OLS Model on Advertising Sales Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a648fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Advertising Sales data\n",
    "Finding the best fit line using OLS (ordinary least square) model\n",
    "Error anlysis is done by plotting the histogram of the errors\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d91f6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing \n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import statsmodels.api as sm \n",
    "\n",
    "# supress the warning \n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37d7a0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     TV  Radio  Newspaper  Sales\n",
       "0             1  230.1   37.8       69.2   22.1\n",
       "1             2   44.5   39.3       45.1   10.4\n",
       "2             3   17.2   45.9       69.3    9.3\n",
       "3             4  151.5   41.3       58.5   18.5\n",
       "4             5  180.8   10.8       58.4   12.9\n",
       "..          ...    ...    ...        ...    ...\n",
       "195         196   38.2    3.7       13.8    7.6\n",
       "196         197   94.2    4.9        8.1    9.7\n",
       "197         198  177.0    9.3        6.4   12.8\n",
       "198         199  283.6   42.0       66.2   25.5\n",
       "199         200  232.1    8.6        8.7   13.4\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "\n",
    "advertising = pd.read_csv('D:Advertising.csv')\n",
    "advertising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2ac778a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peeking the data\n",
    "\n",
    "advertising.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ef38705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  200 non-null    int64  \n",
      " 1   TV          200 non-null    float64\n",
      " 2   Radio       200 non-null    float64\n",
      " 3   Newspaper   200 non-null    float64\n",
      " 4   Sales       200 non-null    float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "advertising.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2f10779",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
       "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
       "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
       "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
       "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
       "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
       "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
       "max    200.000000  296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "118b5be5",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualisation\n",
    "\n",
    "sns.pairplot(advertising, x_vars = ['TV', 'Radio' , 'Newspaper'] , y_vars = 'Sales', size = 3 , kind = 'scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "516f8e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(advertising.corr() , cmap = 'YlGnBu'  , annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4fc2e92c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = advertising['TV']\n",
    "y = advertising['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a854591",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into Train and Test (70:30)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x,y,train_size =0.7, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11499239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116    139.2\n",
       "67     139.3\n",
       "78       5.4\n",
       "42     293.6\n",
       "17     281.4\n",
       "       ...  \n",
       "133    219.8\n",
       "137    273.7\n",
       "72      26.8\n",
       "140     73.4\n",
       "37      74.7\n",
       "Name: TV, Length: 140, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f690033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58     210.8\n",
       "40     202.5\n",
       "34      95.7\n",
       "102    280.2\n",
       "184    253.8\n",
       "Name: TV, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "932bed54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ba3e43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a constant (to get an intercept)\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train) # y = mx +c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6614e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fitting the model using OLS (sm)\n",
    "\n",
    "lr = sm.OLS(Y_train, X_train_sm).fit() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d575d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    6.923520\n",
       "TV       0.048122\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.params   # tv = m(slope) of tv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1bc4471d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   228.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 02 Nov 2022</td> <th>  Prob (F-statistic):</th> <td>4.71e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:40:45</td>     <th>  Log-Likelihood:    </th> <td> -363.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   140</td>      <th>  AIC:               </th> <td>   730.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   138</td>      <th>  BIC:               </th> <td>   736.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    6.9235</td> <td>    0.532</td> <td>   13.025</td> <td> 0.000</td> <td>    5.872</td> <td>    7.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>    <td>    0.0481</td> <td>    0.003</td> <td>   15.112</td> <td> 0.000</td> <td>    0.042</td> <td>    0.054</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.320</td> <th>  Durbin-Watson:     </th> <td>   2.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.852</td> <th>  Jarque-Bera (JB):  </th> <td>   0.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.106</td> <th>  Prob(JB):          </th> <td>   0.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.831</td> <th>  Cond. No.          </th> <td>    322.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.623\n",
       "Model:                            OLS   Adj. R-squared:                  0.621\n",
       "Method:                 Least Squares   F-statistic:                     228.4\n",
       "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           4.71e-31\n",
       "Time:                        17:40:45   Log-Likelihood:                -363.19\n",
       "No. Observations:                 140   AIC:                             730.4\n",
       "Df Residuals:                     138   BIC:                             736.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          6.9235      0.532     13.025      0.000       5.872       7.975\n",
       "TV             0.0481      0.003     15.112      0.000       0.042       0.054\n",
       "==============================================================================\n",
       "Omnibus:                        0.320   Durbin-Watson:                   2.080\n",
       "Prob(Omnibus):                  0.852   Jarque-Bera (JB):                0.427\n",
       "Skew:                          -0.106   Prob(JB):                        0.808\n",
       "Kurtosis:                       2.831   Cond. No.                         322.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a272f017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the regression line - Best fit line \n",
    "   \n",
    "plt.scatter(X_train,Y_train)\n",
    "plt.plot(X_train, 6.92 + 0.048*X_train, 'r'  ) #(x , c+m*x, 'colour-r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "36a5da1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Error analysis\n",
    "y_train_pred = lr.predict(X_train_sm)\n",
    "res= (Y_train-y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebbf44fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the histogram of the errors\n",
    "\n",
    "sns.distplot(res,bins =15)\n",
    "plt.title('Error terms')\n",
    "plt.xlabel ('Residual error')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b315e8a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Constant \n",
    "X_test_sm = sm.add_constant(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "247ec0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting\n",
    "y_test_pred = lr.predict(X_test_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b816a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_test = (Y_test - y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3cc4d354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Histogram of the errors \n",
    "\n",
    "sns.distplot(res_test,bins =15)\n",
    "plt.title('Test data Error terms')\n",
    "plt.xlabel ('Residual errors')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e50d251e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5750618604943311"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "r_sq = r2_score(Y_test, y_test_pred)\n",
    "r_sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "72818110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6233294771480009"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r_sq = r2_score(Y_train, y_train_pred)\n",
    "r_sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ee1aceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "The model has perfommed 57% on test data and 62% on traning data. It\n",
    "is a fairely robust model which perfrom almost same conclusion on both the data sets.\n",
    "           \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "046e8074",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6484af06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e26499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1621341",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a70d465",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c7dab52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d391d71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bea948",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2e1c33d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378820cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "565b47cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898ba043",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6ab22a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39650423",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3364b3d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d709ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56e417ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80302acf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d48055",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1c40b34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "203f136c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60b83758",
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