Final_Project_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Internshala Data Science Challenge - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement\n",
    "\n",
    "Your client is an Insurance company and they need your help in building a model to predict whether the policyholder (customer) will pay next premium on time or not. \n",
    "\n",
    "An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that you pay regularly to an insurance company for this guarantee.\n",
    "\n",
    "Building a model to predict whether a customer would make the premium payment can be extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers who are less likely to pay and convince them to continue making timely payment.\n",
    "\n",
    "Now, in order to predict, whether the customer would pay the next premium or not, you have information about past premium payment history for the policyholders along with their demographics (age, monthly income, area type) and sourcing channel etc.\n",
    "\n",
    "### The Dataset\n",
    "Two csv files are provided namely `train.csv` and `test.csv`\n",
    "\n",
    "| ﻿Variable                         | Definition                                                                                                         |\n",
    "|----------------------------------|--------------------------------------------------------------------------------------------------------------------|\n",
    "| id                               | Unique ID of the policy                                                                                            |\n",
    "| perc_premium_paid_by_cash_credit | Percentage of premium amount paid by cash or credit card                                                           |\n",
    "| age_in_days                      | Age in days of policy holder                                                                                       |\n",
    "| Income                           | Monthly Income of policy holder                                                                                    |\n",
    "| Count_3-6_months_late            | No of premiums late by 3 to 6 months                                                                               |\n",
    "| Count_6-12_months_late           | No  of premiums late by 6 to 12 months                                                                             |\n",
    "| Count_more_than_12_months_late   | No of premiums late by more than 12 months                                                                         |\n",
    "| application_underwriting_score   | Underwriting Score of the applicant at the time of application (No applications under the score of 90 are insured) |\n",
    "| no_of_premiums_paid              | Total premiums paid on time till now                                                                               |\n",
    "| sourcing_channel                 | Sourcing channel for application                                                                                   |\n",
    "| residence_area_type              | Area type of Residence (Urban/Rural)                                                                               |\n",
    "| target                           | 1 - premium paid on time, 0 - otherwise                                                                            |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "1. [Importing Important Libraries](#importing_libraries)\n",
    "2. [Loading the Data](#load_data)\n",
    "3. [Univariate Analysis](#univariate_analysis)\n",
    "4. [Treating NULL Values](#treating_null_values)\n",
    "5. [Bivariate Analysis](#bivariate_analysis)\n",
    "6. [Preprocessing on Test Data](#test_data_preprocessing)\n",
    "7. [Split Data for Model Training](#splitting)\n",
    "8. [Model Building](#model_building)\n",
    "    1. [Using Linear Regression](#linear_regression)\n",
    "    2. [Using Logistic Regression](#logistic_regression)\n",
    "    3. [Using CatBoostRegressor](#catboostregressor)\n",
    "9. [Results and Summary](#summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Importing important libraries <a name=\"importing_libraries\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load and check the shape of the data <a name=\"load_data\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the train and test datasets\n",
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((79853, 12), (34224, 11))"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of the data\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110936</td>\n",
       "      <td>0.429</td>\n",
       "      <td>12058</td>\n",
       "      <td>355060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.02</td>\n",
       "      <td>13</td>\n",
       "      <td>C</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41492</td>\n",
       "      <td>0.010</td>\n",
       "      <td>21546</td>\n",
       "      <td>315150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>21</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31300</td>\n",
       "      <td>0.917</td>\n",
       "      <td>17531</td>\n",
       "      <td>84140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.69</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19415</td>\n",
       "      <td>0.049</td>\n",
       "      <td>15341</td>\n",
       "      <td>250510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.57</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99379</td>\n",
       "      <td>0.052</td>\n",
       "      <td>31400</td>\n",
       "      <td>198680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.87</td>\n",
       "      <td>12</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0  110936                             0.429        12058  355060   \n",
       "1   41492                             0.010        21546  315150   \n",
       "2   31300                             0.917        17531   84140   \n",
       "3   19415                             0.049        15341  250510   \n",
       "4   99379                             0.052        31400  198680   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    2.0                     3.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    0.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.02   \n",
       "1                             0.0                           99.89   \n",
       "2                             1.0                           98.69   \n",
       "3                             0.0                           99.57   \n",
       "4                             0.0                           99.87   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  target  \n",
       "0                   13                C               Urban       1  \n",
       "1                   21                A               Urban       1  \n",
       "2                    7                C               Rural       0  \n",
       "3                    9                A               Urban       1  \n",
       "4                   12                B               Urban       1  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check first 5 rows of train dataset\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>Income</th>\n",
       "      <th>Count_3-6_months_late</th>\n",
       "      <th>Count_6-12_months_late</th>\n",
       "      <th>Count_more_than_12_months_late</th>\n",
       "      <th>application_underwriting_score</th>\n",
       "      <th>no_of_premiums_paid</th>\n",
