main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.cluster import DBSCAN\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neighbors import NearestCentroid\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputFile=\"Churn_Modelling.csv\"\n",
    "inputDataFrame=pd.read_csv(inputFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data size\n",
    "inputDataFrame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
       "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
       "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
       "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
       "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
       "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
       "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
       "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
       "\n",
       "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
       "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
       "mean    76485.889288       1.530200      0.70550        0.515100   \n",
       "std     62397.405202       0.581654      0.45584        0.499797   \n",
       "min         0.000000       1.000000      0.00000        0.000000   \n",
       "25%         0.000000       1.000000      0.00000        0.000000   \n",
       "50%     97198.540000       1.000000      1.00000        1.000000   \n",
       "75%    127644.240000       2.000000      1.00000        1.000000   \n",
       "max    250898.090000       4.000000      1.00000        1.000000   \n",
       "\n",
       "       EstimatedSalary        Exited  \n",
       "count     10000.000000  10000.000000  \n",
       "mean     100090.239881      0.203700  \n",
       "std       57510.492818      0.402769  \n",
       "min          11.580000      0.000000  \n",
       "25%       51002.110000      0.000000  \n",
       "50%      100193.915000      0.000000  \n",
       "75%      149388.247500      0.000000  \n",
       "max      199992.480000      1.000000  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "inputDataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          10000\n",
       "CustomerId         10000\n",
       "Surname             2932\n",
       "CreditScore          460\n",
       "Geography              3\n",
       "Gender                 2\n",
       "Age                   70\n",
       "Tenure                11\n",
       "Balance             6382\n",
       "NumOfProducts          4\n",
       "HasCrCard              2\n",
       "IsActiveMember         2\n",
       "EstimatedSalary     9999\n",
       "Exited                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking number of unique values in each columns\n",
    "inputDataFrame.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d439762b0>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpccweknSdBYfh2NcDPx9VT0LMPkOkOQbwPfbxwlgRV+/5cCBtj2o/jxwSpLF7aqiv70kaZ4cj1tPV9B32ynJWX37PgI82rZ3ApcneXuSs4ER4MfAA8BIm+F0Er3bWDurqoAfAB9t/TcCdx2H8UqSjsGcriiSvAP4EPCJvvJ/SbKG3m2ipyf3VdVjSe4AfgocAa6qqtfaca4GdgOLgG1V9Vg71jXAjiRfBH4C3DKX8UqSjt2cgqKqDtN76Nxf+1hH++uB6wfUdwG7BtT30ZsVJUkaEn+ZLUnqZFBIkjoZFJKkTgaFJKmTQSFJ6mRQSJI6GRSSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTsdjzeynkzyS5KEkY612WpI9Sfa291NbPUluTDKe5OEk7+07zsbWfm+SjX3189rxx1vfQWtpS5JOkON1RfGvq2pNVY22z9cC91TVCHBP+wy99bVH2msTcDP0ggW4DngfvYWKrpsMl9ZmU1+/dcdpzJKkGZjTCncd1gMXtO3twL30ljVdD9zW1sO+P8kpbY3tC4A9VfUiQJI9wLok9wInV9V9rX4bcClw9wka93Gz