Internshala-DS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Problem Statement: \n",
    "\n",
    "To predict whether the customer would pay the next premium or not, given the information about past premium payment history for the policyholders. \n",
    "\n",
    "**Since the target variable is provided for the training dataset, its a supervised learning based classification problem.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importing required libraries \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>110936</td>\n",
       "      <td>41492</td>\n",
       "      <td>31300</td>\n",
       "      <td>19415</td>\n",
       "      <td>99379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>0.429</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>12058</td>\n",
       "      <td>21546</td>\n",
       "      <td>17531</td>\n",
       "      <td>15341</td>\n",
       "      <td>31400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>355060</td>\n",
       "      <td>315150</td>\n",
       "      <td>84140</td>\n",
       "      <td>250510</td>\n",
       "      <td>198680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>99.02</td>\n",
       "      <td>99.89</td>\n",
       "      <td>98.69</td>\n",
       "      <td>99.57</td>\n",
       "      <td>99.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>13</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sourcing_channel</th>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence_area_type</th>\n",
       "      <td>Urban</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0       1      2       3       4\n",
       "id                                110936   41492  31300   19415   99379\n",
       "perc_premium_paid_by_cash_credit   0.429    0.01  0.917   0.049   0.052\n",
       "age_in_days                        12058   21546  17531   15341   31400\n",
       "Income                            355060  315150  84140  250510  198680\n",
       "Count_3-6_months_late                  0       0      2       0       0\n",
       "Count_6-12_months_late                 0       0      3       0       0\n",
       "Count_more_than_12_months_late         0       0      1       0       0\n",
       "application_underwriting_score     99.02   99.89  98.69   99.57   99.87\n",
       "no_of_premiums_paid                   13      21      7       9      12\n",
       "sourcing_channel                       C       A      C       A       B\n",
       "residence_area_type                Urban   Urban  Rural   Urban   Urban\n",
       "target                                 1       1      0       1       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the training set\n",
    "\n",
    "df = pd.read_csv('train.csv')\n",
    "\n",
    "# Viewing the top 5 records of the dataset\n",
    "\n",
    "df.head().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking on the type of variables\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79853, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the dimensions of the dataset\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Hypothesis: (Testing done in 3.2)\n",
    "\n",
    "1. The residence area type of a customer and his ability for premium payment are dependent - Chi square\n",
    "2. The sourcing channel and the customer's ability for premium payment are dependent - Chi-square\n",
    "3. Income of a customer and his ability for premium payment are dependent - Point bi serial correlation\n",
    "4. Age of the customer and his income are related - Correlation\n",
    "5. Underwriting score and the customer's ability for premium payment are dependent - Point bi serial correlation\n",
    "6. No. of premiums paid and the customer's income are related. - correlation\n",
    "7. There is a significant difference in income between the customers who paid the premium and who did'nt. - 2 sample t-test\n",
    "8. There is a significant difference in age between the customers who paid the premium and who did'nt. - 2 sample t-test\n",
    "9. The average age of people who dont pay the next premium is greater than 20075 day.s (55 yrs) - 1 sample t-test\n",
    "10. Income and percentage of premium paid by the customer are dependent - correlation\n",
    "11. There is a significant relationship between the no. of months a customer has paid the late premium and his ability to pay future premiums - Point bi serial correlation\n",
    "12. The average percentage of premium paid by a customer who defaulted is less than 50% - 1 sample t-test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>57167.166368</td>\n",
       "      <td>32928.970160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28640.000</td>\n",
       "      <td>57262.000</td>\n",
       "      <td>85632.000</td>\n",
       "      <td>114076.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.314288</td>\n",
       "      <td>0.334915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.538</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>18846.696906</td>\n",
       "      <td>5208.719136</td>\n",
       "      <td>7670.0</td>\n",
       "      <td>14974.000</td>\n",
       "      <td>18625.000</td>\n",
       "      <td>22636.000</td>\n",
       "      <td>37602.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>208847.171177</td>\n",
       "      <td>496582.597257</td>\n",
       "      <td>24030.0</td>\n",
       "      <td>108010.000</td>\n",
       "      <td>166560.000</td>\n",
       "      <td>252090.000</td>\n",
       "      <td>90262600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>79756.0</td>\n",
       "      <td>0.248671</td>\n",
       "      <td>0.691468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>79756.0</td>\n",
       "      <td>0.078188</td>\n",
       "      <td>0.436507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>79756.0</td>\n",
       "      <td>0.060008</td>\n",
       "      <td>0.312023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>76879.0</td>\n",
       "      <td>99.067291</td>\n",
       "      <td>0.739799</td>\n",
       "      <td>91.9</td>\n",
       "      <td>98.810</td>\n",
       "      <td>99.210</td>\n",
       "      <td>99.540</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>10.863887</td>\n",
       "      <td>5.170687</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>14.000</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.937410</td>\n",
       "      <td>0.242226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count           mean            std  \\\n",
       "id                                79853.0   57167.166368   32928.970160   \n",
       "perc_premium_paid_by_cash_credit  79853.0       0.314288       0.334915   \n",
       "age_in_days                       79853.0   18846.696906    5208.719136   \n",
       "Income                            79853.0  208847.171177  496582.597257   \n",
       "Count_3-6_months_late             79756.0       0.248671       0.691468   \n",
       "Count_6-12_months_late            79756.0       0.078188       0.436507   \n",
       "Count_more_than_12_months_late    79756.0       0.060008       0.312023   \n",
       "application_underwriting_score    76879.0      99.067291       0.739799   \n",
       "no_of_premiums_paid               79853.0      10.863887       5.170687   \n",
       "target                            79853.0       0.937410       0.242226   \n",
       "\n",
       "                                      min         25%         50%         75%  \\\n",
       "id                                    2.0   28640.000   57262.000   85632.000   \n",
       "perc_premium_paid_by_cash_credit      0.0       0.034       0.167       0.538   \n",
       "age_in_days                        7670.0   14974.000   18625.000   22636.000   \n",
       "Income                            24030.0  108010.000  166560.000  252090.000   \n",
       "Count_3-6_months_late                 0.0       0.000       0.000       0.000   \n",
       "Count_6-12_months_late                0.0       0.000       0.000       0.000   \n",
       "Count_more_than_12_months_late        0.0       0.000       0.000       0.000   \n",
       "application_underwriting_score       91.9      98.810      99.210      99.540   \n",
       "no_of_premiums_paid                   2.0       7.000      10.000      14.000   \n",
       "target                                0.0       1.000       1.000       1.000   \n",
       "\n",
       "                                          max  \n",
       "id                                  114076.00  \n",
       "perc_premium_paid_by_cash_credit         1.00  \n",
       "age_in_days                          37602.00  \n",
       "Income                            90262600.00  \n",
       "Count_3-6_months_late                   13.00  \n",
       "Count_6-12_months_late                  17.00  \n",
       "Count_more_than_12_months_late          11.00  \n",
       "application_underwriting_score          99.89  \n",
       "no_of_premiums_paid                     60.00  \n",
       "target                                   1.00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summarizing all the continous variables\n",
    "\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.93741\n",
       "0    0.06259\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking whether the data is a balanced set\n",
    "\n",
    "df['target'].value_counts()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous variables: ['perc_premium_paid_by_cash_credit', 'age_in_days', 'Income', 'Count_3-6_months_late', 'Count_6-12_months_late', 'Count_more_than_12_months_late', 'application_underwriting_score', 'no_of_premiums_paid']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Identifying continuous variables\n",
    "\n",
    "cont_var = []\n",
    "cont_var = df.describe().transpose().index.to_list()\n",
    "\n",
    "#removing the Id column and target variable\n",
    "cont_var.remove('id')\n",
    "cont_var.remove('target')\n",
    "print(\"Continuous variables:\", cont_var)\n",
    "\n",
    "# Creating histograms for all the continuous variables\n",
    "\n",
    "for i in range(len(cont_var)): \n",
    "    \n",
    "    if cont_var[i] == 'Count_more_than_12_months_late' or cont_var[i] == 'Count_3-6_months_late' or cont_var[i] == 'Count_6-12_months_late':\n",
    "        col=df[cont_var[i]][df[cont_var[i]]>0].to_frame() #Excluding value 0 while plotting for count of months variables\n",
    "    else:\n",
    "        col = df[cont_var[i]].to_frame()        \n",
    "    col.plot.hist()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables: ['id', 'sourcing_channel', 'residence_area_type', 'target']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28697f94f88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KULwZnaiKGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identifying all categorical variables\n",
    "\n",
    "cat_var =[]\n",
    "all_cols = df.columns\n",
    "for i in all_cols:\n",
    "    if i not in cont_var:\n",
    "        cat_var.append(i)\n",
    "print(\"Categorical variables:\", cat_var)\n",
    "\n",
    "# Creating bar plots for all categorical variable - sourcing_channel\n",
    " \n",
    "df['sourcing_channel'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sourcing_channel\n",
       "A    54.016756\n",
       "B    20.677996\n",
       "C    15.076453\n",
       "D     9.466144\n",
       "E     0.762651\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing categorical variable - Sourcing channel in %\n",
    "\n",
    "df.groupby('sourcing_channel')['id'].count()/len(df['sourcing_channel'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28698013208>"
      ]
     },
     "execution_count": 10,
     "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 plots for all categorical variable - residence_area_type\n",
    "\n",
    "df['residence_area_type'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "residence_area_type\n",
       "Rural    39.660376\n",
       "Urban    60.339624\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing categorical variable - Residence area type in %\n",
    "\n",
    "df.groupby('residence_area_type')['id'].count()/len(df['residence_area_type'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2869808ff08>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Indetifying the correlated variables\n",
    "\n",
    "corr = df.corr()\n",
    "mask = np.array(corr)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "fig,ax= plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "sn.heatmap(corr, mask=mask,vmax=.9, square=True,annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28697f92dc8>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# Bar plots for customer income by counts of month (3-6) for late premium paid.\n",
    "\n",
    "sn.barplot(x = df['Count_3-6_months_late'][df['Count_3-6_months_late']>0], y = df['Income'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Count_3-6_months_late</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1153</td>\n",
       "      <td>7673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>592</td>\n",
       "      <td>1927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>288</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>158</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>67</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                    0     1\n",
       "Count_3-6_months_late            \n",
       "1.0                    1153  7673\n",
       "2.0                     592  1927\n",
       "3.0                     288   666\n",
       "4.0                     158   216\n",
       "5.0                      67   101\n",
       "6.0                      31    37\n",
       "7.0                      10    13\n",
       "8.0                       6     9\n",
       "9.0                       2     2\n",
       "10.0                      1     0\n",
       "11.0                      1     0\n",
       "12.0                      1     0\n",
       "13.0                      1     0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross tabulation by outcomes for Count_3-6_months_late\n",
    "\n",
    "pd.crosstab(df['Count_3-6_months_late'][df['Count_3-6_months_late']>0],df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28697e9cd48>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# Bar plots for customer income by counts of month (6-12) for late premium paid.\n",
    "\n",
    "sn.barplot(x = df['Count_6-12_months_late'][df['Count_6-12_months_late']>0], y = df['Income'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>828</td>\n",
       "      <td>1852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>334</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>185</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>85</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                    0     1\n",
       "Count_6-12_months_late           \n",
       "1.0                     828  1852\n",
       "2.0                     334   359\n",
       "3.0                     185   132\n",
       "4.0                      85    45\n",
       "5.0                      30    16\n",
       "6.0                      13    13\n",
       "7.0                       7     4\n",
       "8.0                       3     2\n",
       "9.0                       2     2\n",
       "10.0                      1     3\n",
       "11.0                      1     1\n",
       "12.0                      1     0\n",
       "13.0                      1     1\n",
       "14.0                      1     1\n",
       "15.0                      0     1\n",
       "17.0                      1     0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross tabulation by outcomes for Count_6-12_months_late\n",
    "\n",
    "pd.crosstab(df['Count_6-12_months_late'][df['Count_6-12_months_late']>0],df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28697e76908>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Bar plots for customer income by counts of month (above 12) for late premium paid.\n",
    "\n",
    "sn.barplot(x = df['Count_more_than_12_months_late'][df['Count_more_than_12_months_late']>0], y = df['Income'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>835</td>\n",
       "      <td>2161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>228</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                            0     1\n",
       "Count_more_than_12_months_late           \n",
       "1.0                             835  2161\n",
       "2.0                             228   270\n",
       "3.0                              85    66\n",
       "4.0                              25    23\n",
       "5.0                               7     6\n",
       "6.0                               4     2\n",