       "      <th>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0    649                             0.001        27384   51150   \n",
       "1  81136                             0.124        23735  285140   \n",
       "2  70762                             1.000        17170  186030   \n",
       "3  53935                             0.198        16068  123540   \n",
       "4  15476                             0.041        10591  200020   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    0.0                     0.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    1.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.89   \n",
       "1                             0.0                           98.93   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                           99.00   \n",
       "4                             0.0                           99.17   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  \n",
       "0                    7                A               Rural  \n",
       "1                   19                A               Urban  \n",
       "2                    2                B               Urban  \n",
       "3                   11                B               Rural  \n",
       "4                   14                A               Rural  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check first 5 rows of test dataset\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>perc_premium_paid_by_cash_credit</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>7.985300e+04</td>\n",
       "      <td>79756.000000</td>\n",
       "      <td>79756.000000</td>\n",
       "      <td>79756.000000</td>\n",
       "      <td>76879.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "      <td>79853.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57167.166368</td>\n",
       "      <td>0.314288</td>\n",
       "      <td>18846.696906</td>\n",
       "      <td>2.088472e+05</td>\n",
       "      <td>0.248671</td>\n",
       "      <td>0.078188</td>\n",
       "      <td>0.060008</td>\n",
       "      <td>99.067291</td>\n",
       "      <td>10.863887</td>\n",
       "      <td>0.937410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32928.970160</td>\n",
       "      <td>0.334915</td>\n",
       "      <td>5208.719136</td>\n",
       "      <td>4.965826e+05</td>\n",
       "      <td>0.691468</td>\n",
       "      <td>0.436507</td>\n",
       "      <td>0.312023</td>\n",
       "      <td>0.739799</td>\n",
       "      <td>5.170687</td>\n",
       "      <td>0.242226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7670.000000</td>\n",
       "      <td>2.403000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28640.000000</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>14974.000000</td>\n",
       "      <td>1.080100e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.810000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>57262.000000</td>\n",
       "      <td>0.167000</td>\n",
       "      <td>18625.000000</td>\n",
       "      <td>1.665600e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.210000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85632.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>22636.000000</td>\n",
       "      <td>2.520900e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.540000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>114076.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37602.000000</td>\n",
       "      <td>9.026260e+07</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>99.890000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  id  perc_premium_paid_by_cash_credit   age_in_days  \\\n",
       "count   79853.000000                      79853.000000  79853.000000   \n",
       "mean    57167.166368                          0.314288  18846.696906   \n",
       "std     32928.970160                          0.334915   5208.719136   \n",
       "min         2.000000                          0.000000   7670.000000   \n",
       "25%     28640.000000                          0.034000  14974.000000   \n",
       "50%     57262.000000                          0.167000  18625.000000   \n",
       "75%     85632.000000                          0.538000  22636.000000   \n",
       "max    114076.000000                          1.000000  37602.000000   \n",
       "\n",
       "             Income  Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "count  7.985300e+04           79756.000000            79756.000000   \n",
       "mean   2.088472e+05               0.248671                0.078188   \n",
       "std    4.965826e+05               0.691468                0.436507   \n",
       "min    2.403000e+04               0.000000                0.000000   \n",
       "25%    1.080100e+05               0.000000                0.000000   \n",
       "50%    1.665600e+05               0.000000                0.000000   \n",
       "75%    2.520900e+05               0.000000                0.000000   \n",
       "max    9.026260e+07              13.000000               17.000000   \n",
       "\n",
       "       Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "count                    79756.000000                    76879.000000   \n",
       "mean                         0.060008                       99.067291   \n",
       "std                          0.312023                        0.739799   \n",
       "min                          0.000000                       91.900000   \n",
       "25%                          0.000000                       98.810000   \n",
       "50%                          0.000000                       99.210000   \n",
       "75%                          0.000000                       99.540000   \n",
       "max                         11.000000                       99.890000   \n",
       "\n",
       "       no_of_premiums_paid        target  \n",
       "count         79853.000000  79853.000000  \n",
       "mean             10.863887      0.937410  \n",
       "std               5.170687      0.242226  \n",
       "min               2.000000      0.000000  \n",
       "25%               7.000000      1.000000  \n",
       "50%              10.000000      1.000000  \n",
       "75%              14.000000      1.000000  \n",
       "max              60.000000      1.000000  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will be working on the train data to build our model\n",
    "# describe the train data to check the numerical variations on the dataset\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'perc_premium_paid_by_cash_credit', 'age_in_days', 'Income',\n",
       "       'Count_3-6_months_late', 'Count_6-12_months_late',\n",
       "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
       "       'no_of_premiums_paid', 'sourcing_channel', 'residence_area_type',\n",
       "       'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Univariate Analysis <a name=\"univariate_analysis\"></a>\n",
    "We will analyze different variables in train data one at a time to check the variations and treat accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'perc_premium_paid_by_cash_credit'\n",
    "\n",
    "# Let's convert the values to percentages by adding a column 'percentage_by_cash_credit'\n",
    "train['percentage_by_cash_credit'] = train.perc_premium_paid_by_cash_credit.apply(lambda x: x * 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    79853.000000\n",
       "mean        31.428774\n",
