final_training_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the train and test data set\n",
    "\n",
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79853, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34224, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'perc_premium_paid_by_cash_credit', 'age_in_days', 'Income',\n",
       "       'Count_3-6_months_late', 'Count_6-12_months_late',\n",
       "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
       "       'no_of_premiums_paid', 'sourcing_channel', 'residence_area_type',\n",
       "       'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'perc_premium_paid_by_cash_credit', 'age_in_days', 'Income',\n",
       "       'Count_3-6_months_late', 'Count_6-12_months_late',\n",
       "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
       "       'no_of_premiums_paid', 'sourcing_channel', 'residence_area_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>12058</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>21546</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>17531</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>15341</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>31400</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0  110936                             0.429        12058  355060   \n",
       "1   41492                             0.010        21546  315150   \n",
       "2   31300                             0.917        17531   84140   \n",
       "3   19415                             0.049        15341  250510   \n",
       "4   99379                             0.052        31400  198680   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    2.0                     3.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    0.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.02   \n",
       "1                             0.0                           99.89   \n",
       "2                             1.0                           98.69   \n",
       "3                             0.0                           99.57   \n",
       "4                             0.0                           99.87   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  target  \n",
       "0                   13                C               Urban       1  \n",
       "1                   21                A               Urban       1  \n",
       "2                    7                C               Rural       0  \n",
       "3                    9                A               Urban       1  \n",
       "4                   12                B               Urban       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0    649                             0.001        27384   51150   \n",
       "1  81136                             0.124        23735  285140   \n",
       "2  70762                             1.000        17170  186030   \n",
       "3  53935                             0.198        16068  123540   \n",
       "4  15476                             0.041        10591  200020   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    0.0                     0.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    1.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.89   \n",
       "1                             0.0                           98.93   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                           99.00   \n",
       "4                             0.0                           99.17   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  \n",
       "0                    7                A               Rural  \n",
       "1                   19                A               Urban  \n",
       "2                    2                B               Urban  \n",
       "3                   11                B               Rural  \n",
       "4                   14                A               Rural  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "age_in_days                           int64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "sourcing_channel                     object\n",
       "residence_area_type                  object\n",
       "target                                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying categorical and continuous variables\n",
    "\n",
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "age_in_days                           int64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "sourcing_channel                     object\n",
       "residence_area_type                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Age in days to Age in years\n",
    "\n",
    "df_train['Age'] = round((df_train['age_in_days'])/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Age'] = round((df_train['age_in_days'])/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 97\n",
       "Count_6-12_months_late                97\n",
       "Count_more_than_12_months_late        97\n",
       "application_underwriting_score      2974\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "target                                 0\n",
       "Age                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping all rows where missing values are present\n",
    "train = df_train.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "perc_premium_paid_by_cash_credit    0\n",
       "age_in_days                         0\n",
       "Income                              0\n",
       "Count_3-6_months_late               0\n",
       "Count_6-12_months_late              0\n",
       "Count_more_than_12_months_late      0\n",
       "application_underwriting_score      0\n",
       "no_of_premiums_paid                 0\n",
       "sourcing_channel                    0\n",
       "residence_area_type                 0\n",
       "target                              0\n",
       "Age                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 31\n",
       "Count_6-12_months_late                31\n",
       "Count_more_than_12_months_late        31\n",
       "application_underwriting_score      1323\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "Age                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "perc_premium_paid_by_cash_credit    0\n",
       "age_in_days                         0\n",
       "Income                              0\n",
       "Count_3-6_months_late               0\n",
       "Count_6-12_months_late              0\n",
       "Count_more_than_12_months_late      0\n",
       "application_underwriting_score      0\n",
       "no_of_premiums_paid                 0\n",
       "sourcing_channel                    0\n",
       "residence_area_type                 0\n",
       "Age                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(['age_in_days'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.drop(['age_in_days'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inclusion of new column as premium_pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prem_pay(train):\n",
    "    if (((train['Count_3-6_months_late'] == 0) or (train['Count_6-12_months_late'] == 0) or (train['Count_more_than_12_months_late'] == 0) and (train['target'] == 1))\n",
    "        or ((train['Count_3-6_months_late'] == 0) and (train['Count_6-12_months_late'] == 0) and (train['Count_more_than_12_months_late'] == 0) and (train['target'] == 1))\n",
    "        or ((train['perc_premium_paid_by_cash_credit'] < 0.600) and (train['target'] == 1)) or ((train['sourcing_channel'] != 'A') and (train['target'] == '1'))):\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "train['premium_pay'] = train.apply(prem_pay, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def prem_pay(df_test):\n",
    "    if (((df_test['Count_3-6_months_late'] == 0) and (df_test['Count_6-12_months_late'] == 0) and (df_test['Count_more_than_12_months_late'] == 0))\n",
    "        or ((df_test['perc_premium_paid_by_cash_credit'] < 0.600))):\n",
    "        return '1'\n",
    "    else:\n",
    "        return '0'\n",
    "\n",
    "df_test['premium_pay'] = df_test.apply(prem_pay, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "      <th>Age</th>\n",
       "      <th>premium_pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  Income  Count_3-6_months_late  \\\n",
       "0  110936                             0.429  355060                    0.0   \n",
       "1   41492                             0.010  315150                    0.0   \n",
       "2   31300                             0.917   84140                    2.0   \n",
       "3   19415                             0.049  250510                    0.0   \n",
       "4   99379                             0.052  198680                    0.0   \n",
       "\n",
       "   Count_6-12_months_late  Count_more_than_12_months_late  \\\n",
       "0                     0.0                             0.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     3.0                             1.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             0.0   \n",
       "\n",
       "   application_underwriting_score  no_of_premiums_paid sourcing_channel  \\\n",
       "0                           99.02                   13                C   \n",
       "1                           99.89                   21                A   \n",
       "2                           98.69                    7                C   \n",
       "3                           99.57                    9                A   \n",
       "4                           99.87                   12                B   \n",
       "\n",
       "  residence_area_type  target   Age premium_pay  \n",
       "0               Urban       1  33.0           1  \n",
       "1               Urban       1  59.0           1  \n",
       "2               Rural       0  48.0           0  \n",
       "3               Urban       1  42.0           1  \n",
       "4               Urban       1  86.0           1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>Age</th>\n",
       "      <th>premium_pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  Income  Count_3-6_months_late  \\\n",
       "0    649                             0.001   51150                    0.0   \n",
       "1  81136                             0.124  285140                    0.0   \n",
       "2  70762                             1.000  186030                    0.0   \n",
       "3  53935                             0.198  123540                    0.0   \n",
       "4  15476                             0.041  200020                    1.0   \n",
       "\n",
       "   Count_6-12_months_late  Count_more_than_12_months_late  \\\n",
       "0                     0.0                             0.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     0.0                             0.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             0.0   \n",
       "\n",
       "   application_underwriting_score  no_of_premiums_paid sourcing_channel  \\\n",
       "0                           99.89                    7                A   \n",
       "1                           98.93                   19                A   \n",
       "2                            0.00                    2                B   \n",
       "3                           99.00                   11                B   \n",
       "4                           99.17                   14                A   \n",
       "\n",
       "  residence_area_type   Age premium_pay  \n",
       "0               Rural  33.0           1  \n",
       "1               Urban  59.0           1  \n",
       "2               Urban  48.0           1  \n",
       "3               Rural  42.0           1  \n",
       "4               Rural  86.0           1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis of Continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>target</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>7.985300e+04</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57167.166368</td>\n",
       "      <td>0.314288</td>\n",
       "      <td>2.088472e+05</td>\n",
       "      <td>0.248369</td>\n",
       "      <td>0.078093</td>\n",
       "      <td>0.059935</td>\n",
       "      <td>95.377685</td>\n",
       "      <td>10.863887</td>\n",
       "      <td>0.937410</td>\n",
       "      <td>51.607404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32928.970160</td>\n",
       "      <td>0.334915</td>\n",
       "      <td>4.965826e+05</td>\n",
       "      <td>0.691102</td>\n",
       "      <td>0.436251</td>\n",
       "      <td>0.311840</td>\n",
       "      <td>18.773317</td>\n",
       "      <td>5.170687</td>\n",
       "      <td>0.242226</td>\n",
       "      <td>14.270484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.403000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28640.000000</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>1.080100e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.750000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57262.000000</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>1.665600e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.180000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85632.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>2.520900e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.520000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114076.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.026260e+07</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>99.890000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  perc_premium_paid_by_cash_credit        Income  \\\n",
       "count   79853.000000                      79853.000000  7.985300e+04   \n",
       "mean    57167.166368                          0.314288  2.088472e+05   \n",
       "std     32928.970160                          0.334915  4.965826e+05   \n",
       "min         2.000000                          0.000000  2.403000e+04   \n",
       "25%     28640.000000                          0.034000  1.080100e+05   \n",
       "50%     57262.000000                          0.167000  1.665600e+05   \n",
       "75%     85632.000000                          0.538000  2.520900e+05   \n",
       "max    114076.000000                          1.000000  9.026260e+07   \n",
       "\n",
       "       Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "count           79853.000000            79853.000000   \n",
       "mean                0.248369                0.078093   \n",
       "std                 0.691102                0.436251   \n",
       "min                 0.000000                0.000000   \n",
       "25%                 0.000000                0.000000   \n",
       "50%                 0.000000                0.000000   \n",