       "7.0                               2     1\n",
       "8.0                               1     1\n",
       "11.0                              1     0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross tabulation by outcomes for Count_more_than_12_months_late\n",
    "\n",
    "pd.crosstab(df['Count_more_than_12_months_late'][df['Count_more_than_12_months_late']>0],df['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>0.39976</td>\n",
       "      <td>0.396393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Urban</th>\n",
       "      <td>0.60024</td>\n",
       "      <td>0.603607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                     0         1\n",
       "residence_area_type                   \n",
       "Rural                0.39976  0.396393\n",
       "Urban                0.60024  0.603607"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross tabulation by outcomes for residence_area_type\n",
    "\n",
    "pd.crosstab(df['residence_area_type'],df['target'],normalize='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>sourcing_channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.469988</td>\n",
       "      <td>0.544853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.213285</td>\n",
       "      <td>0.206346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.180672</td>\n",
       "      <td>0.148768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.126851</td>\n",
       "      <td>0.092512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.009204</td>\n",
       "      <td>0.007521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target                   0         1\n",
       "sourcing_channel                    \n",
       "A                 0.469988  0.544853\n",
       "B                 0.213285  0.206346\n",
       "C                 0.180672  0.148768\n",
       "D                 0.126851  0.092512\n",
       "E                 0.009204  0.007521"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross tabulation by outcomes for sourcing_channel\n",
    "\n",
    "pd.crosstab(df['sourcing_channel'],df['target'],normalize='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Univariate & Bivariate data analysis Inferences:\n",
    "\n",
    "**1. There are missing data for the variables Count_3-6_months_late, Count_6-12_months_late, Count_more_than_12_months_late and application_underwriting_score.**\n",
    "\n",
    "**2. Majority of the cutomers (54%) in the sample are sourced through the sourcing channel A.**\n",
    "\n",
    "**3. 60% of the customers in the sample are from Urban residential area.**\n",
    "\n",
    "**4. Age of the customers is approximately in normal distribution**\n",
    "\n",
    "**5. There is a strong negative correlation between the nummber of times a customer delays payment in the past months and his ability to pay the next premium.**\n",
    "\n",
    "**6. 60% of the defaulters are from Urban residential area**\n",
    "\n",
    "**7. 46% of the defaulter are sourced through channel A**\n",
    "\n",
    "**8. The dataset is balanced in a decent way.**\n",
    "\n",
    "**9.There is weak correleation among most of the variables.**\n",
    "\n",
    "**10. There is a positive correlation between the nummber of times a customer delays payment in the past months and the percentage of the premium he has paid so far.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Hypothesis Testing:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 Hypothesis 1\n",
    "\n",
    "Ho - The residence area type of a customer and his ability for premium payment are independent\n",
    "\n",
    "Ha - The residence area type of a customer and his ability for premium payment are dependent \n",
    "\n",
    "Test - Chi-square test of independence \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic - 0.2080863817891421 , p_value - 0.6482714305747836\n"
     ]
    }
   ],
   "source": [
    "# chi-square test of indepence for residence_area_type and target\n",
    "\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "chisquare_stat, p_value, dof, expected = chi2_contingency(pd.crosstab(df['residence_area_type'],df['target']))\n",
    "print(\"t_statistic -\",chisquare_stat, \", p_value -\" , p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value > 0.05, we fail to reject the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 Hypothesis 2\n",
    "\n",
    "Ho - The sourcing channel and the customer's ability for premium payment are independent\n",
    "\n",
    "Ha - The sourcing channel and the customer's ability for premium payment are dependent \n",
    "\n",
    "Test - Chi-square test of independence \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic - 141.43673259821205 , p_value - 1.390061884429808e-29\n"
     ]
    }
   ],
   "source": [
    "# chi-square test of indepence for sourcing_channel and target\n",
    "\n",
    "chisquare_stat, p_value, dof, expected = chi2_contingency(pd.crosstab(df['sourcing_channel'],df['target']))\n",
    "print(\"t_statistic -\",chisquare_stat, \", p_value -\" , p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value < 0.05, we reject the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.3 Hypothesis 3\n",
    "\n",
    "Ho - Income of a customer and his ability for premium payment are independent\n",
    "\n",
    "Ha - Income of a customer and his ability for premium payment are dependent \n",
    "\n",
    "Test -  Point bi serial correlation \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.016541208055673, pvalue=2.946615108531472e-06)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for Income and target\n",
    "\n",
    "stats.pointbiserialr(df['Income'], df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value < 0.05, the null hypothes is rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.4 Hypothesis 4\n",
    "\n",
    "Ho - Age of the customer and his income are not related\n",
    "\n",
    "Ha - Age of the customer and his income are related \n",
    "\n",
    "Test - Correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.02930792084733421, 1.195447691481687e-16)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for Income and age\n",
    "\n",
    "stats.pearsonr(df['age_in_days'],df['Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value < 0.05, the null hypothes is rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.5 Hypothesis 5\n",
    "\n",
    "Ho - Underwriting score and the customer's ability for premium payment are independent\n",
    "\n",
    "Ha - Underwriting score and the customer's ability for premium payment are dependent \n",
    "\n",
    "Test - Point bi serial correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.06979924435438947, pvalue=1.2898372569281858e-83)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for target and underwriting score\n",
    "\n",
    "df_copy = df\n",
    "df_copy.dropna(inplace= True)\n",
    "stats.pointbiserialr(df_copy['application_underwriting_score'], df_copy['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value is less than 0.05, the null hypothesis rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.6 Hypothesis 6\n",
    "\n",
    "Ho - No. of premiums paid and the customer's income are not related.\n",
    "\n",
    "Ha - No. of premiums paid and the customer's income are related. \n",
    "\n",
    "Test - Correlation \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10438428230394584, 4.015550261118411e-185)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for Income and no. of premiums paid\n",
    "\n",
    "stats.pearsonr(df['no_of_premiums_paid'],df['Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value is less than 0.05, the null hypothesis rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.7 Hypothesis 7\n",
    "\n",
    "Ho - There is no significant difference in income between the customers who paid the premium and who did'nt.\n",
    "\n",
    "Ha - There is a significant difference in income between the customers who paid the premium and who did'nt.\n",
    "\n",
    "Test - 2 sample t-test , two-tailed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic- -5.557991356676819\n",
      "Tc -/+ -1.9599948522626998\n"
     ]
    }
   ],
   "source": [
    "#  2 sample t-test for income by target\n",
    "\n",
    "t_statistic, _ = stats.ttest_ind(df['Income'][df['target']==0],df['Income'][df['target']==1])\n",
    "print(\"t_statistic-\",t_statistic)\n",
    "dof =df['Income'][df['target']==0].count() + df['Income'][df['target']==1].count() - 1\n",
    "print(\"Tc -/+\",stats.t.ppf(0.05/2,df=dof))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since t_statistic doesnt not lie within the T-critical area, the null hypothesis is rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.8 Hypothesis 8\n",
    "\n",
    "Ho - There is no significant difference in age between the customers who paid the premium and who did'nt. \n",
    "\n",
    "Ha - There is a significant difference in age between the customers who paid the premium and who did'nt.  \n",
    "\n",
    "Test - 2 sample t-test , two-tailed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic- -25.73306874108016\n",
      "Tc -/+ -1.9599948522626998\n"
     ]
    }
   ],
   "source": [
    "#  2 sample t-test for age by target\n",
    "\n",
    "t_statistic, _ = stats.ttest_ind(df['age_in_days'][df['target']==0],df['age_in_days'][df['target']==1])\n",
    "print(\"t_statistic-\",t_statistic)\n",
    "dof =df['age_in_days'][df['target']==0].count() + df['age_in_days'][df['target']==1].count() - 1\n",
    "print(\"Tc -/+\",stats.t.ppf(0.05/2,df=dof))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since t_statistic doesnt not lie within the T-critical area, the null hypothesis is rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.9 Hypothesis 9\n",
    "\n",
    "Ho - The average age of people who dont pay the next premium is less than or equal to 20075 day.s (55 yrs) \n",
    "\n",
    "Ha - The average age of people who dont pay the next premium is greater than 20075 day.s (55 yrs) \n",
    "\n",
    "Test - 1 sample t-test, right tailed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic - -46.525169885732765 , t_critical - -1.6451725360049116\n"
     ]
    }
   ],
   "source": [
    "# 1 sample t-test fot target and age\n",
    "\n",
    "t_statistic, _ = stats.ttest_1samp(df[df['target']==0]['age_in_days'], 20075)\n",
    "N = df[df['target']==0]['age_in_days'].count()\n",
    "\n",
    "Tc = (stats.t.ppf(0.05, df=N-1))\n",
    "\n",
    "print(\"t_statistic -\",t_statistic, \", t_critical -\" , Tc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since T_statistic < T_critical, we fail to reject null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.10 Hypothesis 10\n",
    "\n",
    "Ho - Income and percentage of premium paid by the customer are independent\n",
    "\n",
    "Ha - Income and percentage of premium paid by the customer are dependent \n",
    "\n",
    "Test - correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.037196938557830156, 6.002435969124594e-25)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for Income and % of premium paid \n",
    "\n",
    "stats.pearsonr(df['perc_premium_paid_by_cash_credit'],df['Income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value is less than 0.05, the null hypothesis rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.11 Hypothesis 11\n",
    "\n",
    "Ho - There is no significant relationship between the no. of months a customer has paid the late premium and his ability to pay future premiums\n",
    "\n",
    "Ha - There is a significant relationship between the no. of months a customer has paid the late premium and his ability to pay future premiums \n",
    "\n",
    "Test - Point bi serial correlation \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PointbiserialrResult(correlation=0.018319604148599136, pvalue=3.7934929112420323e-07)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation for target and no.of permiums paid\n",
    "\n",
    "stats.pointbiserialr(df['no_of_premiums_paid'], df['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since P value is less than 0.05, the null hypothesis rejected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.12 Hypothesis 12\n",
    "\n",
    "Ho - The average percentage of premium paid by a customer who defaulted is greater than or equal to 50% \n",
    "\n",
    "Ha - The average percentage of premium paid by a customer who defaulted is less than 50%  \n",
    "\n",
    "Test - 1 sample t-test , left tailed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_statistic - 23.17772731608718 , t_critical - -1.6451725360049116\n"
     ]
    }
   ],
   "source": [
    "# 1 sample t-test for target and % premium paid\n",
    "\n",
    "t_statistic, _ = stats.ttest_1samp(df[df['target']==0]['perc_premium_paid_by_cash_credit'], 0.5)\n",
    "N = df[df['target']==0]['perc_premium_paid_by_cash_credit'].count()\n",
    "\n",
    "Tc = (stats.t.ppf(0.05, df=N-1))\n",
    "\n",
    "print(\"t_statistic -\",t_statistic, \", t_critical -\" , Tc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since T_statistic < T_critical, we reject null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Hypothesis Testing Inferences:\n",
    "\n",
    "**1. The residence area type of a customer and his ability for premium payment are independent**\n",
    "\n",
    "**2. The sourcing channel and the customer's ability for premium payment are dependent**\n",
    "\n",
    "**3. Income of a customer and his ability for premium payment are weakly dependent**\n",
    "\n",
    "**4. Age of the customer and his income are weakly related**\n",
    "\n",
    "**5. Underwriting score and the customer's ability for premium payment are weakly dependent**\n",
    "\n",
    "**6. No. of premiums paid and the customer's income are weakly related.**\n",
    "\n",
    "**7. There is a significant difference in income between the customers who paid the premium and who did'nt.**\n",
    "\n",
    "**8. There is a significant difference in age between the customers who paid the premium and who did'nt.**\n",
    "\n",
    "**9. The average age of people who dont pay the next premium is less than or equal to 20075 day.s (55 yrs)**\n",
    "\n",
    "**10. Income and percentage of premium paid by the customer are weakly and neagatively dependent**\n",
    "\n",
    "**11. There is a significant relationship between the no. of months a customer has paid the late premium and his ability to pay future premiums**\n",
    "\n",
    "**12. The average percentage of premium paid by a customer who defaulted is less than 50%**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Missing Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76855"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of rows in the training dataset\n",
    "\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows having missing values in training dataset\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34224"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the test set\n",
    "\n",
    "final_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Total number of rows in the test dataset\n",
    "\n",
    "final_test.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows having missing values in test dataset\n",
    "\n",
    "final_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Identifying nature of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "         id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0      True                              True         True    True   \n",
       "1      True                              True         True    True   \n",
       "2      True                              True         True    True   \n",
       "3      True                              True         True    True   \n",
       "4      True                              True         True    True   \n",
       "...     ...                               ...          ...     ...   \n",
       "79848  True                              True         True    True   \n",
       "79849  True                              True         True    True   \n",
       "79850  True                              True         True    True   \n",
       "79851  True                              True         True    True   \n",
       "79852  True                              True         True    True   \n",
       "\n",
       "       Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                       True                    True   \n",
       "1                       True                    True   \n",
       "2                       True                    True   \n",
       "3                       True                    True   \n",