6PCLLHniDYv26S3qbf/4awD+3z85ecgj0UKy6PCLwJnDHsa0jkdQFPA3SQr4b1W1FTizqg4CVNXBJGe0tsuAZ/r6TrRaV31iQH1BW7169bCHoAVmfPxlAFa/a+H/UdB8OvNN8ffieATF+6vqQAuDPUme6Gg76PlCzaL++oMmm+jdnmLlypXTj/gE27x587CHoAVmy5YtANxwww1DHol07Ob8jKKqDrT354Dv0XvG8Gy7pUR7f641nwBW9HVfDhyYpr58QP3oMWytqtGqGl26dOlcv5Ikqc+cgiLJP03yzya3gYuAR4GdwOTMpY3AXW17J7ChzX5aC7zUblHtBi5Kcmp7iH0RsLvteznJ2jbbaUPfsSRJ82Cut57OBL7XZqwuBr5VVf8ryQPAHUmuBH4BXNba7wIuAcaBw8DHAarqxSRfAB5o7T4/+WAb+BRwK7CE3kPsBf8gW5J+l8wpKKpqH/CvBtRfAC4cUC/gqimOtQ3YNqA+Bpw7l3FKkmbPX2ZLkjoZFJKkTgaFJKmTQSFJ6mRQSJI6GRSSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKmTQSFJ6jTroEiyIskPkjye5LEkW1r9c0n2J3movS7p6/PZJONJnkzy4b76ulYbT3JtX/3sJD9KsjfJt5OcNNvxSpJmZy5XFEeAz1TVnwBrgauSnNP2fbWq1rTXLoC273Lg3cA64OtJFiVZBHwNuBg4B7ii7zhfbscaAX4JXDmH8UqSZmHWQVFVB6vq79v2y8DjwLKOLuuBHVX1alX9jN662ee313hV7auq3wA7gPXpLcT9QeA7rf924NLZjleSNDvH5RlFklXAe4AftdLVSR5Osi3Jqa22DHimr9tEq01Vfyfwq6o6clR90Pk3JRlLMnbo0KHj8I0kSZPmHBRJfh+4E/h0Vf0auBn4A2ANcBD4ymTTAd1rFvU3Fqu2VtVoVY0uXbr0GL+BJKnL4rl0TvJ79ELim1X1XYCqerZv/zeA77ePE8CKvu7LgQNte1D9eeCUJIvbVUV/e0nSPJnLrKcAtwCPV9Vf9dXP6mv2EeDRtr0TuDzJ25OcDYwAPwYeAEbaDKeT6D3w3llVBfwA+GjrvxG4a7bjlSTNzlyuKN4PfAx4JMlDrfaX9GYtraF3m+hp4BMAVfVYkjuAn9KbMXVVVb0GkORqYDewCNhWVY+1410D7EjyReAn9IJJkjSPZh0UVfV3DH6OsKujz/XA9QPquwb1q6p99GZFSZKGxF9mS5I6GRSSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKmTQSFJ6mRQSJI6GRSSpE4GhSSpk0EhSeq04IMiybokTyYZT3LtsMcjSW81CzookiwCvgZcDJxDb5nVc4Y7Kkl6a5nLmtnz4XxgvC2JSpIdwHp6627rTeCmm25ifHx82MMYusn/DbZs2TLkkSwMq1evZvPmzcMehmZoQV9RAMuAZ/o+T7Ta6yTZlGQsydihQ4fmbXDSTC1ZsoQlS5YMexjSrCz0K4oMqNUbClVbga0Ao6Ojb9iv4fFfjdKb30K/opgAVvR9Xg4cGNJYJOktaaEHxQPASJKzk5wEXA7sHPKYJOktZUHfeqqqI0muBnYDi4BtVfXYkIclSW8pCzooAKpqF7Br2OOQpLeqhX7rSZI0ZAaFJKmTQSFJ6mRQSJI6pep36/dpSQ4BPx/2OKQBTgeeH/YgpCn8i6paOmjH71xQSAtVkrGqGh32OKRj5a0nSVIng0KS1MmgkObP1mEPQJoNn1FIkjp5RSFJ6mRQSJI6GRTSPEiyLsmTScaTXDvs8UjHwmcU0gmWZBHwD8CH6C3G9QBwRVW59rveFLyikE6884HxqtpXVb8BdgDrhzwmacYMCunEWwY80/d5otWkNwWDQjrxMqDmPV+9aRgU0ok3Aazo+7wcODCksUjHzKCQTrwHgJEkZyc5Cbgc2DnkMUkztuDXzJbe7KrqSJKrgd3AImBbVT025GFJM+b0WElSJ289SZI6GRSSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqdP/BzOOpwOy5wMnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=inputDataFrame['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d466c56d8>"
      ]
     },
     "execution_count": 171,
     "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": [
    "sns.boxplot(data=inputDataFrame['Balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d445b66d8>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANqklEQVR4nO3dX2yd5X3A8e8PW5Q/Gg0EJ2J2WFIclYtJZcxCbJWQ1qzTyKaGiyKlQkqEIjwk5mXjYs2umstVmsRCLpCisimRtq4MDRFNERsK5RKE0yJKIcBpNoiTNLhJSCfC3/LbhR8XJzm2XztO3pOn349knfM+73PMzxJ88+rN8SEyE0lSXa5oewBJ0tIz7pJUIeMuSRUy7pJUIeMuSRXqb3sAgBtvvDFXr17d9hiSdFk5cODALzJzoNu5noj76tWrGR8fb3sMSbqsRMTbs53ztowkVci4S1KFjLskVci4S1KFjLskVci4S1KFjLskVagn3ueu3rJz5046nU7bY7TuyJEjAAwODrY8SW8YHh5mbGys7THUkHGXZvHBBx+0PYK0aMZd5/HqbMrWrVsB2LFjR8uTSAvnPXdJqpBxl6QKGXdJqpBxl6QKGXdJqpBxl6QKGXdJqpBxl6QKGXdJqpBxl6QKGXdJqpBxl6QKGXdJqpBxl6QKGXdJqlCjuEfE30TETyPi1Yj4fkRcFRFrIuLFiHgrIn4QEVeWvV8ox51yfvXF/AEkSeebN+4RMQj8FTCSmb8L9AEbge8Cj2TmWuAUsKW8ZAtwKjOHgUfKPknSJdT0tkw/cHVE9APXAMeArwFPlvO7gXvK8w3lmHJ+XUTE0owrSWpi3rhn5hHgH4B3mIr6aeAA8F5mflq2TQDT/xfhQeBwee2nZf/yc79vRIxGxHhEjE9OTl7ozyFJmqHJbZnrmboaXwP8NnAtcHeXrTn9kjnOfb6QuSszRzJzZGBgoPnEkqR5Nbkt88fA/2TmZGZ+AvwH8IfAsnKbBmAIOFqeTwCrAMr5LwInl3RqSdKcmsT9HeDOiLim3DtfB7wG/BD4ZtmzGXi6PN9bjinnn8vM867cJUkXT5N77i8y9RejPwJ+Ul6zC/g28HBEdJi6p/54ecnjwPKy/jCw7SLMLUmaQ//8WyAzvwN855zlQ8AdXfZ+CNx74aNJkhbL31CVpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAoZd0mqkHGXpAr1tz1Ar9i5cyedTqftMdRDpv992Lp1a8uTqNcMDw8zNjbW9hhzMu5Fp9Ph5Vdf51fX3ND2KOoRV3ycABw4dLzlSdRL+s6cbHuERoz7DL+65gY+uHV922NI6mFXH9zX9giNeM9dkipk3CWpQvPGPSK+HBEvz/j6ZUT8dUTcEBHPRsRb5fH6sj8i4tGI6ETEKxFx+8X/MSRJM80b98x8IzNvy8zbgN8HzgBPAduA/Zm5FthfjgHuBtaWr1HgsYsxuCRpdgu9LbMO+Flmvg1sAHaX9d3APeX5BmBPTnkBWBYRNy3JtJKkRhYa943A98vzlZl5DKA8rijrg8DhGa+ZKGuSpEukcdwj4krgG8C/z7e1y1p2+X6jETEeEeOTk5NNx5AkNbCQK/e7gR9l5vRvdByfvt1SHt8t6xPAqhmvGwKOnvvNMnNXZo5k5sjAwMDCJ5ckzWohcf8Wn9+SAdgLbC7PNwNPz1jfVN41cydwevr2jSTp0mj0G6oRcQ3wdeAvZiz/PfBERGwB3gHuLev7gPVAh6l31ty/ZNNKkhppFPfMPAMsP2ftBFPvnjl3bwIPLcl0kqRF8TdUJalCxl2SKmTcJalCxl2SKmTcJalCxl2SKmTcJalCxl2SKmTcJalCxl2SKmTcJalCxl2SKmTcJalCjT4V8jfBkSNH6DtzmqsP7mt7FEk9rO/MCY4c+bTtMebllbskVcgr92JwcJCff9TPB7eub3sUST3s6oP7GBxc2fYY8/LKXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIq1CjuEbEsIp6MiIMR8XpE/EFE3BARz0bEW+Xx+rI3IuLRiOhExCsRcfvF/REkSedqeuW+A3gmM28FvgK8DmwD9mfmWmB/OQa4G1hbvkaBx5Z0YknSvOaNe0RcB9wFPA6QmR9n5nvABmB32bYbuKc83wDsySkvAMsi4qYln1ySNKsmV+5fAiaBf46IH0fE9yLiWmBlZh4DKI8ryv5B4PCM10+UtbNExGhEjEfE+OTk5AX9EJKkszWJez9wO/BYZv4e8D6f34LpJrqs5XkLmbsycyQzRwYGBhoNK0lqpkncJ4CJzHyxHD/JVOyPT99uKY/vzti/asbrh4CjSzOuJKmJeeOemT8HDkfEl8vSOuA1YC+wuaxtBp4uz/cCm8q7Zu4ETk/fvpEkXRr9DfeNAf8SEVcCh4D7mfqD4YmI2AK8A9xb9u4D1gMd4EzZK0m6hBrFPTNfBka6nFrXZW8CD13gXK3oO3OSqw/ua3sM9YgrPvwlAJ9ddV3Lk6iX9J05Caxse4x5Nb1yr97w8HDbI6jHdDr/B8Dwl3r/P2RdSisvi14Y92JsbKztEdRjtm7dCsCOHTtankRaOD9bRpIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqULGXZIqZNwlqUKN4h4R/xsRP4mIlyNivKzdEBHPRsRb5fH6sh4R8WhEdCLilYi4/WL+AJKk8y3kyv2PMvO2zBwpx9uA/Zm5FthfjgHuBtaWr1HgsaUaVpLUzIXcltkA7C7PdwP3zFjfk1NeAJZFxE0X8M+RJC1Q07gn8N8RcSAiRsvaysw8BlAeV5T1QeDwjNdOlDVJ0iXS33DfVzPzaESsAJ6NiINz7I0ua3nepqk/JEYBbr755oZjSJKaaHTlnplHy+O7wFPAHcDx6dst5fHdsn0CWDXj5UPA0S7fc1dmjmTmyMDAwOJ/AknSeeaNe0RcGxG/Nf0c+BPgVWAvsLls2ww8XZ7vBTaVd83cCZyevn0jSbo0mtyWWQk8FRHT+/81M5+JiJeAJyJiC/AOcG/Zvw9YD3SAM8D9Sz61JGlO88Y9Mw8BX+myfgJY12U9gYeWZDpJ0qL4G6qSVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVCHjLkkVMu6SVKHGcY+Ivoj4cUT8ZzleExEvRsRbEfGDiLiyrH+hHHfK+dUXZ3RJ0mwWcuW+FXh9xvF3gUcycy1wCthS1rcApzJzGHik7JMkXUKN4h4RQ8CfAd8rxwF8DXiybNkN3FOebyjHlPPryn5J0iXS9Mr9H4G/BT4rx8uB9zLz03I8AQyW54PAYYBy/nTZf5aIGI2I8YgYn5ycXOT4kqRu5o17RPw58G5mHpi53GVrNjj3+ULmrswcycyRgYGBRsNKkprpb7Dnq8A3ImI9cBVwHVNX8ssior9cnQ8BR8v+CWAVMBER/cAXgZNLPrkkaVbzXrln5t9l5lBmrgY2As9l5n3AD4Fvlm2bgafL873lmHL+ucw878pd6nWffPIJnU6HEydOtD2KtGAX8j73bwMPR0SHqXvqj5f1x4HlZf1hYNuFjSi14/jx47z//vvs2bOn7VGkBWtyW+bXMvN54Pny/BBwR5c9HwL3LsFsUmtOnDjByZNTdxOfeeYZNm3axPLl570vQOpZC4q7fjPs3LmTTqfT9hitmpiYYPpu4kcffcTo6ChDQ0MtT9Wu4eFhxsbG2h5DDfnxA1IXp06dmvNY6nVeues8Xp3B9u3bef755399fNddd7F9+/bW5pEWyit3qYs33nhjzmOp1xl3qYtjx47NeSz1OuMudXHuxyH58Ui63Bh3qYsVK1bMeSz1OuMudXHuh9n54Xa63Bh3qYvPPvtszmOp1xl3SaqQcZekChl3qYu+vr45j6VeZ9ylLtasWXPW8S233NLSJNLiGHepi3M/OO3NN99saRJpcYy7JFXIuEtShYy71MUDDzxw1vGDDz7Y0iTS4hh3qYv77rvvrOONGze2NIm0OMZdmsX01btX7bocxfT/SqxNIyMjOT4+3vYYknRZiYgDmTnS7ZxX7pJUIeMuSRUy7pJUIeMuSRXqib9QjYhJ4O2255C6uBH4RdtDSLP4ncwc6HaiJ+Iu9aqIGJ/t3QhSL/O2jCRVyLhLUoWMuzS3XW0PIC2G99wlqUJeuUtShYy7JFXIuEuziIg/jYg3IqITEdvankdaCO+5S11ERB/wJvB1YAJ4CfhWZr7W6mBSQ165S93dAXQy81Bmfgz8G7Ch5Zmkxoy71N0gcHjG8URZky4Lxl3qLrqseQ9Tlw3jLnU3AayacTwEHG1pFmnBjLvU3UvA2ohYExFXAhuBvS3PJDXW3/YAUi/KzE8j4i+B/wL6gH/KzJ+2PJbUmG+FlKQKeVtGkipk3CWpQsZdkipk3CWpQsZdkipk3CWpQsZdkir0/7Q3KbrHWqVqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=inputDataFrame['CreditScore'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d44437780>"