       "75%                 0.000000                0.000000   \n",
       "max                13.000000               17.000000   \n",
       "\n",
       "       Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "count                    79853.000000                    79853.000000   \n",
       "mean                         0.059935                       95.377685   \n",
       "std                          0.311840                       18.773317   \n",
       "min                          0.000000                        0.000000   \n",
       "25%                          0.000000                       98.750000   \n",
       "50%                          0.000000                       99.180000   \n",
       "75%                          0.000000                       99.520000   \n",
       "max                         11.000000                       99.890000   \n",
       "\n",
       "       no_of_premiums_paid        target           Age  \n",
       "count         79853.000000  79853.000000  79853.000000  \n",
       "mean             10.863887      0.937410     51.607404  \n",
       "std               5.170687      0.242226     14.270484  \n",
       "min               2.000000      0.000000     21.000000  \n",
       "25%               7.000000      1.000000     41.000000  \n",
       "50%              10.000000      1.000000     51.000000  \n",
       "75%              14.000000      1.000000     62.000000  \n",
       "max              60.000000      1.000000    103.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccf31760>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qY5KkhWGPQZLkmVV1H/DbY+qPJGmBebwzko/Re+rv3yX5SFX9y3F0SpK0cDzeGEn/o9qfNcqOSJIWpscLkppnWZIk4PEvbb0wyQ/onZkc3pbh0cH2I0faO0nSAW+PQVJVh4yrI5KkhWnY35FIkjSQQSJJ6mQiQZLk6UmuS/K1JHcl+ZUkRye5KcnX2+tRfduvS7I1yd1Jzuirn5rk9rbukvZcMEnSGE3qjOS9wCeq6nnAC+nN2b4WuLmqlgE3t/ckOQlYBZwMnAlcmmTX2M1lwBp687gva+slSWM09iBJciTwG8DlAFX1cFV9D1gJbGybbQTObssrgWuqakeb2ncrcFqSE4Ajq+qWNlfKVX1tJEljMokzkmcBs8D7k9yW5H1toqzjq2obQHs9rm2/GLi/r/1Mqy1uy3Pru0myJsl0kunZ2dn9+20k6SA3iSA5FHgxcFlVvYjeQyDX7mH7QeMetYf67sWqDVW1vKqWL1q0aG/7K0nag0kEyQwwU1W3tvfX0QuWB9vlKtrr9r7tT+xrvwR4oNWXDKhLksZo7EFSVd8C7k/y3FY6HbgT2AysbrXVwPVteTOwKslhSZbSG1Tf0i5/PZRkRbtb69y+NpKkMRl2PpL97Y+ADyZ5EvAN4HX0Qm1TkvOA+4BzAKrqjiSb6IXNTuCCqnqkfc75wJXA4cCN7U+SNEYTCZKq+gqwfMCq0+fZfj2wfkB9Gjhl//ZOkrQ3/GW7JKkTg0SS1IlBIknqZFKD7ZIOIFNrb5jIfu+96KyJ7Ff7l2ckkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUic+ImUvTOoxEpJ0IPOMRJLUycSCJMkhSW5L8tft/dFJbkry9fZ6VN+265JsTXJ3kjP66qcmub2tu6RNuStJGqNJnpG8Cbir7/1a4OaqWgbc3N6T5CRgFXAycCZwaZJDWpvLgDX05nFf1tZLksZoIkGSZAlwFvC+vvJKYGNb3gic3Ve/pqp2VNU9wFbgtCQnAEdW1S1VVcBVfW0kSWMyqTOS9wBvAX7WVzu+qrYBtNfjWn0xcH/fdjOttrgtz63vJsmaJNNJpmdnZ/fPN5AkARMIkiSvBLZX1ZeGbTKgVnuo716s2lBVy6tq+aJFi4bcrSRpGJO4/felwKuS/A7wZODIJB8AHkxyQlVta5ettrftZ4AT+9ovAR5o9SUD6pKkMRr7GUlVrauqJVU1RW8Q/TNV9VpgM7C6bbYauL4tbwZWJTksyVJ6g+pb2uWvh5KsaHdrndvXRpI0JgfSDxIvAjYlOQ+4DzgHoKruSLIJuBPYCVxQVY+0NucDVwKHAze2P0nSGE00SKrqs8Bn2/LfA6fPs916YP2A+jRwyuh6KEl6PP6yXZLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqZOxT2yV5ETgKuAZwM+ADVX13iRHA9cCU8C9wO9W1Xdbm3XAecAjwBur6pOtfiqPzpD4ceBNVVXj/D6S9t3U2hsmtu97LzprYvv+eTOJM5KdwJ9W1fOBFcAFSU4C1gI3V9Uy4Ob2nrZuFXAycCZwaZJD2mddBqyhN4/7srZekjRGYw+SqtpWVV9uyw8BdwGLgZXAxrbZRuDstrwSuKaqdlTVPcBW4LQkJwBHVtUt7Szkqr42kqQxmegYSZIp4EXArcDxVbUNemEDHNc2Wwzc39dsptUWt+W59UH7WZNkOsn07Ozs/vwKknTQm1iQJHkq8BHgzVX1gz1tOqBWe6jvXqzaUFXLq2r5okWL9r6zkqR5TSRIkjyRXoh8sKo+2soPtstVtNftrT4DnNjXfAnwQKsvGVCXJI3R2IMkSYDLgbuq6t19qzYDq9vyauD6vvqqJIclWUpvUH1Lu/z1UJIV7TPP7WsjSRqTsd/+C7wU+NfA7Um+0mpvAy4CNiU5D7gPOAegqu5Isgm4k94dXxdU1SOt3fk8evvvje1PkjRGYw+SqvpfDB7fADh9njbrgfUD6tPAKfuvd5KkveUv2yVJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUieTeGijJE3cpOaL/3mcK94zEklSJwaJJKkTg0SS1IlBIknqZMEHSZIzk9ydZGuStZPujyQdbBZ0kCQ5BPgvwG8DJwGvSXLSZHslSQeXhX7772nA1qr6BkCSa4CV9OZ3l6QDzqRuO4bR3Xq80INkMXB/3/sZ4B/P3SjJGmBNe/vDJHfvxT6OBb69zz08eHichuNxGo7HaXhDH6u8s9N+fmm+FQs9SDKgVrsVqjYAG/ZpB8l0VS3fl7YHE4/TcDxOw/E4De9AOFYLeoyE3hnIiX3vlwAPTKgvknRQWuhB8jfAsiRLkzwJWAVsnnCfJOmgsqAvbVXVziR/CHwSOAS4oqru2M+72adLYgchj9NwPE7D8TgNb+LHKlW7DSlIkjS0hX5pS5I0YQaJJKkTg6RJcmKS/5nkriR3JHlTqx+d5KYkX2+vR026rweCJIckuS3JX7f3Hqc5kjw9yXVJvtb+ufoVj9NgSf64/Xv31SQfSvJkjxUkuSLJ9iRf7avNe1ySrGuPi7o7yRnj6qdB8qidwJ9W1fOBFcAF7XEra4Gbq2oZcHN7L3gTcFffe4/T7t4LfKKqnge8kN7x8jjNkWQx8EZgeVWdQu/GmVV4rACuBM6cUxt4XNp/r1YBJ7c2l7bHSI2cQdJU1baq+nJbfojev/SL6T1yZWPbbCNw9mR6eOBIsgQ4C3hfX9nj1CfJkcBvAJcDVNXDVfU9PE7zORQ4PMmhwBH0fg920B+rqvo88J055fmOy0rgmqraUVX3AFvpPUZq5AySAZJMAS8CbgWOr6pt0Asb4LjJ9eyA8R7gLcDP+moep8d6FjALvL9dAnxfkqfgcdpNVX0TeBdwH7AN+H5VfQqP1XzmOy6DHhm1eBwdMkjmSPJU4CPAm6vqB5Puz4EmySuB7VX1pUn35QB3KPBi4LKqehHwIw7OSzOPq13jXwksBX4ReEqS1062VwvSUI+MGgWDpE+SJ9ILkQ9W1Udb+cEkJ7T1JwDbJ9W/A8RLgVcluRe4BnhFkg/gcZprBpipqlvb++voBYvHaXe/CdxTVbNV9VPgo8Cv4rGaz3zHZWKPjDJImiShdz37rqp6d9+qzcDqtrwauH7cfTuQVNW6qlpSVVP0BvY+U1WvxeP0GFX1LeD+JM9tpdPpTW/gcdrdfcCKJEe0fw9PpzdG6bEabL7jshlYleSwJEuBZcCWcXTIX7Y3SX4N+AJwO49e+38bvXGSTcAz6f0Df05VzR38OigleRnwZ1X1yiTH4HF6jCT/iN4NCU8CvgG8jt7/vHmc5kjyH4F/Re/uyduA1wNP5SA/Vkk+BLyM3qPiHwQuBD7GPMclyduB36d3HN9cVTeOpZ8GiSSpCy9tSZI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerk/wNwXJoF+tDFAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histogram and box plot of Age Variable\n",
    "\n",
    "train['Age'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcc7093a60>"
      ]
     },
     "execution_count": 29,
     "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": [
    "train['Age'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.loc[train['Age']>90,'Age']=np.mean(train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcc70ecaf0>"
      ]
     },
     "execution_count": 30,
     "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": [
    "# Plotting histogram of income by taking logs.\n",
    "np.log(train['Income']).plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    72081\n",
       "0     4798\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating frequency table for target variable\n",
    "\n",
    "train['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.93759\n",
       "0    0.06241\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating percentage from target frequency\n",
    "\n",
    "train['target'].value_counts()/len(train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdccc7354f0>"
      ]
     },
     "execution_count": 37,
     "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": [
    "# Creating bar plot for target frquencies\n",
    "\n",
    "train['target'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    75784\n",
       "0     1095\n",
       "Name: premium_pay, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating frequency table for premium_pay variable\n",
    "\n",
    "train['premium_pay'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccb53b20>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD1CAYAAABQtIIDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASgklEQVR4nO3db4xddX7f8fcndpbSpBD+DBadMTUVzh9AWrZYlGqlqo3b4mirmAcgzUoNVmTJFSJVVqrUmj6p+sASPCkNUkFCS4qh6RrX7QprIzZFpqsoKrJ3SGiIYQnTZRePTPFkIYQ0gtTk2wf3O8315Xrmju2dMev3Szo6537P73f8O5Klz5zf79yZVBWSJP3Yeg9AknRxMBAkSYCBIElqBoIkCTAQJEnNQJAkAbBxvQdwrq699trasmXLeg9Dkj5TXn755T+qqqlx5z6zgbBlyxbm5ubWexiS9JmS5PtnO+eUkSQJMBAkSc1AkCQBBoIkqRkIkiTAQJAkNQNBkgQYCJKk9pn9YtpnxZa9v7neQ/iR8r2HvrTeQ5B+ZPmEIEkCDARJUjMQJEmAgSBJagaCJAkwECRJzUCQJAEGgiSpGQiSJMBAkCQ1A0GSBEwQCEl+JskrQ9ufJPlKkquTvJDkzd5fNdTnwSTzSd5IctdQ/fYkr/a5R5Ok65clebbrR5Ns+WHcrCTp7FYMhKp6o6puq6rbgNuBPwO+DuwFjlTVVuBIfybJzcAscAuwA3gsyYa+3OPAHmBrbzu6vht4v6puAh4BHr4wtydJmtRqp4y2A/+rqr4P7AT2d30/cHcf7wQOVNXHVfUWMA/ckeR64IqqeqmqCnh6pM/StQ4B25eeHiRJa2O1gTALfK2PN1XVOwC9v67r08CJoT4LXZvu49H6GX2q6jTwAXDNKscmSToPEwdCks8Bvwj855WajqnVMvXl+oyOYU+SuSRzi4uLKwxDkrQaq3lC+AXgd6vq3f78bk8D0ftTXV8ANg/1mwFOdn1mTP2MPkk2AlcC740OoKqeqKptVbVtampqFUOXJK1kNYHwZf5yugjgMLCrj3cBzw3VZ/vNoRsZLB4f62mlD5Pc2esD9430WbrWPcCLvc4gSVojE/0JzSR/FfiHwD8dKj8EHEyyG3gbuBegqo4nOQi8BpwGHqiqT7rP/cBTwOXA870BPAk8k2SewZPB7HnckyTpHEwUCFX1Z4ws8lbVDxi8dTSu/T5g35j6HHDrmPpHdKBIktaH31SWJAEGgiSpGQiSJMBAkCQ1A0GSBBgIkqRmIEiSAANBktQMBEkSYCBIkpqBIEkCDARJUjMQJEmAgSBJagaCJAkwECRJzUCQJAEGgiSpGQiSJGDCQEjyU0kOJflOkteT/J0kVyd5Icmbvb9qqP2DSeaTvJHkrqH67Ule7XOPJknXL0vybNePJtlyoW9UkrS8SZ8Qfg34ZlX9LPB54HVgL3CkqrYCR/ozSW4GZoFbgB3AY0k29HUeB/YAW3vb0fXdwPtVdRPwCPDwed6XJGmVVgyEJFcAfxd4EqCq/ryq/hjYCezvZvuBu/t4J3Cgqj6uqreAeeCOJNcDV1TVS1VVwNMjfZaudQjYvvT0IElaG5M8IfxNYBH4D0l+L8lXk/wEsKmq3gHo/XXdfho4MdR/oWvTfTxaP6NPVZ0GPgCuGR1Ikj1J5pLMLS4uTniLkqRJTBIIG4G/BTxeVV8A/g89PXQW436yr2Xqy/U5s1D1RFVtq6ptU1NTy49akrQqkwTCArBQVUf78yEGAfFuTwPR+1ND7TcP9Z8BTnZ9Zkz9jD5JNgJXAu+t9mYkSeduxUCoqv8NnEjyM13aDrwGHAZ2dW0X8FwfHwZm+82hGxksHh/raaUPk9zZ6wP3jfRZutY9wIu9ziBJWiMbJ2z3z4DfSPI54LvALzMIk4NJdgNvA/cCVNXxJAcZhMZp4IGq+qSvcz/wFHA58HxvMFiwfibJPIMng9nzvC9J0ipNFAhV9Qqwbcyp7Wdpvw/YN6Y+B9w6pv4RHSiSpPXhN5UlSYCBIElqBoIkCTAQJEnNQJAkAQaCJKkZCJIkwECQJDUDQZIEGAiSpGYgSJIAA0GS1AwESRJgIEiSmoEgSQIMBElSMxAkSYCBIElqEwVCku8leTXJK0nmunZ1kheSvNn7q4baP5hkPskbSe4aqt/e15lP8miSdP2yJM92/WiSLRf2NiVJK1nNE8Lfr6rbqmrpbyvvBY5U1VbgSH8myc3ALHALsAN4LMmG7vM4sAfY2tuOru8G3q+qm4BHgIfP/ZYkSefifKaMdgL7+3g/cPdQ/UBVfVxVbwHzwB1JrgeuqKqXqqqAp0f6LF3rELB96elBkrQ2Jg2EAv5bkpeT7Onapqp6B6D313V9Gjgx1Heha9N9PFo/o09VnQY+AK5Z3a1Iks7HxgnbfbGqTia5DnghyXeWaTvuJ/tapr5cnzMvPAijPQA33HDD8iOWJK3KRE8IVXWy96eArwN3AO/2NBC9P9XNF4DNQ91ngJNdnxlTP6NPko3AlcB7Y8bxRFVtq6ptU1NTkwxdkjShFQMhyU8k+WtLx8A/Av4AOAzs6ma7gOf6+DAw228O3chg8fhYTyt9mOTOXh+4b6TP0rXuAV7sdQZJ0hqZZMpoE/D1XuPdCPynqvpmkm8DB5PsBt4G7gWoquNJDgKvAaeBB6rqk77W/cBTwOXA870BPAk8k2SewZPB7AW4N0nSKqwYCFX1XeDzY+o/ALafpc8+YN+Y+hxw65j6R3SgSJLWh99UliQBBoIkqRkIkiTAQJAkNQNBkgQYCJKkZiBIkgADQZLUDARJEmAgSJKagSBJAgwESVIzECRJgIEgSWoGgiQJMBAkSc1AkCQBBoIkqU0cCEk2JPm9JN/oz1cneSHJm72/aqjtg0nmk7yR5K6h+u1JXu1zj6b/UHOSy5I82/WjSbZcuFuUJE1iNU8Ivwq8PvR5L3CkqrYCR/ozSW4GZoFbgB3AY0k2dJ/HgT3A1t52dH038H5V3QQ8Ajx8TncjSTpnEwVCkhngS8BXh8o7gf19vB+4e6h+oKo+rqq3gHngjiTXA1dU1UtVVcDTI32WrnUI2L709CBJWhuTPiH8O+BfAH8xVNtUVe8A9P66rk8DJ4baLXRtuo9H62f0qarTwAfANRPfhSTpvK0YCEn+MXCqql6e8JrjfrKvZerL9Rkdy54kc0nmFhcXJxyOJGkSkzwhfBH4xSTfAw4AP5/kPwLv9jQQvT/V7ReAzUP9Z4CTXZ8ZUz+jT5KNwJXAe6MDqaonqmpbVW2bmpqa6AYlSZNZMRCq6sGqmqmqLQwWi1+sqn8CHAZ2dbNdwHN9fBiY7TeHbmSweHysp5U+THJnrw/cN9Jn6Vr39L/xqScESdIPz8bz6PsQcDDJbuBt4F6Aqjqe5CDwGnAaeKCqPuk+9wNPAZcDz/cG8CTwTJJ5Bk8Gs+cxLknSOVhVIFTVt4Bv9fEPgO1nabcP2DemPgfcOqb+ER0okqT14TeVJUmAgSBJagaCJAkwECRJzUCQJAEGgiSpGQiSJMBAkCQ1A0GSBBgIkqRmIEiSAANBktQMBEkSYCBIkpqBIEkCDARJUjMQJEmAgSBJagaCJAmYIBCS/JUkx5L8zyTHk/ybrl+d5IUkb/b+qqE+DyaZT/JGkruG6rcnebXPPZokXb8sybNdP5pky4W/VUnSciZ5QvgY+Pmq+jxwG7AjyZ3AXuBIVW0FjvRnktwMzAK3ADuAx5Js6Gs9DuwBtva2o+u7gfer6ibgEeDhC3BvkqRVWDEQauBP++OP91bATmB/1/cDd/fxTuBAVX1cVW8B88AdSa4Hrqiql6qqgKdH+ixd6xCwfenpQZK0NiZaQ0iyIckrwCnghao6CmyqqncAen9dN58GTgx1X+jadB+P1s/oU1WngQ+Aa8aMY0+SuSRzi4uLk92hJGkiEwVCVX1SVbcBMwx+2r91mebjfrKvZerL9RkdxxNVta2qtk1NTa00bEnSKqzqLaOq+mPgWwzm/t/taSB6f6qbLQCbh7rNACe7PjOmfkafJBuBK4H3VjM2SdL5meQto6kkP9XHlwP/APgOcBjY1c12Ac/18WFgtt8cupHB4vGxnlb6MMmdvT5w30ifpWvdA7zY6wySpDWycYI21wP7+02hHwMOVtU3krwEHEyyG3gbuBegqo4nOQi8BpwGHqiqT/pa9wNPAZcDz/cG8CTwTJJ5Bk8Gsxfi5iRJk1sxEKrq94EvjKn/ANh+lj77gH1j6nPAp9YfquojOlAkSevDbypLkgADQZLUDARJEmAgSJKagSBJAgwESVIzECRJgIEgSWoGgiQJMBAkSc1AkCQBBoIkqRkIkiTAQJAkNQNBkgQYCJKkZiBIkgADQZLUVgyEJJuT/Pckryc5nuRXu351kheSvNn7q4b6PJhkPskbSe4aqt+e5NU+92iSdP2yJM92/WiSLRf+ViVJy5nkCeE08M+r6ueAO4EHktwM7AWOVNVW4Eh/ps/NArcAO4DHkmzoaz0O7AG29raj67uB96vqJuAR4OELcG+SpFVYMRCq6p2q+t0+/hB4HZgGdgL7u9l+4O4+3gkcqKqPq+otYB64I8n1wBVV9VJVFfD0SJ+lax0Cti89PUiS1saq1hB6KucLwFFgU1W9A4PQAK7rZtPAiaFuC12b7uPR+hl9quo08AFwzWrGJkk6PxMHQpKfBP4L8JWq+pPlmo6p1TL15fqMjmFPkrkkc4uLiysNWZK0ChMFQpIfZxAGv1FV/7XL7/Y0EL0/1fUFYPNQ9xngZNdnxtTP6JNkI3Al8N7oOKrqiaraVlXbpqamJhm6JGlCk7xlFOBJ4PWq+rdDpw4Du/p4F/DcUH223xy6kcHi8bGeVvowyZ19zftG+ixd6x7gxV5nkCStkY0TtPki8EvAq0le6dq/Ah4CDibZDbwN3AtQVceTHAReY/CG0gNV9Un3ux94CrgceL43GATOM0nmGTwZzJ7nfUmSVmnFQKiq32H8HD/A9rP02QfsG1OfA24dU/+IDhRJ0vrwm8qSJMBAkCQ1A0GSBBgIkqRmIEiSAANBktQMBEkSYCBIkpqBIEkCDARJUjMQJEmAgSBJagaCJAkwECRJzUCQJAEGgiSpGQiSJMBAkCS1FQMhya8nOZXkD4ZqVyd5Icmbvb9q6NyDSeaTvJHkrqH67Ule7XOPJknXL0vybNePJtlyYW9RkjSJSZ4QngJ2jNT2AkeqaitwpD+T5GZgFril+zyWZEP3eRzYA2ztbemau4H3q+om4BHg4XO9GUnSuVsxEKrqt4H3Rso7gf19vB+4e6h+oKo+rqq3gHngjiTXA1dU1UtVVcDTI32WrnUI2L709CBJWjvnuoawqareAej9dV2fBk4MtVvo2nQfj9bP6FNVp4EPgGvOcVySpHN0oReVx/1kX8vUl+vz6Ysne5LMJZlbXFw8xyFKksY510B4t6eB6P2pri8Am4fazQAnuz4zpn5GnyQbgSv59BQVAFX1RFVtq6ptU1NT5zh0SdI45xoIh4FdfbwLeG6oPttvDt3IYPH4WE8rfZjkzl4fuG+kz9K17gFe7HUGSdIa2rhSgyRfA/4ecG2SBeBfAw8BB5PsBt4G7gWoquNJDgKvAaeBB6rqk77U/QzeWLoceL43gCeBZ5LMM3gymL0gdyZJWpUVA6GqvnyWU9vP0n4fsG9MfQ64dUz9IzpQJEnrx28qS5IAA0GS1AwESRJgIEiSmoEgSQIMBElSMxAkSYCBIElqBoIkCTAQJEnNQJAkAQaCJKkZCJIkwECQJDUDQZIEGAiSpGYgSJIAA0GS1AwESRIwwd9UXitJdgC/BmwAvlpVD63zkKQfaVv2/uZ6D+FHyvce+tJ6D+G8XRRPCEk2AP8e+AXgZuDLSW5e31FJ0qXloggE4A5gvqq+W1V/DhwAdq7zmCTpknKxTBlNAyeGPi8Af3u0UZI9wJ7++KdJ3liDsV0qrgX+aL0HsZI8vN4j0Drw/+aF9TfOduJiCYSMqdWnClVPAE/88Idz6UkyV1Xb1nsc0ij/b66di2XKaAHYPPR5Bji5TmORpEvSxRII3wa2JrkxyeeAWeDwOo9Jki4pF8WUUVWdTvIrwG8xeO3016vq+DoP61LjVJwuVv7fXCOp+tRUvSTpEnSxTBlJktaZgSBJAgwESVK7KBaVJWlJkp9l8JsKphl8H+kkcLiqXl/XgV0CfELQGZL88nqPQZeuJP+Swa+uCXCMwSvpAb6WZO96ju1S4FtGOkOSt6vqhvUehy5NSf4QuKWq/u9I/XPA8arauj4juzQ4ZXQJSvL7ZzsFbFrLsUgj/gL468D3R+rX9zn9EBkIl6ZNwF3A+yP1AP9j7Ycj/X9fAY4keZO//IWXNwA3Ab+ybqO6RBgIl6ZvAD9ZVa+MnkjyrbUfjjRQVd9M8tMMfiX+NIMfUhaAb1fVJ+s6uEuAawiSJMC3jCRJzUCQJAEGgiSpGQiSJMBAkCS1/wfHgFyziH8xXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating bar plot for premium_pay frquencies\n",
    "\n",
    "train['premium_pay'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    40926\n",
       "B    16076\n",
       "C    11844\n",
       "D     7434\n",
       "E      599\n",
       "Name: sourcing_channel, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Creating frequency table for Sourcing channel\n",
    "\n",
    "train['sourcing_channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccb9c460>"
      ]
     },
     "execution_count": 41,
     "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": [
    "# Creating a bar plot for sourcing channel\n",
    "\n",
    "train['sourcing_channel'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Urban    0.602804\n",
       "Rural    0.397196\n",
       "Name: residence_area_type, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating frequency table percentage for residence type\n",
    "\n",
    "train['residence_area_type'].value_counts()/len(train['residence_area_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccc0a2e0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fvPHJiQ2lNS3J04BfAzYCtwAf7G+/BfhUVe2e4HjNM+hTbOB/ay/+IV783saXT2gkrXFJbgG+DHwc+Al6v9NZD/xGVd0xydnWAoM+hZL89sWH/X8WvV+IfrSq7pnMVBIk+XRV/UD/8QxwH7Clqr422cnWhqsmPYAuy1P7P0/p/zyV3rsIbkuyZ5KDac17+OKD/ieA3mPMrxzP0BuS5Jn0vvLrmrGLpVWQ5BG+9fucAN8OPMS3Lgf65SuryPehN6SqvpQk41dKq6OqZiY9w1rmJZeGJHk5vV9ISVqDPEOfQkk+zWM/+OiZwFl6X0MnaQ3yGvoU6n+xxaAC7q+qB0etl7Q2GHRJaoTX0CWpEQZdkhph0CWpEQZdkhph0CWpEf8HhCDnyprdLMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating bar plot for residence type\n",