       "4                       True                    True   \n",
       "...                      ...                     ...   \n",
       "79848                   True                    True   \n",
       "79849                   True                    True   \n",
       "79850                   True                    True   \n",
       "79851                   True                    True   \n",
       "79852                   True                    True   \n",
       "\n",
       "       Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                                True                            True   \n",
       "1                                True                            True   \n",
       "2                                True                            True   \n",
       "3                                True                            True   \n",
       "4                                True                            True   \n",
       "...                               ...                             ...   \n",
       "79848                            True                            True   \n",
       "79849                            True                            True   \n",
       "79850                            True                            True   \n",
       "79851                            True                            True   \n",
       "79852                            True                            True   \n",
       "\n",
       "       no_of_premiums_paid  sourcing_channel  residence_area_type  target  \n",
       "0                     True              True                 True    True  \n",
       "1                     True              True                 True    True  \n",
       "2                     True              True                 True    True  \n",
       "3                     True              True                 True    True  \n",
       "4                     True              True                 True    True  \n",
       "...                    ...               ...                  ...     ...  \n",
       "79848                 True              True                 True    True  \n",
       "79849                 True              True                 True    True  \n",
       "79850                 True              True                 True    True  \n",
       "79851                 True              True                 True    True  \n",
       "79852                 True              True                 True    True  \n",
       "\n",
       "[79853 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract missing records and check for any pattern within\n",
    "\n",
    "train_df = pd.read_csv(\"train.csv\")\n",
    "df2=train_df.notnull()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     76879\n",
       "False     2974\n",
       "Name: application_underwriting_score, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing \n",
    "\n",
    "df2['application_underwriting_score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    76855\n",
       "0     2998\n",
       "Name: flag, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizind the total missing value counts by flaging them as row with missing value (flag =1) or not (flag =0)\n",
    "\n",
    "flag=[]\n",
    "for x in range(len(df2)):\n",
    "    if df2['Count_3-6_months_late'][x] and df2['Count_6-12_months_late'][x] and df2['Count_more_than_12_months_late'][x] and df2['application_underwriting_score'][x] :\n",
    "        flag.append(1)\n",
    "    else:\n",
    "        flag.append(0)\n",
    "train_df['flag'] = flag\n",
    "train_df['flag'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Urban    1855\n",
       "Rural    1143\n",
       "Name: residence_area_type, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating crosstab for all categorical variable - residence_area_type\n",
    "\n",
    "train_df['residence_area_type'][train_df['flag']==0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2225\n",
       "B     438\n",
       "C     198\n",
       "D     127\n",
       "E      10\n",
       "Name: sourcing_channel, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating crosstab for all categorical variable - residence_area_type\n",
    "\n",
    "train_df['sourcing_channel'][train_df['flag']==0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "      <td>13520.000</td>\n",
       "      <td>20089.000</td>\n",
       "      <td>25930.00</td>\n",
       "      <td>37602.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>2998.0</td>\n",
       "      <td>209048.482322</td>\n",
       "      <td>1.750555e+06</td>\n",
       "      <td>24030.00</td>\n",
       "      <td>72032.500</td>\n",
       "      <td>118615.000</td>\n",
       "      <td>200077.50</td>\n",
       "      <td>90262600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>2901.0</td>\n",
       "      <td>0.097208</td>\n",
       "      <td>3.959221e-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>2901.0</td>\n",
       "      <td>0.111341</td>\n",
       "      <td>5.623341e-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>2901.0</td>\n",
       "      <td>0.029645</td>\n",
       "      <td>2.046448e-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>24.0</td>\n",
       "      <td>99.829583</td>\n",
       "      <td>1.111786e-01</td>\n",
       "      <td>99.34</td>\n",
       "      <td>99.835</td>\n",
       "      <td>99.865</td>\n",
       "      <td>99.88</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>2998.0</td>\n",
       "      <td>5.977318</td>\n",
       "      <td>4.326377e+00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>7.75</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>2998.0</td>\n",
       "      <td>0.927285</td>\n",
       "      <td>2.597117e-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flag</th>\n",
       "      <td>2998.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   count           mean           std  \\\n",
       "id                                2998.0   57443.939960  3.258509e+04   \n",
       "perc_premium_paid_by_cash_credit  2998.0       0.301754  4.425019e-01   \n",
       "age_in_days                       2998.0   19994.162108  7.343642e+03   \n",
       "Income                            2998.0  209048.482322  1.750555e+06   \n",
       "Count_3-6_months_late             2901.0       0.097208  3.959221e-01   \n",
       "Count_6-12_months_late            2901.0       0.111341  5.623341e-01   \n",
       "Count_more_than_12_months_late    2901.0       0.029645  2.046448e-01   \n",
       "application_underwriting_score      24.0      99.829583  1.111786e-01   \n",
       "no_of_premiums_paid               2998.0       5.977318  4.326377e+00   \n",
       "target                            2998.0       0.927285  2.597117e-01   \n",
       "flag                              2998.0       0.000000  0.000000e+00   \n",
       "\n",
       "                                       min        25%         50%        75%  \\\n",
       "id                                   59.00  29171.750   57803.000   85833.50   \n",
       "perc_premium_paid_by_cash_credit      0.00      0.000       0.007       1.00   \n",
       "age_in_days                        7670.00  13520.000   20089.000   25930.00   \n",
       "Income                            24030.00  72032.500  118615.000  200077.50   \n",
       "Count_3-6_months_late                 0.00      0.000       0.000       0.00   \n",
       "Count_6-12_months_late                0.00      0.000       0.000       0.00   \n",
       "Count_more_than_12_months_late        0.00      0.000       0.000       0.00   \n",
       "application_underwriting_score       99.34     99.835      99.865      99.88   \n",
       "no_of_premiums_paid                   2.00      3.000       5.000       7.75   \n",
       "target                                0.00      1.000       1.000       1.00   \n",
       "flag                                  0.00      0.000       0.000       0.00   \n",
       "\n",
       "                                          max  \n",
       "id                                  114075.00  \n",
       "perc_premium_paid_by_cash_credit         1.00  \n",
       "age_in_days                          37602.00  \n",
       "Income                            90262600.00  \n",
       "Count_3-6_months_late                    6.00  \n",
       "Count_6-12_months_late                  13.00  \n",
       "Count_more_than_12_months_late           5.00  \n",
       "application_underwriting_score          99.89  \n",
       "no_of_premiums_paid                     60.00  \n",
       "target                                   1.00  \n",
       "flag                                     0.00  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing the rows with missing values\n",
    "\n",
    "train_df[train_df['flag']==0].describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>57156.369839</td>\n",
       "      <td>32942.474239</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28612.500</td>\n",
       "      <td>57241.000</td>\n",
       "      <td>85624.5000</td>\n",
       "      <td>114076.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>0.314777</td>\n",
       "      <td>0.330002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.5345</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>18801.935983</td>\n",
       "      <td>5102.226406</td>\n",
       "      <td>7670.0</td>\n",
       "      <td>14975.000</td>\n",
       "      <td>18624.000</td>\n",
       "      <td>22280.0000</td>\n",
       "      <td>37240.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>208839.318327</td>\n",
       "      <td>369746.603912</td>\n",
       "      <td>24030.0</td>\n",
       "      <td>109805.000</td>\n",
       "      <td>168330.000</td>\n",
       "      <td>254395.0000</td>\n",
       "      <td>53821900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>0.254388</td>\n",
       "      <td>0.699544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>0.076937</td>\n",
       "      <td>0.430994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>0.061154</td>\n",
       "      <td>0.315304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>99.067053</td>\n",
       "      <td>0.739789</td>\n",
       "      <td>91.9</td>\n",
       "      <td>98.810</td>\n",
       "      <td>99.210</td>\n",
       "      <td>99.5400</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>11.054505</td>\n",
       "      <td>5.106978</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>59.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>76855.0</td>\n",
       "      <td>0.937805</td>\n",
       "      <td>0.241511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count           mean            std  \\\n",
       "id                                76855.0   57156.369839   32942.474239   \n",
       "perc_premium_paid_by_cash_credit  76855.0       0.314777       0.330002   \n",
       "age_in_days                       76855.0   18801.935983    5102.226406   \n",
       "Income                            76855.0  208839.318327  369746.603912   \n",
       "Count_3-6_months_late             76855.0       0.254388       0.699544   \n",
       "Count_6-12_months_late            76855.0       0.076937       0.430994   \n",
       "Count_more_than_12_months_late    76855.0       0.061154       0.315304   \n",
       "application_underwriting_score    76855.0      99.067053       0.739789   \n",
       "no_of_premiums_paid               76855.0      11.054505       5.106978   \n",
       "target                            76855.0       0.937805       0.241511   \n",
       "\n",
       "                                      min         25%         50%  \\\n",
       "id                                    2.0   28612.500   57241.000   \n",
       "perc_premium_paid_by_cash_credit      0.0       0.038       0.174   \n",
       "age_in_days                        7670.0   14975.000   18624.000   \n",
       "Income                            24030.0  109805.000  168330.000   \n",
       "Count_3-6_months_late                 0.0       0.000       0.000   \n",
       "Count_6-12_months_late                0.0       0.000       0.000   \n",
       "Count_more_than_12_months_late        0.0       0.000       0.000   \n",
       "application_underwriting_score       91.9      98.810      99.210   \n",
       "no_of_premiums_paid                   2.0       7.000      10.000   \n",
       "target                                0.0       1.000       1.000   \n",
       "\n",
       "                                          75%          max  \n",
       "id                                 85624.5000    114076.00  \n",
       "perc_premium_paid_by_cash_credit       0.5345         1.00  \n",
       "age_in_days                        22280.0000     37240.00  \n",
       "Income                            254395.0000  53821900.00  \n",
       "Count_3-6_months_late                  0.0000        13.00  \n",
       "Count_6-12_months_late                 0.0000        17.00  \n",
       "Count_more_than_12_months_late         0.0000        11.00  \n",
       "application_underwriting_score        99.5400        99.89  \n",
       "no_of_premiums_paid                   14.0000        59.00  \n",
       "target                                 1.0000         1.00  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Studing the summary of the entire training set by comparing with the summary of the rows with missing values\n",
    "\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2869781c308>"
      ]
     },
     "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": [
    "# Checking income variable values in missing value dataset\n",
    "\n",
    "train_df['Income'][train_df['flag']==0].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>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>target</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002707</td>\n",
       "      <td>0.006821</td>\n",
       "      <td>-0.021642</td>\n",
       "      <td>-0.023749</td>\n",
       "      <td>0.009900</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>-0.206364</td>\n",
       "      <td>-0.020687</td>\n",
       "      <td>0.010813</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>-0.002707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.362120</td>\n",
       "      <td>-0.032044</td>\n",
       "      <td>0.084638</td>\n",
       "      <td>0.265629</td>\n",
       "      <td>0.124886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.356671</td>\n",
       "      <td>-0.334613</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>0.006821</td>\n",
       "      <td>-0.362120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019761</td>\n",
       "      <td>-0.053431</td>\n",
       "      <td>-0.131834</td>\n",
       "      <td>-0.045637</td>\n",
       "      <td>-0.097506</td>\n",
       "      <td>0.299627</td>\n",
       "      <td>0.151250</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>-0.021642</td>\n",
       "      <td>-0.032044</td>\n",
       "      <td>0.019761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009154</td>\n",
       "      <td>-0.009816</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>0.256887</td>\n",
       "      <td>0.047061</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>-0.023749</td>\n",
       "      <td>0.084638</td>\n",
       "      <td>-0.053431</td>\n",
       "      <td>-0.009154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086117</td>\n",
       "      <td>0.372988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.160103</td>\n",
       "      <td>-0.159789</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>0.009900</td>\n",
       "      <td>0.265629</td>\n",
       "      <td>-0.131834</td>\n",
       "      <td>-0.009816</td>\n",
       "      <td>0.086117</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.094162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.119274</td>\n",
       "      <td>-0.238853</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.124886</td>\n",
       "      <td>-0.045637</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>0.372988</td>\n",
       "      <td>0.094162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.024316</td>\n",
       "      <td>-0.210216</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>-0.206364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.097506</td>\n",
       "      <td>0.256887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.422165</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>-0.020687</td>\n",
       "      <td>-0.356671</td>\n",
       "      <td>0.299627</td>\n",
       "      <td>0.047061</td>\n",
