ml-from-scratch-with-loan-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML FROM SCRATCH WITH LOAN PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://drive.google.com/uc?id=1LZz7B6QHtupGwocuejQrx2RFK3ck178F' width=1000 >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this Notebook we will Learn:-\n",
    "* Basic EDA.\n",
    "* Dealing with missing values.\n",
    "* Aplly Scaling on Feature matrix.\n",
    "* Spliting the Dataset into training set and test set.\n",
    "* Dealing with Categorical Dataset.\n",
    "* Dimensionality Reduction (PCA) .\n",
    "* K-Cross validation to check accuracy.\n",
    "* Logistic Regression    \n",
    "* Random Forest\n",
    "* Prediction on new Values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['credit_train.csv', 'credit_test.csv']\n",
      "\n",
      "The files in the dataset are:-\n",
      "credit_test.csv\n",
      "credit_train.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotly.offline import init_notebook_mode, download_plotlyjs, iplot\n",
    "import cufflinks as cf\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "print()\n",
    "print(\"The files in the dataset are:-\")\n",
    "from subprocess import check_output\n",
    "print(check_output(['ls','../input']).decode('utf'))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# Importing the dataset.\n",
    "df_train = pd.read_csv(\"../input/credit_train.csv\")\n",
    "df_test = pd.read_csv(\"../input/credit_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Loan ID</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Loan Status</th>\n",
       "      <th>Current Loan Amount</th>\n",
       "      <th>Term</th>\n",
       "      <th>Credit Score</th>\n",
       "      <th>Annual Income</th>\n",
       "      <th>Years in current job</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Monthly Debt</th>\n",
       "      <th>Years of Credit History</th>\n",
       "      <th>Months since last delinquent</th>\n",
       "      <th>Number of Open Accounts</th>\n",
       "      <th>Number of Credit Problems</th>\n",
       "      <th>Current Credit Balance</th>\n",
       "      <th>Maximum Open Credit</th>\n",
       "      <th>Bankruptcies</th>\n",
       "      <th>Tax Liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14dd8831-6af5-400b-83ec-68e61888a048</td>\n",
       "      <td>981165ec-3274-42f5-a3b4-d104041a9ca9</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>445412.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>709.0</td>\n",
       "      <td>1167493.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Home Improvements</td>\n",
       "      <td>5214.74</td>\n",
       "      <td>17.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>228190.0</td>\n",
       "      <td>416746.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4771cc26-131a-45db-b5aa-537ea4ba5342</td>\n",
       "      <td>2de017a3-2e01-49cb-a581-08169e83be29</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>262328.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Home Mortgage</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>33295.98</td>\n",
       "      <td>21.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229976.0</td>\n",
       "      <td>850784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4eed4e6a-aa2f-4c91-8651-ce984ee8fb26</td>\n",
       "      <td>5efb2b2b-bf11-4dfd-a572-3761a2694725</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>99999999.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2231892.0</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>29200.53</td>\n",
       "      <td>14.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>297996.0</td>\n",
       "      <td>750090.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a</td>\n",
       "      <td>e777faab-98ae-45af-9a86-7ce5b33b1011</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>347666.0</td>\n",
       "      <td>Long Term</td>\n",
       "      <td>721.0</td>\n",
       "      <td>806949.0</td>\n",
       "      <td>3 years</td>\n",
       "      <td>Own Home</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>8741.90</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256329.0</td>\n",
       "      <td>386958.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d4062e70-befa-4995-8643-a0de73938182</td>\n",
       "      <td>81536ad9-5ccf-4eb8-befb-47a4d608658e</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>176220.0</td>\n",
       "      <td>Short Term</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5 years</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>20639.70</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253460.0</td>\n",
       "      <td>427174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Loan ID    ...    Tax Liens\n",
       "0  14dd8831-6af5-400b-83ec-68e61888a048    ...          0.0\n",
       "1  4771cc26-131a-45db-b5aa-537ea4ba5342    ...          0.0\n",
       "2  4eed4e6a-aa2f-4c91-8651-ce984ee8fb26    ...          0.0\n",
       "3  77598f7b-32e7-4e3b-a6e5-06ba0d98fe8a    ...          0.0\n",
       "4  d4062e70-befa-4995-8643-a0de73938182    ...          0.0\n",
       "\n",
       "[5 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us check the top 5 entries in training dataset.\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BASIC ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to convert the values of years in current job into integer format.\n",