    "\n",
    "(train['residence_area_type'].value_counts()/len(train['residence_area_type'])).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>target</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004991</td>\n",
       "      <td>0.001460</td>\n",
       "      <td>-0.005209</td>\n",
       "      <td>-0.002739</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>-0.002124</td>\n",
       "      <td>-0.002450</td>\n",
       "      <td>-0.005517</td>\n",
       "      <td>0.005134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>-0.004991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037407</td>\n",
       "      <td>0.219189</td>\n",
       "      <td>0.211828</td>\n",
       "      <td>0.170251</td>\n",
       "      <td>-0.142670</td>\n",
       "      <td>-0.149822</td>\n",
       "      <td>-0.237376</td>\n",
       "      <td>-0.250829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.001460</td>\n",
       "      <td>-0.037407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000857</td>\n",
       "      <td>-0.022187</td>\n",
       "      <td>-0.016297</td>\n",
       "      <td>0.085746</td>\n",
       "      <td>0.104532</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>0.037309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>-0.005209</td>\n",
       "      <td>0.219189</td>\n",
       "      <td>-0.000857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.210438</td>\n",
       "      <td>0.294580</td>\n",
       "      <td>-0.082882</td>\n",
       "      <td>0.068010</td>\n",
       "      <td>-0.250534</td>\n",
       "      <td>-0.057370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>-0.002739</td>\n",
       "      <td>0.211828</td>\n",
       "      <td>-0.022187</td>\n",
       "      <td>0.210438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274270</td>\n",
       "      <td>0.004726</td>\n",
       "      <td>-0.091049</td>\n",
       "      <td>-0.288496</td>\n",
       "      <td>-0.069440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>0.003453</td>\n",
       "      <td>0.170251</td>\n",
       "      <td>-0.016297</td>\n",
       "      <td>0.294580</td>\n",
       "      <td>0.274270</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031783</td>\n",
       "      <td>-0.026595</td>\n",
       "      <td>-0.240787</td>\n",
       "      <td>-0.059962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>-0.002124</td>\n",
       "      <td>-0.142670</td>\n",
       "      <td>0.085746</td>\n",
       "      <td>-0.082882</td>\n",
       "      <td>0.004726</td>\n",
       "      <td>-0.031783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.270864</td>\n",
       "      <td>0.068715</td>\n",
       "      <td>0.047079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>-0.002450</td>\n",
       "      <td>-0.149822</td>\n",
       "      <td>0.104532</td>\n",
       "      <td>0.068010</td>\n",
       "      <td>-0.091049</td>\n",
       "      <td>-0.026595</td>\n",
       "      <td>-0.270864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019851</td>\n",
       "      <td>0.187335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.005517</td>\n",
       "      <td>-0.237376</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>-0.250534</td>\n",
       "      <td>-0.288496</td>\n",
       "      <td>-0.240787</td>\n",
       "      <td>0.068715</td>\n",
       "      <td>0.019851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.005134</td>\n",
       "      <td>-0.250829</td>\n",
       "      <td>0.037309</td>\n",
       "      <td>-0.057370</td>\n",
       "      <td>-0.069440</td>\n",
       "      <td>-0.059962</td>\n",
       "      <td>0.047079</td>\n",
       "      <td>0.187335</td>\n",
       "      <td>0.092631</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  perc_premium_paid_by_cash_credit  \\\n",
       "id                                1.000000                         -0.004991   \n",
       "perc_premium_paid_by_cash_credit -0.004991                          1.000000   \n",
       "Income                            0.001460                         -0.037407   \n",
       "Count_3-6_months_late            -0.005209                          0.219189   \n",
       "Count_6-12_months_late           -0.002739                          0.211828   \n",
       "Count_more_than_12_months_late    0.003453                          0.170251   \n",
       "application_underwriting_score   -0.002124                         -0.142670   \n",
       "no_of_premiums_paid              -0.002450                         -0.149822   \n",
       "target                           -0.005517                         -0.237376   \n",
       "Age                               0.005134                         -0.250829   \n",
       "\n",
       "                                    Income  Count_3-6_months_late  \\\n",
       "id                                0.001460              -0.005209   \n",
       "perc_premium_paid_by_cash_credit -0.037407               0.219189   \n",
       "Income                            1.000000              -0.000857   \n",
       "Count_3-6_months_late            -0.000857               1.000000   \n",
       "Count_6-12_months_late           -0.022187               0.210438   \n",
       "Count_more_than_12_months_late   -0.016297               0.294580   \n",
       "application_underwriting_score    0.085746              -0.082882   \n",
       "no_of_premiums_paid               0.104532               0.068010   \n",
       "target                            0.020331              -0.250534   \n",
       "Age                               0.037309              -0.057370   \n",
       "\n",
       "                                  Count_6-12_months_late  \\\n",
       "id                                             -0.002739   \n",
       "perc_premium_paid_by_cash_credit                0.211828   \n",
       "Income                                         -0.022187   \n",
       "Count_3-6_months_late                           0.210438   \n",
       "Count_6-12_months_late                          1.000000   \n",
       "Count_more_than_12_months_late                  0.274270   \n",
       "application_underwriting_score                  0.004726   \n",
       "no_of_premiums_paid                            -0.091049   \n",
       "target                                         -0.288496   \n",
       "Age                                            -0.069440   \n",
       "\n",
       "                                  Count_more_than_12_months_late  \\\n",
       "id                                                      0.003453   \n",
       "perc_premium_paid_by_cash_credit                        0.170251   \n",
       "Income                                                 -0.016297   \n",
       "Count_3-6_months_late                                   0.294580   \n",
       "Count_6-12_months_late                                  0.274270   \n",
       "Count_more_than_12_months_late                          1.000000   \n",
       "application_underwriting_score                         -0.031783   \n",
       "no_of_premiums_paid                                    -0.026595   \n",
       "target                                                 -0.240787   \n",
       "Age                                                    -0.059962   \n",
       "\n",
       "                                  application_underwriting_score  \\\n",