       "std         33.491457\n",
       "min          0.000000\n",
       "25%          3.400000\n",
       "50%         16.700000\n",
       "75%         53.800000\n",
       "max        100.000000\n",
       "Name: percentage_by_cash_credit, dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.percentage_by_cash_credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.distplot(train.percentage_by_cash_credit)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'age_in_days'\n",
    "# Let's modify the age by adding a new column 'age_in_years' using 'age_in_days'\n",
    "train['age_in_years'] = train.age_in_days.apply(lambda x: x/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.distplot(train.age_in_years)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Finding and treating the NULL Values, if any. <a name=\"treating_null_values\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 97\n",
       "Count_6-12_months_late                97\n",
       "Count_more_than_12_months_late        97\n",
       "application_underwriting_score      2974\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "target                                 0\n",
       "percentage_by_cash_credit              0\n",
       "age_in_years                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values in train data\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will treat the columns with missing values as follows -\n",
    "\n",
    "- We can assume that there were no cases of late payments for columns `Count_3-6_months_late`, `Count_6-12_months_late`, and `Count_more_than_12_months_late`. Thus, we can replace the NULL values in these with `0`.\n",
    "- For `application_underwriting_score`, we can use the **mean** of present values in the column to replace the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treating null values for 'late payments' with 0s\n",
    "train['Count_3-6_months_late'].fillna(0, inplace=True)\n",
    "train['Count_6-12_months_late'].fillna(0, inplace=True)\n",
    "train['Count_more_than_12_months_late'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treating null values for 'application_underwriting_score' with mean value\n",
    "train['application_underwriting_score'].fillna(train[train['application_underwriting_score'].notnull()]['application_underwriting_score'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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",
       "percentage_by_cash_credit           0\n",
       "age_in_years                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data again for the null values\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, all the null values have been treated accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Bivariate Analysis <a name=\"bivariate_analysis\"></a>\n",
    "Analysing the correlation between all the numeric variables in the data and the possibilty of multi-collinearity before building the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "corr_plot = train.corr()\n",
    "mask = np.zeros_like(corr_plot, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr_plot, linewidth=.1, vmin=-1, vmax=1, cmap='viridis', annot=True, mask = mask)\n",
    "plt.xticks(rotation=75)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We achieve following observation from the heatmap above -\n",
    "\n",
    "- Variables `percentage_by_cash_credit`, `Count_3-6_months_late`, `Count_6-12_months_late`, `Count_more_than_12_months_late` share good enough correlation with `target`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's add up the total late payments into one new column 'total_late_count' and check for correlation again\n",
    "train['total_late_count'] = train['Count_more_than_12_months_late'] + train['Count_6-12_months_late'] + train['Count_3-6_months_late']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "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>total_late_count</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_late_count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.353315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.353315</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  total_late_count    target\n",
       "total_late_count          1.000000 -0.353315\n",
       "target                   -0.353315  1.000000"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['total_late_count','target']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly notice a stronger correlation between late payments and 'target' variables. This could positively affect our prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's calculate the percentage of late payments upon all the paid premiums into a new column 'perc_late_count'\n",
    "train['perc_late_count'] = train['total_late_count']/train['no_of_premiums_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>perc_late_count</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>perc_late_count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.333177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.333177</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 perc_late_count    target\n",
       "perc_late_count         1.000000 -0.333177\n",
       "target                 -0.333177  1.000000"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the correlation of 'perc_late_count' with 'target'\n",
    "train[['perc_late_count','target']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thus, we will include 'total_late_count' and not 'perc_late_count'  as its more correlated for late payments with target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 -0.005064\n",
       "perc_premium_paid_by_cash_credit   -0.240980\n",
       "age_in_days                         0.095103\n",
       "Income                              0.016541\n",
       "Count_3-6_months_late              -0.246469\n",
       "Count_6-12_months_late             -0.284034\n",
       "Count_more_than_12_months_late     -0.238482\n",
       "application_underwriting_score      0.067333\n",
       "no_of_premiums_paid                 0.022664\n",
       "target                              1.000000\n",
       "percentage_by_cash_credit          -0.240980\n",
       "age_in_years                        0.095103\n",
       "total_late_count                   -0.353315\n",
       "perc_late_count                    -0.333177\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the correlation values again for 'target' wrt to other variables\n",
    "train.corr()['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((79853, 16),\n",
       " Index(['id', 'perc_premium_paid_by_cash_credit', 'age_in_days', 'Income',\n",
       "        'Count_3-6_months_late', 'Count_6-12_months_late',\n",
       "        'Count_more_than_12_months_late', 'application_underwriting_score',\n",
       "        'no_of_premiums_paid', 'sourcing_channel', 'residence_area_type',\n",
       "        'target', 'percentage_by_cash_credit', 'age_in_years',\n",
       "        'total_late_count', 'perc_late_count'],\n",
       "       dtype='object'))"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will include variables only with better correlation for model building. \n",
    " Thus, we drop `id`, `perc_premium_paid_by_cash_credit`, `perc_late_count`, `age_in_days` from the model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's convert the categorical columns to numeric using pandas dummies\n",
    "train = pd.get_dummies(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79853 entries, 0 to 79852\n",