    "df_train['Years in current job'] = df_train['Years in current job'].map({'8 years':8, '10+ years':15,\n",
    "                                        '3 years':3, '5 years':5, '< 1 year':0.5, \n",
    "                            '2 years':2, '4 years':4, '9 years':9, '7 years':7, '1 year':1, '6 years':6})\n",
    "\n",
    "df_test['Years in current job'] = df_test['Years in current job'].map({'8 years':8, '10+ years':15,\n",
    "                                        '3 years':3, '5 years':5, '< 1 year':0.5, \n",
    "                            '2 years':2, '4 years':4, '9 years':9, '7 years':7, '1 year':1, '6 years':6})\n",
    "# Run it one time on the secons time the all values become NaN.\n",
    "# To solve this problem, run the code from beginning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us check the null values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of dataset is \t 100514\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Number of null values</th>\n",
       "      <th>Null values in percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Loan ID</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Customer ID</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Loan Status</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Current Loan Amount</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Term</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Credit Score</td>\n",
       "      <td>19668</td>\n",
       "      <td>19.567423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Annual Income</td>\n",
       "      <td>19668</td>\n",
       "      <td>19.567423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Years in current job</td>\n",
       "      <td>4736</td>\n",
       "      <td>4.711781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home Ownership</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Purpose</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Monthly Debt</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Years of Credit History</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Months since last delinquent</td>\n",
       "      <td>53655</td>\n",
       "      <td>53.380624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Number of Open Accounts</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Number of Credit Problems</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Current Credit Balance</td>\n",
       "      <td>514</td>\n",
       "      <td>0.511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maximum Open Credit</td>\n",
       "      <td>516</td>\n",
       "      <td>0.513361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bankruptcies</td>\n",
       "      <td>718</td>\n",
       "      <td>0.714328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Tax Liens</td>\n",
       "      <td>524</td>\n",
       "      <td>0.521320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Column Name            ...              Null values in percentage\n",
       "0                        Loan ID            ...                               0.511372\n",
       "1                    Customer ID            ...                               0.511372\n",
       "2                    Loan Status            ...                               0.511372\n",
       "3            Current Loan Amount            ...                               0.511372\n",
       "4                           Term            ...                               0.511372\n",
       "5                   Credit Score            ...                              19.567423\n",
       "6                  Annual Income            ...                              19.567423\n",
       "7           Years in current job            ...                               4.711781\n",
       "8                 Home Ownership            ...                               0.511372\n",
       "9                        Purpose            ...                               0.511372\n",
       "10                  Monthly Debt            ...                               0.511372\n",
       "11       Years of Credit History            ...                               0.511372\n",
       "12  Months since last delinquent            ...                              53.380624\n",
       "13       Number of Open Accounts            ...                               0.511372\n",
       "14     Number of Credit Problems            ...                               0.511372\n",
       "15        Current Credit Balance            ...                               0.511372\n",
       "16           Maximum Open Credit            ...                               0.513361\n",
       "17                  Bankruptcies            ...                               0.714328\n",
       "18                     Tax Liens            ...                               0.521320\n",
       "\n",
       "[19 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df_train.isnull().sum().reset_index()\n",
    "temp_df['Percentage'] = (temp_df[0]/len(df_train))*100\n",
    "temp_df.columns = ['Column Name', 'Number of null values', 'Null values in percentage']\n",