      ]
     },
     "execution_count": 173,
     "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": [
    "sns.boxplot(data=inputDataFrame['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18d444a1b70>"
      ]
     },
     "execution_count": 174,
     "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": [
    "numericalColumns=['Tenure','NumOfProducts','HasCrCard','IsActiveMember']\n",
    "sns.boxplot(data=inputDataFrame[numericalColumns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a 60 instances of data where NumberOfProducts is 4. This might be a potential cluster of valid datapoints and cannot be considered as outlier.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age feature seems to have a few outliers. Age is one column which needs to be treated for outliers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(i) No Outliers in Estimated Salary\n",
    "(ii) No Outliers in Balance\n",
    "(iii) There are a cluster of data points outside the IQR but very close to the border and hence cannot be considered as Outliers\n",
    "(iv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for missing values\n",
    "inputDataFrame.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No Missing Values found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Data\n",
    "\n",
    "There are two features in this dataset which is categorical. Both of the features are not nominal features and does not require any inherent ordering.\n",
    "\n",
    "One Hot Encoder can be applied because the data does not have huge number of unique values in them. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
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       "      <td>42.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>608.0</td>\n",
       "      <td>41.0</td>\n",
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       "      <td>83807.86</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>112542.58</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113931.57</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>79084.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4      5     6    7          8    9   10   11  \\\n",
       "0  1.0  0.0  0.0  1.0  0.0  619.0  42.0  2.0       0.00  1.0  1.0  1.0   \n",
       "1  0.0  0.0  1.0  1.0  0.0  608.0  41.0  1.0   83807.86  1.0  0.0  1.0   \n",
       "2  1.0  0.0  0.0  1.0  0.0  502.0  42.0  8.0  159660.80  3.0  1.0  0.0   \n",
       "3  1.0  0.0  0.0  1.0  0.0  699.0  39.0  1.0       0.00  2.0  0.0  0.0   \n",
       "4  0.0  0.0  1.0  1.0  0.0  850.0  43.0  2.0  125510.82  1.0  1.0  1.0   \n",
       "\n",
       "          12  \n",
       "0  101348.88  \n",
       "1  112542.58  \n",
       "2  113931.57  \n",
       "3   93826.63  \n",
       "4   79084.10  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One Hot Encoding\n",
    "from sklearn.compose import ColumnTransformer\n",
    "unprocessedDataX=inputDataFrame.drop(['CustomerId', 'RowNumber', 'Surname','Exited'], axis=1)\n",
    "dataY=inputDataFrame.Exited\n",
    "preprocessor= ColumnTransformer([('num',OneHotEncoder(sparse=False, categories='auto'),\\\n",
    "                                 categorialColumns)],remainder='passthrough')\n",
    "transformedData= preprocessor.fit_transform(unprocessedDataX)\n",
    "processedDataX=pd.DataFrame(data=transformedData)\n",
    "processedDataX.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8565\n",