       "      <td>0.160103</td>\n",
       "      <td>-0.119274</td>\n",
       "      <td>0.024316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.111673</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.010813</td>\n",
       "      <td>-0.334613</td>\n",
       "      <td>0.151250</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>-0.159789</td>\n",
       "      <td>-0.238853</td>\n",
       "      <td>-0.210216</td>\n",
       "      <td>-0.422165</td>\n",
       "      <td>0.111673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flag</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  perc_premium_paid_by_cash_credit  \\\n",
       "id                                1.000000                         -0.002707   \n",
       "perc_premium_paid_by_cash_credit -0.002707                          1.000000   \n",
       "age_in_days                       0.006821                         -0.362120   \n",
       "Income                           -0.021642                         -0.032044   \n",
       "Count_3-6_months_late            -0.023749                          0.084638   \n",
       "Count_6-12_months_late            0.009900                          0.265629   \n",
       "Count_more_than_12_months_late    0.004217                          0.124886   \n",
       "application_underwriting_score   -0.206364                               NaN   \n",
       "no_of_premiums_paid              -0.020687                         -0.356671   \n",
       "target                            0.010813                         -0.334613   \n",
       "flag                                   NaN                               NaN   \n",
       "\n",
       "                                  age_in_days    Income  \\\n",
       "id                                   0.006821 -0.021642   \n",
       "perc_premium_paid_by_cash_credit    -0.362120 -0.032044   \n",
       "age_in_days                          1.000000  0.019761   \n",
       "Income                               0.019761  1.000000   \n",
       "Count_3-6_months_late               -0.053431 -0.009154   \n",
       "Count_6-12_months_late              -0.131834 -0.009816   \n",
       "Count_more_than_12_months_late      -0.045637 -0.006363   \n",
       "application_underwriting_score      -0.097506  0.256887   \n",
       "no_of_premiums_paid                  0.299627  0.047061   \n",
       "target                               0.151250  0.015647   \n",
       "flag                                      NaN       NaN   \n",
       "\n",
       "                                  Count_3-6_months_late  \\\n",
       "id                                            -0.023749   \n",
       "perc_premium_paid_by_cash_credit               0.084638   \n",
       "age_in_days                                   -0.053431   \n",
       "Income                                        -0.009154   \n",
       "Count_3-6_months_late                          1.000000   \n",
       "Count_6-12_months_late                         0.086117   \n",
       "Count_more_than_12_months_late                 0.372988   \n",
       "application_underwriting_score                      NaN   \n",
       "no_of_premiums_paid                            0.160103   \n",
       "target                                        -0.159789   \n",
       "flag                                                NaN   \n",
       "\n",
       "                                  Count_6-12_months_late  \\\n",
       "id                                              0.009900   \n",
       "perc_premium_paid_by_cash_credit                0.265629   \n",
       "age_in_days                                    -0.131834   \n",
       "Income                                         -0.009816   \n",
       "Count_3-6_months_late                           0.086117   \n",
       "Count_6-12_months_late                          1.000000   \n",
       "Count_more_than_12_months_late                  0.094162   \n",
       "application_underwriting_score                       NaN   \n",
       "no_of_premiums_paid                            -0.119274   \n",
       "target                                         -0.238853   \n",
       "flag                                                 NaN   \n",
       "\n",
       "                                  Count_more_than_12_months_late  \\\n",
       "id                                                      0.004217   \n",
       "perc_premium_paid_by_cash_credit                        0.124886   \n",
       "age_in_days                                            -0.045637   \n",
       "Income                                                 -0.006363   \n",
       "Count_3-6_months_late                                   0.372988   \n",
       "Count_6-12_months_late                                  0.094162   \n",
       "Count_more_than_12_months_late                          1.000000   \n",
       "application_underwriting_score                               NaN   \n",
       "no_of_premiums_paid                                     0.024316   \n",
       "target                                                 -0.210216   \n",
       "flag                                                         NaN   \n",
       "\n",
       "                                  application_underwriting_score  \\\n",
       "id                                                     -0.206364   \n",
       "perc_premium_paid_by_cash_credit                             NaN   \n",
       "age_in_days                                            -0.097506   \n",
       "Income                                                  0.256887   \n",
       "Count_3-6_months_late                                        NaN   \n",
       "Count_6-12_months_late                                       NaN   \n",
       "Count_more_than_12_months_late                               NaN   \n",
       "application_underwriting_score                          1.000000   \n",
       "no_of_premiums_paid                                          NaN   \n",
       "target                                                 -0.422165   \n",
       "flag                                                         NaN   \n",
       "\n",
       "                                  no_of_premiums_paid    target  flag  \n",
       "id                                          -0.020687  0.010813   NaN  \n",
       "perc_premium_paid_by_cash_credit            -0.356671 -0.334613   NaN  \n",
       "age_in_days                                  0.299627  0.151250   NaN  \n",
       "Income                                       0.047061  0.015647   NaN  \n",
       "Count_3-6_months_late                        0.160103 -0.159789   NaN  \n",
       "Count_6-12_months_late                      -0.119274 -0.238853   NaN  \n",
       "Count_more_than_12_months_late               0.024316 -0.210216   NaN  \n",
       "application_underwriting_score                    NaN -0.422165   NaN  \n",
       "no_of_premiums_paid                          1.000000  0.111673   NaN  \n",
       "target                                       0.111673  1.000000   NaN  \n",
       "flag                                              NaN       NaN   NaN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understanding correlation between variables in the missing value dataset\n",
    "\n",
    "train_df[train_df['flag']==0].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the analysis, its identified that the application underwriting score has a dependency on income and the no.of months of late premium payment has a dependency on the percentage of the premium paid. Since the missing values has no relationship with itself but has relationship with other variables, the data is missing at random (MAR).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Handling missing values\n",
    "\n",
    "**As the dataset is not a well balanced classification set, deletion will lead to loss of data. Hence imputation technique is opted. Both the variables - income and the no.of months of late premium payment have skewed distribution, imputation by median method is chosen.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>57167.166368</td>\n",
       "      <td>32928.970160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28640.000</td>\n",
       "      <td>57262.000</td>\n",
       "      <td>85632.000</td>\n",
       "      <td>114076.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.314288</td>\n",
       "      <td>0.334915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.538</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>18846.696906</td>\n",
       "      <td>5208.719136</td>\n",
       "      <td>7670.0</td>\n",
       "      <td>14974.000</td>\n",
       "      <td>18625.000</td>\n",
       "      <td>22636.000</td>\n",
       "      <td>37602.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>208847.171177</td>\n",
       "      <td>496582.597257</td>\n",
       "      <td>24030.0</td>\n",
       "      <td>108010.000</td>\n",
       "      <td>166560.000</td>\n",
       "      <td>252090.000</td>\n",
       "      <td>90262600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.248369</td>\n",
       "      <td>0.691102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.078093</td>\n",
       "      <td>0.436251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.059935</td>\n",
       "      <td>0.311840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>99.072606</td>\n",
       "      <td>0.726395</td>\n",
       "      <td>91.9</td>\n",
       "      <td>98.830</td>\n",
       "      <td>99.210</td>\n",
       "      <td>99.520</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>10.863887</td>\n",
       "      <td>5.170687</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>14.000</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.937410</td>\n",
       "      <td>0.242226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flag</th>\n",
       "      <td>79853.0</td>\n",
       "      <td>0.962456</td>\n",
       "      <td>0.190092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count           mean            std  \\\n",
       "id                                79853.0   57167.166368   32928.970160   \n",
       "perc_premium_paid_by_cash_credit  79853.0       0.314288       0.334915   \n",
       "age_in_days                       79853.0   18846.696906    5208.719136   \n",
       "Income                            79853.0  208847.171177  496582.597257   \n",
       "Count_3-6_months_late             79853.0       0.248369       0.691102   \n",
       "Count_6-12_months_late            79853.0       0.078093       0.436251   \n",
       "Count_more_than_12_months_late    79853.0       0.059935       0.311840   \n",
       "application_underwriting_score    79853.0      99.072606       0.726395   \n",
       "no_of_premiums_paid               79853.0      10.863887       5.170687   \n",
       "target                            79853.0       0.937410       0.242226   \n",
       "flag                              79853.0       0.962456       0.190092   \n",
       "\n",
       "                                      min         25%         50%         75%  \\\n",
       "id                                    2.0   28640.000   57262.000   85632.000   \n",
       "perc_premium_paid_by_cash_credit      0.0       0.034       0.167       0.538   \n",
       "age_in_days                        7670.0   14974.000   18625.000   22636.000   \n",
       "Income                            24030.0  108010.000  166560.000  252090.000   \n",
       "Count_3-6_months_late                 0.0       0.000       0.000       0.000   \n",
       "Count_6-12_months_late                0.0       0.000       0.000       0.000   \n",
       "Count_more_than_12_months_late        0.0       0.000       0.000       0.000   \n",
       "application_underwriting_score       91.9      98.830      99.210      99.520   \n",
       "no_of_premiums_paid                   2.0       7.000      10.000      14.000   \n",
       "target                                0.0       1.000       1.000       1.000   \n",
       "flag                                  0.0       1.000       1.000       1.000   \n",
       "\n",
       "                                          max  \n",
       "id                                  114076.00  \n",
       "perc_premium_paid_by_cash_credit         1.00  \n",
       "age_in_days                          37602.00  \n",
       "Income                            90262600.00  \n",
       "Count_3-6_months_late                   13.00  \n",
       "Count_6-12_months_late                  17.00  \n",
       "Count_more_than_12_months_late          11.00  \n",
       "application_underwriting_score          99.89  \n",
       "no_of_premiums_paid                     60.00  \n",
       "target                                   1.00  \n",
       "flag                                     1.00  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputing column median for training data\n",
    "\n",
    "train_df.fillna(train_df.median(), inplace=True)\n",
    "train_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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",
       "flag                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking for missing values in the datasaet\n",
    "\n",
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>id</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>56739.956376</td>\n",
       "      <td>32935.402647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28254.750</td>\n",
       "      <td>56488.000</td>\n",
       "      <td>85386.25</td>\n",
       "      <td>114077.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>0.334059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>18824.215346</td>\n",
       "      <td>5246.525604</td>\n",
       "      <td>7671.0</td>\n",
       "      <td>14972.000</td>\n",
       "      <td>18623.000</td>\n",
       "      <td>22636.00</td>\n",
       "      <td>35785.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>202820.127104</td>\n",
       "      <td>270253.607996</td>\n",
       "      <td>24030.0</td>\n",
       "      <td>106397.500</td>\n",
       "      <td>165070.000</td>\n",
       "      <td>250020.00</td>\n",
       "      <td>21914550.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>0.238517</td>\n",
       "      <td>0.685889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>0.454435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>0.058059</td>\n",
       "      <td>0.306912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>99.067623</td>\n",
       "      <td>0.728999</td>\n",
       "      <td>91.9</td>\n",
       "      <td>98.830</td>\n",
       "      <td>99.210</td>\n",
       "      <td>99.52</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <td>34224.0</td>\n",
       "      <td>10.890428</td>\n",
       "      <td>5.216867</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>14.00</td>\n",
       "      <td>59.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count           mean            std  \\\n",
       "id                                34224.0   56739.956376   32935.402647   \n",
       "perc_premium_paid_by_cash_credit  34224.0       0.314457       0.334059   \n",
       "age_in_days                       34224.0   18824.215346    5246.525604   \n",
       "Income                            34224.0  202820.127104  270253.607996   \n",
       "Count_3-6_months_late             34224.0       0.238517       0.685889   \n",
       "Count_6-12_months_late            34224.0       0.080645       0.454435   \n",
       "Count_more_than_12_months_late    34224.0       0.058059       0.306912   \n",
       "application_underwriting_score    34224.0      99.067623       0.728999   \n",
       "no_of_premiums_paid               34224.0      10.890428       5.216867   \n",
       "\n",
       "                                      min         25%         50%        75%  \\\n",
       "id                                    1.0   28254.750   56488.000   85386.25   \n",
       "perc_premium_paid_by_cash_credit      0.0       0.034       0.169       0.54   \n",
       "age_in_days                        7671.0   14972.000   18623.000   22636.00   \n",
       "Income                            24030.0  106397.500  165070.000  250020.00   \n",
       "Count_3-6_months_late                 0.0       0.000       0.000       0.00   \n",
       "Count_6-12_months_late                0.0       0.000       0.000       0.00   \n",
       "Count_more_than_12_months_late        0.0       0.000       0.000       0.00   \n",
       "application_underwriting_score       91.9      98.830      99.210      99.52   \n",
       "no_of_premiums_paid                   2.0       7.000      10.000      14.00   \n",