    "print(f\"The length of dataset is \\t {len(df_train)}\")\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 50% of data in Months since last delinquest column is fill with null values.\n",
    "* We can remove this column but now we are removing this column.\n",
    "* We will fill these values later with it's mean values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's remove unwanted columns\n",
    "try:\n",
    "    df_test.drop(labels=['Loan ID', 'Customer ID'], axis=1, inplace=True)\n",
    "    df_train.drop(labels=['Loan ID', 'Customer ID'], axis=1, inplace=True)\n",
    "    \n",
    "    \n",
    "except Exception as e:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us deal with missing values in categorical data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1). With 'Term' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_train, x='Term')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As maximum are from 'Short Term' then we will replace the missing values with the 'Short Term' ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Term'].fillna(value='Short Term', inplace=True)\n",
    "df_test['Term'].fillna(value='Short Term', inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2). With 'Home Ownership' coulmn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_train, x='Home Ownership')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here Home Mortage and Rent values are at the same number of count.\n",
    "* So we willl replace it by Home Mortgage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Home Mortgage', 'Own Home', 'Rent', 'HaveMortgage', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Home Ownership'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Home Ownership'].fillna(value='Home Mortgage', inplace=True)\n",
    "df_test['Home Ownership'].fillna(value='Home Mortgage', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3). With 'Purpose' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rkhaXtDjw38DOwEbAnvWxERExTvoWVFz8v3pzyXoxJdPxGbV8JrBbvb5rvU29fztJquXfs/2U7d8Acyh73G8JzLF9p+2nge/Vx0ZExDjp65hKPaO4DngAuBD4NfBH28/Uh9wNrFWvrwX8HqDe/wiwWnf5kOeMVD5cOw6QNEvSrLlz57bx0iIiYhh9DSq2n7W9GTCVcmbxin7+vnm04xjb02xPmzJlyng0ISJikbBAZn/Z/iPwM2BrYGVJnX1cpgL31Ov3AGsD1PtXAh7sLh/ynJHKIyJinPRz9tcUSSvX68sC2wO3UoLL7vVhM4Cz6vWz623q/Rfbdi1/d50dtj6wIXAVcDWwYZ1NthRlMP/sfr2eiIiYv1Hv/NiDNYGZdZbWYsBptn8s6Rbge5I+B1wLHFcffxxwsqQ5wEOUIIHtmyWdRtl18hngINvPAkj6EHABsDhwvO2b+/h6IiJiPvoWVGzfALxmmPI7KeMrQ8ufBPYYoa7PA58fpvxc4NzGjY2IiFZkRX1ERLQmQSUiIlqToBIREa1JUImIiNYkqERERGsSVCIiojUJKhER0ZoElYiIaE2CSkREtCZBJSIiWpOgEhERrUlQiYiI1iSoREREaxJUIiKiNQkqERHRmgSViIhoTYJKRES0pp971K8t6WeSbpF0s6SDa/mqki6UdEf9uUotl6SjJM2RdIOkzbvqmlEff4ekGV3lW0i6sT7nKEnq1+uJiIj56+eZyjPAx21vBEwHDpK0EXAIcJHtDYGL6m2AnYEN6+UA4BtQghBwKLAVZRviQzuBqD5m/67n7dTH1xMREfPRt6Bi+17b19TrfwJuBdYCdgVm1ofNBHar13cFTnJxBbCypDWBHYELbT9k+2HgQmCnet+Ktq+wbeCkrroiImIcLJAxFUnrAa8BrgTWsH1vves+YI16fS3g911Pu7uWzav87mHKIyJinPQ9qEh6AfAD4CO2H+2+r55heAG04QBJsyTNmjt3br9/XUTEIquvQUXSkpSA8h3bZ9bi+2vXFfXnA7X8HmDtrqdPrWXzKp86TPnz2D7G9jTb06ZMmdLsRUVExIj6OftLwHHArbb/q+uus4HODK4ZwFld5fvUWWDTgUdqN9kFwA6SVqkD9DsAF9T7HpU0vf6ufbrqioiIcbBEH+veBngvcKOk62rZvwBfAE6TtB9wF/DOet+5wJuAOcDjwL4Ath+S9Fng6vq4w2w/VK8fCJwILAucVy8RETFO+hZUbP8cGGndyHbDPN7AQSPUdTxw/DDls4CNGzQzIiJalBX1ERHRmgSViIhoTYJKRES0JkElIiJak6ASERGtSVCJiIjWJKhERERrElQiIqI1CSoREdGaBJWIiGhNgkpERLQmQSUiIlqToBIREa1JUImIiNYkqERERGsSVCIiojUJKhER0ZoElYiIaE3fgoqk4yU9IOmmrrJVJV0o6Y76c5VaLklHSZoj6QZJm3c9Z0Z9/B2SZnSVbyHpxvqcoySNtHVxREQsIP08UzkR2GlI2SHARbY3BC6qtwF2BjaslwOAb0AJQsChwFbAlsChnUBUH7N/1/OG/q6IiFjA+hZUbF8GPDSkeFdgZr0+E9itq/wkF1cAK0taE9gRuND2Q7YfBi4Edqr3rWj7CtsGTuqqKyIixsmCHlNZw/a99fp9wBr1+lrA77sed3ctm1f53cOUD0vSAZJmSZo1d+7cZq8gIiJGNG4D9fUMwwvodx1je5rtaVOmTFkQvzIiYpG0oIPK/bXrivrzgVp+D7B21+Om1rJ5lU8dpjwiIsbRgg4qZwOdGVwzgLO6yveps8CmA4/UbrILgB0krVIH6HcALqj3PSppep31tU9XXRERMU6W6FfFkr4LvB5YXdLdlFlcXwBOk7QfcBfwzvrwc4E3AXOAx4F9AWw/JOmzwNX1cYfZ7gz+H0iZYbYscF69RETEOOpbULG95wh3bTfMYw0cNEI9xwPHD1M+C9i4SRsjIqJdWVEfERGtSVCJiIjWJKhERERrElQiIqI1CSoREdGavs3+mujmfuOURs+f8vd7t9SSBeus43duXMeu78/s7YgYXs5UIiKiNQkqERHRmgSViIhoTYJKRES0JkElIiJak6ASERGtWWSnFEdMVG854zuN6/jx7nu10JKIscuZSkREtCZBJSIiWpOgEhERrcmYSjT2rZN3bFzHB957QQstiYjxNumDiqSdgCOBxYFjbX9hnJsULfjEGTs1ruPw3c9voSUxkt1/cE3jOs54x+YttCQmkknd/SVpceC/gZ2BjYA9JW00vq2KiFh0TfYzlS2BObbvBJD0PWBX4JZxbVWLrv7WWxs9/7Uf+FFLLZn83vQ/H29cx7m7fXnQ7TefeXTjOs95+4cb1xExUcj2eLehZ5J2B3ay/Xf19nuBrWx/aMjjDgAOqDdfDtw+iupXB/6vxeamztQ5UetLnalzfta1PWU0FU72M5VRsX0McMxYniNplu1pbbYjdabOiVhf6kydbdY5qcdUgHuAtbtuT61lERExDiZ7ULka2FDS+pKWAt4NnD3ObYqIWGRN6u4v289I+hBwAWVK8fG2b26p+jF1l6XO1LkA65wMbUydi2idk3qgPiIiJpbJ3v0VERETSIJKRES0JkElIiJak6DSZ5IWl/RiSet0LuPdpsmo/h2PGO92jJakpeuMxDbqWkzSX7VRV0S/JahUkr4oaUVJS0q6SNJcSXs3rPPDwP3AhcA59fLjhnW+rLbvpnp7E0n/2qC+xSV9tEmbRqh3iqR/kXSMpOM7l17rs/0ssG2LTRxE0rqS/rZeX1bSCj3Ws6mkq4HfAr+TdKWkTZu0zfZfKDnuJjRJB9fPkCQdJ+kaSTuMd7sWhH69dkmbD3PZQFLPM3clzZZ0kKRVmrZvWLZzKTPgrqs/3wYcB6wEXN+wzjnAai2381JKzrNru8pualjnVX34e/4SOBx4J/COzqVhnd+grEN6L/D2zqWFtu5PWfP063p7Q+CiXt9HwPZdt/+2895q2MYj6t9QLf6PvgisCCwJXATMBfZuUN/19eeOwJnAq4BrWmjnj+r/vftyMnAwsEwP9W1DOdD7X+BO4DfAnQ3b2K/XfgXwNDALmA08BVwD/BrYocc6Xwp8vn4/fa+2ubX31aRep9KyJevPNwOn235EUtM6fw880rSSIZazfdWQtj3TsM5fSPoa8H3gsU6h7Sa5zZez/YmG7RpqGeBB4I1dZaZ8iJs4iBKorwSwfYekF/ZYl21f2HXjp5LamLf/AeBjwLOSngBUf9eKDercwfY/S3ob5czq7cBlwCk91td5U74JONn2zWrhQ0T54p8CfLfefhfwJ+BlwLcpBxljcRzwUcqX9LMttA/699r/AOznuv6uZmE/DPhnyvv+J2Ot0PYc4FOS/g