+hkjyTJQp4+uD2+vtpu9e3CdCCCHO5tCEM3OULFlSnn/+ebnrrru0ZuK8886TMXgVjXb2rhiEK9oN8Zs1y9dOKFV2sCyXgZkgvVCLm9s44wzfuu3bRXbt8pUphx0f7aJPPXuK/P336WNxXaBpcGPTd9p3hpUSQkiaZ8A8efKk+uSTT1TevHnVL7/8kq2ZKF26tCpVqpQ699xz1aOPPqoOHz4csp1jx45pCcpYtmzZErFmwqsMhB9/bK8d7GcHLzJBQsuBfuOc+MR3J+fD908/VWr48OBaklhpBJgpkhBC0jQD5sqVK7WvxLFjx6Rw4cIyZcoUOeecc/S2m2++WapUqSIVKlSQFStWyIABA2T16tUyOYTxe8SIETIc6R09xKtwRa8jFfC2366de81DsKRXVpqEYFkpcSzyWgQD+0Nb0qePr6/RtPEzrJQQQuJD3M0cJ06ckM2bN2sVysSJE+Wtt96SefPmZQsUZr755htp2bKlrFu3TmoESdJw/PhxvZhVNJUqVYrIzOFVBkKvIhWcmglwvkCThFEwK7AfVgWzvMqGGe0MjcwUSQgh8TFzxF2YCKRVq1ZaUHj99ddzbDt8+LDWXsyYMUNat24dc58JL4QAu74OkRIq/BQCxbFj1vU0rMZjd5IOx5AhIsOGuROU7PhPxFpYI4SQVOdAMoSGWpGVleWnWTCzbNky/Vk+xhmLvMxAGIvkQ+HCTzHZBhMkrFJWe5Vt8skn3YVo2g31ZKZIQgiJEyqOwKFy3rx5asOGDWrFihX6e0ZGhvr666/VunXr1OOPP66WLFmit0+dOlVVr15dNWvWLC6hocDL0tdOHB+dthsu/NTuYjiC2nVstLM4dch0E+qZbiXKCSEkKUND165dK3PmzJGdO3dqTYKZxx57zHY7CP+cPXu2bNu2TatRkMAKTpZXXnmlbNmyRW699VZZtWqVNm/A76FDhw4yZMgQR+YKL8wcyZSBcPZsmIq8aQshqi1b+sZctmxobYYTnGSxdBuSG+o+Jfo9JISQlPeZePPNN6VHjx5SunRpKVeunM5Kmd1YRoYsXbpUEgmvhQnNpEki77wjMnKkyFlnSaIAtf8994js2eNNezDFvPSSLwrDS2HCrkNmNBwqnUSwEEJIunPA5hzqODT0ySeflKeeekprENKW224TOXpUZNo03/cvvhC59tq4dilY6GYk/PWXr004TXotSNjxxfA61DPYNYIPCdazYBYhhLjDsQPm3r175YYbbpC05oUX/L+3bevTuT/1lLezuU1ClSQPBrpbqlROR1AzRnuG86nXhPOj9TIvh52y7ciFgf0IIYREWZiAIPH1119LWoOc0fAVeeKJnLGPmZkinTr5Yi8dcOKELyLkgQd8n/huF7clyd94Q+S990Lvg4nWK7OJWZCpVMnnqxAKbIcJIlihMbvt2LlGRgQLTCtYPvnE90nhghBCwuPYzFGzZk35v//7P/n++++lXr16kidPHr/tvXv3lrQAMxmEByyff+5zLDDr0wsUEKlSRWThQpEKFUI29cgjIqNG+U9c/fqJ9O0r8txz4bviNHTT7COASdMOJUtCKxW54sVJKK0R6gkTBI6zysthNyTX7jVCNk+z8ER/CkIICY9jB8xq1aoFbywjQ9avXy8p74AZjF9/FalXz6e1CGTRIpGLL7YUJJ5/PniT/fuHFyicJpUyHCshTNg9FhnK4TsBIhEooEmAAODE2dHKadJpO24Tb3mdTMwJjDohhMSbpM2AmdTChAFe4du0Efnhh5zbEAXSrZv+E6aMggVDq9Lx1n3kiEjevMH3CZf5MdQECYWK3ayRSMEdLKtmOIzaHG7DMCOdWJ1eo3hnzmTUCSEkbaqGZmVl6SWR8TJplasMUj16WGdvuv9+NWbkSVuJnkaPtp/cySrBU7DkUUjkhC4GO9YqMZSRbGvIEHvnKVMmZ7KoUAm7opXMy801cloV1ut+xrMCKyGEOJlDXQkT7733nqpbt67Kly+fXurVq6fef/99lYjEVZgw8+abljPUAmmiisq+kJPY/ffbO4VV5ke7EyTKiJcubT9rpN1y6h9+6C8k9OnjEzDM+6DPOE+wEudeTp5W5yhZ0tvS8NHMYGoWAgkhJGmFiZEjR6qCBQuqRx55RKe4xtK/f3+9btSoUSrRSBhhwuC77yxniWOSV9WW311rJgyMSRsCiN0J0mqCxYQ/YUJwbYHdFNvYL5yQE0pTEI238cDxzJqVOJoJJ9eVEEKSVpioWrWq1kwEMm7cOL0t0Ug4YcJgyxaVFWSGvUa+yP6amakUlD6hVP5Wk73dCXL48NAq9f79rbUF0GTgM5ggYLxBYz+3ZoVYvY0b2oBQY8F2XNNomF/caHxioSUhhJD90RImYNZYu3ZtjvVr1qzR2xKNhBUm/sfAPkfUZ3Kd5YzxqDytRLL8JvHAN3Srt/5SpXyLnUmpaFF3k7shaITytYBmw6uiY9F+Gw/lN2Jc00CBKhq+C9RMEEKScQ7NdJNn4tNPP82x/j//+Y/UqlXLsadouvP06AKyoP9UyZ2ZJY/JcL9tI2SQKMmUCXK95JXj2WmfjdLbwUqNI/W13fTXBw447zOmUzB+vAgehWDl1EuXdhf5EQyvSqE7KQ2P/Bog8HoG3guv8DJRFyGExArHoaGTJk2Szp07S6tWreSSSy7R6xYsWKCrf0LIQGVPSffQUBcgTHTsWFRkFdnz7lT55Gj7HPtskKpyqSyQXJUqyLp1IjVqeDtZuwFFtjCxWYVtIiHWzTd7ey67Bb3cYg5BRbrxW24R+fvv2IaMGkIiMP/vjGfOC0JIenLA5hzqWDPRqVMnWbx4sa4a+tlnn+kFf//www8JJ0gkE8gjgVwMKHsy/mg7yRAl58gvcsp0i6rJRtkqFWXzlgyZPGBx3AUJgEkXEykm+S5dfJ/GxGq3tkY4Yvk2bowlX77QgoQ5BTeEj1hoSQyNDwUJQkiiwaRVCYbV23xx2Stfy1VyoSzJsf8d8q68J3dIvAilLYgkUVQ03sbtJr5yWoH14499gpTXMAMmISSlNBNozO5CIsPqbX6flJCL5EfJJSflNbnPb9s46SZKMuRF6S0ZYpHGO0rY0RYYtTWM/d3g1ds4BAQINkipDWENn/ge6PPgpgKrVxqYQIJpfAghJCk1E5mZmbruRijQDPY5lWBlFqPtMxHJ26PVscBOeuv1j74huXv5CxZggTSVa2SaHJBikQ5NOnf2OVi6sd2bxwY/kDffDO/fgXah2h83TmTnTndps60Ip2kwp/pGn+3W8DDuBfxXUM/NbapvQghJi9oc8+bNs33i5s2bS7oIE5HUTwh1LLDjgIc2RnVaIN/JpTnaPyb5pL6skLVS2/X4oL6H74DTIltWY4OQcO+9vrIlODZYFVCvfQIMU4sd/xJcf1x39M8O6DOqu8I05eYZIISQRCcmtTmSgWjlmYikfoKdY63yR1iltzb2O1M2qz+lgmWChjYyLaK8Dk7qZXg5tljmbTDnlLCzIEOokWfDzTOQyESzPgohJDXnUFuaiRUrVtiWYurXry+JRDQ0E+HedkOFDDo5FtgxoSCsFG/+iDzIL0flP9JZrpP/5thvgDwjz8kjOEvYMUL7YDfk0TBpwDTz0EMiu3Z5N7ZIcRqeij5mZoau5FqmjMimTSK1a7t7BhIZVislhERNM5GRkaEyMzP1Z6gF+6SDZiKSLIXRyHBo3WaWekyGWTb+qVyv8sqxkOfHW3e0i4vFAieaiXCaCrPGIRUzVbJaKSEkqhkwN2zYIOvXr9efoRbskw7YzcRotV8kxzrbN0Mel6E6X0V7meK35QaZKMclv/wh1aWcWJ/ohRfCZ3cMloHTXX+jQ7iMksGAU2aoPA/RuI/xJFQUi7EO1yTB/KsJIQlCbjs7ValSJfo9SSLshgJa7RfJsW73nSrts5Ng/SJ1s9dXlw2yTSrovy+SxfKjXGQZ5WClpncTQmm3v15ihKdC6Al0+gwFxg2BKpgpJhr3MZ5gnKGEQnOCrmhnISWEpFHSql9//VU2b94sJ2CwN3HddddJuvhMhAvfDOUz4eZYt/0JpITs0UmwGslPObbdLu/JB3J7yKRUuOV9+4q88oo4wq0fgRfJm6x8ASLpYzTuYzyx61sSmKCLibUISW2iFs3xxx9/qPr16+fwo8DfTn0mxo4dq+rVq6eKFCmil4svvlhNmzYte/vRo0dVz549VcmSJVWhQoVUx44d1fbt2xMqmiOUXd2LY+161gdrM9SSS/5Rr8m9lhvHSG+VIadUnz7+54EvRa5c7vwP3EQ4oIR56dLeVOw0riXGFM4nIt18DNz4gFj5y0SrmiohJMVKkLdt21a1a9dO7dq1SxUuXFj9+uuv6ttvv1UXXXSRmj9/vqO2Pv/8c/Xll1/q8uWrV69WgwYNUnny5FGrVq3S27t3764qVaqkZs+erZYsWaKFjaZNmyZMCfJIQhztHOv0x9qNM6Sx3CuvWW74Vi5RUz/Yny1IuHVmdBP6Gep8kU7WXoSnYl+rUu9Yl2wTKgQtXI9gwijW4/oYwmwqCVKEkDgIE6VKlVLLly/XfxctWlT9/vvv+m9M+Oedd56KlBIlSqi33npL7du3TwsWEyZMyN7222+/6UEtWrQoIYSJSGPygx2Lz+HD3b3hG21++GHON/rAdqw0DJfIt5YHZOXPr+pkrHYkQCAXA/rhJlcBbrsdASWSHAiR3Ltgk6mxJONkaldjZggeoZ6tSO8NISTFhYnixYur9evX67+rV6+uvvnmG/33unXrVIECBdz2V508eVJ98sknKm/evOqXX37RwgkGsHfvXr/9KleurEaNGhW0nWPHjulBG8uWLVuiKkx4DX6wK1YMPYna+bGOJCRST9SySf0l5Sw3tpbpYfsXydspxgVBJFFDL1N5MrWjsUnFsFhCSAxCQ83UrVtXli9frv9u3LixPPfcc7JgwQJ5/PHHpXr16o6dO1auXCmFCxeWfPnySffu3WXKlClyzjnnyPbt2yVv3rxSvHhxv/3Lli2rtwVjxIgR2lnEWCoh+1KSYIRawqkvFHZKX0cakrhFKksF2SYF5Ij8V9r6bZshV+viYo/Is+iN58W5MK5gia8SIfTSSeRDsoF7tnGjz/EWzpb4hBOp+V6mWlgsISRyHAsTQ4YMkawsX3VKCBDIL3HZZZfJtGnT5KWXXnLcgTp16siyZctk8eLF0qNHD+natauOFHHLwIEDtdepsWzBr3oS4CbUMtSPtVchicekgM6miYqkQ2WY37Zn5VFRkinjpbPkleN6Xa9eOScfpziZhOIRepnqk2m4aqWpFhZLCIlRngkzrVu3zv67Zs2a8vvvv8uePXukRIkSYSuLWgHtA9oBF1xwgfz444/y4osvSufOnXXY6b59+/y0Ezt27JBy5coFbQ8aDizJRri3XTs/1kePivTv76vSWaOGL+nSX39ZCyhGhU4cs3u3nbP5kmBhQRKsKXJaWugsn+oFSbAq9f8OPZO5c92HctqdhJDWOlQJ9GiFL6b7ZGokAgsXFmvn3hBC0lSYsKJkyZLiFdB6HD9+XAsWefLkkdmzZ0unTp30ttWrV+vcFk2aNJFUw+lbLCZDsymgfXuRqVOt97Wq0Inv9gUJfz6TDjoJ1rmySlZJvez1NWS9SFVfEqz+8oMskQtdVdE0JqtwwhXyXIQTCuzWmnAicKT7ZBoqEZjxPoHKq8mQX4MQ4hHKJn/99ZcO3TS45JJL1Pnnn5+9NGrUSP3555/KCY8++qiaN2+e2rBhg1qxYoX+jpwVX3/9dXZoKBwu4eSJ0NAmTZroxQnRjubwCjcOk4aTY7t2offLn9//u1U4o9WC/YJFlZiXErJbLZGGlhtvk/dcOWSGi5awUzvEbviim3wJkeQZSRViWf2VEJIi0RxDhgxRPXr0yP6OHBO9e/dWw4YN00vjxo3Vww8/7KiTd955p6pSpYqO4ChTpoxq2bJltiBhTlqFcNGCBQuqDh06qG3btqWkMBEuzj+YMFHBuup4jmXGDF8I5KxZ4aNFihZV6quvfH1y0i8kwXpd7rHcOFr6qMpnnnIcfhk4WSHKwxQtHPZ6hrp2mPiQFCuSUvLpPpmyXDkhqY3nwgRySJiTUkGYQDZMgxkzZqhzzjlHJRrJIky4zWJpd+nVK7JMh077dZ+8arlhb71LcVOiPlnZHWe4XBzmEE+rvnAyJYSkMnbnUNs+Exs3bpRq1aplf7/yyiulUKFCflEZiOwg7oENHyGVdmpIOAVOmW4jEdz063XprpfLZL7Ml+bZ64uv/E6kWDGRggVFli0TqVXLVmSBU+yO8++/g28zh3ju2WPP94IQQtIR26Gh//zzj+wyefxNnjxZ53ww2Lt3r2RmOo40JSHi/IcM8e7yGHO220gEo1+jRzs777fSTDtrVpZNsk1MUThHjojUru3z2JsxQ7zGy0gKOLZalVqHAybWhyvVnorAYRUROygQhk+WJickvbE9+0PzsHDhwqDbv/32W6mNyYFEjPE2PmyY7+03WMQt1lfwBU+E5fnn/SMRQrWJPF/mSARUCYV3PkqSI8WI3XOa+TOjsjQos03Gv31Y/m58rf/Gq6/2nRidhDrAg8mtadPw40RoqR0++MC6W4ZBBNclnSZTCE+omNqiha/SKD7xPR2FKkLI/7BrN3nuued09U6jLoeZZcuW6Zod2CfRSCafCbdRA+GiObDdaZuRVgkNv2SpYfKY9cYbb0RedEfXyCoaA30PNU44cjp1erXjY2JFqvhWsMAXIenFfq8dME+cOKGaNWumcufOra6++mrVp08fveBvrLvsssv0PolGsgsTdqMGggkUgYKEkzbdVAnF+ZxWLu0ok6w31KypVJjonXCTG8YQapz49EI4CizVHu5aJ2Op7lSuSUIIiWwOzcA/YhNkpBw1apSMHz9e1qxZo9fVqlVLunTpIg899FBCZp48cOCArtGB1NpFixaVZMVOUiVzBkz4SMBqUKCAuzZh2oCPpFP1PUwLf/whAosYfApgAgjl5Gg2O7Q8Y6XM3FHfeocffhC58MIc/Yd6PZhTqJE8at06X3/M4wQYO/rYsyeeE4kImEzQfuA9MeqtBP4vM8wvkdQwiTUwH8GkEQ74+7hxmiWESPLOoSrFSQXNRDwYPTpylb+bRFz62N27lTr/fOsdPvgg4uqVVpoCL5bA86TamzxMNHauA/YjhKQGUasaStIDaBfcYoRluil0pY9BevalSxFCJHL33f473Hab77X+oYdk29YsR/0xawq8Dr0NPE8qVhdN95okhJDgUJgglqBQmFuMycTNpOJ3TO7cIm++6Zt1UYjDzJgx0uXWXDJXmksRCW2jOOMM95VZnRA43lSpLmpEysAkBHOOk0ggQkh64MhnIhlJZp8Jr6tdOgH+F4UL+0JBnVC6tC+MFBVJEZ4JoSRYQaxASpVCVdgwY5w/X6T56SRYBoeloJwny2Sd5EyCZSSXgsLDjs3fDWZfEeN+4f61auWtj0GsnwmrQmlWJKMPCCEkPPSZcGjvSTTiGQHglU9BqPDMYIvt8W3apNQZZ1g2cqV8lcM3AQsiLrz2kzBHs1rdr8KFvfOZiPUzEa7YWjrXJCEkXdjvdWgoQOhn9erV1a+//qqShWQUJuIZy+9kAgm3hArP9Mwh8fBhta3hNZYNPizP+7WNImHREibcLnbvZayfiXDOo1hwPT/8MLnzZhBC4uCAmSdPHjl27JiTQ4hDQtn1jXXRyrjo1qcA5gkrjHbGj/eZAMKl4nblkFiwoJT76UuZPTNLnhD//OMvSH9RkiEfSxfJrU4IssHDDBPM5g+wvUQJiQm4bu3aJeYzEc55FOB6wpwFE02szG/xgKnDCYmCA2avXr3k2WeflZMnTzo9lNggnhEAdiYQK3bvDt9f+BKYSrl47pC4c1eGPCZP6DognWSi37YuMl5OSD5ZI7Wke4cdel2gQIHvWB54AHVmJCbgupnvY7BJKx7PRKo4j0YKU4cTYg/bVUMNfvzxR5k9e7Z8/fXXUq9ePb/KoUYBMJKcP+LRnBgMh0E7RBoFMlk6aaGirqyUlXI6CVYtWSdPvFlOnhCRxpk/yg+qUfY21Kjr2zdsEdOoXXMrR0fDcfT4cWdteQHDQIMnHDMKvNHZlJAINBPFixeXTp06SevWraVChQo6UsK8kOT9EY9mfgAj8sBpkTG7WLW9SuppoaKU/C0/y3l++y/OulCbQG6RD/V3aAFeeEFk9WqJKbguwXJfGJOWUT7eTlteEc17lQzE09xISDLC0NAEw0gRHSyc0kgRvWGDtZ3aUJV/843I5s0ilSuLXHGFPbt2uHNbgTd6TGI4JhjFi4vcc4/IsmUi+/ZBu+Ubh/kcoUILQ4VDmrdh0kWlVWDV9oRPTso/d94nNx15J0cfR0pf6S/PS5GimRGn1raDOdU3wmdDpQSHXwJw+0y4xRBygN17lSowdTghMQgN/eeff9TMmTPVa6+9pg4cOKDXbd26VR08eFAlGskazeEmAgDrS5WyPgbr7Xj8B6soGqqwV7BzOllQmRRRH07CIa22oS+B/THCFs3pt3vIK5YdmSPNVWE5ENUIDnMEht2U4MOH26/06iV2CsKlIkwdTkgUQ0PBxo0b1VlnnaUKFiyocuXKpf744w+9vnfv3uq+++5TiUa6CBN2q1/aFSjChQVi8ocg4VUYqTExmvsXKhwyVBvG5BtY7ttqgmgmcy0bOiCFVQ1ZG9F4MOmGq1rqZNKK18SeKuXTneC27gshqUZUqoaC9u3bS5EiReTtt9+WUqVKyfLly6V69eoyd+5cueeee2StXQNvjEi2DJh2K2GaVdrhjjGDYzdutGfyMJsWGjcWef11X4gn1PL33SdSu7a3NS7MYwN2xxSqHfM4Q6muK8sm+VEulDNkV45tV8lXMlOuym4bmTTz5/c37cB/YOTI09VDzeaYUGYap+r0eGZFTSciNTcSkipEzcxRsmRJ9fvvv+u/CxcunK2Z2LBhgypQoIBKNJJNM+HmjchpdU4v3qbcVAR10j8v2g9WxTOUZqOgHFLTpI3lxr7ygv4TmoBQb+tO3uTt9AnJoY4fj/yepTLR0J4EM/nFInkcISlfNTQrK0tOWbgw//nnn1pjQWIfGuo0JNCLEMJoh5FGo494g0SoZSiOSCG5RqZLhuRMgjVS+ukIkI4Tb5Zcp05oTUGXLv7OrU7zEpj7FCxyAsmhoA1i1HVsc0HAuRROpoYDrAE0EqnsfEqIGxwLE1dddZWMQSWn/5GRkSGHDh2SoUOHyjXXXOOqEySy0FCnIYFehBBGO4w0Wn00JghkugzN6SRY18sE/03IKpUvn0idOroymRFB89BDIp06BQ/xDDa5BZu0nLSRroQLq/VCoIBZEGamjz/2fcK0QUGCEH8c+0xAA4EcEzgM/hGNGjXSn6VLl5b58+fLGUa95wQhWX0mnNhqo+EzYdUvs63eaUVQu6BfR474Pr32mTCPAamsb7lF5O+/7bdbT1bICmlgue0CWSJL5QLHfTJz4oRvH2gigrUBgWPcOJGdO+kz4ca/iBCSYKGhH374oerfv7/q0aOHevPNN9WRI0cct/P000+rRo0aad+LMmXKqHbt2mX7Yxg0b95c22vMi5OokWTzmXBrq/UymsNueKbTiqBOfB1Chbmaz2f3GlmNITPTXf9KyS61TOpbbjxPljry4zDjxk8kVpVkExFGXBCSpKGhixYtUoMGDVL9+vVT06ZNi7SPqnXr1urdd99Vq1atUsuWLVPXXHONqly5sjp06JCfMHHPPfeobdu2ZS9OBINkFCaAmzBAL/JMOK1WabciqJMFpcJDCSjGWOxeIy8roZqXXPKPelu65djwlVz5vzLoWTmOgYNgMOyGidoRntIB5oIgJAmFiQkTJqjMzExVqFAhVbx4cf33888/r7xk586dutPz5s3zEyYefPBB120mqzDh1kMd+8yapdSgQUrdeqvvE9/deLeHK0NtlAxHpMFXX3k3YZcuHf5t3BhPuGtkp5R24OJGY9FLXla/SR11Uk4fvFzqqa7yrsorx6KmmTDfh3TI/2CGmglCklCYaNiwoTYvnPzfLxZMFCVKlFBesnYtkgSJWrlypZ8wUbp0aVWqVCl17rnnqkcffVQdPnw4aBvHjh3TgzaWLVu2JK0wkSw/1qNHKzVyZORCBCZFhEF6Gd7qdoLGmCCg9Orl7LgqskGNkj466ZWxcquUVwPlaVW3wu6Iw0S9uCapQrjrla5CFiEJLUxAI4HJ3uD48eMqd+7caseOHcoLTp06pa699lp1ySWX+K1//fXX1YwZM9SKFSu0n0bFihVVhw4dgrYzdOjQHD4WFCbc4UTtXriwc8Eh8DsWmDjsHB/KXOB2DFbt33+/u+OLyV7VX55VW6Ri9sp/8hX0NbhunWfpzM3Lhx+mX6ZK5oIgJMmEiYyMjByCgzlpVaR0795dValSRWsSQjF79mw9sHVBfpCpmfCOaCWmQqrrYL4OXquu3Y7BaB8aikjGmkeOq+6FP1B7qzbwl5w6dlRq4ULX6cztmIfSxTkzXeuHEJKU6bQzMzPlySeflMKFC2evGzBggPTv31+HhRr07t3bYeCJyP333y9Tp07VoaXVqlULue/hw4d1H2bMmKFDVFMtNDSRQKgiQhGdhE+Go0IFkfffF9m+3RcCifTTOIc59bSd0FhU21y4MHwlUUQqd+0q8tdf1u2FCyfENShYMLJS0zocd4OSXPO+8eXcnj799MYmTUQeflhO/au9fLswl1/orTE+lEV/8kl3fcB40iHBEtOME5IkoaHQGlStWjXkUq1aNUcST1ZWlurVq5eqUKGCWrNmja1jvvvuOy0lLV++POUdMOOJ27fjcEtgtInV23O4MNe2bYO/hQerJBpJZAQKmkU6bj9NyqpVSt11l1J582bvsDFXde3EiXTegeOJ1LEV408mk0c6FhYjJO2qhnoJclQUK1ZMzZ071y/008hZAVPG448/rpYsWaJrf0ydOlVVr15dNWvWzPY5KEw4J9IJzOrY/PntT+B2c2aEO6edbbEKK7X08di2Tf12/RD1t5TM3nG3lFBPyUBVXv7S5w0nCNmNPIFpKRkIVXKeEBJ7kkKYsHKUxILcE2Dz5s1acEBxsXz58qmaNWvqRFnpkGciXrgJpQxcvv5aqRdeUKp9e194KlKS2Akxxbm9OL+bxaqYlpd9sfLxQPsQFgrIYdVdxqo1UjP7gOOSR70jd6i6ssKT85csmfhv+OFymlCgICSFSpAnG/SZcIbdktjBMEpw2y39bQZ1D0Ak548Eo8y3V9ciXErnxx8XGTr09PdMOSX/kv/KwzJSLpPvstd/JVfJSHlYZsqVumaIV+NLJJgam5DknkMdF/oiqU2k1TpR7yJw0nRSCTWa1UjtnD/Ud6cYVUBRFy/wmmDyDKxgmiW5ZKq0l2byrTSW7+VTuUFOSaa0lq/la2kty6WB3C7vSR454Xec3WK98by24YDDbKg6LHjl2bLFtx8hJPGgMEH8iLRaZ7t2kVVCjWY1UjvnD/XdKZjk//Mf60gKTIp79gQ/9gdpLJ3lU6kp62SMPCiHpJDUl5XyntwhG6WqPCojpKTskUqVRPr2tdefeF7bcDgROAkhiQeFCeIHQiyhljfeqp2AiQ3HGyW5Uakbn40bhy75jXMZx0ZyfrfgXDDPIBwV/TVCMBGeGb5UeXAOHPCVJbcqg213UtxRoJo8JGPkTPlTHpFnZatUkAqyTUbIINkslWRChd4y6Kb1ugqqneubqDgROFOBwP8jkYQeE5IQOHXG+Omnn3Q2SoPPPvtMV/scOHCgzoqZaNAB0zlOszCaHeSsvPFz5bJ3rNvzW7UZibOiURHVK+dLK+dBt8m0kATrVnlf/Synk2ChHsjy2p3UxbIwaZ0X0yk1NiNWSDIRtWgOlAyfOHGi/hvZL/Pnz6+6dOmiIy0iKcgVLShMeJtV0GqSNUIq3YRRBstU6CbPBSIjPv3Ufl6JWC1WE2GkdThQkfQKmaWmSRu/DT/mbaI6yCSVKSdDXt9EJB1SYzNihSQbURMmihYtmp3K+plnnlFXXXVVdjKpM/HrmGBQmPA+eZDVejdhlFbhmMHOjzwJoYQEY7LBMRUrhj/v+++Hr07q9RIYHhqpBsZYzpFV6m25Ux2T00mwDpStodb0flmd3H9IJROpnBrbbhXeVNC+kNQhasJEkSJFsrNVtmrVSo0ZM0b/vWnTJq2lSDQoTMSGSGtg2AE/shAqkDMh2GTjpNJpLAWJYImrvMw0Wla2qY23Dva/QKjsizr0f/2VFNkl0Z9Zs5QaMsS34O9E66NbWDKdJCN251DHDpiNGjXSNTo++OADmTdvnlx77bV6/YYNG6Rs2bLRcOsgSYBbL3snxyG88rHHRHbu9OVM+Phj3ydyOBgRE3bb++MPiTlWzoPo98aNp8czZIj79ndIOVl4zZMimzeLvPKKSM2aInv3ijz9tC54sumKbtK64iqdO+Pmm305NFAHxcpBNB6gH+hPq1a+WiRY7rhDZOpUSQkYsUJSGcfCxJgxY2Tp0qW6ONfgwYOlJn6wBMWEJkpTuL+TtMStl72b4yBUIPlSly6+T3MOB7vt1aghMSNcNIV5PHnyRHYuPf5ChUR69hT5/XeRKVNELrlEVyyrMmeczNpRT6ZLG2klM3XCWUSwXH99/AUKnB/9CMw1kSj984J0i1gh6YVnGTCPHTsmuXLlkjyR/hp6DDNgxoZw1T6dZIYMbBc5GdAuKo0iN0Nmpm/yDRQk7PTDXHUUAoXd/rrFCHE1Kneax7Njh8ju3afHg7F17uyuP6GuJ87ZvvxiuW3XSOkkkySXZOn1y6W+jJK+Ml66SNlKecPei8A2jcqsgVVbnR6LdxDci2BJq+w+K4mO3Wcz2cdJUgvPq4aa2bt3r3rzzTfVo48+qnbv3p0dMvrnn3+qRIM+E7HDrkOhXe/8cP4EcMgMFgkSqg+ISHHSX3O/rf42fw90EjX7c4Qbj92iXcH6Fux6mm31VWW9GiO91UEplL1yq5RXA2SE+vbzPVEvxmV1rF1HWCf+NYlKOkSskNQiag6YKP1dunRpHQqaO3duHR4KBg8erG677TaVaFCYiC128kzY8c53EmZq1RYEBjsTr9sQ2GDbgjk4elV9NJyAZAX6Erh/cdmjHpFn1J9SIXvliXyFlHrgAcR8O74vdibDSK+BZeXVJCSVI1ZI6hG1Ql+tWrWShg0bynPPPSdFihSR5cuXS/Xq1WXhwoVy8803y0Z4kyUQNHPEHis19sKF9lXi4Yo+BQLVMB47o02nRaOCqexDqfKdqPmxb5UqPvV2NAinHg9VsAx1PjrLf6SfvCANZIVvJewusMk8/LDIxRf7jcPJdY3kniZboTKnRGImIiQlzBzmPBOFCxfO1kxs3LhRlwlPNKiZSI8wU7MK3E0IXiQhk+GORThrtDQSdswAtrJLnpmlTs6YqVQb/yRYqmnTbJVLJKGNbkOHs/vH/AuEpFZoaL58+bSkEsiaNWukDAocEBKHMFPzMU5D8IyQRDchk+GOxae5zHg0CTZuvPEaFUoDa55kVzZ9MUNytW4lMn26yMqVIt26ieTN61MpdeokUqeOFH7vFSkoh8P2wyqU023ocKjKq4SQxMGxMHHdddfJ448/Lv/884/+npGRIZs3b5YBAwZIJ/zoEBIhbkLjzMc4CcGLJCQx3LETJog8+KDEjFDjhtUCESUVK/qvh1nCiDTJpm5dkXfeEdm0SWTwYJGSJXVijkbj7pfNUlmelMFSToJLB5j4A6+b3XsS+D5i2T9CSMLh2GcCdpPrr79elixZIgcPHpQKFSrI9u3bpUmTJjJt2jQphBj3BII+E/Eh0tBBJ2Gmhs8EMMIu+/QR+ftv6/0Dw0ND+QBg8h03zpcoK9CfIpz/ACqO7toljjHaN4/v6FFf6Giw64GqoSh3bg6XtboHwPF9OXxYXwQ1erRk/C/b13HJKx/JLTq09BepG9Z3wknIrhP/GkJIYsyhrvNMfPfdd7JixQo5dOiQdsiEY2YiQmEi9uCtFG/k5okWEwVU7XbfMI23fjtP56RJvs/Ac4Y7Bi/cwRwTg2GMw82xdoFGA4KIeUKF6QDXA4S6Jkb/QKT3IAenTsn3gz6Xk8+NlEtlQfbqGdJaRsrDMkvwG5AR1GHSuKeBYwjMxUEISZM8E8kEHTCTtyoi9g1XARQhkW5CDnFMnz7unAGxuDk23BIsb4aTOh52rgP6HkldDhzfWBapT+V6Xf7caHiZ1Fe3yXu6TLqTWiQMiyQkTUJDX3rpJdtSTO/evSWRoGYidkQSOugmpNIwQwAnIYfGcTAdIAOlU5yYMOADAHNLsP9lRYqIPPCAyBVXWGf0tLomCPW88Uaf2SMS3GoqzKGm1WS9PCgvyl3ythT+n3PmVqkgL8sD0vbz++TSf5XIcfyJEyJjx/rqo+B5qVfPdx9o1iAkxc0c1apVs3VSOGOuX79eEgkKE7EjVD4DN/kC7LYXL0IJCobgNGqUb+L3UrXv1XUJ1odw/i5W/g/FZa/cJ6/LA/KyVJS/9DpVqJBk3HWXz4Hlf78hViYwM7hmo0eLlCjhGycIljqdEBJ9aOZwqKIhkWOVaTGSTIZ224vXAnW/ndTIwdJPI/+Em7wWXl6XwBwOdlNlB0sLnVeOq9vlPbW3Sn3/POHXX6++GfG96wyY4UxAhJAkyzNBSKyqIiZ69cR27eyFWwaWGR8+3DdFIv+Em1LgXl4X9GPLFp8mwkmYbLBQ08Il80qN4bdLkXXLRL7+WqR1a5GsLL1zi4EXy3x1qbSXKZIppnAVG8AMgshzJ9VDDZPQJ5/4Ps0RMoQQb7Fl5ujbt6888cQTOuwTf4diFPS6CQTNHLHDTvhfsFDLSNoDTqp/Gsdhfzcpru2m47Yaz1NPWSexcmJyQFXxm27ydnIcNEjk/fft+bsAo09nnOH7++WX/X04/PwxVq2Sbf1HSckZH0k+OaG3r5WaMkb6yDi5Q46I/XDywNTpkUYUMa01ITE0c1x++eW6Uqjxd7ClRYsWjtQnTz/9tGrUqJFOy12mTBnVrl079fvvv/vtc/ToUdWzZ09VsmRJVahQIdWxY0e1fft22+egmSNxqiIa6upwKnSr9uxEc9hRoZtNEKGKgQX2O5gJw8l1qVgxcpNDuOiWaC8wzYSLKLG6TjDNlJO/1JMySO2WEtk7/i0l1RMyWJWVbbb7EK56qN2IokiqnxKSLuyPVtVQL2ndurV699131apVq9SyZcvUNddcoypXrqwOHTqUvU/37t1VpUqV1OzZs9WSJUvUxRdfrJqiXoBNKEzEHieToJ3J2U4F0GDnDFYS3K6Q4kUYo9PQVUyWbitswj1h/HilPv00Z7XWWC9m4chcm6OgHFI95d9qrdTIXnlM8qq3pZs6R1ZF5HNj1CEJ1ydcH69CmAlJZZJCmAhk586dutPz5s3T3/ft26fy5MmjJkyYkL3Pb7/9pvdZtGiRrTYpTMQHc/GrWbPs/cBbOSHanRywn1XBrWDrImnT6XWw8yZvXj780Pkx5qVMmdgVF7OzGNctcEyZclJ1kEnqO2nqt2GatFEtZaYSyQraXjDsFhQrXdrd80hIurHfpjCRW2xy55132trvHeT0dwlsMqAk0guKyE8//aRrgJiza5511llSuXJlWbRokVxsKo9scPz4cb0YWBUlI9EHNm0j/BPOb6HyQJidAANDRrHOybFWIaeB65z2J5Ky1+H6bwXyV0RSqhvHx6q4mB3gW4HnASGfN9xwen2W5JIp0lEvjeV7eVhGSkeZLFfLDL0skwY6Xfd4uUn+kbzZfg9GWvBg57JDsFTr4Z5HQog1tqM5xo0bJ3PmzJF9+/bJ3r17gy5uycrKkj59+sgll1widVFoSETX/MibN68UL17cb9+yZcvqbVaMGDFCO4sYS6VKlVz3iXiD0yqeXh0bjf44xUkbcHLE45pqxXeN6BMk+grGYrlYbpQJUkvWykvygBySQnKeLJf3patskGryiDyrc1nAgTKU86WXkS7me8fIEEJCY1sz0aNHD/nkk09kw4YN0q1bN7n11luzNQhe0KtXL1m1apWu+REJAwcO9Is4gWaCAkV8iSQUNBphpLEMTXXaBipuevjfKq4YESCGJsGOYLVBqsugQi/J0MPDdRKs3vKSToL1rDwqT+V/QnLPu0vk/NNJsAKjMhCdA6EllOYBWUcPHrR/77yoNUNIyuPEdnLs2DH18ccfq1atWqmCBQuqG264Qc2YMUNlZWVFZJPp1auXOvPMM9X69ev91sPpEl00IkkM4KQ5atQoW23TZyL+GPbyUA6FJUsq9cILPn8Bs29CuGPd2LfttInt8PVw6yvhZOyBSaxw3nCRH04Wt4mivFgefPD09XPjz4A6H70Kj1N7K9czOVtkKnXDDUp9/73tmiVOr5fxTHlZa4aQZCTqDpgbN25Uw4YNU9WrV9eT+8GDBx23ASEEgkSFChXUmjVrcmw3HDAnTpyYvQ6ho3TATD6chG8ak6s5hM9Opsl4hrBGMvbOnXMKD5gvvRAQrEI5jbHFUshAHxBBYUewspy4JUt9+9jXCAHLscM4uV3llhOeCRLGM+XEUZeQVCXqwsTmzZvV8OHDVbVq1VTFihVdCRM9evRQxYoVU3PnzlXbtm3LXo4cOeIXGgph5ZtvvtGhoU2aNNGLXaiZSBycvkGGywkQabVJr0NYnZ4L/UcIajQm9XARKXZDaREZ4mWf3I7Xb+JesUKpO+7IsdMKqatKyS6/Y6DlgNbLTdpuu5qUcHkvCElmoiJMmM0c+fPnV9dff7368ssv1alTp1x1Eh20WpB7IjBpVYkSJbRppUOHDlrgsAuFicQCkwHU+HZ/4M1vfpGGadoJYQ1lXoj0TTSw/8ePe6Oed6uxsRNKC7OTl0IOrh/yYLjNgWGeuH98bUnQHevLMlft434Y99frWjOEJCOeCxPQImBCr1+/vhozZozatWuXSgYoTCQedt/4Yv3mF+s3UafXIdgSqD2IVGMTjT6al9Gj3R9rnriNyb6M7FC/ylmWB1wvn7oWWGL9PERDWCYk4fJMvPbaazq/Q/Xq1WXevHl6sWKyk0o8JC1xGnLpRYiml+fxqj9etYP8Dag1Eq42iBvQFqJLzHU3IuWPP7yJjjH+3iVnyDnym+SRE/K23CW3yYfZ+0wQX/33p2WgDJanEGMS9hxTp/ryS2DsiNoIVRsmXN4LuzBihKRNnonbb79dWrRooXM+mPM4BC6EeB0uGavqobGuZupVOxAkMPl16eL79EqQAGgLYZFeUqOG82OMHBzmiRvJucwgsdXt8oFkiJK+MtJv2yAZIUoyZYa0loJyOGx4LiZ3jB3hn8b5A/tj7BusqJvdiqVOqrUSktRVQ5MZVg2NHK8rK4arBmoGE4hRnTPa2KlSaq4Waqc9I/cBJj4ko8LEb1w/J9chGNAaDB6MRG6n2wZO7pedfqJ9lAGPFNzPdet8AoXTcUOoad/+9Bhx7cJlCn3v1ply+4dX5Vj/t5SSRrJENknVoP007rOV1gDbIUigDH3gtYZmw25eCuMZsFOtNRb/BwiJatXQZIY+E5ERrcqKdgpZxSOO36sw1FCRK3bCXt0uVhEZoe6X3X7aqbDqNDrH7bjRL4TTOjmmuqxT+6So5cbmMiesL4STiJhgY7d6fhgxQhKdpCz0FQ0oTLgn2gl7Qk1kXjoRetEvJ/1xKihZnc/Lip/B7pfdfoYL52zXzl5pdLQT7jpHeyksB9QsucJyYy952XaUhpuqrlbRQIwYIakyh9LMQSyJlfo1nIo92Uw74a5bsOsXeL6mTX3fb7zRG+dHq/PZ6Scw9g/V7urVPtV/MFNIsOflxAnf/Q6V/joaZEiWPCsDpL+8kGPbu3KH3CtvyMw5ebQPitW9gZnGbTG2OXP8i+C1aOHsGEJiCc0cDqUq4g/Vr/EPe41GWKbTsEevwz0DxxuNMTpdbhbrZBpZ9eqp/767K4f2JNJEXkOG5Mw14lW6+EjDSxmeStzOobajOUh6EeswyVTBy7DXaFxbo02v27Yb7hl43kR4fj6WW3QEyAWyxG99xsqV0rZbGdnyZ4bUkxVBo0ic8uSTIjff7NNIQMOBKBw3ESOBwFEU2ia0a7SP73ajQSI9nqQ3FCZIQoRJpgpehr1G49oabXrdtt1wz8DzJtLzs1QukMqVlHz59nZRder4bVshDURJhnSSiZ6eE+a9F14Q6dfPZ+4xA7PQxIn2KpNGGl7K8FQSMSrFoZnDHdGo1pkOhCsO5eT62W3LzhJ4PrvVTO2261Zd71U/Il2Q3h3p1I3+wUSAiqUfys2WBzwpg5RIlqf3BtfQjYki0oJkLGhGQkEzB4mISBL2pDPGdQu8ZlaEu35O2gqF1f0y399IwHSFdvPmDf+83H23yKef+idxCvWcxQKcE8ubb/ocHOFoiURTs2f7kmDdKh9pE8jDAY6ag+VpnQRrurSRAnLErz1QqpSza7hli+/cbpKP4XqGcgY1t28F1kdyfCQJu0gKoVIcaiYiIxrVOtMBL8NenYRQWuWZCHW+SMMz+/QJ356d3BexCBMNdW3snv9K+cpyw04prSrLxuz2UG4dFUudakec/r/C/naL5gULdfUyPDVaeWlI/GBo6P9gBszEy4CZLngZ9hrYFt58EYZpfJrbBk7uF+zyN9zgboxWIYvm52XtWpGhQ3MeZ7zBm30CzGPs2RP/d8WTuiXI3mlcB2CVsRJ+BZj67FJT1snPGedLYXUox7b5j8+Vy4c2d9Se+bo49ZOwe55g4aVehacG64/VvSapN4cyzwQhaYyTfBNO84y4zVXiRfpuu3lQ3Izfb3K88qDIddf5ZuQAesm/Zaz0ctxvCIXjxons3BlcGHTS73DXwos08nb6A4EX22ESI6knTDCag5A0Jpy93Aq7PjNubfH4bkeQ6Nz5tM+Dm/7Z6aMVflEWRYr4Xtkxm/bt67ffK3K/jgB5W+6UXHLSVtu4JuhPq1ahwzOd9jvUtfDCP8pOf6BRg6DEUNPUhMIEIWmMmzwPdkMWneYqMRz3Jk2ydxyKbKEfkYRU2u3jkCEiH3/skxvwhp6j7cxMmXzJSO2seYupBDq4U96Vk5JHlkkDKSnO1S1W4Z12+w0zmJ1rge2RXEu7/UGmU1ZCTU1o5iAkjbFrLw/0PbDj8+HEFo+U4YGVNu0cZ5Xu2olPilf+AlZqfiTBWiIXWu6PJFirpJ69TlqYGuz2e9YskZYtbZ/G9bW02x/ASqjJBX0mHF4IQtKRaJVdx2R0xhkiXbuK/PVX6LZHjfLVILHrSOjFZGR29uzTJ3htEDv+BmgHoaTIbGnFGbJD5kszqSNrcmxDEqzJ0smVAOX0vgUTFKxqjyxcGH4/s6CBGis4n5PsoKEENK+dvulE7h7W5nAY1kJIuhLNsutGKGawthFC6TQkNNKKtXbDQMON32k4K5JgfSw3WW58QgbbSoJlDs90ct+ChWyikmu4irXB9jNCPt2G9QYLNfU6vJThqpHBEuQOLwQh6Uy0yq4b64Lld3BT6CuwlLnTcdrNthkuP4ebrJ2dO/uO6ysvWO4wTdqoAnLYdqE0O/fNbV/DLZG2aVXkLtRz5EaI9Lq9dGQ/S5A7VNEQkuZEq+x6qHBHZElE1IIToE7fuNFdro5w/Sxd2ucfEiofiNtwWsO5EaYJg6vkK/lK2uTYd6eUkQvlR9ksVcKaW2BiGDvWV2wNNVKQo8MIv3Tb12gSKiTYTShxMLxuL105YHcOVSkONROEJG65erclyEOVbo9GPyPps/EWPHx48H1qyFp1QApbbmwmc4NqZMKp8BOhxLtdjYBX9yda7aUr+1mCnBCS6OXq8faPt0OndTnchLRG0s9Izm2EV9aqFXyfP6SmFJWDUlT2yzxp5rdtnlyu81Ucev5Vv/BQO5U+E6HEu91QU6/uT7T2I6FhnglCSNzK1bstOOamdHkk/XTTDnJTmPNS2DkO4sTlMk8y5ZSMFP8kWGOlp3TslCFZd94lp46f1KG0eLcOxFiHKBUnBceiBcxGIXN0eHx/orUfCQ3zTBBCIvKdsBumuG6ddcghwNtqjx7BQzSd+kxYjQG47Scw2oNfBfw87ISTmo9DqOwddwQ/fzBulQ/kA7k9x3okwWops2WPBJcYnn9epH9/iRtIoY2xhwqrNYekwufDyzBlL8Oe05UDyeAzMW/ePNW2bVtVvnx5HXExZcoUv+1du3bV681L69atHZ2DPhOERD+MLlyYopehhXb6EupcTvtpVW00nF9AsHEVtnaLsLU0kh+CbqwrKyw3FSoUXx+Jtm2d3SNcfy/ClO0+l4zmSJHQ0GnTpqnBgweryZMnBxUm2rRpo7Zt25a97Nmzx9E5KEwQEpswumBhisYEYdWu12GhdsbgtJ9u+ugmHBNho3b2WzBpmzpcsablxg4yKeL+Z2ZaXxsnApV5CXxuwt0jK4HOSZiy3eeSgkSKhoZmZGTIlClTpH379tnr7rjjDtm3b5989tlnrttlaCghsQujC6a69iI00W71SztjANHqJ86BX1VzCKjd44AdtTyoVeWEPLW1q3SR8Tn2fUKGyGPyOI5ybJZ47z2fmQcgQ6WRpdJtiGmlSqfvmd17FMok5gZmwExxM4eZYJqJYsWKqTJlyqjatWur7t27q7///jtkO8eOHdMSlLFs2bKFSasIiVMYXTRCE4P1IR4hql4vCB91ktXS2O9hed6ywS/l6pBJsMzt4zNQ+2CYhyK9PsY1Z7hm8pESoaFt2rSR999/X2bPni3PPvuszJs3T66++mo5BTEzCCNGjNBSlLFUglhMCIlLGF00wu6CtRnJGBIlPBDho3ardxqVPkuWFBkp/XTF0jYy3e+4a2S6HJFCsl3KSiXZHPS8aAMEln43wkynTo1sXMb1Zbhm6pJbEpibbrop++969epJ/fr1pUaNGjJ37lxpGaQU3sCBA6Vv375+KhoKFITEJ4wuGmF3wdqMZAyJEh6IfsCsgPLqdiJqIFAUKybSqpXvO7JpQqioKWtlmZwnheSIXl9WdmZn02wm8+RbUy6LF17whW9aAb0CTA8ffRT5uMyfdvcnyUNCayYCqV69upQuXVrWwaAWhHz58mm7jnkhhJwGij2UjEYqa3zie7jkUVgPJZ8RJmkXt0mp3PTBzrngE4C3bWPc5mMDtQFu+4g+oC0nYw4cGwQHCBVduvg+g/mIYBzbt/vGZT7fOqklheWwToI1X/wv2HxprpNgdZdX9XH164f274BAgWqgRYqIKxBKa1xz+KYYviHBQJ+wX6QY1weC0Jgxvs/A+048RCWwz0Qg8H/IyMhQU6dOtd0uozkI8SZsMhJv+mDtWv0dzKZvN6LE6lxWiznkFZ9uoxUC+xnqWkYytsBxOgmnzZBTapT0sdz4Xr67VS75JyY+IbjGdsJjsV+0KsNGUoE0HdmfDKGhBw8eVD///LNe0NlRo0bpvzdt2qS39evXTy1atEht2LBBzZo1SzVs2FDVqlVLO1nahcIEIZGHTUb64xuqXattgWWwnVYwdVJiPFxIKCY/O4KGVbVOOzkrnF7fSKuA3irvW25YKuepErI7JkKF3cXNc2fn+jDHRIqFhsL3oUWLFjnWd+3aVV599VUdJvrzzz/r8NAKFSrIVVddJU888YSULVvW9jkYGkpIZGGTkYbl2QnPswopjSQ00GgP6vWHHvKp6YNhhCwGA9cFFTmN/iCTJYB5Ae1CLR+symiwTJxur6+XVUAvlB/kB2lsua2urJRfpK7EG6cVYu1eH2a/FM/n0ITJMxEtKEwQ4rMVW8jtOUD9BCOvQDqNO1mui1fjMVNWtst3cqnUlD9ybOsok2SKBCmmESOcXHun1ydR7msqzKFJ5YBJCHFHuobkeTWeRLku0ejHDikntWSd5JNj8h+50W/bZOmknTWHydD/VTRI7DEnQuhyukJhgpA0IF1D8rwaT6Jcl2j244Tkk5vkP5IhWdJfnvPbNlQeFyWZ8l9pK/nlqCTqmBMhdDldoZmDkDTATQXFVEhBHG7cAGPKyvLuugTb7sVxbiuPugVJsKbLNTnWb5NycpH8IH9KdJMClighMmFC8PDYSHwmELKK/BrwdzF8dHBdw/nBJCqnovT/NenSaUcLRnMQ4sNJ6Kfb6qGJiJ1KoV5dl1DVML06zogG8aIomd2llqxWh6WA5cZLZX7Uz+/k2XMT7RIYPZRsz/ykKP5/TYrQ0FhAYYKQ09gJ/Yy0emgyjtuL6+K06mgkx0VazjxwnFZCi9UEW1T2qflyqWVD98prnvUp0mfPq7whxrkT+ZmfFOX/r0kRGhoLGM1BiLMQTS+qhyYibs0Mdq9LZqbz7Ipuj7MT0moFzvXFFyJ584rs3BnaDAPVP77fcIPI3r0B/ZYsGSV9pY+8mOMcb8g90lPGyimPqzU4efYwlipVnFdutVP5NJE4FYP/rzRzOJSqCCGs6hiMRKkq6sUyerS3475dxlluWCINo5IEy07l2lhWq40nc6JU7TflqoYSQmJLuoaQptN4kYDLy3G/L111cbGLZLHf+gtkqeyRUjq09FxZ5aKn7vsUy2q18WRbAv1/pTBBCJF0DyFNp/HWqBGdcf8oF2mhorz8JX9Idb9tq6SeFirayWcOeuq+T7GsVhtPyifQ/1f6TBBCIgohTQfsXJd4+EwEC2kNdcyRIz6fCTs4CbU0qq4a1yivHJcP5Da5USbk2B9JsIbrRFgZUfOZCBcSHG2fiVMWPjhY9/LLIt99J1K4sMhtt4m0bOn+/1Is/r/SZ8KhvYcQ4iNa1UOTHbchpsGWWB+HBce4Hbf7aqlZaoA8Y3ng59JW5ZcjtvvvpiBapCG0bp75SRbRQYh0seoL1kdaITWa/18ZGurwQhBC/H+golE9NNlxG2JqFXoZreOKFFEqMzNnmKcbQSLUuIM9F6Gu0XeDp1k28peUU2fKZk+FCSeVW4PlmXDzzE9yWdXV65LrXv1/ZWioUxUNISSHCjXZM2BGg3hmwLR7HNaNHetztoSPRM+e9k0b4cZtJ0tkuDDbHz5YLed1O08KyLEc57lUvpUFcqlnKns7lVu9yoB5KoKqrjjfpk2RmTzimQGTPhOEEEJiilHds6jsly/lWrlUFuTY5155Xd6Ue5Oq0ufcCKu6JuLYWDWUEEJIQmKEKh6QYnKZfCeZckpelN5++7wh9+kIkNfkPsklJ3Mcm4hsi7BviTy2cDA0lBBCSEwJDFVERVJk00Ro6R3yrt+2++QNOSl5ZIlcICVkT0KGaBpE2rdEHls4aOYghKQ99A+JDqH8QMKFbiIJ1mK52HrjqlUi557r+LzRJpKQVFQxHTMm8aqV0sxBCCE2mDzZNwHA1n3zzb5PfMd6Ep3rionyxRdPO1Za8YM0zk6CtV6q+W+sW9d34NSpCXU/c9kYVzD+/lvk1luT9/mjmYMQkrbgB/v663N63+PNEuuT7Qc9ma5rx44iEyeeTnZlUKqUbzHYLuXl8krr5bPxx3wHm2nf3jdrDx+uVQGJcD87BhlXkSL2BYxkfP5o5iCEpCWpXCE1ma6rndBNP7U/7AfPPCMyaFCOtmfmbyvXHftUjkmBsOeNNqfCZMAsWFDk6699GolEfv4YGurwQhBC0gu7YXyJGK6XyMT0un75pUjbtjlWb5UK0lgWy1Y5MzrnTaPn74DNOZRmDkJIWpJIFRdTiZhe12uv9Wkqfv9dTuU+nZWrovwlf0olHVraNCCHRaLcz20p9vxRmCCEpCWJVHExlYjLda1TR76deVyKyT5ZIE39NiGbJoSKu+VN788bAeVT7PmjMEEISUtgw4ZNOphTHNajWqRhwyeJfV3RXtEzi+lsmkhy9bLc77cd2TQhVDT7pLu7Mq0ec1mKPX9xFSbmz58v//rXv6RChQqSkZEhn33mX+teKSWPPfaYlC9fXgoUKCCtWrWStWvXxq2/hJDUIVQYn/Edcf90vnR+Xbt0CZ1nIRrXFVGiR4/6/s6SXNJbXtahpd3kHb/9Mt94XSR3bpELLxTZu1fiRa4Ue/7iKkwcPnxYGjRoIK+88orl9ueee05eeuklee2112Tx4sVSqFAhad26tRw7lrM4DCGEeBXGhzdGrMd24gyEM77wQvDt/fp5f12NkNDdu3NuGyfdpHQpJXNGLPLfsGSJSMmSIpmZIr/+KvGgYwo9fwkTGgrNxJQpU6Q94ob/p5WAxuLhhx+Wfnj6RLQ3admyZWXcuHFy00032WqX0RyEkHAwA6Y32KmaCdW9l+GOds6JyXnjxv+d86+/RC65xLfCSr1x3XUSa04lcIXepI/m2LBhg2zfvl2bNgwwoMaNG8uiRQESponjx4/rwZsXQggJBX64EX4H9Tw+E+WHPNnAhBiu/PaWLb79YnlObM8+Z4UKPmkGNpHAV/927Xw2hscfd54PO82fv4QVJiBIAGgizOC7sc2KESNGaKHDWCpBDCaEEJKS4Y6uz5k/v8ikSSJZWSJPP+2/behQn/kDwoXhiEGSU5hwy8CBA7U6xli2QAwmhBCSkuGOEZ8TmoiBA32aiP/+13/b55/7UlXipTSc+iPNSVhholy5cvpzx44dfuvx3dhmRb58+bRdx7wQQghJzXBHL84JnwVkpPzkYFuZO0fJqVW/ieQ9nQRLCxJoBI0tXOhd51OIhBUmqlWrpoWG2bNnZ6+D/wOiOpo0aRLXvhFCCEmMcMdIz2lZZbTNWTL5k+O+0NGLA8qgw3kTDb/pS4JFEkCYOHTokCxbtkwvhtMl/t68ebOO7ujTp488+eST8vnnn8vKlSvl9ttv1xEeRsQHIYSQxCIe4Y5uzxm2yug3xUXg8H/ypMj9/kmw5N57fUJFjx4JkQQrrUND586dKy0sKp107dpVh3+ia0OHDpU33nhD9u3bJ5deeqmMHTtWateubfscDA0lhBBJi3BHJ+d0XTX2nXdE7ror5wEXXSTy1VcixYtLKsGqoQ4vBCGEkPQh4qqd338vYmVyz8wUWbVK5OyzJRVI+jwThBBCSMKGscKXAop9qDYqVz69HqGm55zjU20gGiRNoDBBCCEk7fAsjBWOGps2hU6C9cQTMU2CFQ8oTBBCCEk7PA9jzW9KgvXkk/7bHnvMZ/7o0EEkRWtLUZgghBCSdkQtjDUjQ2TwYOskWKiMXaCAT0pByEgKQWGCEEJIWhL1MNa2bX1CBaqSouy5AfwsDLVIiFpTyUTCVA2NFozmIIQQkhBhrPv2iVx9tS8SJJC33rIOOY0zDA11eCEIIYSQmEkvvXuLjB2bc1vPniIvvZQwpUMZGkoIIYQkIrlyibzyis8EEpiWGwIGTCKNG/s0GUkCfSYIIYSQeHH33T6hYsEC//U//CBSooRPsPjtN0l0KEwQQggh8aZpU+skWDCJGEmwAqNDEggKE4QQQkiiUNGUBCuwqOV11/mEiqeeSrgkWBQmCCGEkEQjf36RKVN8SbCQQdPMkCEJlwSLwgQhhBCSqGRk+IQHaCKmTrVOggWzSJyTYFGYIIQQQpKB6647nQTLHDq6ZcvpJFgwkcQBChOEEEJIMnH22SInT4rs2SNy0UX+2x58MC5dojBBCCGEJCMlSogsXuwTLJDsqmBBkS5d4tIVptMmhBBCiCXMgEkIIYSQmEAzByGEEEIigsIEIYQQQiKCwgQhhBBCIoLCBCGEEEIigsIEIYQQQiKCwgQhhBBCUleYGDZsmGRkZPgtZ511Vry7RQghhBATuSXBOffcc2XWrFnZ33PnTvguE0IIIWlFws/MEB7KlSsX724QQgghJBnNHGDt2rVSoUIFqV69utxyyy2yefPmkPsfP35cp/80L4QQQghJ09oc06dPl0OHDkmdOnVk27ZtMnz4cNm6dausWrVKihQpEtTPAvsFsmXLFilatGgMek0IIYSkBnghr1Spkuzbt0+KFSuWnMJEIBhMlSpVZNSoUXLXXXcF1UxgMYDwcc4558Swl4QQQkhqgRfyM888M3l9JswUL15cateuLevWrQu6T758+fRiULhwYX0RoMlANIiVxJUuWguON7Xh/U1teH9TmwMJOh9B33Dw4EHtbhCKpBImYPL4448/5LbbbrN9TGZmZkhpCuDGJdLNizYcb2rD+5va8P6mNkUTcD4KZd5ICgfMfv36ybx582Tjxo2ycOFC6dChg+TKlUu6dOkS764RQgghJBk0E3/++acWHHbv3i1lypSRSy+9VL7//nv9NyGEEEISg4QWJsaPHx/V9uFbMXToUD8fi1SG401teH9TG97f1CZfks9HSRXNQQghhJDEI6F9JgghhBCS+FCYIIQQQkhEUJgghBBCSERQmCCEEEJIegsT8+fPl3/96186OxcyXH722Wd+2++44w693ry0adPGb589e/boImJIFIIsm0jVjQRZZlasWCGXXXaZ5M+fX2cpe+655yQejBgxQi688EKd0fOMM86Q9u3by+rVq/32OXbsmPTq1UtKlSqlM4B26tRJduzY4bcPCqZde+21UrBgQd1O//795eTJk377zJ07Vxo2bKi9i2vWrCnjxo2TRBzv5ZdfnuMed+/ePSnH++qrr0r9+vWzE9c0adJE16hJxXtrZ7ypdG8DeeaZZ/R4+vTpk7L3N9x4U+3+Dhs2LMd4zjrrrLS4v0iVmdRMmzZNDR48WE2ePBlRKWrKlCl+27t27aratGmjtm3blr3s2bPHbx9sb9Cggfr+++/Vt99+q2rWrKm6dOmSvX3//v2qbNmy6pZbblGrVq1Sn3zyiSpQoIB6/fXXVaxp3bq1evfdd3U/li1bpq655hpVuXJldejQoex9unfvripVqqRmz56tlixZoi6++GLVtGnT7O0nT55UdevWVa1atVI///yzvoalS5dWAwcOzN5n/fr1qmDBgqpv377q119/VS+//LLKlSuXmjFjRsKNt3nz5uqee+7xu8e4Z8k43s8//1x9+eWXas2aNWr16tVq0KBBKk+ePHr8qXZv7Yw3le6tmR9++EFVrVpV1a9fXz344IPZ61Pt/oYbb6rd36FDh6pzzz3Xbzy7du1K+fsLkl6YMBNMmGjXrl3QY3AzcNyPP/6YvW769OkqIyNDbd26VX8fO3asKlGihDp+/Hj2PgMGDFB16tRR8Wbnzp26//PmzdPf9+3bp3+MJ0yYkL3Pb7/9pvdZtGiR/o4HNDMzU23fvj17n1dffVUVLVo0e4yPPPKI/k9hpnPnznpyT6TxGj9I5h+oQJJ5vADP3ltvvZXy9zZwvKl6bw8ePKhq1aqlZs6c6Te+VL2/wcabivd36NCh+sXUilS9vwZJb+awA1RCUBehlHmPHj10Rk2DRYsWadNGo0aNste1atVK1/RYvHhx9j7NmjWTvHnzZu/TunVrrW7fu3evxJP9+/frz5IlS+rPn376Sf755x89BgOo2SpXrqzHAfBZr149KVu2rN94UGjml19+yd7H3Iaxj9FGoozX4KOPPpLSpUtL3bp1ZeDAgXLkyJHsbck63lOnTunEbYcPH9bq/1S/t4HjTdV7CzU31NiBfUrV+xtsvKl6f9euXavN7tWrV9fmc5gtUvn+JkUGTC+Af0THjh2lWrVqukjYoEGD5Oqrr9YXHnU+tm/frgUNM7lz59aTFbYBfOJ4M8bNxrYSJUpIPMjKytL2x0suuUT/RzT6A6EHAlJgf83jMT+sxnZjW6h98FAfPXpUChQoIIkwXnDzzTfr0vT4DwzflgEDBmhBb/LkySHHYmxLtPGuXLlST6awr8KuOmXKFDnnnHNk2bJlKXlvg403Fe8thKWlS5fKjz/+mGNbKv7fDTXeVLy/jRs31v4LeHHdtm2bDB8+XPvarVq1KiXvb1oJEzfddFP235D44OxVo0YNra1o2bKlJDOQ+PGQfvfdd5IOBBvvvffe63ePy5cvr+8thEfc62QDP0QQHKCFmThxonTt2lUXvEtVgo0XAkUq3VuUln7wwQdl5syZ2pE71bEz3lS6vwAvqgaYayBcQFj69NNP4zbJx4q0MHOYgeoJKrV169bp7+XKlZOdO3f67QPPWUR4YJuxT6DHrfHd2CfW3H///fLFF1/InDlz/Eqsoz8nTpyQffv25eivk/EE2wce9/H4TxFsvFbgPzAw3+NkGi/eXuChfcEFF+holgYNGsiLL76Ysvc22HhT7d5CzY3fGnjhQ/uJBULTSy+9pP/G22Uq3d9w44VZK5XurxXQQtSuXVuPJ1X//6atMIFKpPCZgAQMoF7FzcWDb/DNN99olbrxYGMfhKDC3mUAaRtvVLE2ccDPFBMrVMHoZ6D5BT/IefLkkdmzZ2evg9oQdjvDDo1PqJbNQhTGg4fRUC9jH3Mbxj5mW3YijNcKvOUC8z1OlvFagWfx+PHjKXdvw4031e4t3rjRV4zBWOCrBbu68Xcq3d9w44WZOZXurxVIMQAtC8aT8v9/VZIDT2GE0GDBcEaNGqX/3rRpk97Wr18/7Sm7YcMGNWvWLNWwYUPtWXzs2DG/0NDzzz9fLV68WH333Xd6uzk0FF64CA297bbbdMja+PHjdWhOPEJDe/TooYoVK6bmzp3rF3505MgRv/AjhE9+8803OvyoSZMmegkMP7rqqqt0uCVCisqUKWMZftS/f3/tcfzKK6/EJfwo3HjXrVunHn/8cT1O3OOpU6eq6tWrq2bNmiXleB999FEdqYKxrFixQn9HZNHXX3+dcvc23HhT7d5aERjNkGr3N9R4U/H+Pvzww/q3CuNZsGCBDvFEaCei0FL9/ia9MDFnzhwtRAQuCAnFhIObgpuBkJwqVaromGZz2A3YvXu3Fh4KFy6sQ3C6deumBREzy5cvV5deeqnKly+fqlixonrmmWdUPLAaKxbkYjA4evSo6tmzpw6xw0PXoUMHPQGb2bhxo7r66qt1vgw87PhP8M8//+S4tuedd57Kmzev/k9uPkeijHfz5s36x6dkyZL63iBHCP6TmWPVk2m8d955p35O0Qc8ty1btswWJFLt3oYbb6rdWzvCRKrd31DjTcX727lzZ1W+fHndD8wT+A6hKR3uL0uQE0IIISQi0s5nghBCCCHeQmGCEEIIIRFBYYIQQgghEUFhghBCCCERQWGCEEIIIRFBYYIQQgghEUFhghBCCCERQWGCEEIIIRQmCCHJR9WqVWXMmDG299+4caNkZGRk128ghCQO1EwQQuLCjz/+6FeC2gvGjRunKzUSQmJL7hifjxBCNGXKlOGVICRFoGaCEGKLL774Qr/1nzp1Sn+HuQFmh0cffTR7n7vvvltuvfVW/fd3330nl112mRQoUEAqVaokvXv3lsOHDwc1c/z+++9y6aWXSv78+XW55VmzZun2P/vsM79+rF+/Xlq0aCEFCxaUBg0ayKJFi/T6uXPnSrdu3WT//v36OCzDhg3j3SUkBlCYIITYAoLBwYMH5eeff9bf582bJ6VLl9aTuAHWXX755fLHH39ImzZtpFOnTrJixQr5z3/+o4WL+++/37JtCCjt27fXAsLixYvljTfekMGDB1vui/X9+vXTwkzt2rWlS5cucvLkSWnatKkWTooWLSrbtm3TC/YjhEQfChOEEFsUK1ZMzjvvvGzhAZ8PPfSQFi4OHTokW7dulXXr1knz5s1lxIgRcsstt0ifPn2kVq1aeqJ/6aWX5P3335djx47laHvmzJlaAMF2aBugoXjqqacs+wEB4dprr9WCxPDhw2XTpk36vHnz5tV9hEaiXLlyeilcuDDvLiExgMIEIcQ2EBQgRCil5Ntvv5WOHTvK2WefrbUO0EpUqFBBCw/Lly/XzpCYzI2ldevWkpWVJRs2bMjR7urVq7UpBAKAwUUXXWTZh/r162f/Xb58ef25c+dO3kVC4ggdMAkhtoEJ45133tHCQp48eeSss87S6yBg7N27VwsbAJqK++67T/tJBFK5cuWIrjjOawAtBICQQgiJHxQmCCGO/SZGjx6dLThAmHjmmWe0MPHwww/rdQ0bNpRff/1VatasaavdOnXqyJYtW2THjh1StmzZ7NBRp8DUYTiIEkJiB80chBDblChRQpsZPvroIy1EgGbNmsnSpUtlzZo12QLGgAEDZOHChdrhEo6Sa9eulalTpwZ1wLzyyiulRo0a0rVrV+2wuWDBAhkyZIif9sEOiBCBVmT27Nny999/y5EjR3h3CYkBFCYIIY6AwIC3f0OYKFmypA7lhL8DNAwAAgd8KCBgQJtx/vnny2OPPaZ9KqzIlSuXDgGFIHDhhRfqEFMjmgOhonaBo2f37t2lc+fOOo/Fc889x7tLSAzIUPCkIoSQBAPaCUR1IFIDWgtCSOJCYYIQkhBMmTJFR30gGgQCxIMPPqjNKogUIYQkNnTAJIQkBHDshK/F5s2bdTKsVq1ayciRI+PdLUKIDaiZIIQQQkhE0AGTEEIIIRFBYYIQQgghEUFhghBCCCERQWGCEEIIIRFBYYIQQgghEUFhghBCCCERQWGCEEIIIRFBYYIQQgghEgn/D1ttRS0CTQDtAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
     "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
@@ -225,7 +560,14 @@
    "id": "f67e57ab",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "\n",
+    "1. acceleration vs mpg: Positive association in general but the trend is weak. In general, cars with higher acceleration have higher mpg.\n",