       "id                                                     -0.002124   \n",
       "perc_premium_paid_by_cash_credit                       -0.142670   \n",
       "Income                                                  0.085746   \n",
       "Count_3-6_months_late                                  -0.082882   \n",
       "Count_6-12_months_late                                  0.004726   \n",
       "Count_more_than_12_months_late                         -0.031783   \n",
       "application_underwriting_score                          1.000000   \n",
       "no_of_premiums_paid                                    -0.270864   \n",
       "target                                                  0.068715   \n",
       "Age                                                     0.047079   \n",
       "\n",
       "                                  no_of_premiums_paid    target       Age  \n",
       "id                                          -0.002450 -0.005517  0.005134  \n",
       "perc_premium_paid_by_cash_credit            -0.149822 -0.237376 -0.250829  \n",
       "Income                                       0.104532  0.020331  0.037309  \n",
       "Count_3-6_months_late                        0.068010 -0.250534 -0.057370  \n",
       "Count_6-12_months_late                      -0.091049 -0.288496 -0.069440  \n",
       "Count_more_than_12_months_late              -0.026595 -0.240787 -0.059962  \n",
       "application_underwriting_score              -0.270864  0.068715  0.047079  \n",
       "no_of_premiums_paid                          1.000000  0.019851  0.187335  \n",
       "target                                       0.019851  1.000000  0.092631  \n",
       "Age                                          0.187335  0.092631  1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining co-relation of the data frame\n",
    "\n",
    "train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous-Continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccc59b20>"
      ]
     },
     "execution_count": 45,
     "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": [
    "# Plotting a scatter plot of Age and Income Variable\n",
    "\n",
    "train.plot.scatter('Age', 'Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03730939030449797"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].corr(train['Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccebeeb0>"
      ]
     },
     "execution_count": 47,
     "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": [
    "train.plot.scatter('Age', 'no_of_premiums_paid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18733531575463236"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].corr(train['no_of_premiums_paid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccf6a5e0>"
      ]
     },
     "execution_count": 49,
     "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": [
    "train.plot.scatter('application_underwriting_score', 'Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08574635573581679"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['application_underwriting_score'].corr(train['Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccfc4100>"
      ]
     },
     "execution_count": 51,
     "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": [
    "train.plot.scatter('no_of_premiums_paid', 'Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10453245004911926"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['no_of_premiums_paid'].corr(train['Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical-Continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "      <th>Age</th>\n",
       "      <th>premium_pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  Income  Count_3-6_months_late  \\\n",
       "0  110936                             0.429  355060                    0.0   \n",
       "1   41492                             0.010  315150                    0.0   \n",
       "2   31300                             0.917   84140                    2.0   \n",
       "3   19415                             0.049  250510                    0.0   \n",
       "4   99379                             0.052  198680                    0.0   \n",
       "\n",
       "   Count_6-12_months_late  Count_more_than_12_months_late  \\\n",
       "0                     0.0                             0.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     3.0                             1.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             0.0   \n",
       "\n",
       "   application_underwriting_score  no_of_premiums_paid sourcing_channel  \\\n",
       "0                           99.02                   13                C   \n",
       "1                           99.89                   21                A   \n",
       "2                           98.69                    7                C   \n",
       "3                           99.57                    9                A   \n",
       "4                           99.87                   12                B   \n",
       "\n",
       "  residence_area_type  target   Age premium_pay  \n",
       "0               Urban       1  33.0           1  \n",
       "1               Urban       1  59.0           1  \n",
       "2               Rural       0  48.0           0  \n",
       "3               Urban       1  42.0           1  \n",
       "4               Urban       1  86.0           1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "sourcing_channel                     object\n",
       "residence_area_type                  object\n",
       "target                                int64\n",
       "Age                                 float64\n",
       "premium_pay                          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcccc594c0>"
      ]
     },
     "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": [
    "train.groupby('residence_area_type')['Income'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the scipy library for the ttest\n",
    "\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "rural = train[train['residence_area_type']=='Rural']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "urban = train[train['residence_area_type']=='Urban']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.6443871975384211, pvalue=0.5193263068977849)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(rural['Income'], urban['Income'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "paid = train[train['premium_pay']=='1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "other = train[train['premium_pay']=='0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-47.194117740707156, pvalue=0.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(paid['perc_premium_paid_by_cash_credit'], other['perc_premium_paid_by_cash_credit'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=6.2430694644375295, pvalue=4.312985166539086e-10)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(paid['application_underwriting_score'], other['application_underwriting_score'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=13.26912787462067, pvalue=3.870923580157875e-40)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(paid['no_of_premiums_paid'], other['no_of_premiums_paid'], nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdccfae55b0>"
      ]
     },
     "execution_count": 65,