      "Confusion Matrix:\n",
      "[[1531   54]\n",
      " [ 233  182]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "beforeScaling_X=processedDataX\n",
    "beforeScaling_y=dataY\n",
    "def evaluate_performance(X,y):\n",
    "    bs_train_X, bs_test_X, bs_train_y, bs_test_y = train_test_split(X, y, test_size = 0.20, random_state=1)\n",
    "    clf = RandomForestClassifier()\n",
    "    clf.fit(bs_train_X,bs_train_y)\n",
    "    print(\"Accuracy:\",clf.score(bs_test_X,bs_test_y))\n",
    "    results=clf.predict(bs_test_X)\n",
    "    print(\"Confusion Matrix:\")\n",
    "    confusionMatrix= metrics.confusion_matrix(y_true= bs_test_y, y_pred=results )\n",
    "    print(confusionMatrix)\n",
    "evaluate_performance(beforeScaling_X,beforeScaling_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "processedDataX.columns=[ 'geo_france','geo_germany','geo_spain' ,'gender_female','gender_male',\\\n",
    "                        'creditscore', 'age', 'tenure', 'balance','numofproducts', 'hascrcard', \\\n",
    "                        'isactivemember', 'estimatedsalary']\n",
    "#Taking only the numerical columns for scaling and ignoring the categorical columns\n",
    "num_columns=['creditscore', 'age', 'tenure', 'balance','numofproducts','estimatedsalary']\n",
    "\n",
    "scaler = preprocessing.StandardScaler()\n",
    "minmaxscaler = preprocessing.MinMaxScaler()\n",
    "processedDataX[num_columns]= scaler.fit_transform(processedDataX[num_columns])\n",
    "\n",
    "minmaxprocesseddata=minmaxscaler.fit_transform(processedDataX[num_columns])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard scaled data\n",
      "Accuracy: 0.8485\n",
      "Confusion Matrix:\n",
      "[[1530   55]\n",
      " [ 248  167]]\n",
      "Minmax scaled data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.835\n",
      "Confusion Matrix:\n",
      "[[1522   63]\n",
      " [ 267  148]]\n"
     ]
    }
   ],
   "source": [
    "#After scaling results\n",
    "print(\"Standard scaled data\")\n",
    "standard_scaling_X=processedDataX\n",
    "standard_scaling_y=dataY\n",
    "evaluate_performance(standard_scaling_X,standard_scaling_y)\n",
    "#MinMax Scaled data\n",
    "print(\"Minmax scaled data\")\n",
    "minmax_scaling_X=minmaxprocesseddata\n",
    "minmax_scaling_y=dataY\n",
    "evaluate_performance(minmax_scaling_X,minmax_scaling_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7963\n",
       "1    0.2037\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataY.columns=['target']\n",
    "\n",
    "dataY.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output we could say that the data is imbalanced. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "[1 1 2 1 3 1 1 1 1 1 1 1 1]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'(slice(None, None, None), array([ True,  True, False,  True, False,  True,  True,  True,  True,\n        True,  True,  True,  True]))' is an invalid key",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-179-c23d3f21d0af>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# ranking of each