      "Data columns (total 21 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  percentage_by_cash_credit         79853 non-null  float64\n",
      " 11  age_in_years                      79853 non-null  float64\n",
      " 12  total_late_count                  79853 non-null  float64\n",
      " 13  perc_late_count                   79853 non-null  float64\n",
      " 14  sourcing_channel_A                79853 non-null  uint8  \n",
      " 15  sourcing_channel_B                79853 non-null  uint8  \n",
      " 16  sourcing_channel_C                79853 non-null  uint8  \n",
      " 17  sourcing_channel_D                79853 non-null  uint8  \n",
      " 18  sourcing_channel_E                79853 non-null  uint8  \n",
      " 19  residence_area_type_Rural         79853 non-null  uint8  \n",
      " 20  residence_area_type_Urban         79853 non-null  uint8  \n",
      "dtypes: float64(9), int64(5), uint8(7)\n",
      "memory usage: 9.1 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((79853, 21), None)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see `sourcing_channel` and `residence_area_type` columns has been sub divided into 5 and 2 different numeric cols respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Performing the same preprocessing on the test data as performed on train data. <a name=\"test_data_preprocessing\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'perc_premium_paid_by_cash_credit', 'age_in_days', 'Income',\n",
       "       'Count_3-6_months_late', 'Count_6-12_months_late',\n",
       "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
       "       'no_of_premiums_paid', 'sourcing_channel', 'residence_area_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of test data\n",
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the values of 'perc_premium_paid_by_cash_credit' to percentages\n",
    "test['percentage_by_cash_credit'] = test.perc_premium_paid_by_cash_credit.apply(lambda x: x * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "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>sourcing_channel</th>\n",
       "      <th>residence_area_type</th>\n",
       "      <th>percentage_by_cash_credit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>649</td>\n",
       "      <td>0.001</td>\n",
       "      <td>27384</td>\n",
       "      <td>51150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.89</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81136</td>\n",
       "      <td>0.124</td>\n",
       "      <td>23735</td>\n",
       "      <td>285140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.93</td>\n",
       "      <td>19</td>\n",
       "      <td>A</td>\n",
       "      <td>Urban</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>17170</td>\n",
       "      <td>186030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>Urban</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53935</td>\n",
       "      <td>0.198</td>\n",
       "      <td>16068</td>\n",
       "      <td>123540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11</td>\n",
       "      <td>B</td>\n",
       "      <td>Rural</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15476</td>\n",
       "      <td>0.041</td>\n",
       "      <td>10591</td>\n",
       "      <td>200020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.17</td>\n",
       "      <td>14</td>\n",
       "      <td>A</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  perc_premium_paid_by_cash_credit  age_in_days  Income  \\\n",
       "0    649                             0.001        27384   51150   \n",
       "1  81136                             0.124        23735  285140   \n",
       "2  70762                             1.000        17170  186030   \n",
       "3  53935                             0.198        16068  123540   \n",
       "4  15476                             0.041        10591  200020   \n",
       "\n",
       "   Count_3-6_months_late  Count_6-12_months_late  \\\n",
       "0                    0.0                     0.0   \n",
       "1                    0.0                     0.0   \n",
       "2                    0.0                     0.0   \n",
       "3                    0.0                     0.0   \n",
       "4                    1.0                     0.0   \n",
       "\n",
       "   Count_more_than_12_months_late  application_underwriting_score  \\\n",
       "0                             0.0                           99.89   \n",
       "1                             0.0                           98.93   \n",
       "2                             0.0                             NaN   \n",
       "3                             0.0                           99.00   \n",
       "4                             0.0                           99.17   \n",
       "\n",
       "   no_of_premiums_paid sourcing_channel residence_area_type  \\\n",
       "0                    7                A               Rural   \n",
       "1                   19                A               Urban   \n",
       "2                    2                B               Urban   \n",
       "3                   11                B               Rural   \n",
       "4                   14                A               Rural   \n",
       "\n",
       "   percentage_by_cash_credit  \n",
       "0                        0.1  \n",
       "1                       12.4  \n",
       "2                      100.0  \n",
       "3                       19.8  \n",
       "4                        4.1  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    34224.000000\n",
       "mean        31.445743\n",
       "std         33.405873\n",
       "min          0.000000\n",
       "25%          3.400000\n",
       "50%         16.900000\n",
       "75%         54.000000\n",
       "max        100.000000\n",
       "Name: percentage_by_cash_credit, dtype: float64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.percentage_by_cash_credit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cfQ9QCUw+7CrTyGs76lhWXcPFY0o4YWB+wsudVFLIBaOLeXHDblZtrunBCkVEpDclEtQLgTIzG2lm2cC1wJx2beYAN1jgHKDG3beY2aBwSxoz6wN8EFjTjfWnnMp12ynMzeSisuLDXvZDp5YwtKgPf1iyiZoDTT1QnYiI9LYug9rdm4HpwDxgNfB7d19pZjeb2c1hs7nABqAKuA/4+3D6EOAZM1tGEPhPuPsj3dyHlPHm3lY27KjjgtHFRzSYSWYsxjVnD6OppZW5y7f0QIUiItLbEvodtbvPJQjj+Gkz4h47cEsHyy0DzjzKGtPGU9Wt9MnKYOLIAUf8GsUFOVScPIgnV2/nhaqdnDf68LfMRUQkOjQGZUSs27aPFbucc08cSE7me3+KdTguLBvEgPxsbp+zkqaW1m6qUEREkkFBHRH3Vr5GdgzOGzXwqF8rKyPG5acNYf32Wn71whtHX5yIiCSNgjoCNu7ez5xXN3PukBh5Od0z/PopQ/py8ZjjuOPJ9WzXQCgiIscsBXUEPPTyWwC8b2j3fhz/dsVYGptb+cHja7v1dUVEpPcoqJOspdX549JNXFRWTFHOoUcgO1wjivO56fwRzF5azUr9tlpE5Jiky1wm2Quv7WRLTT3/evlYGjbu6fbX//v3j+Z3izbyvbmr+fVnJxFc6ExEpHf99qW3umxzfC/UcSzSFnWS/WFxNX1zM/nAKcf1yOv365PFrR8o4/mqXVSu29Ej6xARkZ6joE6iffVNPL5yKx8543hyO7g6Vnf51KQTGDEwj+89uppm/VxLROSYoqBOornLt1Df1MrHzyrt0fVkZ8a4bcoY1m+v5feLqnt0XSIi0r0U1En0h8WbGDUonzOGFfX4ui49dTDlJ/Tnx0+so7ahucfXJyIi3UNBnSRv7qrj5Td2c9WE0l45wcvM+JfLT2FnbQMzn32tx9cnIiLdQ0GdJH9cugkz+OiZQ3ttnWcO78+Hxx/PzL9uYGuNBkERETkWKKiTZO7yLUwcMYAh/fr06nq/dunJtLbCj+ZrEBQRkWOBgjoJqrbXsm5bLZedNqTX1z1sQB43nj+Ch5dUs2rz3l5fv4iIHB4FdRI8viK4VvSlpw5OyvpvqRhNvz5ZfHfuKoIrlIqISFQpqJPgsRVbOWt4EYP75SZl/f3y3hkEZd7KrUmpQUREEqOg7mVv7drPys17mTKu93d7x/u7c05gzOBCvv3Iag40tiS1FhER6VxCQW1mk81srZlVmdltHcw3M7srnL/MzM4Kpw8zs2fMbLWZrTSzW7u7A8eax1cGu70nj0vObu82mRkxvvWRU9m05wD3VFYltRYREelclxflMLMM4G7gEqAaWGhmc9x9VVyzKUBZeJsE3BveNwP/6O5LzKwQWGxmT7RbNq08tmIr44b2ZdiAvB5fVyKD4F95xvH87NkNfPysUkYU5/d4TSIicngS2aKeCFS5+wZ3bwRmAVPbtZkKPOiBF4EiMxvi7lvcfQmAu+8DVgO998PhiNlSc4Clb+1J+m7veN+47BSyMoxv/WVlsksREZEOJBLUQ4GNcc+reW/YdtnGzEYAZwIvHW6RqeLxFcGJW1OSvNs7XknfXL7ywZN4Zu0OHlm2OdnliIhIO4lcj7qj8S3b/6bnkG3MrAD4A/AVd+/wx7tmNg2YBlBSUkJlZWUCpR1bZr10gNIC462Vi+hop3SsuZ7cnWt6tabKyg2ManVG9ovxjf9bSuuWtfTNSc41q2tra1Pycz8c6f4eqP+p2//cusYu29R6Y8r2/2gkEtTVwLC456VA+02vTtuYWRZBSP/G3Wd3thJ3nwnMBCgvL/eKiooESjt27NjXwLp5T/Lli8uoqDipwzazH51PffGYXq2rYtJwAIafuo/L71rA4zv7cs+nJvRqDW0qKytJtc/9cKX7e6D+p27/EzlnZsCBDSnb/6ORyK7vhUCZmY00s2zgWmBOuzZzgBvCs7/PAWrcfYsFV5v4BbDa3X/crZUfY+av2oo7TDktOru945WVFPKVS8qYu3yrdoGLiERIl0Ht7s3AdGAewclgv3f3lWZ2s5ndHDabC2wAqoD7gL8Pp58P/B1wsZm9Et4u6+5OHAseW76VUcX5nFxSmOxSOjXtwlGML+3Hv/15JTv2NSS7HBERIbFd37j7XIIwjp82I+6xA7d0sNwCOj5+nVbermvkbxt28YWLRvXKJS2PVGZGjP++ejwf/skCvvq7V/jVZyaSEYtuvSIi6UAjk/WCJ1Zvo6XVI/WzrM6cVFLIf0w9lQVVO7nzqfXJLkdEJO0pqHvB4yu2Utq/D+OG9k12KQn5RPkwrppQyk+eXk/l2u3JLkdEJK0pqHvYvvomFqzfyeRTB0d6t3c8M+PbU8dxckkhX/3dK2zacyDZJYmIpC0FdQ97es12GltamZKEa08fjT7ZGdzzqbNoanE++8BCag40JbskEZG0pKDuYXOXb6Gkbw5nDitKdimHbdSgAmZcP4HXdtQy7cFF1DfpKlsiIr1NQd2D6hqaeXbdDiafOpjYMXr29AVlxfzw6vG89Ppu/uH3r9DS2n5QOhER6UkJ/TxLjszTa7ZT39TK5acfn+xSOpXIaEEA/3r5KXzn0dX067OC71457pj94iEicqxRUPegR5dt4bjCHMpP6J/sUo7a5y4cxe66Ru6pfI2Gphb+66rTyczQDhkRkZ6moO4htQ3NPLN2O9dNHJ4yW5//fOnJ5GVn8MP566hrbOau684kJzMj2WWJiKQ0bRL1kKdWb6OhuZXLTz+2zvY+FDNj+sVl3P7hscxbuY3PPLCQPfu7viKOiIgcOQV1D3l0WXC294Thx/5u7/ZuOn8kP7x6PC+/vpuP/PR51mzt8MqlIiLSDRTUPWBffROV63YwZdyQlNnt3d5VE0qZNe1c6pta+Ng9L/Dosi3JLklEJCUpqHvA02u209jcyhUptNu7IxNO6M9fvnQBYwYXcstvl/D1h5exr14Do4iIdCcFdQ94ZNkWBvfN5awU3O3dXknfXB6adg5frDiR/1u8kcl3/JUXN+xKdlkiIilDZ313s731TTy7bgfXTzohpXZ7d/V762H98/j8haN4fOVWrrvvRa4pH8Y/X3oyAwtyeqlCEZHUpC3qbvbosi00Nrfy4fGpvdu7IycMzOexWy/ks+eP5OHF1Vz8o2f537+9odHMRESOgoK6mz28uJrRxxVwxjE4tnd3yMvO5F+vGMtjt17Iqcf35Zt/XsmldzzHY8u34K7AFhE5XArqbrRhRy2L33ybqyaUHjOXtOwpZSWF/OZzk5hx/QQAvvibJXz4pwt4avU2BbaIyGFI6Bi1mU0G7gQygJ+7+/fbzbdw/mXAfuBGd18SzrsfuALY7u7jurH2yPnDkmpiBh87c2iyS0majo5l33jeCF7ZuIenVm/js79aREnfHL4+eQwfHn88WRqGVETkkLr8K2lmGcDdwBRgLHCdmY1t12wKUBbepgH3xs17AJjcHcVGWUur84fFm3jfSYM4rm9ussuJlJgZZw3vzz9ccjJXTyjFHf7h969y0X89w0+eWs+OfQ3JLlFEJLIS2aKeCFS5+wYAM5sFTAVWxbWZCjzowT7NF82syMyGuPsWd3/OzEZ0c92R83zVTrbureebV7T/DiNtMmLGmcP7M35YEUP65fLAC2/woyfWcdfT65kybggnZ7VwUaun1NnyIiJHK5GgHgpsjHteDUxKoM1QIOHhqsxsGsHWOCUlJVRWVia6aCTMeLWe/CzI3rmGysq1R/QaseZ6cneu6ebKoqlmF3x0MJzfN5Pnt7TyxMrNzGmG+5bPpfy4GGcNijH2uPT7aVdtbe0x92+/O6n/qdv/3LqurwtQ640p2/+jkUhQd7R50/5soETaHJK7zwRmApSXl3tFRcXhLJ5UNQeaWPrkk1xz9glccvGRH4af/eh86ovHdGNl0dcXmDIcPtjSymvr1vDi2314amMtT25s5aSSXC4/7Xg+OPY4xg7pmxYn6FVWVnIs/dvvbup/6va/q7EYAAYc2JCy/T8aiQR1NTAs7nkpsPkI2qSs2UuqaWhu5aoJpcku5ZiVlRHjjEExxpwykn31TazYvJdte+u546l1/M+T6xhUmMNFZYOoOHkQF5YVU5SXneySRUR6RSJBvRAoM7ORwCbgWuCT7drMAaaHx68nATXunhZXaWhpde5//nUmnNCf00vT87fT3a0wN4tzRw3kk5OGs31fPc+t20nl2u08uXrbwTPrxw8r4oLRxZw5vIgzhvVnQL6CW0RSU5dB7e7NZjYdmEfw86z73X2lmd0czp8BzCX4aVYVwc+zbmpb3sweAiqAYjOrBm539190d0eSZd7KrWzcfYB/ueyUZJeSko4rzOWqCaVcNaGUllbn1eo9PLt2B5XrdnD3M1W0DXp2wsA8zhxWdPBktZNLCumTnZHc4kUEgPqmFlZv2cvGt/ez6e0DbK6pp7mllYyYkREzBuRlM7qkgHP7tXBBSyuZ+tnmuyT0O2p3n0sQxvHTZsQ9duCWTpa97mgKjLr7/rqBEwbmccnYwckuJeV0dkyrpG8u15QP48ozjmfTngMUF+Sw9K23eeG1XfzpleCIi1kw/vhJJQWUlRRyckkhZSUFjCouUICL9AJ355WNe3h4cTVzXt3MvvpmYhb8/x1TUkhOVoyWVqel1dlSU8/Tq7fzFHDfqqeZ/v7RfOLsYeRk6v8q6KIcR2Xxm7tZ+tYevvWRU8nQT4p6XU5mBqOKC/jkpOFA8IdhS009y6r3sHZrLeu272P9tn1Urt1Bc9x444W5mQzMz2ZAfg4D8rMZWJAdPs/mcxeOSlZ3RFKCu/Pc+p3c+eQ6lry1h9ysGFPGDaEoL4sRA/M7HeRof0MzuzZtYFFNHt/880pmPLuBWz9QxlUTStP+J5sK6qNw33Ov069PFleX6ySyZOpoy3tQYQ6DCnM4/8RiWlqdnbUNbN/XwM7aBnbXNrKrroGq7fvYW9/8ruV+8nQVJwzMo7R/H4YWBbfS/nkM7d+H0v59KMzN6q1uiRxT3J1n1m7nzqeqeHXjHoYW9eHbU0/lyjOHUpib1eVZ33k5mYweksnXrj2Xv67fyY+eWMfX/rCMvyzbzI+uHp/WA0kpqI/Qm7vqmLdqK19834nkZettjLKMmFHSN5eSDv6jNza3snt/48Hw3l3XyO66Rl5+/W327N/2ri1xgNysGP3zshk3tF8Y4sHt+KI+DO6by8CCHO1dkbTi7jy5ejt3PbWe5ZtqKO3fh//82Gl8/KxSsjMP/1izmXHRScGvO37z0lt859FVXHrHc3z/46dz6anpeYhRCXOE7n6misyY8enzRiS7FDkK2ZkxBvfNZXAHIe7u1DY0s2d/E2/vb3zX/Zu76nihaid1jS3vWiYjZhxXmENJ+JqD++WGXxJyGNw3l+P65jAwP4d+fbLSfneeHNtaW535q7Zy11NVrNqyl+ED8vivj5/OR88a2i1j+JsZ159zAueMGshXfreUL/zvYr5YcSL//KGT0+7/joL6CKzYVMP/La7mcxeM7HArTVKDmVGYm0VhbhbDBuS9a94nJw3H3dmzv4lNew6wac8Btu2tZ2tNPdv2NrBtbz1VO2p5vmon+xqa3/PaGTELjo/nZzOoMIeB+dkMLMihZnsj2/M3MrAgm+KCnIP3uVk6qUaiobmllcdWbOXuZ6pYs3UfI4vz+dHV45l6xvE9crb26OMKmP3F8/n3v6zk3srXeGvXfn70ifFp9X9CQX2Y3J3/eGQV/fOymX5xWbLLkSTp6HhbZixGaf88Svu/O9QbmlvYd6CZmvomahuaqWtofue+vpk3dtaxcvNeahuaaWxu5eF1y97z2m1b/gPCk97edcvLpn+75337ZKbFSG7Se3bXNTJr4Vv8+m9vsrmmnlGD8rnjmjO44vQhPf5zquzMGN+9chwjB+bzvcdWs2nPAe67oZxBhekxzLCC+jA9vmIrL7++m+9cOY5+fXRikXQtJzODnMIMihP4oxLbtppdhSceDPH4YG97vH1fPXUNLdQ1NL/nGHqbzJhRlJfNgPysgwHePy/Ygm8L9aK8bPrmZtK3TxZ9c7MozM1Mq60U6Vp9UwvPrNnOn1/ZzNNrt9PY3Mr5ox/VUI8AABDjSURBVAfyranjuHjMcb16PoaZ8fmLRjF8YB63zlrKR+95nl/eeDZlJYW9VkOyKKgPQ31TC997bDVjBhdy7dnDul5A5DBlZxj984JQTURjcyt1jc3sb2ihrjEI8rrGFvaH93UNzWytqee1HXXUNTRzoLHlkIPwZ2fG6JubRd8+meF91rvCvG16YW5wX5CbSUFOcCvMzSQ/J1PXGD+GuTtv7NrPgvU7WFC1k+erdlHb0ExxQQ6fnDic6yYO5+TByQ3GS08dzO+/cC6f/dUiPnbPC9x7/QQuKCtOak09TUF9GH6x4HU27j7Abz43SSPnSCRkZ8bIzsym3d72TrW6cyAM8ANNLdQ3tVDf1Br3uIUDTa3UNwVtdtY2vKtNSydb8PFys2IU5ARh3hbiBbmZFOYEQd4W7u+dn8XWulZ27GugMDeTnMyYdt/3oJr9Tby5u443du1n7da9rNi0lxWbatgVXuVqaFEfPjx+CJefdjznjBrQ6d+8RC620d1OLy3iT7ecz2d+uZAbf/ky37lyHNdOHN7rdfQWBXWCFr/5Nnc8uY4p4wZz/ujU/vYmqStmRn4YmEeiqSUI8QONLTQ0t1Lf3EJDUysNzUGYNzQHjxua3pm3pf4ADbuC5Rqag/tD5v1fnwSC3ffxW+wF7UK+T1YmuVkxcjIzyM2KkZuVQU5mcN82PSczRmZGjIyYkRkOVxn/ODMWIyMjeB6zcHpGXFsL7qP2haG5pZX65lYamlqoD9/Tg1+oGlvYW9/E3gNN4X0ze+ubqDnQxI59DazfXsu++ibqm1oPvl7biGEjBuZzQVkxowcVMP3i0ZHrd7yhRX14+IvnMv23S7lt9nJe21HLbVNOScmfRyqoE7CrtoHpv13C4H65fP9jpye7HJGkycqIkZURO6qBX9yd5lYPQj0MmoYwxFverqY2t4QxQwoPHqOvjTtev7uukbd27WdvffPBcOrsOH13M4IvOhkZRswgw4JwNwvO4o+ZEYsF82LW9jxcJmwXiwv+WLhcWxi2tLZSs/cAfZY+S3Or09rqB+/3NTQffN7U0nroLzrtxIyDhy6KC7I5rjCHEwflHzxnYUBB8KuD9ocsohzSbQpzs/jFp8v59iOruO+vr/P6zv3cee0ZR/xFNKpSqzc9oKXVuXXWK+yqa2T2F8+jX55OIBM5GmZGVoaRlRGjoN0f1NysGPXFAwEo6pPYcfqWVqe5tZWmFqe5pZXmFqepNbwPQ63VPbi1xj1um97q727jhNOCmzs4vPPYgy8b75pGsNzBeeE0D1/X4Z3H4eu1tDrHFeYEyxHsQchqqmPwoAJibVv9YbC/vrOOWMzIihmZGTEyM4ysWOzg+5iZETzOjMXIzozRJ9yr0Ccrg+wjPISQjF3aRyIzI8a3po5j1KACvvWXlXz83he451NnMWpQQbJL6zYK6i78zxPrWFC1k+9/7DTGDe2X7HJEpJ1gd3YGqbARlbvzbeqLB71n+pnD+yehmmPLp88bwcjifG6dtZQP/2QB//nx0/nI+OOTXVa30BlRnXB3fjhvLT99popPlJdyjc7yFhGJtItOGsSjX76QMUP68uWHlvL//ric2g4GHDrWKKg70NzSyjdmL+enz1Rx7dnD+N5HTzsmjteIiKS744v6MGvaOXzholE89PJbXPLjZ5m/cmuyyzoqCup23q5r5Iu/WcKshRuZ/v7R/OfHTtNPsUREjiFZGTG+cdkpPHzzefTrk8W0/13MtAcXUbW9NtmlHZEUOKrTPVpbnd8t2sgPHl/Dvvpmbv/wWG46f2SyyxIRkSM04YT+/OVLF/CLBa9z55PreWL1s1x+2hCmXzyaMYP7Jru8hKV9UDc0t/DU6u387LkNvLpxDxNHDuDbU8clffQdERE5elkZMW5+34lcNaGUXyx4nQdfeINHlm1h0sgBXHnmUC4bNyTyv+ZJKKjNbDJwJ5AB/Nzdv99uvoXzLwP2Aze6+5JElk2GPfsbWbpxz8ExbGsONDGkXy4//sR4PnrmUB2PFhFJMcUFOXx98hi+cNEofv3im8xesolvzF7O7X9eyaRRA5g4YgATRw5g/LCiyI1532VQm1kGcDdwCVANLDSzOe6+Kq7ZFKAsvE0C7gUmJbhsj5q7fAvVb+9n857gEoTrtu1jw846IBh+8dJTB3P1hFLOH12ckiPaiIjIO4rCKx/e8v7RrNi0lz+/sokFVTv50RPrADALRj0bWZzPiIH5DCx456I2OZmxg4P+jBqUz/FFfXql5kS2qCcCVe6+AcDMZgFTgfiwnQo86O4OvGhmRWY2BBiRwLI96ruPBpdEK8jJZEi/XE48roCryks5Y1gR40uLUm4EGxER6ZqZcVppP04rDcbH2LO/kYVvvM3KzTVs2FHH6zvrmFMd7HHtyL9dMZbPXNA75zElklJDgY1xz6sJtpq7ajM0wWUBMLNpwLTwaa2ZrU2gtsOysrtfsPsVAzuTXUQSpXv/Qe+B+q/+HxP9/+wP4LPd+5IndDYjkaDuaH9w+5FmO2uTyLLBRPeZwMwE6klZZrbI3cuTXUeypHv/Qe+B+q/+p3P/O5NIUFcD8cNylQKbE2yTncCyIiIi0olERvJYCJSZ2UgzywauBea0azMHuMEC5wA17r4lwWVFRESkE11uUbt7s5lNB+YR/MTqfndfaWY3h/NnAHMJfppVRfDzrJsOtWyP9CQ1pPWuf9R/0Hug/qe3dO9/hyw4UVtERESiSINYi4iIRJiCWkREJMIU1BFhZpPNbK2ZVZnZbcmup6eZ2TAze8bMVpvZSjO7NZw+wMyeMLP14X3/ZNfak8wsw8yWmtkj4fO06X84MNLDZrYm/Hdwbpr1/6vhv/0VZvaQmeWmev/N7H4z225mK+KmddpnM/tG+DdxrZldmpyqk09BHQFxQ61OAcYC15nZ2ORW1eOagX9091OAc4Bbwj7fBjzl7mXAU+HzVHYrsDrueTr1/07gcXcfA4wneB/Sov9mNhT4MlDu7uMITra9ltTv/wPA5HbTOuxz+PfgWuDUcJl7wr+VaUdBHQ0Hh2l190agbajVlOXuW9ou3OLu+wj+SA8l6Pevwma/Aq5MToU9z8xKgcuBn8dNTov+m1lf4CLgFwDu3ujue0iT/ocygT5mlgnkEYwxkdL9d/fngN3tJnfW56nALHdvcPfXCX5VNLFXCo0YBXU0dDYEa1owsxHAmcBLQEn4G3zC++OSV1mPuwP4GtAaNy1d+j8K2AH8Mtz1/3MzyydN+u/um4AfAm8BWwjGnphPmvS/nc76nNZ/F+MpqKMh4aFWU42ZFQB/AL7i7nuTXU9vMbMrgO3uvjjZtSRJJnAWcK+7nwnUkXq7eTsVHoedCowEjgfyzez65FYVOWn7d7E9BXU0JDJMa8oxsyyCkP6Nu88OJ28Lr7xGeL89WfX1sPOBj5jZGwSHOi42s1+TPv2vBqrd/aXw+cMEwZ0u/f8g8Lq773D3JmA2cB7p0/94nfU5Lf8udkRBHQ1pN9SqmRnB8cnV7v7juFlzgE+Hjz8N/Lm3a+sN7v4Ndy919xEEn/fT7n496dP/rcBGMzs5nPQBgsvfpkX/CXZ5n2NmeeH/hQ8QnKeRLv2P11mf5wDXmlmOmY0EyoCXk1Bf0mlksogws8sIjlm2DbX63SSX1KPM7ALgr8By3jlG+/8IjlP/HhhO8Mfsandvf/JJSjGzCuCf3P0KMxtImvTfzM4gOJEuG9hAMPRwjPTp/7eAawh+AbEU+BxQQAr338weAioILme5Dbgd+BOd9NnM/gX4DMF79BV3fywJZSedglpERCTCtOtbREQkwhTUIiIiEaagFhERiTAFtYiISIQpqEVERCJMQS0iIhJhCmqRiDCzr5hZXi+t60Yz+2lvrCuqdZjZG2ZWHD5+IbwfYWafTEY9Ip1RUIschfDKR93lKwRXUZIjdKSfh7ufFz4cASioJVIU1JL2wq2oNWb2KzNbZmYPh0M7TjCzZ81ssZnNixuPuNLMvmdmzwK3mtnZZvaCmb1qZi+bWaGZZZjZf5vZwvA1vxAuWxEu/3C4zt9Y4MsEF2d4xsyeCdvea2aLzGxlOIpVW72XhcsuMLO7zOyRcHq+md0frnOpmXV1qdRhZva4ma01s9vD1/i2md0at67vhrV19t59zcyWh33/fjjt82ENr5rZH9r2EpjZ1Wa2Ipz+XNzLHB/Wsd7M/quLz2qymS0JX+OpcNq/m9lMM5sPPGhmg8L1Lgxv54ftBprZ/PC9+RlxF30ws9rw4feBC83sFTP7ahfvn0jvcHfddEvrG8FWlAPnh8/vB/4ZeAEYFE67hmBoV4BK4J7wcdvwl2eHz/sSXBlqGvCv4bQcYBHBlZIqgBqCCwzEgL8BF4Tt3gCK4+oaEN5nhOs8HcgluPTfyHDeQ8Aj4ePvAdeHj4uAdUB+J32+keDyigOBPsAKoDx8L5aEbWLAa8DATl5jSvge5bWrd2Bcm+8AXwofLweGttUXV8cGoF/YtzeBYZ2sb1C7vret79+BxUCf8Plv497T4QTjyQPcBfxb+Pjy8DMvDp/XhvcVbe+nbrpF5dadu+1EjmUb3f358PGvCcYdHwc8EVwzgQyCYGvzu/D+ZGCLuy8E8PBSnWb2IeB0M7sqbNeP4KICjcDL7l4dtnuFIBwXdFDTJ8xsGkHwDwHGEoTnBnd/PWzzEMGXAoAPEVyR65/C57mEQdVJn59w911hHbMJwu0OM9tlZmcCJcDStjYd+CDwS3ffH/a9bUzqcWb2HYIvCwXAvHD688ADZvZ7gqtFtXnK3WvCOlYBJ/Du6xC3OQd4rq3v/u4xsOe4+4G4usaGnxtAXzMrBC4CPhYu+6iZvd1Jv0QiRUEtEmg/6P0+YKW7n9tJ+7rw3jpYtm36l9x93rsmBhfgaIib1EIH/w8tuFrQPxFsqb9tZg8QBG9H1+iNX+fH3X3tIdrEa1932/OfE2zpDibYu3Co9XXU9weAK939VTO7kWArFXe/2cwmEWzNvmLBRTkggfeji/XBO58HBF9mzo0L7mDhILh1cQM55ugYtUhguJm1hfJ1wIvAoLZpZpZlZqd2sNwagmOsZ4ftCsMTmuYBX7TgmtuY2Ulmlt9FDfuAwvBxX4LwqTGzEoLdzG3rG2VmI8Ln18QtPw/4koWJFG4VH8olZjbAzPoAVxJs8QL8EZgMnM07W8MdmQ98Ju4Y9IBweiGwJez7p9oam9mJ7v6Su/8bsJN3X2s4EX8D3hd+iYlfX0d1TY9bb9sXgufa6jGzKUD/DpaN/wxEIkFb1CKB1cCnw5OM1gM/IQipu8ysH8H/lTuAlfELuXujmV0D/CQMvAMEu15/Tni8NwzOHQRheCgzgcfMbIu7v9/Mlobr20AYou5+wMz+HnjczHby7uvzfjuscVm4zjeAKw6xvgXA/wKjgd+6+6K4Pj0D7HH3ls4WdvfHwxBcZGaNwFyCQwbfJLhc6ZsEx6Xbgu+/zayMYMv4KeBV4Iz3vHDn69sRHgqYbWYxYDtwSQdNvwzcbWbLCD6354CbgW8BD5nZEuBZgksqtrcMaDazV4EH3P1/Eq1PpKfoMpeS9sKt00fcfVySS0mImRW4e20YxncD67szUMIQXEJwXeD13fW6InJktOtb5Njz+fAktJUEJ6n9rLte2MzGAlUEJ3gppEUiQFvUIinMzC4FftBu8uvu/tHDeI3TCHaRx2tw90lHW98h1vkSwc/a4v2duy/vqXWKRJWCWkREJMK061tERCTCFNQiIiIRpqAWERGJMAW1iIhIhP1/jkYSB9fjG9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.distplot(test.percentage_by_cash_credit)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting 'age_in_days' to 'age_in_years'\n",
    "test['age_in_years'] = test.age_in_days.apply(lambda x: x/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.distplot(test.age_in_years)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                     0\n",
       "perc_premium_paid_by_cash_credit       0\n",
       "age_in_days                            0\n",
       "Income                                 0\n",
       "Count_3-6_months_late                 31\n",
       "Count_6-12_months_late                31\n",
       "Count_more_than_12_months_late        31\n",
       "application_underwriting_score      1323\n",
       "no_of_premiums_paid                    0\n",
       "sourcing_channel                       0\n",
       "residence_area_type                    0\n",
       "percentage_by_cash_credit              0\n",
       "age_in_years                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check and treat the missing values, if any.\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We treat the null values in the same way as treated for train data.\n",
    "# Treating null values for 'late payments' with 0s\n",
    "test['Count_3-6_months_late'].fillna(0, inplace=True)\n",
    "test['Count_6-12_months_late'].fillna(0, inplace=True)\n",
    "test['Count_more_than_12_months_late'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treating null values for 'application_underwriting_score' with mean value\n",
    "test['application_underwriting_score'].fillna(test[test['application_underwriting_score'].notnull()]['application_underwriting_score'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "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",
       "percentage_by_cash_credit           0\n",
       "age_in_years                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check back again for null values\n",
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Totaling up all the late payments to 'total_late_count'\n",
    "test['total_late_count'] = test['Count_more_than_12_months_late'] + test['Count_6-12_months_late'] + test['Count_3-6_months_late']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating percentage of late payments\n",
    "test['perc_late_count'] = test['total_late_count']/test['no_of_premiums_paid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the categorical columns to numeric\n",
    "test = pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34224 entries, 0 to 34223\n",
      "Data columns (total 20 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   percentage_by_cash_credit         34224 non-null  float64\n",
      " 10  age_in_years                      34224 non-null  float64\n",
      " 11  total_late_count                  34224 non-null  float64\n",
      " 12  perc_late_count                   34224 non-null  float64\n",
      " 13  sourcing_channel_A                34224 non-null  uint8  \n",
      " 14  sourcing_channel_B                34224 non-null  uint8  \n",
      " 15  sourcing_channel_C                34224 non-null  uint8  \n",
      " 16  sourcing_channel_D                34224 non-null  uint8  \n",
      " 17  sourcing_channel_E                34224 non-null  uint8  \n",
      " 18  residence_area_type_Rural         34224 non-null  uint8  \n",
      " 19  residence_area_type_Urban         34224 non-null  uint8  \n",
      "dtypes: float64(9), int64(4), uint8(7)\n",
      "memory usage: 3.6 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((34224, 20), None)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape, test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have completed with the usual exploration/transformation to test data\n",
    "\n",
    "### 7. Split data for model training <a name=\"splitting\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the train data using train_test_split needed for model training\n",
    "train_data, val_data = train_test_split(train, test_size=0.3, stratify=train['target'], random_state=121)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_data.drop(['id','target','perc_late_count','perc_premium_paid_by_cash_credit','age_in_days'], axis=1)\n",
    "y_train = train_data['target']\n",
    "x_val = val_data.drop(['id','target','perc_late_count','perc_premium_paid_by_cash_credit','age_in_days'], axis=1)\n",
    "y_val = val_data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55897, 16)\n",
      "Index(['Income', 'Count_3-6_months_late', 'Count_6-12_months_late',\n",
      "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