     "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": [
    "train.groupby('target')['Income'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdccc731cd0>"
      ]
     },
     "execution_count": 66,
     "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": [
    "train.groupby('premium_pay')['Income'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorical-Categorical|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence_area_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>1922</td>\n",
       "      <td>28614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>2876</td>\n",
       "      <td>43467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                  0      1\n",
       "residence_area_type             \n",
       "Rural                1922  28614\n",
       "Urban                2876  43467"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train['residence_area_type'], train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.23046908003248764,\n",
       " 0.6311762306702822,\n",
       " 1,\n",
       " array([[ 1905.74445557, 28630.25554443],\n",
       "        [ 2892.25554443, 43450.74445557]]))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(train['residence_area_type'], train['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132.10639524416905,\n",
       " 1.3800037098441687e-27,\n",
       " 4,\n",
       " array([[2.55418187e+03, 3.83718181e+04],\n",
       "        [1.00329931e+03, 1.50727007e+04],\n",
       "        [7.39181207e+02, 1.11048188e+04],\n",
       "        [4.63954162e+02, 6.97004584e+03],\n",
       "        [3.73834467e+01, 5.61616553e+02]]))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(train['sourcing_channel'], train['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10391.864783853072,\n",
       " 0.0,\n",
       " 1,\n",
       " array([[6.83386881e+01, 1.02666131e+03],\n",
       "        [4.72966131e+03, 7.10543387e+04]]))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(train['premium_pay'], train['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9381202748761918,\n",
       " 0.33276172814546595,\n",
       " 1,\n",
       " array([[  434.92917442,   660.07082558],\n",
       "        [30101.07082558, 45682.92917442]]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(train['premium_pay'], train['residence_area_type']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76879, 13)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34224, 12)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Including Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.get_dummies(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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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       "    }\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>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>target</th>\n",
       "      <th>Age</th>\n",
       "      <th>sourcing_channel_A</th>\n",
       "      <th>sourcing_channel_B</th>\n",
       "      <th>sourcing_channel_C</th>\n",
       "      <th>sourcing_channel_D</th>\n",
       "      <th>sourcing_channel_E</th>\n",
       "      <th>residence_area_type_Rural</th>\n",
       "      <th>residence_area_type_Urban</th>\n",
       "      <th>premium_pay_0</th>\n",
       "      <th>premium_pay_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>33.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
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       "      <td>59.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  Income  Count_3-6_months_late  \\\n",
       "0  110936                             0.429  355060                    0.0   \n",
       "1   41492                             0.010  315150                    0.0   \n",
       "2   31300                             0.917   84140                    2.0   \n",
       "3   19415                             0.049  250510                    0.0   \n",
       "4   99379                             0.052  198680                    0.0   \n",
       "\n",
       "   Count_6-12_months_late  Count_more_than_12_months_late  \\\n",
       "0                     0.0                             0.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     3.0                             1.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             0.0   \n",
       "\n",
       "   application_underwriting_score  no_of_premiums_paid  target   Age  \\\n",
       "0                           99.02                   13       1  33.0   \n",
       "1                           99.89                   21       1  59.0   \n",
       "2                           98.69                    7       0  48.0   \n",
       "3                           99.57                    9       1  42.0   \n",
       "4                           99.87                   12       1  86.0   \n",
       "\n",
       "   sourcing_channel_A  sourcing_channel_B  sourcing_channel_C  \\\n",
       "0                   0                   0                   1   \n",
       "1                   1                   0                   0   \n",
       "2                   0                   0                   1   \n",
       "3                   1                   0                   0   \n",
       "4                   0                   1                   0   \n",
       "\n",
       "   sourcing_channel_D  sourcing_channel_E  residence_area_type_Rural  \\\n",
       "0                   0                   0                          0   \n",
       "1                   0                   0                          0   \n",
       "2                   0                   0                          1   \n",
       "3                   0                   0                          0   \n",
       "4                   0                   0                          0   \n",
       "\n",
       "   residence_area_type_Urban  premium_pay_0  premium_pay_1  \n",
       "0                          1              0              1  \n",
       "1                          1              0              1  \n",
       "2                          0              1              0  \n",
       "3                          1              0              1  \n",
       "4                          1              0              1  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>Age</th>\n",
       "      <th>sourcing_channel_A</th>\n",
       "      <th>sourcing_channel_B</th>\n",
       "      <th>sourcing_channel_C</th>\n",
       "      <th>sourcing_channel_D</th>\n",
       "      <th>sourcing_channel_E</th>\n",
       "      <th>residence_area_type_Rural</th>\n",
       "      <th>residence_area_type_Urban</th>\n",
       "      <th>premium_pay_0</th>\n",
       "      <th>premium_pay_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>59.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  Income  Count_3-6_months_late  \\\n",
       "0    649                             0.001   51150                    0.0   \n",
       "1  81136                             0.124  285140                    0.0   \n",
       "2  70762                             1.000  186030                    0.0   \n",
       "3  53935                             0.198  123540                    0.0   \n",
       "4  15476                             0.041  200020                    1.0   \n",
       "\n",