feature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mrfecv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mranking_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mreduced_X\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprocessedDataX\u001b[0m\u001b[1;33m[\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mrfecv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport_\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2993\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2994\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2995\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2996\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2997\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2895\u001b[0m                 )\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2899\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '(slice(None, None, None), array([ True,  True, False,  True, False,  True,  True,  True,  True,\n        True,  True,  True,  True]))' is an invalid key"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "estimator = RandomForestClassifier(n_estimators=250, random_state=0)\n",
    "rfecv= RFECV(estimator, cv=10)\n",
    "rfecv.fit(processedDataX,dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "[1 1 2 1 3 1 1 1 1 1 1 1 1]\n",
      "(10000, 13)\n"
     ]
    }
   ],
   "source": [
    "# optimal number of features\n",
    "print (rfecv.n_features_)\n",
    "# ranking of each feature\n",
    "print (rfecv.ranking_)\n",
    "print(processedDataX.shape)\n",
    "array_data=processedDataX.to_numpy()\n",
    "reduced_X = array_data[ : , rfecv.support_ ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Feature Selection results\n",
      "Accuracy: 0.8545\n",
      "Confusion Matrix:\n",
      "[[1528   57]\n",
      " [ 234  181]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#After Feature Selection results\n",
    "print(\"After Feature Selection results\")\n",
    "after_feature_selection_X=reduced_X\n",
    "after_feature_selection_y=dataY\n",
    "evaluate_performance(after_feature_selection_X,after_feature_selection_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=reduced_X\n",
    "target=dataY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(features, target, test_size = 0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the model performance before balancing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.7925\n",
      "1    0.2075\n",
      "Name: Exited, dtype: float64\n",
      "Accuracy: 0.852\n",
      "Confusion Matrix:\n",
      "[[1525   60]\n",
      " [ 236  179]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "print(test_y.value_counts(normalize=True))\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(train_X,train_y)\n",
    "print(\"Accuracy:\",clf.score(test_X,test_y))\n",
    "results=clf.predict(test_X)\n",
    "print(\"Confusion Matrix:\")\n",
    "confusionMatrix= metrics.confusion_matrix(y_true= test_y, y_pred=results )\n",
    "print(confusionMatrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Score: 0.8095\n",
      "[[1557   28]\n",
      " [ 353   62]]\n",
      "Prediction Score: 0.8385\n",
      "[[1507   78]\n",
      " [ 245  170]]\n",
      "Prediction Score: 0.8\n",
      "[[1381  204]\n",
      " [ 196  219]]\n",
      "Prediction Score: 0.812\n",
      "[[1533   52]\n",
      " [ 324   91]]\n",
      "Prediction Score: 0.823\n",
      "[[1505   80]\n",
      " [ 274  141]]\n",
      "Prediction Score:"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0.8545\n",
      "[[1527   58]\n",
      " [ 233  182]]\n",
      "Prediction Score: 0.8585\n",
      "[[1523   62]\n",
      " [ 221  194]]\n",
      "Prediction Score: 0.7235\n",
      "[[1153  432]\n",
      " [ 121  294]]\n"
     ]
    }
   ],
   "source": [