+    "2. cylinders vs mpg: Negative association. Cars with more cylinders have lower mpg\n",
+    "3. displacement vs mpg: Negative association. Cars with bigger engine displacement have lower mpg\n",
+    "4. horsepower vs mpg: Negative association. Cars with greater horsepower have lower mpg\n",
+    "5. model_year vs mpg: Positive assocaition. Newer model cars have higher mpg. \n",
+    "6. weight vs mpg: Negative association: Heavier cars have lower mpg"
    ]
   },
   {
@@ -241,7 +583,9 @@
    "id": "843f9eef",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "The plotted line represents simple linear regression involving one predictor and one response variable. It predicts by creating a straight line of best fit. It minimizes the sum of squared differences between the observed values and the values predicted by the line. THe equation to represent it is: y= mx+b.\n",
+    "Where y is the response variable, m is the slope of the line, x is the predictor variable, and b is the intercept"
    ]
   },
   {
@@ -257,7 +601,8 @@
    "id": "2ea782fc",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here...\n",
+    "No, the data points do not fall perfectly along the plotted line. The deviations occur because other factors influence the response variable (mpg), and their spread indicates the strength of the linear relationship between the predictor and mpg. A larger spread of the data points around the line represent a weaker linear relationship while points closer to the line indicate a stronger linear relationship."
    ]
   },
   {
@@ -274,12 +619,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "399225f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "mpg_train, mpg_test = train_test_split(mpg_data,train_size=0.75,random_state=42)"
    ]
   },
   {
@@ -292,19 +638,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "ac1e1117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      predictor     slope  intercept\n",
+      "0     cylinders -0.160143 -15.047371\n",
+      "1  displacement  0.000373 -15.047371\n",
+      "2    horsepower -0.001899 -15.047371\n",
+      "3        weight -0.006457 -15.047371\n",
+      "4  acceleration  0.057588 -15.047371\n",
+      "5    model_year  0.762270 -15.047371\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here ...\n",
     "\n",
-    "numeric_predictors = 🤷‍♂️\n",
+    "X_train = mpg_train.select_dtypes(include=[float,int]).drop(columns=['mpg'])\n",
+    "\n",
+    "#response variable\n",
+    "y_train = mpg_train['mpg']\n",
+    "\n",
+    "#create the model\n",
+    "lm = LinearRegression()\n",
     "\n",
+    "#fit the model\n",
+    "lm.fit(X_train,y_train)\n",
     "\n",
     "# Create a DataFrame containing the slope (coefficients) and intercept\n",
     "coefficients_df = pd.DataFrame({\n",
-    "    \"predictor\": numeric_predictors.columns,\n",
+    "    \"predictor\": X_train.columns,\n",
     "    \"slope\": lm.coef_,\n",
     "    \"intercept\": [lm.intercept_] * len(lm.coef_)\n",
     "})\n",
@@ -330,12 +698,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "ffefa9f2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "\n",
+    "mpg_test['predicted'] = lm.predict(mpg_test[X_train.columns])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "131abd0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1854749513391605\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate RMSPE\n",
+    "rmspe = mean_squared_error(\n",
+    "    y_true=mpg_test[\"mpg\"], y_pred=mpg_test[\"predicted\"]) **(1/2)\n",
+    "print (rmspe)"
    ]
   },
   {
@@ -386,7 +777,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -400,12 +791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_3.ipynb b/02_activities/assignments/assignment_3.ipynb
index 889b10f21..c06c4067e 100644
--- a/02_activities/assignments/assignment_3.ipynb
+++ b/02_activities/assignments/assignment_3.ipynb
@@ -224,7 +224,8 @@
    "metadata": {},
    "source": [
     "**Question:**\n",
-    "- Do you notice any patterns or relationships between the different features? How might these patterns help in distinguishing between different species?"
+    "- Do you notice any patterns or relationships between the different features? How might these patterns help in distinguishing between different species?\n",
+    "**Positive or negative associations, between two features, etc. Pick a couple of plots to comment on**"
    ]
   },
   {