       "\n",
       "                                          max  \n",
       "id                                  114077.00  \n",
       "perc_premium_paid_by_cash_credit         1.00  \n",
       "age_in_days                          35785.00  \n",
       "Income                            21914550.00  \n",
       "Count_3-6_months_late                   12.00  \n",
       "Count_6-12_months_late                  10.00  \n",
       "Count_more_than_12_months_late           7.00  \n",
       "application_underwriting_score          99.89  \n",
       "no_of_premiums_paid                     59.00  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputing column median for test data\n",
    "\n",
    "test_df=pd.read_csv('test.csv')\n",
    "test_df.fillna(test_df.median(), inplace=True)\n",
    "test_df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking for missing values in the datasaet\n",
    "\n",
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The values are impute for both the training and the test set.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier count -  3307\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAANj0lEQVR4nO3df2xd513H8c/Hcegqe1MgjVBJx+6EtyGxDArekChCbdQgF0+wf6a1KmsnJk1IW1oUIX5UluJIVv/sqCINVBVQK6YVtgFaqGqWKl3HgC293pplpd24Yq5INqlNULfZirLY/vKHzzU3znWufTnnfq+v3y8pqn18z/M856R+9/TYPnZECADQe0PZCwCAnYoAA0ASAgwASQgwACQhwACQZHgrL77pppuiVqtVtBQAGExzc3MXImLf+u1bCnCtVlO9Xi9vVQCwA9h+td12bkEAQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAk2dLvhOvW8ePH9fzzz2vPnj16/PHHezElAPS9ngS40WjowoULunTpUi+mA4BtgVsQAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0CSngT4/PnzkqTLly/r+PHjvZgSAPpeTwJ86dIlSdLKyooajUYvpgSAvsctCABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIElPA7yysqIzZ87o9ttv7/rPHXfccc22ubk51et1HTx4UHNzc5Kkixcv6oEHHtDFixevWkNze6PRWPt4c98TJ05ocnJS9Xr9mn3b7Xe9eTpp3a/bMbpR9lyNRkOTk5NqNBqljLed9PLvbdD187mscm3b7go4Iq7ZdvToUU1PT2tlZUVHjx6VJD3xxBM6e/asnnzyyate29w+MzOz9vHmvo888ogWFxc1PT19zb7t9rvePJ207tftGN0oe66ZmRktLi5qZmamlPG2k17+vQ26fj6XVa5t2wW4nYWFBS0sLKy9ferUKc3OzioiNDs7e9XVanP7/Py8IkJPP/302r7NuC8sLFy1b7v9Zmdn1Wg02s7TSet4zzzzTFdjdKN13jLmajQamp+flyTNz8/vqKvgss/lTtbP57LqtQ1EgNd7+OGHtbKyIklaXl6+6mq1ub1paWlpw3Ga+7bbb3l5WTMzM23n6aR1vCtXrujKlStbHqMbrfOWMdf6q96ddBVc9rncyfr5XFa9to4Btv0x23Xb9ddff73UyauytLS0FtalpSWdPHlSkvTss89eN7jtxjl58mTb/ZaWljQ/P992nk5ax4uItSvvrYzRjdZ5y5irefW70fuDrOxzuZP187msem0dAxwRj0XEeESM79u3r9TJqzI8PKzh4eG1tw8dOiRJuvPOO9e2b3acQ4cOtd1veHhYtVqt7TydtI5nW7a3PEY3WuctY65arXbd9wdZ2edyJ+vnc1n12gbyFsRDDz2koaHVQ9u1a5fuu+8+SdL999+/tr3pekFu7ttuv127dmlqaqrtPJ20jrd7927t3r17y2N0o3XeMuaampq67vuDrOxzuZP187msem0DEeDR0VGNjo6uvX3w4EFNTEzItiYmJrR3715J0t69e9e212o12dbk5OTavs0r0dHR0av2bbffxMSExsbG2s7TSet4d911V1djdKN13jLmGhsbW7vqrdVqGhsbK2GV20PZ53In6+dzWfXatl2Am5FsdezYMU1PT2toaEjHjh2TtPpfrgMHDlzzX6zm9qmpqbWPN/c9cuSIRkZGND09fc2+7fa73jydtO7X7RjdKHuuqakpjYyM7Kir36Ze/r0Nun4+l1Wuze2+r3Yj4+PjUa/XtzzJ5OSkFhcXNTQ0pAMHDujRRx/d8hgAsF3ZnouI8fXbt90VMAAMCgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEl6EuAbb7xxdbKhIY2NjfViSgDoez0J8P79+yVJN9xwgw4fPtyLKQGg73ELAgCSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIM92KSsbExnT9/Xnv27OnFdACwLTgiNv3i8fHxqNfrFS4HAAaP7bmIGF+/nVsQAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACTZ0i/ltP26pFe7nOsmSRe63Hc7GPTjkzjGQTDoxyf15zG+LSL2rd+4pQD/f9iut/utoINi0I9P4hgHwaAfn7S9jpFbEACQhAADQJJeBvixHs6VYdCPT+IYB8GgH5+0jY6xZ/eAAQBX4xYEACQhwACQpPIA256w/W3bDdt/UvV8vWb7r2y/Zvtb2Wupgu232n7O9su2X7L9YPaaymb7TbZP2z5THOOx7DVVwfYu29+w/U/Za6mC7XnbZ22/aLuevZ7NqPQesO1dkr4j6ZCkc5JekHRPRPxHZZP2mO3fkLQg6cmIeHf2espm+2ZJN0fE122/WdKcpA8M2N+hJY1ExILt3ZK+IunBiPhq8tJKZfuIpHFJb4mI92evp2y25yWNR0S//RDGhqq+An6fpEZE/FdE/FjSU5J+p+I5eyoivizpf7LXUZWI+H5EfL14+0eSXpa0P3dV5YpVC8W7u4s/A/XVadu3SJqU9Hj2WvB/qg7wfkn/3fL+OQ3YJ+9OYrsm6VZJX8tdSfmK/z1/UdJrkk5GxKAd459J+iNJK9kLqVBI+qLtOdsfy17MZlQdYLfZNlBXFjuF7VFJn5f0BxHxw+z1lC0iliPilyTdIul9tgfmdpLt90t6LSLmstdSsdsi4pcl3SXp48Xtwb5WdYDPSXpry/u3SPpexXOiZMV90c9L+nRE/H32eqoUEW9I+pKkieSllOk2Sb9d3CN9StJB23+Tu6TyRcT3in++JukftHoLtK9VHeAXJL3D9ttt/4SkuyV9oeI5UaLiC1R/KenliHgkez1VsL3P9p7i7Rsl3SnpldxVlSci/jQibomImlY/B09FxO8mL6tUtkeKLxLL9oik35TU99+ZVGmAI2JJ0ick/bNWv3jzdxHxUpVz9prtz0j6d0nvsn3O9kez11Sy2yR9WKtXTS8Wf34re1Elu1nSc7a/qdWLhpMRMZDfqjXAflrSV2yfkXRa0tMRMZu8po74UWQASMJPwgFAEgIMAEkIMAAkIcAAkIQAA8AGtvKwLdufbPlOoe/YfqPjPnwXBLLZXoiI0ex1AOt1+7At24cl3RoRv3e913EFDAAbaPewLds/Z3u2eObEv9j++Ta73iPpM53GJ8DoG7Zvt/0l25+z/YrtTxc/iSfb77X9b8Uze0/bfnPxHN+/Lp4B+w3bdxSv/Yjtf7R9wvZ3bX/C9pHiNV+1/VPF6zbziQSs95ikwxHxK5L+UNKnWj9o+22S3i7pVKeBhitZHtC9WyX9glafGfKvkm6zfVrS30r6UES8YPstki5JelCSIuJAEc8v2n5nMc67i7HeJKkh6Y8j4lbbn5R0n1afDvaYpN+PiP+0/ata/UQ62KsDxfZTPJTq1yR9trg2kKQb1r3sbkmfi4jlTuMRYPSb0xFxTpKKx0PWJP1A0vcj4gVJaj6NzfavSzpebHvF9quSmgF+rnh+8Y9s/0DSiWL7WUnv2eQnErDekKQ3iifnbeRuSR/fzGAEGP3mcsvby1r9d9Rq/xjTdo87bTfOSsv7K8WYm/lEAq4SET8sbmt9MCI+W9wie09EnJEk2++S9JNafT5MR9wDxnbwiqSfsf1eSSru/w5L+rKke4tt75T0s5K+vZkBi6vo79r+YLG/bf9iFYvH9rXBw7bulfTR4sE/L+nq3/Jzj6SnYpPfXsYVMPpeRPzY9ockHS8eF3lJq4+M/JSkv7B9VtKSpI9ExOWWWwqd3Cvpz21PafXXED0l6UzpB4BtKyLu2eBDbZ8XHRHTWxmf7wMGgCTcggCAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEjyv6Vw2zgGCNakAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating boxplots for all the continuous variables\n",
    "\n",
    "sn.boxplot(df['Income'])\n",
    "\n",
    "IQR = df['Income'].quantile(0.75) - df['Income'].quantile(0.25)\n",
    "print(\"Outlier count - \", df[df['Income']>(df['Income'].quantile(0.75)+ (1.5* IQR))]['id'].count() + df[df['Income']<(df['Income'].quantile(0.25) - (1.5* IQR))]['id'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2869b844108>"
      ]
     },
     "execution_count": 57,
     "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 boxplots for all the continuous variables\n",
    "\n",
    "sn.boxplot(df['perc_premium_paid_by_cash_credit'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier count -  80\n"
     ]
    },
    {
     "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 boxplots for all the continuous variables\n",
    "\n",
    "sn.boxplot(df['age_in_days'])\n",
    "\n",
    "IQR = df['age_in_days'].quantile(0.75) - df['age_in_days'].quantile(0.25)\n",
    "print(\"Outlier count - \", df[df['age_in_days']>(df['age_in_days'].quantile(0.75)+ (1.5* IQR))]['id'].count() + df[df['age_in_days']<(df['age_in_days'].quantile(0.25) - (1.5* IQR))]['id'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier count -  3381\n"
     ]
    },
    {
     "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 boxplots for all the continuous variables\n",
    "\n",
    "sn.boxplot(df['application_underwriting_score'])\n",
    "\n",
    "IQR = df['application_underwriting_score'].quantile(0.75) - df['application_underwriting_score'].quantile(0.25)\n",
    "print(\"Outlier count - \", df[df['application_underwriting_score']>(df['application_underwriting_score'].quantile(0.75)+ (1.5* IQR))]['id'].count() + df[df['application_underwriting_score']<(df['application_underwriting_score'].quantile(0.25) - (1.5* IQR))]['id'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier count -  1407\n"
     ]
    },
    {
     "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 boxplots for all the continuous variables\n",
    "\n",
    "sn.boxplot(df['no_of_premiums_paid'])\n",
    "\n",
    "IQR = df['no_of_premiums_paid'].quantile(0.75) - df['no_of_premiums_paid'].quantile(0.25)\n",
    "print(\"Outlier count - \", df[df['no_of_premiums_paid']>(df['no_of_premiums_paid'].quantile(0.75)+ (1.5* IQR))]['id'].count() + df[df['no_of_premiums_paid']<(df['no_of_premiums_paid'].quantile(0.25) - (1.5* IQR))]['id'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The outliers in the data looks reasonable due to the distribution being skewed hence they are left as such.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Variable encoding & Dataset split for validation\n",
    "\n",
    "**The categorical variables are encoded into 0s and 1s before building the model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id', 'sourcing_channel', 'residence_area_type', 'target']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical variables in the dataset\n",
    "\n",
    "cat_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_var.remove('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_var.remove('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dummy variables for both training ang and the test set\n",
    "\n",
    "final_train_df= pd.get_dummies(train_df,columns=cat_var, drop_first = True )\n",
    "final_test_df= pd.get_dummies(test_df,columns=cat_var, drop_first = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79853 entries, 0 to 79852\n",
      "Data columns (total 16 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                79853 non-null  int64  \n",
      " 1   perc_premium_paid_by_cash_credit  79853 non-null  float64\n",
      " 2   age_in_days                       79853 non-null  int64  \n",
      " 3   Income                            79853 non-null  int64  \n",
      " 4   Count_3-6_months_late             79853 non-null  float64\n",
      " 5   Count_6-12_months_late            79853 non-null  float64\n",
      " 6   Count_more_than_12_months_late    79853 non-null  float64\n",
      " 7   application_underwriting_score    79853 non-null  float64\n",
      " 8   no_of_premiums_paid               79853 non-null  int64  \n",
      " 9   target                            79853 non-null  int64  \n",
      " 10  flag                              79853 non-null  int64  \n",
      " 11  sourcing_channel_B                79853 non-null  uint8  \n",
      " 12  sourcing_channel_C                79853 non-null  uint8  \n",
      " 13  sourcing_channel_D                79853 non-null  uint8  \n",
      " 14  sourcing_channel_E                79853 non-null  uint8  \n",
      " 15  residence_area_type_Urban         79853 non-null  uint8  \n",
      "dtypes: float64(5), int64(6), uint8(5)\n",
      "memory usage: 7.1 MB\n"
     ]
    }
   ],
   "source": [
    "# Checking the dummy variables created on training set\n",
    "\n",
    "final_train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 14 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                34224 non-null  int64  \n",
      " 1   perc_premium_paid_by_cash_credit  34224 non-null  float64\n",
      " 2   age_in_days                       34224 non-null  int64  \n",
      " 3   Income                            34224 non-null  int64  \n",
      " 4   Count_3-6_months_late             34224 non-null  float64\n",
      " 5   Count_6-12_months_late            34224 non-null  float64\n",
      " 6   Count_more_than_12_months_late    34224 non-null  float64\n",
      " 7   application_underwriting_score    34224 non-null  float64\n",
      " 8   no_of_premiums_paid               34224 non-null  int64  \n",
      " 9   sourcing_channel_B                34224 non-null  uint8  \n",
      " 10  sourcing_channel_C                34224 non-null  uint8  \n",
      " 11  sourcing_channel_D                34224 non-null  uint8  \n",
      " 12  sourcing_channel_E                34224 non-null  uint8  \n",
      " 13  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(5), int64(4), uint8(5)\n",
      "memory usage: 2.5 MB\n"
     ]
    }
   ],
   "source": [
    "# Checking the dummy variables created on test set\n",
    "\n",
    "final_test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Training and validation data split\n",
    "\n",
    "**Training dataset is further split into 80% training set and 20% validation set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79853 entries, 0 to 79852\n",
      "Data columns (total 16 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                79853 non-null  int64  \n",
      " 1   perc_premium_paid_by_cash_credit  79853 non-null  float64\n",
      " 2   age_in_days                       79853 non-null  int64  \n",
      " 3   Income                            79853 non-null  int64  \n",
      " 4   Count_3-6_months_late             79853 non-null  float64\n",
      " 5   Count_6-12_months_late            79853 non-null  float64\n",
      " 6   Count_more_than_12_months_late    79853 non-null  float64\n",
      " 7   application_underwriting_score    79853 non-null  float64\n",
      " 8   no_of_premiums_paid               79853 non-null  int64  \n",
      " 9   target                            79853 non-null  int64  \n",
      " 10  flag                              79853 non-null  int64  \n",
      " 11  sourcing_channel_B                79853 non-null  uint8  \n",
      " 12  sourcing_channel_C                79853 non-null  uint8  \n",
      " 13  sourcing_channel_D                79853 non-null  uint8  \n",
      " 14  sourcing_channel_E                79853 non-null  uint8  \n",
      " 15  residence_area_type_Urban         79853 non-null  uint8  \n",
      "dtypes: float64(5), int64(6), uint8(5)\n",
      "memory usage: 7.1 MB\n"
     ]
    }
   ],
   "source": [
    "final_train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating the dependent variable from the independent variables in the dataset\n",
    "\n",
    "Y = final_train_df.filter(['target'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79848</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79849</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79850</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79851</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79852</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79853 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       target\n",
       "0           1\n",
       "1           1\n",
       "2           0\n",
       "3           1\n",
       "4           1\n",
       "...       ...\n",
       "79848       1\n",
       "79849       1\n",
       "79850       1\n",
       "79851       1\n",
       "79852       0\n",
       "\n",
       "[79853 rows x 1 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing all variables that are not independent variabes - target\n",
    "\n",
    "X = final_train_df.copy()\n",
    "\n",
    "X.drop(['target'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Removing all variables that are not independent variabes - flag\n",
    "\n",
    "X.drop(['flag'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing all variables that are not independent variabes - ID\n",
    "\n",
    "X.drop(['id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79853 entries, 0 to 79852\n",
      "Data columns (total 13 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   perc_premium_paid_by_cash_credit  79853 non-null  float64\n",
      " 1   age_in_days                       79853 non-null  int64  \n",
      " 2   Income                            79853 non-null  int64  \n",
      " 3   Count_3-6_months_late             79853 non-null  float64\n",
      " 4   Count_6-12_months_late            79853 non-null  float64\n",
      " 5   Count_more_than_12_months_late    79853 non-null  float64\n",
      " 6   application_underwriting_score    79853 non-null  float64\n",
      " 7   no_of_premiums_paid               79853 non-null  int64  \n",
      " 8   sourcing_channel_B                79853 non-null  uint8  \n",
      " 9   sourcing_channel_C                79853 non-null  uint8  \n",
      " 10  sourcing_channel_D                79853 non-null  uint8  \n",
      " 11  sourcing_channel_E                79853 non-null  uint8  \n",
      " 12  residence_area_type_Urban         79853 non-null  uint8  \n",
      "dtypes: float64(5), int64(3), uint8(5)\n",
      "memory usage: 5.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Analysing structure of the resultant dataset \n",
    "\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into training and valuadation in the ratio 80-20 with stratified outcomes in both split sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.2, random_state = 42, stratify = Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63882, 13), (15971, 13), (63882, 1), (15971, 1))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the split datasets\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Model Building - Logistic Regression\n",
    "\n",
    "Logistic regression is used here to predict the yes/ no outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Creating a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a model for logistic regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg=LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.2 Fitting training data in model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vinu\\Python3.7\\lib\\site-packages\\sklearn\\utils\\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the training data into the model\n",
    "\n",
    "logreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.3 Predicting the outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04169144, 0.95830856],\n",
