ensemble_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73e3fa86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed7fc4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "479b5d90",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee5489c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "target = 'Survived'\n",
    "X = data[features]\n",
    "y = data[target]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3f28465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13cb9bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0.000000\n",
       "Sex          0.000000\n",
       "Age         19.865320\n",
       "SibSp        0.000000\n",
       "Parch        0.000000\n",
       "Fare         0.000000\n",
       "Embarked     0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ceb133f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\3834349751.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['Age'].fillna(X['Age'].mean(), inplace=True)\n",
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\3834349751.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['Embarked'].fillna(X['Embarked'].mode()[0], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Handle missing values\n",
    "X['Age'].fillna(X['Age'].mean(), inplace=True)\n",
    "X['Embarked'].fillna(X['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "041ea99b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "073553f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outliers detection\n",
    "plt.boxplot(X['Fare'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "816396e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X['SibSp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c52d9bcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babam\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(np.log(data['Fare']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5467ca28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "616e7d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAKS6//PIoFAr92+WXX17rkYAaqCos2tvbY/bs2dHQ0BAnn3xyLFmyJN56663hmg0YJQqFQqxdu3bAfWvXro1CoVCjiYBaqSosfvOb38RNN90UL7/8cvzqV7+Kf//733HZZZfFnj17hms+4Ch3uHgQFzC+VBUW69evj2984xtxzjnnxMyZM+PRRx+NnTt3xmuvvTZc8wFHsQPf7rj55pujUqn0bzfffPNB1wFjW6FSqVQG++COjo6YNm1abNu2Lc4999yDrunt7Y3e3t7+2+VyOZqamqJUKkVjY+NgDw0cBQ48G3Gw/0oOtx8YPcrlchSLxcO+fg/64s2+vr645ZZbYu7cuYeMioj/XJdRLBb7t6ampsEeEgA4yg36jMWNN94Y69ati5deeilOO+20Q65zxgLGLmcsYPwY1jMW3/72t2PNmjXx61//+kOjIiKirq4uGhsbB2zA2LB48eL+f99yyy0D9h14+8B1wNhW1RmLSqUS3/nOd+KZZ56JF154IaZNm1b1AY+0eIDR4Ug+9eFsBYx+R/r6fUw1T3rTTTfF448/Hr/4xS+ioaEh3nnnnYiIKBaLcdxxxw1tYmBUqlQqHxoXogLGl6reClm1alWUSqW4+OKLY8qUKf3bk08+OVzzAaNApVL5wNsdixcvFhUwDlV1xsJ/EsChrFmzptYjAEcBvxUCAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBpDjrrLOiUCj0b2eddVatRwJqoOqwePHFF+OKK66IqVOnRqFQiNWrVw/DWMBoUigUYvv27QPu2759exQKhRpNBNRK1WGxZ8+emDlzZjzwwAPDMQ8wyhwuHsQFjC9Vh8WiRYvizjvvjKVLlw7HPMAocuDbHYsWLYpKpdK/LVq06KDrgLHtmOE+QG9vb/T29vbfLpfLw31IYIQc+PbHs88+O2Dfs88+23+24r/fJgHGrmG/eLO9vT2KxWL/1tTUNNyHBABqZNjDoq2tLUqlUv/W1dU13IcEAGpk2MOirq4uGhsbB2zA2DBt2rT+f3/xi18csO/A2weuA8a2Yb/GAhi73n777f7rKNatW3fIT4C8/fbbIzkWUENVh0VPT090dHT0396xY0ds3bo1TjzxxGhubk4dDjj6VSqVD/1IaaVSGcFpgFqr+q2QV199NWbNmhWzZs2KiIgVK1bErFmz4vbbb08fDhgdKpXKB97umDZtmqiAcajqMxYXX3yx/yyAD/B2BxDht0IAgETCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDSDCosHHnggzjjjjDj22GPjggsuiC1btmTPBQCMQlWHxZNPPhkrVqyIlStXxuuvvx4zZ86MBQsWxHvvvTcc8wEAo0jVYXHffffFDTfcENddd12cffbZ8eCDD0Z9fX385Cc/GY75AIBR5JhqFv/rX/+K1157Ldra2vrvmzBhQlx66aWxefPmgz6mt7c3ent7+2+Xy+VBjgrs97fdXbHxmf9Nea69e/dEZ+cfU54rW0vLJ6K+ftKQn+d//mdqzFm0LOKj9QlTAR+mqrD429/+Fvv27YtTTjllwP2nnHJK/OEPfzjoY9rb2+OOO+4Y/ITAB2x85n9j6Xv/N+8JTzn8kpro+f/bUL0XseOkk+PMi5YkPBnwYaoKi8Foa2uLFStW9N8ul8vR1NQ03IeFMW3+0uvjmWdynmvcnLE4/7KEiYDDqSosPv7xj8fEiRPj3XffHXD/u+++G6eeeupBH1NXVxd1dXWDnxD4gI9PaYql/+dHtR4D4AOqunjzox/9aHz2s5+N559/vv++vr6+eP755+PCCy9MHw4AGF2qfitkxYoVce2118b5558fc+bMifvvvz/27NkT11133XDMBwCMIlWHxVe/+tX461//Grfffnu88847cd5558X69es/cEEnADD+FCqVSmUkD1gul6NYLEapVIrGxsaRPDQAMEhH+vrtt0IAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTD/rPp/23/F32Wy+WRPjQAMEj7X7cP94XdIx4W3d3dERHR1NQ00ocGAIaou7s7isXiIfeP+G+F9PX1xa5du6KhoSEKhcJIHhoYZuVyOZqamqKrq8tvAcEYU6lUoru7O6ZOnRoTJhz6SooRDwtg7PIjg4CLNwGANMICAEgjLIA0dXV1sXLlyqirq6v1KECNuMYCAEjjjAUAkEZYAABphAUAkEZYAABphAUwZC+++GJcccUVMXXq1CgUCrF69epajwTUiLAAhmzPnj0xc+bMeOCBB2o9ClBjI/4jZMDYs2jRoli0aFGtxwCOAs5YAABphAUAkEZYAABphAUAkEZYAABpfCoEGLKenp7o6Ojov71jx47YunVrnHjiidHc3FzDyYCR5tdNgSF74YUX4pJLLvnA/ddee208+uijIz8QUDPCAgBI4xoLACCNsAAA0ggLACCNsAAA0ggLACCNsAAA0ggLACCNsAAA0ggLACCNsAAA0ggLACCNsAAA0vw/PJM6q/IkfKgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X['Parch'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "296b971c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4344880940129925"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Age'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd6e05e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3dc8200f",
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>820 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass     Sex        Age  SibSp  Parch     Fare Embarked\n",