    "classifiers=[LinearSVC(),\\\n",
    "             KNeighborsClassifier(),\\\n",
    "             DecisionTreeClassifier(),\\\n",
    "             LogisticRegression(),\\\n",
    "             GaussianNB(),\\\n",
    "             RandomForestClassifier(),\\\n",
    "             AdaBoostClassifier(),\\\n",
    "             NearestCentroid()]\n",
    "for c in classifiers:\n",
    "    \n",
    "    c.fit(train_X, train_y)\n",
    "    print(\"Prediction Score:\",c.score(test_X,test_y))\n",
    "    \n",
    "    ###Confusion Matrix implemetation\n",
    "    results=c.predict(test_X)\n",
    "    cmatrix= metrics.confusion_matrix(y_true= test_y, y_pred=results )\n",
    "    print(cmatrix)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balancing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sm = SMOTE(random_state=0)\n",
    "balanced_X, balanced_y = sm.fit_sample(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.7042960175603636\n",
      "[[4542. 1836.]\n",
      " [1936. 4442.]]\n",
      "\n",
      "Accuracy: 0.8494825964252116\n",
      "[[4747. 1631.]\n",
      " [ 289. 6089.]]\n",
      "\n",
      "Accuracy: 0.839448102853559\n",
      "[[5294. 1084.]\n",
      " [ 964. 5414.]]\n",
      "\n",
      "Accuracy: 0.705236751332706\n",
      "[[4532. 1846.]\n",
      " [1914. 4464.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.7191125744747571\n",
      "[[4739. 1639.]\n",
      " [1944. 4434.]]\n",
      "\n",
      "Accuracy: 0.8949513954217623\n",
      "[[5840.  538.]\n",
      " [ 802. 5576.]]\n",
      "\n",
      "Accuracy: 0.8297271872060206\n",
      "[[5352. 1026.]\n",
      " [1146. 5232.]]\n",
      "\n",
      "Accuracy: 0.6999843211037944\n",
      "[[4530. 1848.]\n",
      " [1979. 4399.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "kf = model_selection.StratifiedKFold(n_splits=6, shuffle=True, random_state=1)\n",
    "\n",
    "def cross_validation_scores(X,y):\n",
    "    accuracy=[]\n",
    "    for c in classifiers:\n",
    "\n",
    "        allResults=[] \n",
    "        totalConfusionMatrix= np.zeros((2,2))\n",
    "\n",
    "        for train_index, test_index in kf.split(X, y):  \n",
    "\n",
    "            c.fit(X[train_index], y[train_index])\n",
    "            results=c.predict(X[test_index])\n",
    "            allResults.append(c.score(X[test_index],y[test_index]))            \n",
    "            confusionMatrix= metrics.confusion_matrix(y_true= y[test_index], y_pred=results )\n",
    "            totalConfusionMatrix+= confusionMatrix\n",
    "\n",
    "        print(\"\\nAccuracy:\",np.mean(allResults))\n",
    "        print (totalConfusionMatrix)\n",
    "        accuracy.append(np.mean(allResults))\n",
    "    \n",
    "    return accuracy\n",
    "    \n",
    "    \n",
    "\n",
    "cross_validation_scores(balanced_X,balanced_y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "training_X=balanced_X\n",
    "training_y=balanced_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning\n",
    "\n",
    "From the cross hold validation we can see that the below models performed well\n",
    "\n",
    " 1. KNearestNeighbors()\n",
    " 2. Decision Tree Classifier()\n",
    " 3. RandomForestClassifier() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighbors classifier\n",
      "\n",
      " Best parameters set found on development set:\n",
      "{'n_neighbors': 1, 'p': 1} with a score of  0.9132173095014111\n",
      "Random Forest Classifier\n",