       "       [0.06784235, 0.93215765],\n",
       "       [0.20172412, 0.79827588],\n",
       "       ...,\n",
       "       [0.04026071, 0.95973929],\n",
       "       [0.11558342, 0.88441658],\n",
       "       [0.03533228, 0.96466772]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the outcome probability in vaidation set\n",
    "\n",
    "pred_prob= logreg.predict_proba(X_test)\n",
    "pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the outcome in vaidation set for cut-off probability of default 0.5\n",
    "\n",
    "pred=logreg.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4 Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4.1 Calculating the model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.937386513054912"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validating on the split test set\n",
    "\n",
    "logreg.score(X_test,y_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model accuracy on test data (split from original training set for validation) = 93.74%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9374158604927836"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validating on the split training set\n",
    "\n",
    "logreg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model accuracy on training data (split from original training set for training) = 93.74%**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4.2 Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,  1000],\n",
       "       [    0, 14971]], dtype=int64)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating confusion matrix for the validation set for cut-off probability of default 0.5\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4.3 AUC ROC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6165003005811235\n"
     ]
    }
   ],
   "source": [
    "# AUC-ROC for the validation set for cut-off probability of default 0.5\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc=roc_auc_score(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "print(roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**At 0.5 cut-off probability the model doest classify any true negatives correctly. Hence an optimal cut-off probaibility is to be identified.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.4.4 Identifying optimal cut-off probability\n",
    "\n",
    "**Given the problem statement, the ultimate goal is to identify all the customers who might default the next payment with more accuracy than the customers who will pay the next payment without fail. Hence the more wightage is provided for a true negative classification than a true positive classification.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Cut-off = 0.05\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.1\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.15\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.2\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.25\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.3\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.35\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.4\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.45\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.5\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.55\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.6\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.65\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.7\n",
      "Accuracy= 0.937386513054912\n",
      "ROC= 0.5\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.75\n",
      "Accuracy= 0.9370734456201866\n",
      "ROC= 0.4998330104869414\n",
      "TNR= 0.0\n",
      "FPR= 1.0\n",
      "\n",
      "\n",
      "Cut-off = 0.8\n",
      "Accuracy= 0.9255525640222904\n",
      "ROC= 0.5104854719123639\n",
      "TNR= 0.036\n",
      "FPR= 0.964\n",
      "\n",
      "\n",
      "Cut-off = 0.85\n",
      "Accuracy= 0.8776532465092981\n",
      "ROC= 0.5278634693741233\n",
      "TNR= 0.128\n",
      "FPR= 0.872\n",
      "\n",
      "\n",
      "Cut-off = 0.9\n",
      "Accuracy= 0.7670152150773276\n",
      "ROC= 0.5519045487943357\n",
      "TNR= 0.306\n",
      "FPR= 0.694\n",
      "\n",
      "\n",
      "Cut-off = 0.95\n",
      "Accuracy= 0.4791184021038132\n",
      "ROC= 0.5803158105670964\n",
      "TNR= 0.696\n",
      "FPR= 0.304\n",
      "\n",
      "\n",
      "Cut-off = 0.99\n",
      "Accuracy= 0.07256903136935695\n",
      "ROC= 0.503910460223098\n",
      "TNR= 0.997\n",
      "FPR= 0.003\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Performing gridsearch for optimal cut-off probability\n",
    "\n",
    "pred_proba_df = pd.DataFrame(logreg.predict_proba(X_test))\n",
    "threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99]\n",
    "for i in threshold_list:\n",
    "    print ('\\n\\nCut-off =',format(i))\n",
    "    \n",
    "    #Mapping the probabilities with outcomes for iterated threshold\n",
    "    y_test_pred = pred_proba_df.applymap(lambda x: 1 if x >i else 0)\n",
    "    \n",
    "    #Calcuating the accuracy of the model\n",
    "    test_accuracy = metrics.accuracy_score(y_test.to_numpy().reshape(y_test.to_numpy().size,1),\n",
    "                                           y_test_pred.iloc[:,1].to_numpy().reshape(y_test_pred.iloc[:,1].to_numpy().size,1))\n",
    "    print('Accuracy=',format(test_accuracy))\n",
    "    \n",
    "    # Calculating AUC-ROC for for iterated threshold \n",
    "    y_test_ROC_pred = []\n",
    "    for z in range(len(y_test_pred)):\n",
    "        y_test_ROC_pred.append(y_test_pred[0][z] or y_test_pred[1][z])\n",
    "    #y_test_ROC_pred.drop([1], axis =1)\n",
    "    #print(y_test_ROC_pred[0:5])\n",
    "    print('ROC=',roc_auc_score(y_test,y_test_ROC_pred))\n",
    "\n",
    "    # Generating confusion matrix for iterated threshold\n",
    "    conf_matrix = confusion_matrix(y_test.to_numpy().reshape(y_test.to_numpy().size,1),\n",
    "                           y_test_pred.iloc[:,1].to_numpy().reshape(y_test_pred.iloc[:,1].to_numpy().size,1))\n",
    "    \n",
    "    #True negative rate\n",
    "    tnr = (conf_matrix[0][0]/ (conf_matrix[0][0] + conf_matrix[0][1]))\n",
    "    \n",
    "    #True positive rate\n",
    "    fpr = (conf_matrix[0][1]/ (conf_matrix[0][0] + conf_matrix[0][1]))\n",
    "    \n",
    "    print(\"TNR=\",tnr)  \n",
    "    print(\"FPR=\",fpr)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After analysing the range of cut-off probabilites and the model presformances respectively, the prediction on the given test is done**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.5. Predicting on the original test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 14 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                34224 non-null  int64  \n",
      " 1   perc_premium_paid_by_cash_credit  34224 non-null  float64\n",
      " 2   age_in_days                       34224 non-null  int64  \n",
      " 3   Income                            34224 non-null  int64  \n",
      " 4   Count_3-6_months_late             34224 non-null  float64\n",
      " 5   Count_6-12_months_late            34224 non-null  float64\n",
      " 6   Count_more_than_12_months_late    34224 non-null  float64\n",
      " 7   application_underwriting_score    34224 non-null  float64\n",
      " 8   no_of_premiums_paid               34224 non-null  int64  \n",
      " 9   sourcing_channel_B                34224 non-null  uint8  \n",
      " 10  sourcing_channel_C                34224 non-null  uint8  \n",
      " 11  sourcing_channel_D                34224 non-null  uint8  \n",
      " 12  sourcing_channel_E                34224 non-null  uint8  \n",
      " 13  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(5), int64(4), uint8(5)\n",
      "memory usage: 2.5 MB\n"
     ]
    }
   ],
   "source": [
    "# Summarizing the given test set\n",
    "\n",
    "final_test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating the ID column from rest of the independent variables \n",
    "\n",
    "ID = final_test_df.filter(['id'],  axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ID_map = ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the columns that are not useful for prediction - ID\n",
    "\n",
    "final_test_df.drop(['id'], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 13 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   perc_premium_paid_by_cash_credit  34224 non-null  float64\n",
      " 1   age_in_days                       34224 non-null  int64  \n",
      " 2   Income                            34224 non-null  int64  \n",
      " 3   Count_3-6_months_late             34224 non-null  float64\n",
      " 4   Count_6-12_months_late            34224 non-null  float64\n",
      " 5   Count_more_than_12_months_late    34224 non-null  float64\n",
      " 6   application_underwriting_score    34224 non-null  float64\n",
      " 7   no_of_premiums_paid               34224 non-null  int64  \n",
      " 8   sourcing_channel_B                34224 non-null  uint8  \n",
      " 9   sourcing_channel_C                34224 non-null  uint8  \n",
      " 10  sourcing_channel_D                34224 non-null  uint8  \n",
      " 11  sourcing_channel_E                34224 non-null  uint8  \n",
      " 12  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(5), int64(3), uint8(5)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "final_test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"# Removing the columns that are not useful for prediction - flag\\n\\nfinal_test_df.drop(['Flag'], axis = 1, inplace = True)\""
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"# Removing the columns that are not useful for prediction - flag\n",
    "\n",
    "final_test_df.drop(['Flag'], axis = 1, inplace = True)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 13 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   perc_premium_paid_by_cash_credit  34224 non-null  float64\n",
      " 1   age_in_days                       34224 non-null  int64  \n",
      " 2   Income                            34224 non-null  int64  \n",
      " 3   Count_3-6_months_late             34224 non-null  float64\n",
      " 4   Count_6-12_months_late            34224 non-null  float64\n",
      " 5   Count_more_than_12_months_late    34224 non-null  float64\n",
      " 6   application_underwriting_score    34224 non-null  float64\n",
      " 7   no_of_premiums_paid               34224 non-null  int64  \n",
      " 8   sourcing_channel_B                34224 non-null  uint8  \n",
      " 9   sourcing_channel_C                34224 non-null  uint8  \n",
      " 10  sourcing_channel_D                34224 non-null  uint8  \n",
      " 11  sourcing_channel_E                34224 non-null  uint8  \n",
      " 12  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(5), int64(3), uint8(5)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Summarizing the given test set\n",
    "\n",
    "final_test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02202723, 0.97797277],\n",
       "       [0.02543076, 0.97456924],\n",
       "       [0.06865971, 0.93134029],\n",
       "       ...,\n",
       "       [0.05887605, 0.94112395],\n",
       "       [0.06940725, 0.93059275],\n",
       "       [0.03065415, 0.96934585]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the probabilites for each outcome\n",
    "\n",
    "test_pred_prob= logreg.predict_proba(final_test_df)\n",
    "test_pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Conveting the predicted probabilites to the outcomes as per the cut-off probability - 0.85\n",
    "\n",
    "test_pred_proba_df = pd.DataFrame(test_pred_prob)\n",
    "\n",
    "test_pred_outcome = []\n",
    "\n",
    "test_pred_prob_df = test_pred_proba_df.applymap(lambda x: 1 if x >0.95 else 0)\n",
    "for z in range(len(test_pred_prob_df)):\n",
    "        test_pred_outcome.append(test_pred_prob_df[0][z] or test_pred_prob_df[1][z])\n",
    "\n",
    "print(test_pred_outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34224, 34224)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking on dimenetions for errors\n",
    "\n",
    "len(ID_map), len(test_pred_outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34219</th>\n",
       "      <td>81988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34220</th>\n",
       "      <td>30326</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34221</th>\n",
       "      <td>65749</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34222</th>\n",
       "      <td>65686</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34223</th>\n",
       "      <td>93826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34224 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  target\n",
       "0        649       1\n",
       "1      81136       1\n",
       "2      70762       0\n",
       "3      53935       0\n",
       "4      15476       0\n",
       "...      ...     ...\n",
       "34219  81988       1\n",
       "34220  30326       0\n",
       "34221  65749       0\n",
       "34222  65686       0\n",
       "34223  93826       1\n",
       "\n",
       "[34224 rows x 2 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Appending ID and outcomes to a dataframe\n",
    "\n",
    "final_outcome = pd.DataFrame()\n",
    "final_outcome['id'] = ID_map['id'].tolist()\n",
    "final_outcome['target'] = test_pred_outcome\n",
    "final_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18722\n",
       "1    15502\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing the outcome results\n",
    "\n",
    "final_outcome['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the outcome to a file\n",
    "\n",
    "final_outcome.to_csv('outcomes_LR.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Model building - Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Creating a model and fitting in training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "\n",
    "from sklearn import tree\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/graphviz-2.44.1-win32/Graphviz/bin/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating decision tree model and fitting the training data\n",
    "\n",
    "dt = tree.DecisionTreeClassifier(criterion = \"gini\",\n",
    "                                 random_state = 42,\n",
    "                                 max_depth=3, \n",
    "                                 min_samples_split=80, \n",
    "                                 min_samples_leaf=50, \n",
    "                                 class_weight = 'balanced')\n",
    "\n",
    "dt_model = dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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       "<title>8</title>\r\n",
       "<path fill=\"#f0b68c\" stroke=\"black\" d=\"M1121.5,-306C1121.5,-306 933.5,-306 933.5,-306 927.5,-306 921.5,-300 921.5,-294 921.5,-294 921.5,-235 921.5,-235 921.5,-229 927.5,-223 933.5,-223 933.5,-223 1121.5,-223 1121.5,-223 1127.5,-223 1133.5,-229 1133.5,-235 1133.5,-235 1133.5,-294 1133.5,-294 1133.5,-300 1127.5,-306 1121.5,-306\"/>\r\n",
       "<text text-anchor=\"start\" x=\"929.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Count_6&#45;12_months_late ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"990\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.416</text>\r\n",
       "<text text-anchor=\"start\" x=\"971.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20490</text>\r\n",
       "<text text-anchor=\"start\" x=\"929.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [22481.734, 9428.046]</text>\r\n",
       "<text text-anchor=\"start\" x=\"966.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\">\r\n",
       "<title>0&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M892.97,-341.91C910.55,-331.92 929.48,-321.17 947.43,-310.98\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"949.19,-314 956.16,-306.02 945.73,-307.92 949.19,-314\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"949.51\" y=\"-326.42\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n",
       "</g>\r\n",
       "<!-- 2 -->\r\n",
       "<g id=\"node3\" class=\"node\">\r\n",
       "<title>2</title>\r\n",
       "<path fill=\"#72b9ed\" stroke=\"black\" d=\"M400.5,-187C400.5,-187 212.5,-187 212.5,-187 206.5,-187 200.5,-181 200.5,-175 200.5,-175 200.5,-116 200.5,-116 200.5,-110 206.5,-104 212.5,-104 212.5,-104 400.5,-104 400.5,-104 406.5,-104 412.5,-110 412.5,-116 412.5,-116 412.5,-175 412.5,-175 412.5,-181 406.5,-187 400.5,-187\"/>\r\n",
       "<text text-anchor=\"start\" x=\"208.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Count_6&#45;12_months_late ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"269\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.349</text>\r\n",
