feature selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8617965",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55437a06",
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=load_boston()\n",
    "df=pd.DataFrame(data.data,columns=data.feature_names)\n",
    "df['MEDV']=data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b2101ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7099ff90",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(\"MEDV\",axis=1)\n",
    "y=df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b289266",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "98cf61e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "#Using Pearson Correlation\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = x_train.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.CMRmap_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c288684",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9216b49a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(x_train, 0.7)\n",
    "len(set(corr_features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e57f48d4",
   "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>Wine</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic.acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Acl</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid.phenols</th>\n",
       "      <th>Proanth</th>\n",
       "      <th>Color.int</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</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</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wine  Alcohol  Malic.acid   Ash   Acl   Mg  Phenols  Flavanoids  \\\n",
       "0     1    14.23        1.71  2.43  15.6  127     2.80        3.06   \n",
       "1     1    13.20        1.78  2.14  11.2  100     2.65        2.76   \n",
       "2     1    13.16        2.36  2.67  18.6  101     2.80        3.24   \n",
       "3     1    14.37        1.95  2.50  16.8  113     3.85        3.49   \n",
       "4     1    13.24        2.59  2.87  21.0  118     2.80        2.69   \n",
       "\n",
       "   Nonflavanoid.phenols  Proanth  Color.int   Hue    OD  Proline  \n",
       "0                  0.28     2.29       5.64  1.04  3.92     1065  \n",
       "1                  0.26     1.28       4.38  1.05  3.40     1050  \n",
       "2                  0.30     2.81       5.68  1.03  3.17     1185  \n",
       "3                  0.24     2.18       7.80  0.86  3.45     1480  \n",
       "4                  0.39     1.82       4.32  1.04  2.93      735  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('https://gist.githubusercontent.com/tijptjik/9408623/raw/b237fa5848349a14a14e5d4107dc7897c21951f5/wine.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ba7d075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Wine\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "26326388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Wine                  178 non-null    int64  \n",
      " 1   Alcohol               178 non-null    float64\n",
      " 2   Malic.acid            178 non-null    float64\n",
      " 3   Ash                   178 non-null    float64\n",
      " 4   Acl                   178 non-null    float64\n",
      " 5   Mg                    178 non-null    int64  \n",
      " 6   Phenols               178 non-null    float64\n",
      " 7   Flavanoids            178 non-null    float64\n",
      " 8   Nonflavanoid.phenols  178 non-null    float64\n",
      " 9   Proanth               178 non-null    float64\n",
      " 10  Color.int             178 non-null    float64\n",
      " 11  Hue                   178 non-null    float64\n",
      " 12  OD                    178 non-null    float64\n",
      " 13  Proline               178 non-null    int64  \n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 19.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6897e3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df.drop(labels=['Wine'], axis=1),\n",
    "    df['Wine'],\n",
    "    test_size=0.3,\n",
    "    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b1ef798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.41971352, 0.30800752, 0.16259813, 0.24951392, 0.17742819,\n",
       "       0.46694041, 0.71974178, 0.11743744, 0.26165475, 0.62986347,\n",
       "       0.56272615, 0.55220705, 0.52211509])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mf=mutual_info_classif(X_train,y_train)\n",
    "mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "859e96c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Flavanoids              0.719742\n",
       "Color.int               0.629863\n",
       "Hue                     0.562726\n",
       "OD                      0.552207\n",
       "Proline                 0.522115\n",
       "Phenols                 0.466940\n",
       "Alcohol                 0.419714\n",
       "Malic.acid              0.308008\n",
       "Proanth                 0.261655\n",
       "Acl                     0.249514\n",
       "Mg                      0.177428\n",
       "Ash                     0.162598\n",
       "Nonflavanoid.phenols    0.117437\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mf_info=pd.Series(mf)\n",
    "mf_info.index=X_train.columns\n",
    "mf_info.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "faf23623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "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": [
    "mf_info.sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d0d18522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Phenols', 'Flavanoids', 'Color.int', 'Hue', 'OD', 'Proline'], dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "sel_cols=SelectKBest(mutual_info_classif,k=6)\n",
    "sel_cols.fit(X_train,y_train)\n",
    "X_train.columns[sel_cols.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7347df7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df=sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "18a87ccb",
   "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>sex</th>\n",
       "      <th>embarked</th>\n",
       "      <th>alone</th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sex  embarked  alone  pclass  survived\n",
       "0    1         2      0       2         0\n",
       "1    0         0      0       0         1\n",
       "2    0         2      1       2         1\n",
       "3    0         2      0       0         1\n",
       "4    1         2      1       2         0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df[['sex','embarked','alone','pclass','survived']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1df6e372",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "\n",
    "for i in ['sex','embarked','alone','pclass']:\n",
    "    df[i]=le.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7d0ac806",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df[['sex','embarked','alone','pclass']],\n",
    "                                              df['survived'],test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1a3afa78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "f_p_values=chi2(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "12c47766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([65.67929505,  6.8436521 , 10.88471585, 39.0116624 ]),\n",
       " array([5.30603805e-16, 8.89567920e-03, 9.69610546e-04, 4.21281334e-10]))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_p_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c43ee2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex         5.306038e-16\n",
       "embarked    8.895679e-03\n",
       "alone       9.696105e-04\n",
       "pclass      4.212813e-10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=pd.Series(f_p_values[1])\n",
    "p.index=X_train.columns\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8665aace",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}