       "0         3    male  22.000000      1      0   7.2500        S\n",
       "1         1  female  38.000000      1      0  71.2833        C\n",
       "2         3  female  26.000000      0      0   7.9250        S\n",
       "3         1  female  35.000000      1      0  53.1000        S\n",
       "4         3    male  35.000000      0      0   8.0500        S\n",
       "..      ...     ...        ...    ...    ...      ...      ...\n",
       "886       2    male  27.000000      0      0  13.0000        S\n",
       "887       1  female  19.000000      0      0  30.0000        S\n",
       "888       3  female  29.699118      1      2  23.4500        S\n",
       "889       1    male  26.000000      0      0  30.0000        C\n",
       "890       3    male  32.000000      0      0   7.7500        Q\n",
       "\n",
       "[820 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check = ['Age','Parch','Fare','SibSp']\n",
    "z_scores = np.abs(zscore(X[check]))\n",
    "z_score_threshold=3\n",
    "outlier_mask = np.any(z_scores>z_score_threshold,axis=1)\n",
    "new_X=X[~outlier_mask]\n",
    "new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3710307b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "efea2d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(820, 7)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f086c6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(X['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "19afdfcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b693c0f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\2750445987.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['Sex'] = label_encoder.fit_transform(X['Sex'])\n",
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\2750445987.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['Embarked'] = label_encoder.fit_transform(X['Embarked'].astype(str))\n"
     ]
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "X['Sex'] = label_encoder.fit_transform(X['Sex'])\n",
    "X['Embarked'] = label_encoder.fit_transform(X['Embarked'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bbb21c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\596200898.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[['Age', 'Fare']] = scaler.fit_transform(X[['Age', 'Fare']])\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X[['Age', 'Fare']] = scaler.fit_transform(X[['Age', 'Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6e90f4bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babam\\AppData\\Local\\Temp\\ipykernel_9912\\2920139348.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['FamilySize'] = X['SibSp'] + X['Parch']\n"
     ]
    }
   ],
   "source": [
    "X['FamilySize'] = X['SibSp'] + X['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4e2bd171",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b655b5c7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'imblearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mimblearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mover_sampling\u001b[39;00m \u001b[39mimport\u001b[39;00m RandomOverSampler\n\u001b[0;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mimblearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39munder_sampling\u001b[39;00m \u001b[39mimport\u001b[39;00m RandomUnderSampler\n\u001b[0;32m      5\u001b[0m ros \u001b[39m=\u001b[39m RandomOverSampler(random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m)\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'imblearn'"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_resampled, y_resampled = ros.fit_resample(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd521fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbbbc0da",
   "metadata": {},
   "source": [
    "# Doing hyperparameter tunning and making objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92b04b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a287861",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eaaf71e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8db10f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48d39ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4c18a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 500],\n",
    "    'max_depth': [None, 5, 10],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b267535",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_rf = GridSearchCV(random_forest, param_grid=param_grid, cv=5)\n",
    "grid_search_rf.fit(X_train, y_train)\n",
    "random_forest_best = grid_search_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "982c0ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boosting = GradientBoostingClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9895816b",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 500],\n",
    "    'learning_rate': [0.1, 0.05, 0.01],\n",
    "    'max_depth': [3, 5, 10],\n",