14C3A85X11AnCk7YeaNDhBZcCPJN0GPAH8vaQpwJMN67wTuETSOZQjDABs/1eDOv9P0gaUL9NOUs17G7USNqs/D+sqM4O/vMfqx5LeZPvcBnUMYnvftuoa4inbT3e+A2rXQq+B4GeSjqR8AZryBXhRZ0sG2z1l0LbdU3fcfHQ+/20dSM2W9BNgfeCTtQvxLw3bCPBXtl/bdftHkq62/VpJvSx2fsT2eS20q1u/XvvL3LWg2/Ytkl5h+84m/ytJmwD7UoLgD4DvULqXL2bg+6C3uuvp0CJP0tLA8pQ33LOSlgdeYPv+BnUeOly57c80qPMllFWwfwU8TDl139v2b3utsx8k/Yny93wK+DMtHFlLWgbYj9K1sEyn3Pb7G7b1i8AfgX2ADwMHArfY/lQPdf1qHnfbdk8D7vWody9gfduflbQ2sKbtq3qpr9b5BWA3yoHUlsDKwI9tb9VjfYtRvpDutP1HSasBa9m+odc21npvBXa0/bt6ex3gAtuvlHSt7deMsb4vUDJwnMngg72ez8z7+Nq/DzxE6aaCcpCyOuXs7OdDgu1o65xNeb8fB/zA9lNd951p++2N2pygUki6xvbm8yvrse4XANj+f03r6qpzeWAx239qoa41gP8AXmx753pUvbXt45rW3SZJpwO3Ae+hnFXtBdxq++CG9S5GCVY7UILfBZRdRCfMh0PSNyhHvm+sX6arAD/p5UtlSL2rMnAgtRywou37xljHPD8jDbtRkfQm4JuUcQRRzgYOBC4B9rf91THW97Phm+mez8y7gv5LbB9WA9+LmgT9Wu+ylNfamaTyC+DrlF6U5Xr5TpH0Etc9qPphkQ8qkl4ErEXpR34PA32jKwLftP2KBnVvTBlQXLUW/R+wjxvkJ5N0MHACpU/528DmwCG2x9y32lXnebXOT9netHb/XGv71b3WWetdhTLo3X1WcVmD+q61/RpJN9jeRNKSwOW2pzdp55DfsSowdaxHmJL2sH26pAOHu9/21xu26xrbm3cfmUu63nbPM8sk7QGcb/tPKjMINwc+N9YgMMKXdEejL+uu37E00Pks3m67add0q/oV9PtB0seGKX4EmG37uqb1Z0ylzHx4HyVtfvdYx5+Af2lY9zHAx2z/DEDS6ymBoMmag/fbPlLSjsBqlNPgk+lhwK7L6rZPk/RJeC5RZ6MBTEl/R5mdM5UyI2o68CuajdP8uf78Yw3Y9wG9Dqg/R9IlwC6Uz8Ns4AFJv7Q9lqnWL6o/157no3r3Z5XtsztjaVNo3mf/bzUQbkuZpfYlygy7MXV/2X5Dw3aMxhbAepT/0aaSsH1SLxVJWgk4FHhdLboUOMx2k0k1W3WCPoDth9XCOiVJ2wD/DqxL1/e17Zc0qHZavXS2hX0LcAPwQUmn2/5ig7oTVGzPBGZKeoftH7Rc/fKdgFJ/1yW126qJ7lkmJ7U0y+Sx2gfc+cKaTvNZawcDrwWusP0GSa+gdLE1cUw9AvxXyrTSFwCfblgnwEq2H62B8CTbh0oa05mK7aPrl/7dtvuxpuQo4IfACyV9Htid8ndoonPg8GbgGNvnSPrcWCuRNM8+eNuNZudJOhnYgHJw0mmzgZ6CCmW2002U6e5QDsxOoMx+61U/gj70Z6baVGDzTtdZHfs9hxJkZ1OmmvdskQ8qXX4s6T0MHA0BYPuwEZ8xf3fWaXsn19t7U2aENdGPWSYfo3xJbyDpF5Tpm7s3rPNJ209KQtLStm+T9PImFdo+tl69DGhypDbUEpLWpHzJjHlwvqOOS8ygDwsVbX+nDrBuRzmw2M32rQ2rvUfSt4DtgcNrF1MvC6LfOo/72pjyPQ3YqMUxrg1sv6Pr9mckNe326UfQh/7MVHshXRMUKD0Aa9h+QtJTIzxn1BJUBpxF7Vdk8B+8ifcDn2HgQ3V5LWtiPwZmmTxezzAaTbW1fY2kvwFeTvnCut32n+fztPm5W9LKwP8AF0p6GLirl4pqH/AjQycOSNoPWGGsA7XDOIwyOP9z21fXGXZ39FjX5ZK+TJmt073mp6epxEPcATxK/dxKWqczI6pH7wR2Ao6oM5bWBP5prJW4f1O9O26idC82nTrf8YSkbW3/HJ7rYnqiSYV9CvpQpqh/iRZnqlGmD18p6ax6+63AqbUXpfH7dJEfqO+QdJPtjce7HfMj6XXDlTcZAK/1/hXPP0vrtXthaN1/Q8lQcL7tp3t4/mxg+tBAV/usZ9nepI12tkHDTynueSpxV70fpowD3E/pBulM0W702ut4yoa2T6hdNi+w/Zsx1rG37VNGGABuui6rMxFgM+AqBn+x7tJjfZsBMynvSVGm7L7P9vUN2rgBpevzqTp2ugmlK/WPvdZZ6219plqtdxolswDAL2zPalJft5ypDPilpFfbvrFpRZK+avsjkn7EMIvoev0wVN1HkstQ1hfMpsEAeB/6rDv1bgr8db15eS8BpVpiuDMnlwWLjVctq4X1L5J2tX2W7a2btmcEBwMvt/1gWxXWvvRplDPUEyhZJU5h4MtmtDrjhP1YoAlloLo1dYbTppJWrLcfbaHaHwDTJL0U+BalO/lUythnz9qeBFHHfW6us1pbCyTdElQGbAu8T9JvKEdDTY4EO2MorWfVtT2o/1plEVzT7p+2+6w7U5/3Z6Dr7xRJx9g+uofqFpO0hocsRFVZX9OGkynrX3aka/3LGOs4lNKF2i/9SPnzNuA1lFxS2P6Dekikaftb9WfPi3oRwkOLAAAgAElEQVTnU/+lbdQz0hlV57ik4RnVX+qsybcDX6sTN65tu60dvba1jvvd3kLX6YgSVAbs3FZFtmfXq5vZPrL7vvpl28qHpLobeGXDOtrus4Zy5L+V7ccAJB1OmVLcS1D5EnCOpI9TvwApU0y/RDuB+6W296hnGzMlnUoZ/xp3XV8q/Uj587Rtq+Ym63VmYj3Texclw8OPKGfTr6MsVvys7f/rsd6f295WJTtD9wFPr9kZ5nVG1fSA6s+S9qRkZegc+C05j8fPTz/P/lYBbpZ0FYPH/Zr0oDwnQaWyfddw/csNq50BHDmk7H3DlI2apKMZ+AB0UkP0NGjX1T23AnBLfZM17rPuVM/gKZCdcYAxs32SpLmUs4iNKW2+Gfh0SzNj2lj/8gpJw/0fOl+AvWZm6Hyp/K5elqoXaP5FeFqd/bWypP0pk0i+3UM9J1H+hssDH6ccpHyNcvZ/ImUdxJjZ3rb+bOWLtXNGBfzU9i+676uD9U3sC3wQ+Lzt30han4EeizHrauvXbc9t2Lah/q3l+gbJQH3V3b9s+2WSXkxJsjfmN1s9YnkP5UPVfcS7AuU0ebsG7ZzRdfMZ4LdDPyBjqOtv5nV/k26HeoQ9gzLNEkqOqZm2v9Jrnf1S16f8gDK4egJ1/Yvtb46hjpspCyiHZfvXDdu4h+3T51fWQ73b05Wexl0ZlsdQx022N1bJxHC37Rd13dfzqn+V7AYjco/ZdNXHlExtk/S/lAzS3wfOtP1wS/WuSzmA/qlKep7F3ULKJ0hQeU6dp/4ayh4InTQYN/QyplL/YesD/wkc0nXXn4AbbDdKVV9nPb2s3mw8/VfS4R6Spn64sh7q3ZyBnEWX2+65j3miUw+JDcdY/4T9Iuxux9A2NWljHd80JeCtQ+leEyXx5e9srz/G+ramZLP4CNB9cLMi8LZeg1+te+jK984ZauP1VJK2BN5NOTC7Bfie7V63J6CelR4ArGp7A0kbUlJS9Xyw2y3dXwNa6V+G0pVGWZPR+kygOl1xJuXoRcDakmY0nFK8PTA0gOw8TNmYuMylf65LSNLvbE+47ZRVFv29g2YLX69ouVkASNqZMoNoLUlHdd21Ig330amDyodTuvpE72MVU2vb1HWdenutXtvXCRqSvg380HUbhfo32a2HKpeinIUuweCxikdpvti3HyvfAXBJSnmVpP+gpJKaSe973kC7+wc9T4LKgLb6l5+jku7kaMpA+lKUdNuP9fCh7fZlyuZKt9ff8TLK3h1b9NC+v6dkQN1Ag9OSrEDZubFtbWxa1A+NF77a/vtWWzTgD5Spn3tQdiqEEkzup3yJNfFF4K1uvkive5r70GmqbUxbnW57/84N2+epbFcwJrU791JJJ9YDvzb1Y+U7ddrz2yhnKhtQupO3bFhtm/sHPU+CSmX7iNq//Chl3v6ne+lfHuJrlDfD6ZTxmn0Y6Lbq1ZKdgAJg+39VsvX24lTgPEo33RcYSLD38z51VTV+40p6M89fT9IklQ6UrMQ7NayjX26hTHFeioFsDOtQxn5+3LDu+1sIKJ38ef30B5Usyp2j870owbZXj6usUh/6PmqyoLAfK98BrqdkpTjM9rz26hmLSyX9C7Bs/c47kIHkko0lqHSxfaGkKxlIg7Fqr4OBXXXOkbS47WeBE+rc9U82qHKWpGMZ/AHr6WjQJSvrI5KuqPWdSTmbmCnp272sKRlpXn2tt9FsOknfBJYD3gAcS+myaLRfRdXawtc++CLl77ZuZyC1Hr0eUS9N9pKZpbIJ1P8w+Iuwaa6utu1JWQfUmfRxWS3r1XcoA99voczYmgE0nWHVyew8raus6e6pUPZnsaQXSHqB29mT6RDKlP8bKdtUn0v5PLUiA/WVpA9Q8nQ9SUnQ2HigTdJllJTix1Kmqd5LSQfRZEBwaUqf6HMD4JRphz3nK6tdX1t3rSlZHvhVj5MUht3tssPNdr3s7KPS+fkC4Dzbfz3fJw9f342UD/4SlH1f7qT5wldUMjJvxOCj4FN7rOsOypayHlK+OHCb7Q17qbfWccIwxXbDnTQnOkmzbW/RPRFHdXvi8W7bUBq8J5MowW+G7ZvGtWHzkDOVAf8IbOweF2qN4L2UcZQPUfq/16YMCPesBo//YvDeL021uaakL6uqq07Sv8frlO8HgTUb1NfT+ol5qd00O1A2lLqAskr/55Suxl54aECphc92JpX0yv1PBNmIRkhz1OHe11F1ZkveW7tT/8DARno9Uf92Tx1uT6bOduK9trVvM9UgQaXbr4HH26ywazDwCcpZUM+6jqpH+l1NEgueQMla2r2mZEJtJVz9WCXz8Zcos8pMg8kUnf9PnVBx85DupVfSW1bld1EXpNp+r0rm3xN7bSNlUeo+HpLcU9LelNQyYybpn21/UYMX0j7H9j+Msb5h6+m1vi6dbAlvp2R86HT57kmZqNCrz6ls1PVxykSaFWk+6eFE6u6p9fb/UrrYmn6O+rEnU99mqkG6v54j6TXUL1cG9y+P+QPRjwBQ175AOao4hyGJ6prOZplsa0pqN+AybrZbX6euaymbFnWmky9GyX485vUVkq6yvaVKZuXXA/8PuNU9bkstaS3KWNcTlC8BKP32y1LWVtzTQ51vtf0jDV5I+5yxDryPVE+v9Q1T/yzb0+ZXNp463WcavN3zdbY3a1jvDykHUN17Mm1h+20N6rzS9ph29xyLnKkM+BZwMWXwqummV51ulYPqz+43RE9RvDtoSHqq7SmRQ9eUTHS1G7CtfW/U3cVk+y91mmUvrq1nU8dTJlA8SoPJBDVobCXpjZTZSgDn2r6oQZ0/qj+f+7KvgfQF7iFj7wKY/bW8pJfYvhNAJQVKz0frkmYCB7umpVfZTfTLDceS+rF7KgzsydTZlfZyGu6fRP9mqgE5U3mO+rAierg61cIq6Dbq6DeVxJknULIIHEvJVnCI7Z+Ma8OGIelM4BLK/uxQpli+wXYvC+y6630psGJbH9a2qSTO/CClC+RqSjfQkba/1GN9UygLZodOUmi698dOlHGEOyln6usCH7B9QY/1Dfe5bPT5r2f6R1Ny091E3T3V9pi2pR6m3mmULrX1GDgJ6HkSSa2zL3u0dORMZcB5kg6gzNfujt5NphRL0jauublUNsLqZbvWzpu2Y9naXffcYPoE/OJ6v+0jJe1IyYr6XsoZ24QLKpQv1qMo278auIiSxqIR23Oa1tFnG9l+VNJelPVKh1C62HoKKgxM1X0z7U3Vxfb5KqlEOl2ItzWZ7UjZSmEV1zxaKjnGGn0Xuj+7p0L5m/4jJVC1sed963u0DJWgMqAz7717DYlpthf6fsDxdVBQlNxFvZ5if7nr+n0Mnv3Vxnz4tnUC3puAk23fLDXbUKueURxHmUbcygesTs3dy/a726hvklmyLpzdjbIHyJ8bzihbzfZxkg72wOr1q5s2UiXh4ccoa3X2l7ShpJfb7nXx55eBX0k6nfI+3R34fMM2LkM5w92W8nm8XNI3bT/ZpF5gbqe7si19nKlW6k/3V//VoNJZbLhIqGsg1qIk1tyUMrX6EttjTifTVeffUvqTp1OyFJzgruwCDeq9ynbT1BeTjqR/oHRXXU85u1gHOKXBup8rbE+XdAHlzO8PwBm2N2jYzu9TzqD2ccmGvBzwyyaD4JJeRVlEC3Cx7UZ7s0s6jdLV25mh9h5gZdt7NKx3O8oB70W0tEBV0nnUmWq2N63jh9fafnWTtj5Xf4JK0XU0tI7tA+rpdk9HQ+rznt2TQR343Qy40/YfaxfD1KZ9zLXulSgftE9RdkT8NuXLsKfuBklfoWyo9H0Gb1rU6z410ykLFk+qg7fLu0+77LVN0hLuMYu2pLdQBpLXZmCq7mdsn92wTbNsTxsys6rnlPpd9b6QwWM/Pf+PJN1ie6P5lfVQ7ymUbr+bGej+cpNJBf2aqdaR7q8BJ1COhjqLiu6hHA33cord7z27J4OtgetsP1bXVGxOg83JOuqX9N6UMZprKX3O21L671/fY7WdD1N3DrGeuhRVFj9uQ0n+dxLlS+tUBqZrTxiSPj3CXT3lUus6AHuEgbOANjwtaVkGZlZtQIOZf5J2oXSBvRh4gDLwfysDs+t6cY2k6bavqL9jK9pJpvla2y9voZ5u/ZqpVtjOpZytzao/r+0qu3682zVZL8ANlP7qTSlf/gcBlzas84eUBIufBNYc7v833hfguvq6u99HN4x3u0Zo68e7Lp+ibPd8fIP6ZlK6fDq3V2lSX1c921O24J5LOYj4LfD6BvVdD6zW+R9RAuBxDdt4K+VM4rf18pdadmOT/z/lYHejlv/vmwO/oASSX1AWam7SVv05UxnQ2tGQBu978TzufYVxXwar++QZ25a0K2UQ+DhJ+zWs8yh3rS7u5gYL4UY6Yndv2Y+fqq+78z5artd29Zvt7skfSDqCklqmV5u4rv2o9T9cZyk2NYOy4PcMyrTig90sndKfbT8oaTFJi9n+maSvNmxjv7JcTweuU9mwrHFeOpj/TDVJ27tBhvYElQH/DpxP2fTqO5QujPf1WNfs+T+kZ1+nDFYfVWevnOAWBqv74E+SPknppvrrOsbSa4r+jrUl7TO00ENSmPTgsa7ry1AWr/aaEv5MSf8NrCRpX8oMwOESN05EywFTGzy/9am61XHAX1POWDagLDC9zHav3al/VElGejnwHUkPMPg90IsNbf+0u0Bl87ymC0P7Eqxcxs1uHuHuw4Geg0oG6rvUfsbplOh9RcOjoe56XwDgdtJWd+psdbC6bZJeRJkBc7XtyyWtQ+my6DkAqOSY6lgG2I6SY6vprn1Df8/SlP3aX9/j83dm8L7vrW/e1AYNTie0OGXB3mG2v9ZjffsA/0IZi3xuqq7tk+f5xNHVvTjwWkpX1QeBJ9x76ptPUXJ13UcZn1sJ+I7tBxu07zLKl/Q/UrYqOJZy1trqe3NBaLwQNEGlUMmIeipwtmsK+BbqHC5t9T62RzpCGG293YPVf2BgsPrVvX4R9oNKvrINbf+0dgMt7pq0saX6V6bs193q0ZxK2o6rbb+0h+fu4CFZAyT9ne3W9qtoiwbyyUHdTdI9zvzqqnMjBiY4NJ6qW+u8iDL55VeUs4uf236gQX2HAu8EHqLM+DvddpMEldQ1WB+n7E8CZZO/7zapc7yoYcaOnlZ3L6SOoJxi3yLpDEm71wVNTXTSVq/rsjf7x2m+RfEPKR+s5Shbwe5i+/u2P0zDTbDapLIl8xmUnGpQ1qz8T8u/5jHKOphGJN0o6YZ6uRm4Hei1j/0ztb+6U/fHKF9gE45L/rjVgF0pmYB7WqegktW50911H+Xg7FTgvlrW1A3A05QUKJsAG9fxz57Y/oztV1Emj6xJWaT50/k8bX5WoWzz+2vK2Me6NdAscjKmUnlgBfDilCOt/SlJAZvsJ9+PtNV9Gazug4MoH7IrAWzfUdcF9EyD99dYjJJj6rQmdVbd+6o0PWLfhZKi/6OUvVQ2pQ/7trShTlDYg5JYEOBESafb/twYqzqV8hpnMzhhqmielQLbH63tXYEyznkCJRX+0k3qpUwnvo+yL0+j9yZwBfAF28fXgHc4ZWZVz/uejKPfNnlyur+61DfDWyl7YmwO/LieAfRaXz/SVj9voBpaGaxulWp67U7/rMqq3WuazFrpPgOgfPnfZfvuxo3ludxqnRQbP3eD1P91POlCyvTiGRN1lp6k24FNXVOJ1Pf/dW5/XUQjkj5E6UXYgvKFdzlle4aLe6zvQMrZ4xTK+M9pTbvpJK3jIYsnJb3O9mVN6u2H2hX9ccpC7/3VYKH3cHKmUqmkWdiSMgPsa5Q1FU2/DDppqztHgpfRe+6vju4tT58brKYstJtILpX0L5Tkl9tT8iI1ymFUzyYBkLQ65QizsTaO2CU9zOCj9KUpK8sflGTbbXQDte0PlPdQJz/V0pRFv2OiwclOn8fNk50uQ8l1N7vpmE+1NvAR29c1rUg1e4bt36kreWy1CeUzP9F0FnpvXW83Wej9PDlTqVSy6f7Udus7odX6F6d0h415v4r51NuXweqm6hTi/eiaBQUc6x7ecHXF7xcoA6ufpZz5rU7pAtvH9vkN29r4iL3+f0fUr/dVLzSwU+M6lIOUC+vt7YGrbL99jPV1umOXoWwgdj3lf74JZVHq1iM9d7LrHtQeOsDddMC7X9SntDcdOVMZcDFwkKTX1duXAt9sMkVXw+xXIann/SpG0MpgddvqWd63aTgxofoaZarqSpT/0862r5D0CuC7lLPLJhofsXcHjTrdewO68koBv2zYxjZ10ofMpmQp6Likl8pcU6mrLMzd3PaN9fbGlPVfCzONcH242xNFq2lvhkpQGfANyuK8r9fb761lf9egzrb3q+jnYHWrJG1D+UJZl/I+66wE7mXQdonONF1Jh7nmV7J9W0sTbB4BbpY06IhdNTOCx5ABQSVrwMcos91upJwJXEHveclaN9oFeZJ+YPsdY6j65Z2AUn/PTZJeOeYGTi4e4fpwtyeKQ2lvoffzJKgMeO2Q07+LJV3fsM6296uAMvW5o9XB6pYdB3yUEkSbdv10j209MeS+Nj64P6SFI/bqI5QuoF/Z/muVFOs9JWicAMZ6AHCDpGMZSP++F2U68MLsFZI6ee42qNeptxvNeusX2xdKuoaBhd5N094MkqAy4FlJG9j+NYCkl9D8y/BblNkq1wOX1cVmjcZU+jVY3QePtLiSfFNJj1I+AMvW69TbTdcSzffIfYxH7E/afkISkpZy2ZxsQs2mGoOxBux9gb8HDq63L2Ngi+aF1aQ5ExtmQsW99ec6dfZa9qhvk8pmOCcweB/sfUdaE9Lg9/S0X0W/B6vbJukLlNQfZzJ4c6GJtu3xfGkUaSs6/1dJZwP7UKZsbkv5fy0/0SZSjEYvA821r34dT8x8dIs0Db83fYfd0h71CSo8N1NpOqWrpnNUebub7YPdySH1DmA9us4K3UP2W0mzGBisPoYhg9Xz+9Jb0EZ4A7f2xl2QRvPlOtxj6oHKSsA5Td9L42E0wXTI43ehjBcuZXt9SZtRcont0rdGxoST7i/KTCVJ/10/QG32AZ9FGQSeTfPZFf0erG5VZ0bQIuR5/wTbF41HQ1r0iTE+/lDKWq9LAGxfJ2nCzUxc1KmknzqQgcW+l1Nmuj45zyeOUoLKgIskvQM4s5e1FCOY2mK3R78Hq1uhhXMr5dFE7SkjvWaYWK9bg7MTD7qLrr06PCQx5ij82fYjQw5yJsx7M55zEvAnypbPULKJn0xZANxYgsqAD1Cmgj4r6QkGPmBNcn/9UtKru6dZNtDXweoWTaqtlOuixZNs7zWPh43miH1xSkLPiXfa+Hz9ykV2s6T3AIvX1B//wMRan9O60QboCWZj2xt13f6ZpMbZpDsyptJH9R/1UqC1XduifZJ+DrzR9tMN6piQq6cXpJpT6lMMzqLw2ba6VSYiDd4+4HlcMkFPKJJOoSxxuKLe3go4yPaweQXHXH+CygBJb6ern9F2o1TtI73hJuIbrW2Svgh8jtJVdz4lZcdHbZ8yzyeOA0knUaaGnk3XDoBj6bIa66D2eJL0J+Z9dN3k7DwmqK6zqiUpE5J+V2+vC9w25Oyl99+ToFJI+jrlrKKzsc67gF/bPqhhvZtSMqxCCVRNF1ROCpKus72ZpLdRuls+BlzWVn6hNqls2vQ8tj8zhjpWtf1Qe62aPOo06hEtCrO/6pT/oykHJ0tRukMfm0gBekGdVWVMZcAbgVd2BuklzWTkPZxHRdLBlH1ZOtlvT5F0jO2j5/G0hUXnvfVmys56QwdwJ4xO8FCDbZ8nc0BR2efmuXE5D0nhPgpbU7a0/i5l/5yJ+Y/ur68B76Zk+51GWav0snFt0RBDg8bQ/3tbsvPjgDmUrK0da9eyJvYDtrL9adufpqyF2b9hnZPFjyXdRtkD4yJJUxhI2DihSNpY0rWUg4ibJc2u6VUWapJ2kXQHZczvUkr2h16yILyIsoZqY+BISu60/7N9aXcGiIWd7TmULbOftX0CMCEXvLb4fx9WgsqAFYBbJV0i6RLgFkpW4bPnd3o/D2JwqpdnWUSO4mwfQtn1blrN9PwYZdvaiah72+d1aWHb50nis5QDnf+1vT5lb54rxlpJ/RI93/aMWt8c4BKVzbUWFY9LWgq4TtIXVXb+nKjfr63830eS7q8Bn+5DnScAV6rsAAklseRxffg9E466dqgc0u010TYTg/5s+zwZ/Nn2g5IWk7SY7Z9J+movFdXsEW8G9qRkkDiKwUk6F3bvpQSRD1ESqa5NyaYxEbX2fx9OgkrVOU2XtCKDU6r03Fdu+7/qWc+2tWhfN9imdpKZLDtUAtwp6d8YvO3znePYngXlj3Uc6TLgO5IeoGv222jV2XMbA+cCn7F9U7vNnPi6xiuepOz2OpG18n8fSWZ/VZIOoKQof5Kyer3n/T8kvRZYfWiWXklvAu63PbuFJk8qmqA7VAJIWoXyRdAJ/pcD/2774fFrVf/Vs7EnKe/1vSh5yr5je0yZryX9hYEvpe4vlEVmirKev38QAL18f/Rb/b8/QTmz6vn/PmL9CSpFHbja2i3sKyDpYspZydDZFusCJ0zGpIpNqewrc5PHsEVvLBhtnp0vquqklOftH9TWF3WbVDaSu8z2Hf2oP91fA34NPN5SXSsMN+fb9l0qe6As9DQJdqiU9FXbHxnS1g5T0tZ/q7PyeGEj6QOUM7RBZ+dM0M2lJrg29w/qt3WAb9Vkn7Mo3WCX276ujcpzplJJeg11YJ3B+3+MeivZrrrm2H7pWO9bmEj6m66bE3KHSklb2J49pK3dVqekGWllpfFE0+bZ+aJOk3D/IJW9b/YH/hFYy/bibdSbM5UB3wIupuwr/pf5PHZ+firp88C/di2mFOWo8OKGdU8KngQ7VHbGtua1lkJSz/nAJoE2z84XdVvVn9O6ykxZVD2hSPpXyr70LwCupQSVy1urP2cqRZu5m+pA2LGUvSU6p5SbUk41/66XFduThSbZDpUANaPuf1K66LpXli/U3UBtnp3H5KGyP/0zwDmUxY+/coubyCWoVJL+g7Ky9EcM/oD1PGipss99Z2X2zbYX+mmqmmQ7VMJzWYoPBb4CvJWy1/piNQvCQkvSVcDPGXJ2bnvmuDVqkpK0EuU99LpadCll18tHxq9VI6uTM7ahzHjcA3jA9rbzftYo605QKST9ZpjinqYUL8o6iSTr9Vttv7LrvgmZyVfSbNtbSLrR9qu7y8a7bf00Uf8fk5GkHwA3AZ2A/F5gU9tvH79WDU/SxpQkt39D6a77PWWgvpWDqIypVDVdQTQ3KXaoHOIpSYsBd9TUIvdQ+psXdufV9VmtnZ0vwjaw3b2C/jOSWplN1QdfoIyhHAVcXdMotWaRP1Ope6iMyPaZ87o/BpP0LGUhnIBlGRgIFrCM7SXHq20jqYtVbwVWpowDrQh80faV49qwPhtydv7cF0HOzsdO0q+Af7L983p7G+AI21uPb8uGV2d+rWP79rbrzplK6UMfiRlIWz9mkk62/d75lS1M2pqWuICZMqFgXcoGRlASSi7sO3R+Ajjf9qM1Tc3mlKAaY/f3wMw6tiLKRJX3jWuLRiDprcARlH1f1pe0GWX8p5V9bxb5M5V+0pAtZlX2Q79xYV33MFlJuh34J54/YL1Q79Ap6Qbbm0jalhJMjgA+bXur+Tw1RlAHwLH96Hi3ZSSSZlOmOl/SGVPrHk9sKmcqfSDpk5QZUMtKepSBdPdPU2ZExcQy13av2xtMZp10Im8Gvm37HEmfG88GTTaS9rZ9iqSPDSkHxrYl9QL0Zz9/07zWzi4SVPrA9n8C/ynpP21/crzbE/N1qKRjgYsYPGC9sI+n3SPpW5RNtQ6v6esn6h4gE1Vni4QVhrlvonYD3SzpPcDidY3WPwC/bKvydH/1UV1F/zbKXHBTpu39z/i2KoaSdArwCsrOj53uL9t+//i1qv8kLUfZnfBG23dIWhN4te2fjHPTJh1J29j+xfzKJoL6f/8UsAOlF+UCSjqiVnZmTVCp6h/645QZEfvXCP5y2z9uUOfXgZdS9u4GeBfwa9sHNW5wtEbS7cmeHE0MHT8dqWxRkO6vASdQ0lZ3pgDeA5wO9BxUKINhr+zK/zWTcjQcE8svJW1k+5bxbkhMLpK2pmybPWXIuMqKlASTE46kl1Hyfa3H4C0PWslTlqAyYAPb75K0J4DtxzVkJKsHcyhppjuziNauZTGxTKfsLf4byphKZ3OphX1KcTS3FGWh7BIMHld5FNh9XFo0f6cD36TkJ3x2Po8dswSVAU/XBUGds4oN6Bq0HYuu/TlWAG6tOZZMyWR6VTvNjRZNuN0oY3KoGa4vlXTiJJqC/oztb/Sr8gSVAYcC5wNrS/oOJdna+3qs64i2GhX9N4m+DGLielzSlygJZLszXU+41PfAjyQdCPyQPqTnyUB9F0mrUbpCBFzR0tbC6wIb2v5pPRNawvafmtYbEROHpJ8A36eMVXwQmEFZ//SJcW3YMPqdPDdBpYukTXj+4FWTNC37AwcAq9reoM4o+6bt7Zq2NSImjq5M1zd0xuIkXW37tePdtgUt3V+VpOMpuZ4GrVWgQe4v4CDKRl1XAtS1AC9s0s6ImJA6mX7vlfRm4A/AquPYnhHV5RMfoyyfOKCN5RPdElQGTO9DTq6nbD/dmUQmaQkm7irbiOjd52oyyY8DR1OmFH90fJs0os7yib+qt9tYPvGcBJUBv+rDWoVLJXVygG0PHEjZuyIiFiJdR/mPAG8Yz7aMQj+WTzwneX4GnEQJLLdLukHSjZJuaFjnIcBcSvbbDwDnAv/asM6ImGAkzZS0ctftVWqX+kTU2vKJ4WSgvpI0h9LP2Gr6c0lTaj1zGzUwIias4bZmnojbNdczkvcC+wEbAT+hLp+wfUkbvyPdXwNaS39e/3GHAh+ing3WHRGPtn1YG78jIiaUxSStYtQuw4IAAAS1SURBVPthAEmrMgG/X21b0j8Br2dg+cTBbSyf6JhwL3ocXSvpVJ6/X3cvs78+Son+r7X9GwBJLwG+Iemjtr/SRoMjYsL4MqX7/HTKF/XuwOfHt0kjugZ4ie1z+lF5ur8qSScMU9xT+nNJ1wLbD43+tSvsJxPtlDgimpO0ESWJLMDFEzVBqaTbKNnT7wIeo+VcdwkqfSDpJtsbj/W+iJicJK0zXLnt3y3otsxPzfLxPG2lK0r3VyVpKmV++Ta16HJKX+PdPVT3dI/3RcTkdA4Da9CWBdYHbqfkAptQ+p3rLmcqlaQLgVOBk2vR3sBetrfvoa5nKaeVz7sLWMb2kj03NCImPEmbAwfa/rvxbsuClqBSSbrO9mbzK4uIGA1JN9p+9Xi3Y0FL99eAByXtzcDWv3sCD45jeyJikhiy6+NiwOaU/F+LnKyoH/B+4J3AfcC9lCmB+45riyJislih67I0ZYxl13Ft0ThJ91dERLRmke/+knQ088gcbPsfFmBzImIS6do6fFi2d1mAzZkQFvmgAszquv4ZSnqViIjR6Gwd/nbgRcAp9faewP3j0qJxlu6vLhMxAVxETHySZtmeNr+yRUEG6gdLhI2IXixf8/sBIGl9YPlxbM+4SfdXRERzHwUukXQnZZHzupQ9lBY5i3z3l6Q/MXCGshzweOcuSpK1FcelYRExqUhaGnhFvXmb7dY2vppMFvnuL9sr2F6xXpbour5CAkpEzIukf+66uYvt6+vlKUn/MW4NG0eLfFCJiGjg3V3XPznkvp0WZEMmigSViIjeaYTrw91eJCSoRET0ziNcH+72ImGRH6iPiOhV1zYXouyj0j3RZ5Hc5iJBJSIiWpPur4iIaE2CSkREtCZBJSIiWpM0LREN1IHaGymfpVuBGbYfn/ezIhZeOVOJaOYJ25vZ3hh4GvjgaJ8oKQd1sdBJUIloz+XASyWtJ+mmTqGkf5T07/X6JZK+KmkWcLCkEyV9U9IsSf8r6S31cctIOkHSjZKulfSGWv4qSVdJuk7SDZI2rOV7d5V/S9LiC/zVR5CgEtGKetaxM6UrbH6Wsj3N9pfr7fWALYE3A9+UtAxwECWh6aspGz7NrOUfBI60vRkwDbhb0iuBdwHb1PJngb3ae3URo5fT74hmlpV0Xb1+OXAc8OL5POf7Q26fZvsvwB01dforgG2BowFs3ybpLuBlwK+AT0maCpxp+w5J2wFbAFdLgrII74HmLy1i7BJUIpp5op4dPEfSMwzuBVhmyHMeG3J71Ok9bJ8q6UrKWc25kj5AWb090/bQhIYRC1y6vyLadz/wQkmr1T023jKfx+8haTFJGwAvAW6nnPXsBSDpZcA6wO11d8E7bR8FnAVsAlwE7C7phfXxq0patx8vLGJ+cqYS0TLbf5Z0GHAVcA9w2/9v7w5tEAqCKIreRwGUhyNBUgSaEsBSABJFG1QACoUm+SSD4GswQ765R+8ms+pldpPZH1tu49o5sK6qZ5IdsE9yAV7AavyjYwEskwzAHdhW1SPJBjgnmQEDnzeZ618OKH3h7C9pQkkOwKmqjlPXInXw+kuS1MZORZLUxk5FktTGUJEktTFUJEltDBVJUhtDRZLU5g1iuxoC6wcZDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_train, x='Purpose')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we will replace missing values with 'Debt Consolidation'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Purpose'].fillna(value='Debt Consolidation', inplace=True)\n",
    "df_test['Purpose'].fillna(value='Debt Consolidation', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4). With 'Loan Status' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_train, x='Loan Status')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "responsive": true,
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(0, 191, 255, 0.6)",
          "line": {
           "color": "rgba(0, 191, 255, 1.0)",
           "width": 1
          }
         },
