Feature Selection in ML using Python. Part 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate\n",
    "## Using F-Score and chi2-Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This method is only used for categorical data (i.e. all features as well as targets should be categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_selection import SelectKBest , SelectPercentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>survived</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",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex  sibsp  parch     fare embarked  class    who  \\\n",
       "0         0       3    male      1      0   7.2500        S  Third    man   \n",
       "1         1       1  female      1      0  71.2833        C  First  woman   \n",
       "2         1       3  female      0      0   7.9250        S  Third  woman   \n",
       "3         1       1  female      1      0  53.1000        S  First  woman   \n",
       "4         0       3    male      0      0   8.0500        S  Third    man   \n",
       "\n",
       "   adult_male  embark_town alive  alone  \n",
       "0        True  Southampton    no  False  \n",
       "1       False    Cherbourg   yes  False  \n",
       "2       False  Southampton   yes   True  \n",
       "3       False  Southampton   yes  False  \n",
       "4        True  Southampton    no   True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.drop(labels=['age', 'deck'], inplace=True, axis = 1)\n",
    "titanic.dropna(inplace=True)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "889\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0\n",
       "pclass         0\n",
       "sex            0\n",
       "sibsp          0\n",
       "parch          0\n",
       "fare           0\n",
       "embarked       0\n",
       "class          0\n",
       "who            0\n",
       "adult_male     0\n",
       "embark_town    0\n",
       "alive          0\n",
       "alone          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Selecting only categorical feature and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['survived', 'pclass', 'sex', 'sibsp', 'parch', 'fare', 'embarked',\n",
      "       'class', 'who', 'adult_male', 'embark_town', 'alive', 'alone'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(titanic.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = titanic[[ 'pclass', 'sex', 'sibsp', 'parch', 'embarked', 'who', 'alone' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass     sex  sibsp  parch embarked    who  alone\n",
       "0       3    male      1      0        S    man  False\n",
       "1       1  female      1      0        C  woman  False\n",
       "2       3  female      0      0        S  woman   True\n",
       "3       1  female      1      0        S  woman  False\n",
       "4       3    male      0      0        S    man   True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "female    312\n",
      "male      577\n",
      "Name: sex, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('sex').sex.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "who\n",
      "child     83\n",
      "man      537\n",
      "woman    269\n",
      "Name: who, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('who').who.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alone\n",
      "False    354\n",
      "True     535\n",
      "Name: alone, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('alone').alone.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "embarked\n",
      "C    168\n",
      "Q     77\n",
      "S    644\n",
      "Name: embarked, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('embarked').embarked.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = {'female':0, 'male':1}\n",
    "b = {'child':0, 'man':1, 'woman':2}\n",
    "c = {True:1, False:0}\n",
    "d = {'C':0, 'Q':1, 'S':2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Saurav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "data['sex'] = data['sex'].map(a)\n",
    "data['who'] = data['who'].map(b)\n",
    "data['alone'] = data['alone'].map(c)\n",
    "data['embarked'] = data['embarked'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>embarked</th>\n",
       "      <th>who</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  sex  sibsp  parch  embarked  who  alone\n",
       "0       3    1      1      0         2    1      0\n",
       "1       1    0      1      0         0    2      0\n",
       "2       3    0      0      0         2    2      1\n",
       "3       1    0      1      0         2    2      0\n",
       "4       3    1      0      0         2    1      1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data\n",
    "y = titanic['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(889, 7)\n",
      "(889,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_score = chi2(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([25.61688434, 70.60026519,  0.26361308,  5.12723077,  7.28959069,\n",
       "        22.90453046, 15.27941133]),\n",
       " array([4.16380069e-07, 4.37458254e-17, 6.07648716e-01, 2.35532772e-02,\n",
       "        6.93552756e-03, 1.70250110e-06, 9.27216443e-05]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_value = f_score[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_value = pd.Series(p_value, index = X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      4.163801e-07\n",
       "sex         4.374583e-17\n",
       "sibsp       6.076487e-01\n",
       "parch       2.355328e-02\n",
       "embarked    6.935528e-03\n",
       "who         1.702501e-06\n",
       "alone       9.272164e-05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_value.sort_values(ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19d2b366898>"
      ]
     },
     "execution_count": 33,
     "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": [
    "p_value.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_3 = X_train[['sex', 'pclass', 'alone', 'who']]\n",
    "X_test_3 = X_test[['sex', 'pclass', 'alone', 'who']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8161434977578476\n",
      "Wall time: 1.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = RandomForestClassifier(n_estimators=1000, max_depth=4)\n",
    "model.fit(X_train_3, y_train)\n",
    "y_pred = model.predict(X_test_3)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8161434977578476\n",
      "Wall time: 1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = RandomForestClassifier(n_estimators=1000, max_depth=4)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}