    "    'subsample': [0.8, 1.0]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4858e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_gb = GridSearchCV(gradient_boosting, param_grid=param_grid, cv=5)\n",
    "grid_search_gb.fit(X_train, y_train)\n",
    "gradient_boosting_best = grid_search_gb.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea5d9da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "svm = make_pipeline(StandardScaler(), SVC(random_state=42))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672c690e",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'svc__C': [0.1, 1, 10],\n",
    "    'svc__gamma': [0.1, 1, 10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dd617ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_svm = GridSearchCV(svm, param_grid=param_grid, cv=5)\n",
    "grid_search_svm.fit(X_train, y_train)\n",
    "svm_best = grid_search_svm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95d893d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e2d2de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = [\n",
    "    ('Random Forest', random_forest_best),\n",
    "    ('Gradient Boosting', gradient_boosting_best),\n",
    "    ('Support Vector Machine', svm_best),\n",
    "    ('Logistic regression',lr)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35d6e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.86\n",
      "Gradient Boosting Accuracy: 0.9\n",
      "Support Vector Machine Accuracy: 0.83\n",
      "Logistic regression Accuracy: 0.8\n"
     ]
    }
   ],
   "source": [
    "for classifier_name, classifier in classifiers:\n",
    "    classifier.fit(X_train, y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    print(classifier_name, 'Accuracy:', np.round(accuracy, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1acd4268",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "058fde28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e544fd9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0c73f62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c636eca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b7426fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc1341e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4ed7db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "717ac616",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7093df26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1924b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05f73a63",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7ea0d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43f8cf47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a6a92a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55cef0f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5824bd2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d1fc160",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd63f6a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb13f4c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73e13056",
   "metadata": {},
   "source": [
    "# Bagging start from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54df1fdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ded3eea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22720d45",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod1 = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0591725e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(clf,X,y):\n",
    "    clf.fit(X,y)\n",
    "    plot_tree(clf)\n",
    "    plt.show()\n",
    "    #plot_decision_regions(X.values,y.values,clf=clf,legend=2)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d80602b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "evaluate(mod1,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd31c4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod2 = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e405d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "evaluate(mod2,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c47c47",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (Temp/ipykernel_9488/457312854.py, line 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\hello\\AppData\\Local\\Temp/ipykernel_9488/457312854.py\"\u001b[1;36m, line \u001b[1;32m6\u001b[0m\n\u001b[1;33m    X_test = df_val.iloc[:,0:7].values\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# data = data.sample(100)\n",
    "# df_train=data.iloc[:60,:].sample(20)\n",
    "# df_val=data.iloc[60:80,:].sample(10)\n",
    "# df_test=data.iloc[80:,:].sample(10\n",
    "                                \n",
    "# X_test = df_val.iloc[:,0:7].values\n",
    "                                \n",
    "# y_test = df_val.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d79800a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb01d82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad6c3de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285bc245",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b55f06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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