       "   Count_6-12_months_late  Count_more_than_12_months_late  \\\n",
       "0                     0.0                             0.0   \n",
       "1                     0.0                             0.0   \n",
       "2                     0.0                             0.0   \n",
       "3                     0.0                             0.0   \n",
       "4                     0.0                             0.0   \n",
       "\n",
       "   application_underwriting_score  no_of_premiums_paid   Age  \\\n",
       "0                           99.89                    7  33.0   \n",
       "1                           98.93                   19  59.0   \n",
       "2                            0.00                    2  48.0   \n",
       "3                           99.00                   11  42.0   \n",
       "4                           99.17                   14  86.0   \n",
       "\n",
       "   sourcing_channel_A  sourcing_channel_B  sourcing_channel_C  \\\n",
       "0                   1                   0                   0   \n",
       "1                   1                   0                   0   \n",
       "2                   0                   1                   0   \n",
       "3                   0                   1                   0   \n",
       "4                   1                   0                   0   \n",
       "\n",
       "   sourcing_channel_D  sourcing_channel_E  residence_area_type_Rural  \\\n",
       "0                   0                   0                          1   \n",
       "1                   0                   0                          0   \n",
       "2                   0                   0                          0   \n",
       "3                   0                   0                          1   \n",
       "4                   0                   0                          1   \n",
       "\n",
       "   residence_area_type_Urban  premium_pay_0  premium_pay_1  \n",
       "0                          0              0              1  \n",
       "1                          1              0              1  \n",
       "2                          1              0              1  \n",
       "3                          0              0              1  \n",
       "4                          0              0              1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "target                                int64\n",
       "Age                                 float64\n",
       "sourcing_channel_A                    uint8\n",
       "sourcing_channel_B                    uint8\n",
       "sourcing_channel_C                    uint8\n",
       "sourcing_channel_D                    uint8\n",
       "sourcing_channel_E                    uint8\n",
       "residence_area_type_Rural             uint8\n",
       "residence_area_type_Urban             uint8\n",
       "premium_pay_0                         uint8\n",
       "premium_pay_1                         uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "perc_premium_paid_by_cash_credit    float64\n",
       "Income                                int64\n",
       "Count_3-6_months_late               float64\n",
       "Count_6-12_months_late              float64\n",
       "Count_more_than_12_months_late      float64\n",
       "application_underwriting_score      float64\n",
       "no_of_premiums_paid                   int64\n",
       "Age                                 float64\n",
       "sourcing_channel_A                    uint8\n",
       "sourcing_channel_B                    uint8\n",
       "sourcing_channel_C                    uint8\n",
       "sourcing_channel_D                    uint8\n",
       "sourcing_channel_E                    uint8\n",
       "residence_area_type_Rural             uint8\n",
       "residence_area_type_Urban             uint8\n",
       "premium_pay_0                         uint8\n",
       "premium_pay_1                         uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0\n",
       "perc_premium_paid_by_cash_credit    0\n",
       "Income                              0\n",
       "Count_3-6_months_late               0\n",
       "Count_6-12_months_late              0\n",
       "Count_more_than_12_months_late      0\n",
       "application_underwriting_score      0\n",
       "no_of_premiums_paid                 0\n",
       "Age                                 0\n",
       "sourcing_channel_A                  0\n",
       "sourcing_channel_B                  0\n",
       "sourcing_channel_C                  0\n",
       "sourcing_channel_D                  0\n",
       "sourcing_channel_E                  0\n",
       "residence_area_type_Rural           0\n",
       "residence_area_type_Urban           0\n",
       "premium_pay_0                       0\n",
       "premium_pay_1                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seperating into Train and Test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "training = train[0:69999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation = train[70000:76855]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69999, 19)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6855, 19)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seperation of Dependant and Independant variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = training.drop('target',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = training['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = validation.drop('target', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_p = validation['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Prediction model using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=logreg.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9380014587892049"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(x_val, true_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.937556250803583"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_test_pred = logreg.predict(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_train = training.drop('target',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_train = training['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_val = validation.drop('target', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_dt_p = validation['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Decision Tree Classifier\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(a_train, b_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9465706652952185"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(a_train, b_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9458789204959883"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(a_val, true_dt_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = clf.predict(a_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_test_prediction = clf.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a target column and saving the predictions in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sub6= pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a count column and saving the predictions in it\n",
    "new_sub6['target'] = dt_test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sub6.to_csv('new_sub6.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Submission of Predicted values using Decision Tree Prediction Model"
   ]
  }
 ],
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