         "name": "Loan Status",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "uid": "c0fef612-c467-42ea-9e10-e1f06284d0f6",
         "x": [
          "Fully Paid",
          "Charged Off"
         ],
         "y": [
          77361,
          22639
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "Fully Paid VS Charged off"
        },
        "xaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Loan Status"
         },
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Frequency"
         },
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"5554ccb4-327b-44df-8b39-3a3a2d4988f3\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
       "                    \n",
       "                if (document.getElementById(\"5554ccb4-327b-44df-8b39-3a3a2d4988f3\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        '5554ccb4-327b-44df-8b39-3a3a2d4988f3',\n",
       "                        [{\"marker\": {\"color\": \"rgba(0, 191, 255, 0.6)\", \"line\": {\"color\": \"rgba(0, 191, 255, 1.0)\", \"width\": 1}}, \"name\": \"Loan Status\", \"orientation\": \"v\", \"text\": \"\", \"type\": \"bar\", \"uid\": \"189a68f0-9469-43b9-9d24-6e34749c6ccf\", \"x\": [\"Fully Paid\", \"Charged Off\"], \"y\": [77361, 22639]}],\n",
       "                        {\"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}}, \"paper_bgcolor\": \"#F5F6F9\", \"plot_bgcolor\": \"#F5F6F9\", \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Fully Paid VS Charged off\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Loan Status\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Frequency\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('5554ccb4-327b-44df-8b39-3a3a2d4988f3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Let us plot the same graph but inter-active this time with the help of plotly.\n",
    "    \n",
    "count = df_train['Loan Status'].value_counts().reset_index()\n",
    "count.iplot(kind='bar', x='index', y='Loan Status', xTitle='Loan Status', yTitle='Frequency',\n",
    "           color='deepskyblue', title='Fully Paid VS Charged off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we eill replace missing values with Fully Paid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['Loan Status'].fillna(value='Fully Paid', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ============================================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREDICTION WITH ML MODELS:-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us Import the Important Libraries  to train our Model for Machine Learning \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder # To deal with Categorical Data in Target Vector.\n",
    "from sklearn.model_selection import train_test_split  # To Split the dataset into training data and testing data.\n",
    "from sklearn.model_selection import cross_val_score   # To check the accuracy of the model.\n",
    "from sklearn.preprocessing import Imputer   # To deal with the missing values\n",
    "from sklearn.preprocessing import StandardScaler   # To appy scaling on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert DataFrame into array.\n",
    "x_train = df_train.drop(labels='Loan Status', axis=1).values\n",
    "y_train = df_train['Loan Status'].values\n",
    "x_test = df_test.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us deal with Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/utils/deprecation.py:58: DeprecationWarning:\n",
      "\n",
      "Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "imputer = Imputer()\n",
    "x_train[:, [0,2,3,4,7,8,9,10,11,12,13,14,15]]  = imputer.fit_transform(x_train[:, [0,2,3,4,7,8,9,10,11,12,13,14,15]])\n",
    "x_test[:, [0,2,3,4,7,8,9,10,11,12,13,14,15]]  = imputer.fit_transform(x_test[:, [0,2,3,4,7,8,9,10,11,12,13,14,15]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us deal with the categorical values in the training and test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py:392: DeprecationWarning:\n",
      "\n",
      "The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py:392: DeprecationWarning:\n",
      "\n",
      "The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labelencoder_x = LabelEncoder()\n",
    "x_train[:, 1 ] = labelencoder_x.fit_transform(x_train[:,1 ])\n",
    "x_train[:, 5 ] = labelencoder_x.fit_transform(x_train[:,5 ])\n",
    "x_train[:, 6 ] = labelencoder_x.fit_transform(x_train[:,6 ])\n",
    "\n",
    "#this is need to done when we have more than two categorical values.\n",
    "onehotencoder_x = OneHotEncoder(categorical_features=[1,5,6]) \n",
    "x_train = onehotencoder_x.fit_transform(x_train).toarray()\n",
    "\n",
    "# Let's apply same concept on test set.\n",
    "x_test[:, 1 ] = labelencoder_x.fit_transform(x_test[:,1 ])\n",
    "x_test[:, 5 ] = labelencoder_x.fit_transform(x_test[:,5 ])\n",
    "x_test[:, 6 ] = labelencoder_x.fit_transform(x_test[:,6 ])\n",
    "\n",
    "onehotencoder_x = OneHotEncoder(categorical_features=[1,5,6]) \n",
    "x_test = onehotencoder_x.fit_transform(x_test).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_y=LabelEncoder()\n",
    "y_train = labelencoder_y.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us apply scaling on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X=StandardScaler()\n",
    "x_train=sc_X.fit_transform(x_train)\n",