      "\n",
      " Best parameters set found on development set:\n",
      "{'criterion': 'entropy', 'n_estimators': 102} with a score of  0.9157259328943242\n",
      "Decision Tree Classifier\n",
      "\n",
      " Best parameters set found on development set:\n",
      "{'criterion': 'entropy', 'splitter': 'random'} with a score of  0.8573220445280653\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def gridsearch(param_grid, clf, train_X, train_y):\n",
    "    classifier=GridSearchCV(clf, param_grid, cv=10, n_jobs=-1)\n",
    "    classifier.fit(train_X,train_y)\n",
    "    print(\"\\n Best parameters set found on development set:\")\n",
    "    print(classifier.best_params_ , \"with a score of \", classifier.best_score_)\n",
    "\n",
    "knn_pgrid = {'n_neighbors': list(range(1, 20)),  'p':[1, 2, 3, 4, 5] }\n",
    "rfc_pgrid = {'n_estimators': list(range(90, 110)),  'criterion':['gini', 'entropy'] }\n",
    "dtc_pgrid = {'criterion':['gini', 'entropy'], 'splitter':['best','random']}\n",
    "print(\"KNeighbors classifier\")\n",
    "gridsearch(knn_pgrid, KNeighborsClassifier(), training_X,training_y)\n",
    "print(\"Random Forest Classifier\")\n",
    "gridsearch(rfc_pgrid, RandomForestClassifier(), training_X,training_y)\n",
    "print(\"Decision Tree Classifier\")\n",
    "gridsearch(dtc_pgrid, DecisionTreeClassifier(), training_X,training_y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying the model with and without the ideal hyper parameter settings on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers_without_hyperparameter_optimization=[KNeighborsClassifier(),\\\n",
    "                  RandomForestClassifier(),\\\n",
    "                  DecisionTreeClassifier()]\n",
    "finalclassifiers=[KNeighborsClassifier(n_neighbors=1,p=1),\\\n",
    "                  RandomForestClassifier(criterion='entropy', n_estimators=102),\\\n",
    "                  DecisionTreeClassifier(criterion='entropy', splitter='random')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results before hyperparameter optimization\n",
      "\n",
      "0.775\n",
      "[[1259  326]\n",
      " [ 124  291]]\n",
      "0.845\n",
      "[[1475  110]\n",
      " [ 200  215]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7815\n",
      "[[1344  241]\n",
      " [ 196  219]]\n",
      "Results after hyperparameter optimization\n",
      "\n",
      "0.804\n",
      "[[1398  187]\n",
      " [ 205  210]]\n",
      "0.852\n",
      "[[1475  110]\n",
      " [ 186  229]]\n",
      "0.7805\n",
      "[[1339  246]\n",
      " [ 193  222]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "def get_results(classifiers, training_X, training_y, test_X, test_y):\n",
    "    for classifier in classifiers:\n",
    "        classifier.fit(training_X,training_y)\n",
    "        print(classifier.score(test_X, test_y))\n",
    "        results=classifier.predict(test_X)\n",
    "        confusionMatrix= metrics.confusion_matrix(y_true= test_y, y_pred=results)\n",
    "        print(confusionMatrix)\n",
    "\n",
    "print(\"Results before hyperparameter optimization\\n\")\n",
    "get_results(classifiers_without_hyperparameter_optimization, training_X, training_y, test_X, test_y)\n",
    "print(\"Results after hyperparameter optimization\\n\")\n",
    "get_results(finalclassifiers, training_X, training_y, test_X, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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