      "       'no_of_premiums_paid', 'percentage_by_cash_credit', 'age_in_years',\n",
      "       'total_late_count', 'sourcing_channel_A', 'sourcing_channel_B',\n",
      "       'sourcing_channel_C', 'sourcing_channel_D', 'sourcing_channel_E',\n",
      "       'residence_area_type_Rural', 'residence_area_type_Urban'],\n",
      "      dtype='object')\n",
      "----------------------------------------------------------------------------------------------------\n",
      "(23956, 16)\n",
      "Index(['Income', 'Count_3-6_months_late', 'Count_6-12_months_late',\n",
      "       'Count_more_than_12_months_late', 'application_underwriting_score',\n",
      "       'no_of_premiums_paid', 'percentage_by_cash_credit', 'age_in_years',\n",
      "       'total_late_count', 'sourcing_channel_A', 'sourcing_channel_B',\n",
      "       'sourcing_channel_C', 'sourcing_channel_D', 'sourcing_channel_E',\n",
      "       'residence_area_type_Rural', 'residence_area_type_Urban'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_train.columns, sep='\\n')\n",
    "print('--'*50)\n",
    "print(x_val.shape, x_val.columns, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test.drop(['id','perc_late_count','perc_premium_paid_by_cash_credit','age_in_days'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Model Building <a name=\"model_building\"></a>\n",
    "#### Using Linear Regression <a name=\"linear_regression\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lin_reg.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98745833, 1.03189449, 0.95489563, ..., 0.84676596, 1.00421326,\n",
       "       0.95631295])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15708938952768015"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15504330206873773"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, r2 score value does not differ much for the train and validation data. We can depict that our model fits well to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make prediction on the test data\n",
    "new_pred = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can now save the obtained prediction as 'target' values on 'submission' file\n",
    "new_frame = pd.DataFrame()\n",
    "new_frame['id'] = test['id']\n",
    "new_frame['target'] = new_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the new_frame as output to 'submission' file\n",
    "new_frame.to_csv('submission_Lin_Reg.csv', header=True, index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using Logistic Regression <a name=\"logistic_regression\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression(solver = 'liblinear', penalty = 'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9385957588913008"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9396926489793728"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_pred = log_reg.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01192128, 0.98807872],\n",
       "       [0.02151781, 0.97848219],\n",
       "       [0.08619831, 0.91380169],\n",
       "       ...,\n",
       "       [0.03792764, 0.96207236],\n",
       "       [0.2505939 , 0.7494061 ],\n",
       "       [0.02255561, 0.97744439]])"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prob = np.array(list(map(lambda x: x[1], log_reg_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_frame = pd.DataFrame()\n",
    "log_frame['id'] = test['id']\n",
    "log_frame['target'] = final_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_frame.to_csv('submission_log_reg.csv', header=True, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using CatBoostRegressor <a name=\"catboostregressor\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbr = CatBoostRegressor(loss_function='RMSE',\n",
    "                        eval_metric='AUC',\n",
    "                        use_best_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.09857\n",
      "0:\ttest: 0.8130533\tbest: 0.8130533 (0)\ttotal: 16.3ms\tremaining: 16.3s\n",
      "1:\ttest: 0.8208001\tbest: 0.8208001 (1)\ttotal: 30ms\tremaining: 15s\n",
      "2:\ttest: 0.8249546\tbest: 0.8249546 (2)\ttotal: 43.1ms\tremaining: 14.3s\n",
      "3:\ttest: 0.8347196\tbest: 0.8347196 (3)\ttotal: 56.5ms\tremaining: 14.1s\n",
      "4:\ttest: 0.8361806\tbest: 0.8361806 (4)\ttotal: 71ms\tremaining: 14.1s\n",
      "5:\ttest: 0.8356806\tbest: 0.8361806 (4)\ttotal: 84.5ms\tremaining: 14s\n",
      "6:\ttest: 0.8363008\tbest: 0.8363008 (6)\ttotal: 99.1ms\tremaining: 14.1s\n",
      "7:\ttest: 0.8365509\tbest: 0.8365509 (7)\ttotal: 113ms\tremaining: 14.1s\n",
      "8:\ttest: 0.8379002\tbest: 0.8379002 (8)\ttotal: 129ms\tremaining: 14.2s\n",
      "9:\ttest: 0.8390410\tbest: 0.8390410 (9)\ttotal: 142ms\tremaining: 14s\n",
      "10:\ttest: 0.8396971\tbest: 0.8396971 (10)\ttotal: 154ms\tremaining: 13.9s\n",
      "11:\ttest: 0.8410365\tbest: 0.8410365 (11)\ttotal: 172ms\tremaining: 14.2s\n",
      "12:\ttest: 0.8411044\tbest: 0.8411044 (12)\ttotal: 186ms\tremaining: 14.1s\n",
      "13:\ttest: 0.8413241\tbest: 0.8413241 (13)\ttotal: 199ms\tremaining: 14s\n",
      "14:\ttest: 0.8415291\tbest: 0.8415291 (14)\ttotal: 212ms\tremaining: 14s\n",
      "15:\ttest: 0.8414772\tbest: 0.8415291 (14)\ttotal: 226ms\tremaining: 13.9s\n",
      "16:\ttest: 0.8415762\tbest: 0.8415762 (16)\ttotal: 241ms\tremaining: 13.9s\n",
      "17:\ttest: 0.8416884\tbest: 0.8416884 (17)\ttotal: 255ms\tremaining: 13.9s\n",
      "18:\ttest: 0.8423187\tbest: 0.8423187 (18)\ttotal: 272ms\tremaining: 14.1s\n",
      "19:\ttest: 0.8424781\tbest: 0.8424781 (19)\ttotal: 286ms\tremaining: 14s\n",
      "20:\ttest: 0.8424503\tbest: 0.8424781 (19)\ttotal: 304ms\tremaining: 14.2s\n",
      "21:\ttest: 0.8425876\tbest: 0.8425876 (21)\ttotal: 319ms\tremaining: 14.2s\n",
      "22:\ttest: 0.8424968\tbest: 0.8425876 (21)\ttotal: 337ms\tremaining: 14.3s\n",
      "23:\ttest: 0.8424821\tbest: 0.8425876 (21)\ttotal: 353ms\tremaining: 14.4s\n",
      "24:\ttest: 0.8426508\tbest: 0.8426508 (24)\ttotal: 372ms\tremaining: 14.5s\n",
      "25:\ttest: 0.8428152\tbest: 0.8428152 (25)\ttotal: 386ms\tremaining: 14.5s\n",
      "26:\ttest: 0.8427875\tbest: 0.8428152 (25)\ttotal: 400ms\tremaining: 14.4s\n",
      "27:\ttest: 0.8430655\tbest: 0.8430655 (27)\ttotal: 414ms\tremaining: 14.4s\n",
      "28:\ttest: 0.8430971\tbest: 0.8430971 (28)\ttotal: 428ms\tremaining: 14.3s\n",
      "29:\ttest: 0.8431330\tbest: 0.8431330 (29)\ttotal: 444ms\tremaining: 14.3s\n",
      "30:\ttest: 0.8434217\tbest: 0.8434217 (30)\ttotal: 460ms\tremaining: 14.4s\n",
      "31:\ttest: 0.8434594\tbest: 0.8434594 (31)\ttotal: 474ms\tremaining: 14.4s\n",
      "32:\ttest: 0.8435293\tbest: 0.8435293 (32)\ttotal: 489ms\tremaining: 14.3s\n",
      "33:\ttest: 0.8436873\tbest: 0.8436873 (33)\ttotal: 503ms\tremaining: 14.3s\n",
      "34:\ttest: 0.8437205\tbest: 0.8437205 (34)\ttotal: 520ms\tremaining: 14.3s\n",
      "35:\ttest: 0.8437597\tbest: 0.8437597 (35)\ttotal: 543ms\tremaining: 14.5s\n",
      "36:\ttest: 0.8439629\tbest: 0.8439629 (36)\ttotal: 560ms\tremaining: 14.6s\n",
      "37:\ttest: 0.8440979\tbest: 0.8440979 (37)\ttotal: 573ms\tremaining: 14.5s\n",
      "38:\ttest: 0.8440704\tbest: 0.8440979 (37)\ttotal: 587ms\tremaining: 14.5s\n",
      "39:\ttest: 0.8442218\tbest: 0.8442218 (39)\ttotal: 600ms\tremaining: 14.4s\n",
      "40:\ttest: 0.8443674\tbest: 0.8443674 (40)\ttotal: 614ms\tremaining: 14.4s\n",
      "41:\ttest: 0.8446133\tbest: 0.8446133 (41)\ttotal: 627ms\tremaining: 14.3s\n",
      "42:\ttest: 0.8445646\tbest: 0.8446133 (41)\ttotal: 640ms\tremaining: 14.2s\n",