       "<text text-anchor=\"start\" x=\"250.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 38459</text>\r\n",
       "<text text-anchor=\"start\" x=\"208.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5840.138, 20123.406]</text>\r\n",
       "<text text-anchor=\"start\" x=\"257\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will pay</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\">\r\n",
       "<title>1&#45;&gt;2</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M515.29,-224.05C485.27,-212.9 452.45,-200.71 421.91,-189.37\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"423.11,-186.08 412.51,-185.88 420.67,-192.64 423.11,-186.08\"/>\r\n",
       "</g>\r\n",
       "<!-- 5 -->\r\n",
       "<g id=\"node6\" class=\"node\">\r\n",
       "<title>5</title>\r\n",
       "<path fill=\"#f6d4bc\" stroke=\"black\" d=\"M715.5,-187C715.5,-187 527.5,-187 527.5,-187 521.5,-187 515.5,-181 515.5,-175 515.5,-175 515.5,-116 515.5,-116 515.5,-110 521.5,-104 527.5,-104 527.5,-104 715.5,-104 715.5,-104 721.5,-104 727.5,-110 727.5,-116 727.5,-116 727.5,-175 727.5,-175 727.5,-181 721.5,-187 715.5,-187\"/>\r\n",
       "<text text-anchor=\"start\" x=\"523.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Count_6&#45;12_months_late ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"584\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.479</text>\r\n",
       "<text text-anchor=\"start\" x=\"570\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4933</text>\r\n",
       "<text text-anchor=\"start\" x=\"528\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3619.128, 2389.548]</text>\r\n",
       "<text text-anchor=\"start\" x=\"560.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\">\r\n",
       "<title>1&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M621.5,-222.91C621.5,-214.65 621.5,-205.86 621.5,-197.3\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"625,-197.02 621.5,-187.02 618,-197.02 625,-197.02\"/>\r\n",
       "</g>\r\n",
       "<!-- 3 -->\r\n",
       "<g id=\"node4\" class=\"node\">\r\n",
       "<title>3</title>\r\n",
       "<path fill=\"#6bb6ec\" stroke=\"black\" d=\"M183,-68C183,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,0 12,0 12,0 183,0 183,0 189,0 195,-6 195,-12 195,-12 195,-56 195,-56 195,-62 189,-68 183,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"60\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.324</text>\r\n",
       "<text text-anchor=\"start\" x=\"41.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 37962</text>\r\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5073.17, 19909.52]</text>\r\n",
       "<text text-anchor=\"start\" x=\"48\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will pay</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
       "<g id=\"edge3\" class=\"edge\">\r\n",
       "<title>2&#45;&gt;3</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M228.97,-103.88C209.52,-93.69 188.72,-82.79 169.55,-72.75\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"171.05,-69.58 160.57,-68.04 167.8,-75.78 171.05,-69.58\"/>\r\n",
       "</g>\r\n",
       "<!-- 4 -->\r\n",
       "<g id=\"node5\" class=\"node\">\r\n",
       "<title>4</title>\r\n",
       "<path fill=\"#eca470\" stroke=\"black\" d=\"M388,-68C388,-68 225,-68 225,-68 219,-68 213,-62 213,-56 213,-56 213,-12 213,-12 213,-6 219,0 225,0 225,0 388,0 388,0 394,0 400,-6 400,-12 400,-12 400,-56 400,-56 400,-62 394,-68 388,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"269\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.341</text>\r\n",
       "<text text-anchor=\"start\" x=\"259\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 497</text>\r\n",
       "<text text-anchor=\"start\" x=\"221\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [766.967, 213.886]</text>\r\n",
       "<text text-anchor=\"start\" x=\"245.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\">\r\n",
       "<title>2&#45;&gt;4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M306.5,-103.73C306.5,-95.52 306.5,-86.86 306.5,-78.56\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"310,-78.3 306.5,-68.3 303,-78.3 310,-78.3\"/>\r\n",
       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\">\r\n",
       "<title>6</title>\r\n",
       "<path fill=\"#fbe9dd\" stroke=\"black\" d=\"M601,-68C601,-68 430,-68 430,-68 424,-68 418,-62 418,-56 418,-56 418,-12 418,-12 418,-6 424,0 430,0 430,0 601,0 601,0 607,0 613,-6 613,-12 613,-12 613,-56 613,-56 613,-62 607,-68 601,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"478\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.496</text>\r\n",
       "<text text-anchor=\"start\" x=\"464\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4599</text>\r\n",
       "<text text-anchor=\"start\" x=\"426\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2740.311, 2270.07]</text>\r\n",
       "<text text-anchor=\"start\" x=\"454.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\">\r\n",
       "<title>5&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M582.03,-103.73C573.11,-94.51 563.65,-84.74 554.74,-75.53\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"557.21,-73.05 547.74,-68.3 552.18,-77.92 557.21,-73.05\"/>\r\n",
       "</g>\r\n",
       "<!-- 7 -->\r\n",
       "<g id=\"node8\" class=\"node\">\r\n",
       "<title>7</title>\r\n",
       "<path fill=\"#e99254\" stroke=\"black\" d=\"M806,-68C806,-68 643,-68 643,-68 637,-68 631,-62 631,-56 631,-56 631,-12 631,-12 631,-6 637,0 643,0 643,0 806,0 806,0 812,0 818,-6 818,-12 818,-12 818,-56 818,-56 818,-62 812,-68 806,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"687\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.211</text>\r\n",
       "<text text-anchor=\"start\" x=\"677\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 334</text>\r\n",
       "<text text-anchor=\"start\" x=\"639\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [878.817, 119.477]</text>\r\n",
       "<text text-anchor=\"start\" x=\"663.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\">\r\n",
       "<title>5&#45;&gt;7</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M659.85,-103.73C668.43,-94.61 677.53,-84.93 686.11,-75.81\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"688.87,-77.98 693.18,-68.3 683.78,-73.19 688.87,-77.98\"/>\r\n",
       "</g>\r\n",
       "<!-- 9 -->\r\n",
       "<g id=\"node10\" class=\"node\">\r\n",
       "<title>9</title>\r\n",
       "<path fill=\"#f5cdb1\" stroke=\"black\" d=\"M1117.5,-187C1117.5,-187 937.5,-187 937.5,-187 931.5,-187 925.5,-181 925.5,-175 925.5,-175 925.5,-116 925.5,-116 925.5,-110 931.5,-104 937.5,-104 937.5,-104 1117.5,-104 1117.5,-104 1123.5,-104 1129.5,-110 1129.5,-116 1129.5,-116 1129.5,-175 1129.5,-175 1129.5,-181 1123.5,-187 1117.5,-187\"/>\r\n",
       "<text text-anchor=\"start\" x=\"933.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Count_3&#45;6_months_late ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"994\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.47</text>\r\n",
       "<text text-anchor=\"start\" x=\"971.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18138</text>\r\n",
       "<text text-anchor=\"start\" x=\"934\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14388.63, 8713.849]</text>\r\n",
       "<text text-anchor=\"start\" x=\"966.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\">\r\n",
       "<title>8&#45;&gt;9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1027.5,-222.91C1027.5,-214.65 1027.5,-205.86 1027.5,-197.3\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1031,-197.02 1027.5,-187.02 1024,-197.02 1031,-197.02\"/>\r\n",
       "</g>\r\n",
       "<!-- 12 -->\r\n",
       "<g id=\"node13\" class=\"node\">\r\n",
       "<title>12</title>\r\n",
       "<path fill=\"#e78c4a\" stroke=\"black\" d=\"M1501,-187C1501,-187 1250,-187 1250,-187 1244,-187 1238,-181 1238,-175 1238,-175 1238,-116 1238,-116 1238,-110 1244,-104 1250,-104 1250,-104 1501,-104 1501,-104 1507,-104 1513,-110 1513,-116 1513,-116 1513,-175 1513,-175 1513,-181 1507,-187 1501,-187\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1246\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Count_more_than_12_months_late ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"1338\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.149</text>\r\n",
       "<text text-anchor=\"start\" x=\"1324\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2352</text>\r\n",
       "<text text-anchor=\"start\" x=\"1286\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8093.105, 714.197]</text>\r\n",
       "<text text-anchor=\"start\" x=\"1314.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\">\r\n",
       "<title>8&#45;&gt;12</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1133.8,-227.76C1168.94,-215.95 1208.44,-202.67 1245.17,-190.32\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1246.58,-193.54 1254.94,-187.03 1244.35,-186.9 1246.58,-193.54\"/>\r\n",
       "</g>\r\n",
       "<!-- 10 -->\r\n",
       "<g id=\"node11\" class=\"node\">\r\n",
       "<title>10</title>\r\n",
       "<path fill=\"#fbeee4\" stroke=\"black\" d=\"M1027,-68C1027,-68 848,-68 848,-68 842,-68 836,-62 836,-56 836,-56 836,-12 836,-12 836,-6 842,0 848,0 848,0 1027,0 1027,0 1033,0 1039,-6 1039,-12 1039,-12 1039,-56 1039,-56 1039,-62 1033,-68 1027,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"900\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.497</text>\r\n",
       "<text text-anchor=\"start\" x=\"881.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13883</text>\r\n",
       "<text text-anchor=\"start\" x=\"844\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7957.288, 6873.684]</text>\r\n",
       "<text text-anchor=\"start\" x=\"876.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\">\r\n",
       "<title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M993.99,-103.73C986.57,-94.7 978.7,-85.12 971.27,-76.08\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"973.92,-73.8 964.87,-68.3 968.52,-78.25 973.92,-73.8\"/>\r\n",
       "</g>\r\n",
       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\">\r\n",
       "<title>11</title>\r\n",
       "<path fill=\"#eca572\" stroke=\"black\" d=\"M1248,-68C1248,-68 1069,-68 1069,-68 1063,-68 1057,-62 1057,-56 1057,-56 1057,-12 1057,-12 1057,-6 1063,0 1069,0 1069,0 1248,0 1248,0 1254,0 1260,-6 1260,-12 1260,-12 1260,-56 1260,-56 1260,-62 1254,-68 1248,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1121\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.346</text>\r\n",
       "<text text-anchor=\"start\" x=\"1107\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4255</text>\r\n",
       "<text text-anchor=\"start\" x=\"1065\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6431.342, 1840.165]</text>\r\n",
       "<text text-anchor=\"start\" x=\"1097.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\">\r\n",
       "<title>9&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1076.28,-103.73C1087.63,-94.24 1099.69,-84.16 1110.99,-74.72\"/>\r\n",
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       "</g>\r\n",
       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\">\r\n",
       "<title>13</title>\r\n",
       "<path fill=\"#e88f50\" stroke=\"black\" d=\"M1461,-68C1461,-68 1290,-68 1290,-68 1284,-68 1278,-62 1278,-56 1278,-56 1278,-12 1278,-12 1278,-6 1284,0 1290,0 1290,0 1461,0 1461,0 1467,0 1473,-6 1473,-12 1473,-12 1473,-56 1473,-56 1473,-62 1467,-68 1461,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1338\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.183</text>\r\n",
       "<text text-anchor=\"start\" x=\"1324\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1597</text>\r\n",
       "<text text-anchor=\"start\" x=\"1286\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4721.644, 536.581]</text>\r\n",
       "<text text-anchor=\"start\" x=\"1314.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\">\r\n",
       "<title>12&#45;&gt;13</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1375.5,-103.73C1375.5,-95.52 1375.5,-86.86 1375.5,-78.56\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1379,-78.3 1375.5,-68.3 1372,-78.3 1379,-78.3\"/>\r\n",
       "</g>\r\n",
       "<!-- 14 -->\r\n",
       "<g id=\"node15\" class=\"node\">\r\n",
       "<title>14</title>\r\n",
       "<path fill=\"#e68843\" stroke=\"black\" d=\"M1674,-68C1674,-68 1503,-68 1503,-68 1497,-68 1491,-62 1491,-56 1491,-56 1491,-12 1491,-12 1491,-6 1497,0 1503,0 1503,0 1674,0 1674,0 1680,0 1686,-6 1686,-12 1686,-12 1686,-56 1686,-56 1686,-62 1680,-68 1674,-68\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1551\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.095</text>\r\n",
       "<text text-anchor=\"start\" x=\"1541\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 755</text>\r\n",
       "<text text-anchor=\"start\" x=\"1499\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3371.461, 177.616]</text>\r\n",
       "<text text-anchor=\"start\" x=\"1527.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Will not pay</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\">\r\n",
       "<title>12&#45;&gt;14</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1454.51,-103.88C1474.34,-93.69 1495.53,-82.79 1515.07,-72.75\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1516.93,-75.73 1524.22,-68.04 1513.73,-69.5 1516.93,-75.73\"/>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x2869d3d6588>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizing the tree\n",
    "\n",
    "import graphviz \n",
    "\n",
    "vis_tree = tree.export_graphviz(dt_model,out_file=None,\n",
    "                                feature_names=X_train.columns,  \n",
    "                                class_names=['Will not pay','Will pay'],  \n",
    "                                filled=True, rounded=True,  \n",
    "                                special_characters=True) \n",
    "\n",
    "graph = graphviz.Source(vis_tree) \n",
    "#graph.render(\"decision_tree\") \n",
    "graph.view\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Predicting outcomes for validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a base template for prediction\n",
    "\n",
    "def get_predictions ( test_class, model, test_data ):\n",
    "    y_pred_df = pd.DataFrame( { 'actual_class': test_class,\n",
    "                               'predicted_value': dt_model.predict(test_data)})\n",
    "    return y_pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>actual_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_target\n",
       "13124      Will Pay\n",
       "15228      Will Pay\n",
       "36808  Will not pay\n",
       "71838  Will not pay\n",
       "66579      Will Pay"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping prediction outcomes with classes\n",
    "\n",
    "ser = y_test\n",
    "status_dict = {1:\"Will Pay\", 0:\"Will not pay\"}\n",
    "y_test = ser.replace(dict(actual_target=status_dict))\n",
    "#y_test.rename({'target': 'actual_target'}, axis='columns', inplace=True )\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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",
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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>actual_class</th>\n",
       "      <th>predicted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_class  predicted_value\n",
       "13124      Will Pay                0\n",
       "15228      Will Pay                1\n",
       "36808  Will not pay                0\n",
       "71838  Will not pay                0\n",
       "66579      Will Pay                1"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the predictions with the actual output\n",
    "\n",
    "dt_model_df = pd.DataFrame(get_predictions(y_test.actual_target, dt_model, X_test))\n",
    "dt_model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dt_model_df.drop(['predicted_class'], axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>actual_class</th>\n",
       "      <th>predicted_value</th>\n",
       "      <th>predicted_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_class  predicted_value predicted_class\n",
       "13124      Will Pay                0    Will not pay\n",
       "15228      Will Pay                1        Will Pay\n",
       "36808  Will not pay                0    Will not pay\n",
       "71838  Will not pay                0    Will not pay\n",
       "66579      Will Pay                1        Will Pay"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the actula and predicted values\n",
    "\n",
    "dt_model_df['predicted_class'] = dt_model_df.predicted_value.map(lambda x: 'Will Pay' if x >= 1 else 'Will not pay')\n",