    "x_test = sc_X.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### =========================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DIMENSIONALITY REDUCTION\n",
    "* We are doing this to reduce the number of dimensions/features in the dataset.\n",
    "* The features which have less effect on the prediction , we will remove those features.\n",
    "* It also boosts the process.\n",
    "* It saves time.\n",
    "* Here we will use Principal Component Analysis (PCA) with 'rbf' kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.46401324e-02, 5.89820326e-02, 5.53990596e-02, 5.19091859e-02,\n",
       "       4.59729254e-02, 4.43030899e-02, 3.47104977e-02, 3.16583001e-02,\n",
       "       3.11493410e-02, 3.08239159e-02, 2.96192250e-02, 2.94699583e-02,\n",
       "       2.92059189e-02, 2.91268947e-02, 2.88830852e-02, 2.87206991e-02,\n",
       "       2.86685862e-02, 2.86184640e-02, 2.85913748e-02, 2.84037804e-02,\n",
       "       2.83321582e-02, 2.81589909e-02, 2.74701442e-02, 2.66346162e-02,\n",
       "       2.54490425e-02, 2.46751617e-02, 2.20863778e-02, 2.11902534e-02,\n",
       "       1.44422739e-02, 1.13770797e-02, 9.71799237e-03, 1.60944203e-03,\n",
       "       3.59773827e-31, 6.85813237e-32, 2.88134414e-34])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=None)\n",
    "x_train = pca.fit_transform(x_train)\n",
    "x_test = pca.fit_transform(x_test)\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we will take n_componemt = 25, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=25)\n",
    "x_train = pca.fit_transform(x_train)\n",
    "x_test = pca.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### =================================================================\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPLY LOGISTIC REGRESSION AND RANDOM FOREST MODEL AND CHECK ACCURACY FOR EACH MODEL \n",
    "### AND SELECT THE BEST ONE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1). Apply Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regressor Model is \t 0.8201643649205694\n",
      "The deviation in the accuracy is \t 0.0021906833955243556\n"
     ]
    }
   ],
   "source": [
    " # Apply Logistic regression\n",
    "    # First step is to train our model .\n",
    "\n",
    "classifier_logi = LogisticRegression()\n",
    "classifier_logi.fit(x_train,y_train)\n",
    "\n",
    "# Let us check the accuracy of the model with k-cross validation.\n",
    "accuracy = cross_val_score(estimator=classifier_logi, X=x_train, y=y_train, cv=10)\n",
    "print(f\"The accuracy of the Logistic Regressor Model is \\t {accuracy.mean()}\")\n",
    "print(f\"The deviation in the accuracy is \\t {accuracy.std()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2). Apply Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In Random Forest Model we are getting the accuracy of 79%\n"
     ]
    }
   ],
   "source": [
    "# Train Model\n",
    "\"\"\"classifier_ran = RandomForestClassifier()\n",
    "classifier_ran.fit(x_train,y_train)\n",
    "\n",
    "# Check the accuracy and deviation in the accuracy\n",
    "accuracy = cross_val_score(estimator=classifier_ran, X=x_train, y=y_train, cv=10)\n",
    "print(f\"The accuracy of the Random Forest Model is \\t {accuracy.mean()}\") \n",
    "print(f\"The deviation in the accuracy is \\t {accuracy.std()}\")\"\"\"\n",
    "# Here we are getting the accuracy of 79%. \n",
    "print(\"In Random Forest Model we are getting the accuracy of 79%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we will choose Logistic Regression model.\n",
    "* In Logistic Regression we are getting the accuracy of 82% .\n",
    "* Here deviation in the accuracy is 2%, means if we predict on new values then our accuracy will lies in range of 80% to 84% . \n",
    "* It also indicates that the problem is Linear.\n",
    "* By this We can eliminate all other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n",
      "[1 1 1 1 1 0 1 0 1 1]\n",
      "Here 1 indicates 'Fully Paid'. And 0 indicates 'Charged Off' \n"
     ]
    }
   ],
   "source": [
    "print(np.unique(y_train))\n",
    "print(y_train[:10])\n",
    "print(\"Here 1 indicates 'Fully Paid'. And 0 indicates 'Charged Off' \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us Predict on Test Set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Fully Paid', 'Fully Paid', 'Charged Off',\n",
       "       'Fully Paid'], dtype='<U11')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier_logi.predict(x_test)\n",
    "\n",
    "# Let us convert 1 and 0 into Fully Paid and Charged off respectively\n",
    "y_pred = list(map(lambda x: 'Fully Paid' if x==1 else 'Charged Off' ,y_pred))\n",
    "y_pred = np.array(y_pred)\n",
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### =======================================================================\n",
    "### =======================================================================\n",
    "### =======================================================================\n",
    "### ======================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IF THIS KERNEL IS HELPFUL, THEN PLEASE UPVOTE.\n",
    "<img src='https://drive.google.com/uc?id=1LBdaJj2pTM0cq9PY6k70RaGfUFDakUzG' width=500 >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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