      "43:\ttest: 0.8445854\tbest: 0.8446133 (41)\ttotal: 654ms\tremaining: 14.2s\n",
      "44:\ttest: 0.8445827\tbest: 0.8446133 (41)\ttotal: 671ms\tremaining: 14.2s\n",
      "45:\ttest: 0.8446950\tbest: 0.8446950 (45)\ttotal: 686ms\tremaining: 14.2s\n",
      "46:\ttest: 0.8447850\tbest: 0.8447850 (46)\ttotal: 702ms\tremaining: 14.2s\n",
      "47:\ttest: 0.8448479\tbest: 0.8448479 (47)\ttotal: 735ms\tremaining: 14.6s\n",
      "48:\ttest: 0.8448343\tbest: 0.8448479 (47)\ttotal: 751ms\tremaining: 14.6s\n",
      "49:\ttest: 0.8451461\tbest: 0.8451461 (49)\ttotal: 765ms\tremaining: 14.5s\n",
      "50:\ttest: 0.8453619\tbest: 0.8453619 (50)\ttotal: 780ms\tremaining: 14.5s\n",
      "51:\ttest: 0.8453508\tbest: 0.8453619 (50)\ttotal: 793ms\tremaining: 14.5s\n",
      "52:\ttest: 0.8454192\tbest: 0.8454192 (52)\ttotal: 809ms\tremaining: 14.5s\n",
      "53:\ttest: 0.8454381\tbest: 0.8454381 (53)\ttotal: 823ms\tremaining: 14.4s\n",
      "54:\ttest: 0.8454123\tbest: 0.8454381 (53)\ttotal: 837ms\tremaining: 14.4s\n",
      "55:\ttest: 0.8454398\tbest: 0.8454398 (55)\ttotal: 850ms\tremaining: 14.3s\n",
      "56:\ttest: 0.8455041\tbest: 0.8455041 (56)\ttotal: 863ms\tremaining: 14.3s\n",
      "57:\ttest: 0.8454790\tbest: 0.8455041 (56)\ttotal: 877ms\tremaining: 14.2s\n",
      "58:\ttest: 0.8454586\tbest: 0.8455041 (56)\ttotal: 894ms\tremaining: 14.3s\n",
      "59:\ttest: 0.8454382\tbest: 0.8455041 (56)\ttotal: 912ms\tremaining: 14.3s\n",
      "60:\ttest: 0.8454405\tbest: 0.8455041 (56)\ttotal: 926ms\tremaining: 14.3s\n",
      "61:\ttest: 0.8454435\tbest: 0.8455041 (56)\ttotal: 939ms\tremaining: 14.2s\n",
      "62:\ttest: 0.8455217\tbest: 0.8455217 (62)\ttotal: 953ms\tremaining: 14.2s\n",
      "63:\ttest: 0.8456362\tbest: 0.8456362 (63)\ttotal: 967ms\tremaining: 14.1s\n",
      "64:\ttest: 0.8456498\tbest: 0.8456498 (64)\ttotal: 981ms\tremaining: 14.1s\n",
      "65:\ttest: 0.8456757\tbest: 0.8456757 (65)\ttotal: 995ms\tremaining: 14.1s\n",
      "66:\ttest: 0.8457059\tbest: 0.8457059 (66)\ttotal: 1.01s\tremaining: 14.1s\n",
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    {
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "955:\ttest: 0.8294350\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 625ms\n",
      "956:\ttest: 0.8293803\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 611ms\n",
      "957:\ttest: 0.8293851\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 597ms\n",
      "958:\ttest: 0.8294889\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 582ms\n",
      "959:\ttest: 0.8294491\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 568ms\n",
      "960:\ttest: 0.8294143\tbest: 0.8466448 (103)\ttotal: 13.6s\tremaining: 554ms\n",
      "961:\ttest: 0.8293424\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 540ms\n",
      "962:\ttest: 0.8292215\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 525ms\n",
      "963:\ttest: 0.8292451\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 511ms\n",
      "964:\ttest: 0.8292002\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 497ms\n",
      "965:\ttest: 0.8290804\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 483ms\n",
      "966:\ttest: 0.8292716\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 469ms\n",
      "967:\ttest: 0.8291428\tbest: 0.8466448 (103)\ttotal: 13.7s\tremaining: 454ms\n",
      "968:\ttest: 0.8290682\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 440ms\n",
      "969:\ttest: 0.8290756\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 426ms\n",
      "970:\ttest: 0.8290293\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 412ms\n",
      "971:\ttest: 0.8291174\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 397ms\n",
      "972:\ttest: 0.8290745\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 383ms\n",
      "973:\ttest: 0.8289985\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 369ms\n",
      "974:\ttest: 0.8289834\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 355ms\n",
      "975:\ttest: 0.8288990\tbest: 0.8466448 (103)\ttotal: 13.8s\tremaining: 340ms\n",
      "976:\ttest: 0.8288690\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 326ms\n",
      "977:\ttest: 0.8288636\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 312ms\n",
      "978:\ttest: 0.8288478\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 298ms\n",
      "979:\ttest: 0.8287356\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 284ms\n",
      "980:\ttest: 0.8287961\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 270ms\n",
      "981:\ttest: 0.8288002\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 255ms\n",
      "982:\ttest: 0.8287713\tbest: 0.8466448 (103)\ttotal: 13.9s\tremaining: 241ms\n",
      "983:\ttest: 0.8288175\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 227ms\n",
      "984:\ttest: 0.8287378\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 213ms\n",
      "985:\ttest: 0.8288621\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 198ms\n",
      "986:\ttest: 0.8288989\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 184ms\n",
      "987:\ttest: 0.8288596\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 170ms\n",
      "988:\ttest: 0.8287966\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 156ms\n",
      "989:\ttest: 0.8287734\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 142ms\n",
      "990:\ttest: 0.8288076\tbest: 0.8466448 (103)\ttotal: 14s\tremaining: 128ms\n",
      "991:\ttest: 0.8288781\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 113ms\n",
      "992:\ttest: 0.8288789\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 99.1ms\n",
      "993:\ttest: 0.8288621\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 85ms\n",
      "994:\ttest: 0.8288882\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 70.8ms\n",
      "995:\ttest: 0.8288687\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 56.7ms\n",
      "996:\ttest: 0.8288461\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 42.5ms\n",
      "997:\ttest: 0.8287750\tbest: 0.8466448 (103)\ttotal: 14.1s\tremaining: 28.3ms\n",
      "998:\ttest: 0.8287677\tbest: 0.8466448 (103)\ttotal: 14.2s\tremaining: 14.2ms\n",
      "999:\ttest: 0.8287345\tbest: 0.8466448 (103)\ttotal: 14.2s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.8466448206\n",
      "bestIteration = 103\n",
      "\n",
      "Shrink model to first 104 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x2c0eb57dd60>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbr.fit(x_train, y_train, eval_set=(x_val, y_val), plot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = cbr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['id'] = test['id']\n",
    "submission['target'] = pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission-CatBoost.csv',header=True, index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Results and Summary <a name=\"summary\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have settled to split train data in the ratio **65:35** to train the model, with **random_state=1234** as obtained for best results in all 3 models.\n",
    "- Using Linear Regression - Submission score obtained is ~0.8414...\n",
    "- Using Logistic Regression - Submission score obtained is ~0.8333...\n",
    "- Using CatBoostRegressor - Submission score obtained is ~0.8512...\n",
    "\n",
    "Submitting `submission-CatBoost.csv`, since catboost gives the best score out of all 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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