    "dt_model_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.3 Model accuracy in validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dt model with dummy variable coding output: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Will Pay       0.98      0.62      0.76     14971\n",
      "Will not pay       0.13      0.85      0.23      1000\n",
      "\n",
      "    accuracy                           0.64     15971\n",
      "   macro avg       0.56      0.74      0.49     15971\n",
      "weighted avg       0.93      0.64      0.73     15971\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generating a confusion matrix for model accuracy\n",
    "\n",
    "print(\"The dt model with dummy variable coding output: \")\n",
    "\n",
    "confusion_matrix(dt_model_df.actual_class, dt_model_df.predicted_class)\n",
    "\n",
    "dt_report = (classification_report(dt_model_df.actual_class, dt_model_df.predicted_class))\n",
    "print(dt_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating model accuracy - parameters\n",
    "\n",
    "def measure_performance (clasf_matrix):\n",
    "    measure = pd.DataFrame({\n",
    "                        'sensitivity': [round(clasf_matrix[0,0]/(clasf_matrix[0,0]+clasf_matrix[0,1]),2)], \n",
    "                        'specificity': [round(clasf_matrix[1,1]/(clasf_matrix[1,0]+clasf_matrix[1,1]),2)],\n",
    "                        'recall': [round(clasf_matrix[0,0]/(clasf_matrix[0,0]+clasf_matrix[0,1]),2)],\n",
    "                        'precision': [round(clasf_matrix[0,0]/(clasf_matrix[0,0]+clasf_matrix[1,0]),2)],\n",
    "                        'overall_acc': [round((clasf_matrix[0,0]+clasf_matrix[1,1])/\n",
    "                                              (clasf_matrix[0,0]+clasf_matrix[0,1]+clasf_matrix[1,0]+clasf_matrix[1,1]),2)]\n",
    "                       })\n",
    "    return measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>sensitivity</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>overall_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sensitivity  specificity  recall  precision  overall_acc\n",
       "0         0.62         0.85    0.62       0.98         0.64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing model accuracy\n",
    "\n",
    "cm = metrics.confusion_matrix(dt_model_df.actual_class, dt_model_df.predicted_class)\n",
    "\n",
    "dt_model_metrics_df = pd.DataFrame(measure_performance(cm))\n",
    "dt_model_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.4 Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': ['gini', 'entropy'],\n",
       " 'max_features': [None, 'auto', 'log2', 'sqrt'],\n",
       " 'max_depth': [2, 3, 4],\n",
       " 'min_samples_split': [50, 75, 100, 120],\n",
       " 'min_samples_leaf': [50, 75],\n",
       " 'class_weight': ['balanced', None]}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the optimal paramters for the decision tree\n",
    "\n",
    "criterion = ['gini','entropy']     \n",
    "max_features = [None, 'auto', 'log2','sqrt']\n",
    "max_depth = [2,3,4]\n",
    "min_samples_split = [50,75,100,120]\n",
    "min_samples_leaf = [50, 75]\n",
    "class_weight = ['balanced',None]\n",
    "\n",
    "# Create the grid\n",
    "random_grid = {'criterion': criterion,\n",
    "               'max_features' :  max_features,\n",
    "               'max_depth' :  max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf' : min_samples_leaf,\n",
    "               'class_weight' : class_weight}\n",
    "random_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort='deprecated',\n",
       "                                              random_state=42,\n",
       "                                              splitter='best'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'class_weight': ['balanced', None],\n",
       "                         'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 4],\n",
       "                         'max_features': [None, 'auto', 'log2', 'sqrt'],\n",
       "                         'min_samples_leaf': [50, 75],\n",
       "                         'min_samples_split': [50, 75, 100, 120]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='precision', verbose=0)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the random grid to search for best hyperparameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "tree_model = tree.DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# Random search of parameters, using 3 fold cross validation \n",
    "best_tree_model = GridSearchCV(estimator = tree_model, \n",
    "                               param_grid = random_grid, \n",
    "                               scoring = \"precision\",\n",
    "                               cv = 3, verbose=0)\n",
    "# Fitting the random search model\n",
    "best_tree_model.fit(X_train, y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': 'balanced',\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 4,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 50,\n",
       " 'min_samples_split': 50}"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tuned parameters\n",
    "\n",
    "best_tree_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>actual_class</th>\n",
       "      <th>predicted_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_class  predicted_value\n",
       "13124      Will Pay                0\n",
       "15228      Will Pay                1\n",
       "36808  Will not pay                0\n",
       "71838  Will not pay                0\n",
       "66579      Will Pay                1"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PRedicting the outcomes with the optimal DT parameters\n",
    "\n",
    "best_tree_model_df = pd.DataFrame(get_predictions(y_test.actual_target, best_tree_model, X_test))\n",
    "best_tree_model_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_class</th>\n",
       "      <th>predicted_value</th>\n",
       "      <th>predicted_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74603</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40910</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55600</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57420</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>1</td>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7245</th>\n",
       "      <td>Will Pay</td>\n",
       "      <td>0</td>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       actual_class  predicted_value predicted_class\n",
       "13124      Will Pay                0    Will not pay\n",
       "15228      Will Pay                1        Will Pay\n",
       "36808  Will not pay                0    Will not pay\n",
       "71838  Will not pay                0    Will not pay\n",
       "66579      Will Pay                1        Will Pay\n",
       "74603      Will Pay                1        Will Pay\n",
       "40910      Will Pay                1        Will Pay\n",
       "55600      Will Pay                0    Will not pay\n",
       "57420      Will Pay                1        Will Pay\n",
       "7245       Will Pay                0    Will not pay"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mapping predicted outcomes to classes\n",
    "\n",
    "best_tree_model_df['predicted_class'] = best_tree_model_df.predicted_value.map(lambda x: 'Will Pay' \n",
    "                                                                               if x >= 1 else 'Will not pay')\n",
    "best_tree_model_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>sensitivity</th>\n",
       "      <th>specificity</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>overall_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sensitivity  specificity  recall  precision  overall_acc\n",
       "0         0.62         0.85    0.62       0.98         0.64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gerenating model accuracy \n",
    "\n",
    "cm = metrics.confusion_matrix(best_tree_model_df.actual_class, best_tree_model_df.predicted_class)\n",
    "\n",
    "best_dt_model_metrics_df = pd.DataFrame(measure_performance(cm))\n",
    "best_dt_model_metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.5 Predicting the outcomes in original test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 13 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   perc_premium_paid_by_cash_credit  34224 non-null  float64\n",
      " 1   age_in_days                       34224 non-null  int64  \n",
      " 2   Income                            34224 non-null  int64  \n",
      " 3   Count_3-6_months_late             34224 non-null  float64\n",
      " 4   Count_6-12_months_late            34224 non-null  float64\n",
      " 5   Count_more_than_12_months_late    34224 non-null  float64\n",
      " 6   application_underwriting_score    34224 non-null  float64\n",
      " 7   no_of_premiums_paid               34224 non-null  int64  \n",
      " 8   sourcing_channel_B                34224 non-null  uint8  \n",
      " 9   sourcing_channel_C                34224 non-null  uint8  \n",
      " 10  sourcing_channel_D                34224 non-null  uint8  \n",
      " 11  sourcing_channel_E                34224 non-null  uint8  \n",
      " 12  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(5), int64(3), uint8(5)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Summarizing the original test data\n",
    "\n",
    "final_test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': 'balanced',\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': 4,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 50,\n",
       " 'min_samples_split': 50}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing the tuned parameters\n",
    "\n",
    "best_tree_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a DT model with tunes parameters\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a DT model with tunes parameters\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(class_weight = 'balanced',\n",
    "                                 criterion = 'gini',\n",
    "                                 max_depth = 4,\n",
    "                                 max_features = 'auto',\n",
    "                                 min_samples_leaf = 50,\n",
    "                                 min_samples_split= 50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the training data\n",
    "\n",
    "clf_model = clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7653642653642654"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating score for the training\n",
    "\n",
    "clf.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>actual_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13124</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15228</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36808</th>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71838</th>\n",
       "      <td>Will not pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66579</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11658</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68955</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73494</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49078</th>\n",
       "      <td>Will Pay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15971 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_target\n",
       "13124      Will Pay\n",
       "15228      Will Pay\n",
       "36808  Will not pay\n",
       "71838  Will not pay\n",
       "66579      Will Pay\n",
       "...             ...\n",
       "3320       Will Pay\n",
       "11658      Will Pay\n",
       "68955      Will Pay\n",
       "73494      Will Pay\n",
       "49078      Will Pay\n",
       "\n",
       "[15971 rows x 1 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking witht the original outcomes\n",
    "\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping classes with outcome values\n",
    "\n",
    "y_test_mapped = y_test.actual_target.map(lambda x: 1 if x == 'Will Pay' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7625696575042265"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating score for validation data\n",
    "\n",
    "clf.score(X_test,y_test_mapped.to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting outcomes for original test data\n",
    "\n",
    "clf.predict(final_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retriving ID from the original test data\n",
    "\n",
    "temp_file = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          649\n",
       "1        81136\n",
       "2        70762\n",
       "3        53935\n",
       "4        15476\n",
       "         ...  \n",
       "34219    81988\n",
       "34220    30326\n",
       "34221    65749\n",
       "34222    65686\n",
       "34223    93826\n",
       "Name: id, Length: 34224, dtype: int64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_file['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34219</th>\n",
       "      <td>81988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34220</th>\n",
       "      <td>30326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34221</th>\n",
       "      <td>65749</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34222</th>\n",
       "      <td>65686</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34223</th>\n",
       "      <td>93826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34224 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  target\n",
       "0        649       1\n",
       "1      81136       1\n",
       "2      70762       1\n",
       "3      53935       1\n",
       "4      15476       1\n",
       "...      ...     ...\n",
       "34219  81988       1\n",
       "34220  30326       1\n",
       "34221  65749       1\n",
       "34222  65686       0\n",
       "34223  93826       1\n",
       "\n",
       "[34224 rows x 2 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe of respective IDs and the predicted outcomes\n",
    "\n",
    "final_outcome = pd.DataFrame()\n",
    "final_outcome['id'] = temp_file['id']\n",
    "final_outcome['target'] = clf.predict(final_test_df)\n",
    "final_outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    25468\n",
       "0     8756\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing the outcome results\n",
    "\n",
    "final_outcome['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the outcome to a file\n",
    "\n",
    "final_outcome.to_csv('outcomes_DT.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **End of document**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79853 entries, 0 to 79852\n",
      "Data columns (total 12 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   id                                79853 non-null  int64  \n",
      " 1   perc_premium_paid_by_cash_credit  79853 non-null  float64\n",
      " 2   age_in_days                       79853 non-null  int64  \n",
      " 3   Income                            79853 non-null  int64  \n",
      " 4   Count_3-6_months_late             79756 non-null  float64\n",
      " 5   Count_6-12_months_late            79756 non-null  float64\n",
      " 6   Count_more_than_12_months_late    79756 non-null  float64\n",
      " 7   application_underwriting_score    76879 non-null  float64\n",
      " 8   no_of_premiums_paid               79853 non-null  int64  \n",
      " 9   sourcing_channel                  79853 non-null  object \n",
      " 10  residence_area_type               79853 non-null  object \n",
      " 11  target                            79853 non-null  int64  \n",
      "dtypes: float64(5), int64(5), object(2)\n",
      "memory usage: 7.3+ MB\n"
     ]
    }
   ],
   "source": [
    "temp_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_train['Count_more_than_12_months_late'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_train['Count_more_than_12_months_late'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2869c0e1388>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2/ajtI7a/Y/uj3fz7o/909DxqIEE3SPplzb0W7t8l3Wz7SUmHJW2JiKdsf0DSzyTtlKSI+JV2ZB+z/ZH2ccrtYxUlvSRpd0TcYPsLkv5U0hc1d0nfjoj4nu0bJd0v6ZaV+osChBp59WRETEmS7WclbZL0hqTjEfGUJEXET9qfb5Y02t72ou1XJZ0JdSMi3pT0pu03JH29vf2opF+1fbmkmyR99ayX5K7O+O8GnINQI69OnvX705r7t2xJC90YcP7XkJ97nNmz/jzbPuaApNcj4uMXPypwaVijRi95UdKHbH9Cktrr06skfVvSHe1tH5G0UdJ3Ozlg+1v5y7Y/0/55274+i+GB8yHU6BkR8bakLZJGbT8n6V81t/Z8v6SC7aOaW8O+MyJOnv9I73GHpEr7mM9LunV5JwcujFvIASBxfKMGgMQRagBIHKEGgMQRagBIHKEGgMQRagBIHKEGgMT9PxrvByS+wSFLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_train_df['Income'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2869d4dec08>"
      ]
     },
     "execution_count": 172,
     "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": [
    "final_test_df['Income'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "z = np.abs(stats.zscore(boston_df))\n",
    "print(z)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}