chi1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Chi-Square test for Bank Churn prediction (find out different patterns on customer leaves the bank)  . Here I am considering only few columns to make things clear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as numpy\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_df = pd.read_csv('bank.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geography  Gender  HasCrCard  IsActiveMember  Exited\n",
       "0    France  Female          1               1       1\n",
       "1     Spain  Female          0               1       0\n",
       "2    France  Female          1               0       1\n",
       "3    France  Female          0               0       0\n",
       "4     Spain  Female          1               1       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here we have 4 category predictors and one category response. Exited, the response column represnts customer left the bank or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before performig Ch-Square test we have to make sure data is label encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "churn_df['Geography'] = label_encoder.fit_transform(churn_df['Geography'])\n",
    "churn_df['Gender'] = label_encoder.fit_transform(churn_df['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Geography  Gender  HasCrCard  IsActiveMember  Exited\n",
       "0          0       0          1               1       1\n",
       "1          2       0          0               1       0\n",
       "2          0       0          1               0       1\n",
       "3          0       0          0               0       0\n",
       "4          2       0          1               1       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chi-Square test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sgajawad\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = churn_df.drop('Exited',axis=1)\n",
    "y = churn_df['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "chi_scores = chi2(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 11.85325057,  51.53992627,   0.15004097, 118.19941432]),\n",
       " array([5.75607838e-04, 7.01557451e-13, 6.98496209e-01, 1.56803624e-27]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### here first array represents chi square values and second array represnts p-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_values = pd.Series(chi_scores[1],index = X.columns)\n",
    "p_values.sort_values(ascending = False , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x137589d20f0>"
      ]
     },
     "execution_count": 19,
     "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_values.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since HasCrCard has higher the p-value, it says that this variables is independent of the repsone and can not be considered for model training"
   ]
  },
  {
   "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
}