Predict_Default_Loans_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iSgpq05VqQ1p"
   },
   "source": [
    "# **Predict Default Loans Data Exploration**\n",
    "\n",
    "Credit Lending aims to determine the credit risk and the status of the loans approved. The dataset used for this project is taken from Lending Club. It consists of 52 features and around 42K instances spaaning over 5 years from 2007-2011. The target feature is Loan Status which describes if the loan has been fully paid/current, charged off/defaulted.\n",
    "\n",
    "This notebook aims to explore and understand the dataset by generating visuals between the features and the target label. The following questions are answered in this EDA notebook.\n",
    "\n",
    "1.   Check the distribution of the descriptive features: Loan amount, funded amount, interest rate and DTI\n",
    "2.   What is the average loan amount approved and the recoveries made per year based on term?\n",
    "3.   How does grad, emp_length, purpose, home ownership, term affect the loan amount and interest rate and to study the recoveries collected every year.\n",
    "4.   How does grade, sub grade, term, verification status affect if the loans are fully paid or charged off?\n",
    "5.   Determine the ratio of fully paid to charged off loans with loan amount and interest rates in different states in the USA.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Install Chart Studio for visualizing maps using Plotly**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: chart_studio in c:\\users\\rashm\\anaconda3\\lib\\site-packages (1.1.0)\n",
      "Requirement already satisfied: requests in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from chart_studio) (2.22.0)\n",
      "Requirement already satisfied: plotly in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from chart_studio) (4.8.1)\n",
      "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from chart_studio) (1.3.3)\n",
      "Requirement already satisfied: six in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from chart_studio) (1.12.0)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from requests->chart_studio) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from requests->chart_studio) (2019.9.11)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from requests->chart_studio) (2.8)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\rashm\\anaconda3\\lib\\site-packages (from requests->chart_studio) (1.24.2)\n"
     ]
    }
   ],
   "source": [
    "#install chart studio module\n",
    "!pip install chart_studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6EBKbj3EqqOV"
   },
   "source": [
    "**Import all the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "abnsNYDFl0QW"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LswG_EXIqz3q"
   },
   "source": [
    "**Load the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2IWPTphDl15W"
   },
   "outputs": [],
   "source": [
    "loan = pd.read_csv(\"loans_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "35eGvWv2q_aa"
   },
   "source": [
    "**Print the dataset to analyse the columns and their data types**\n",
    "\n",
    "It is seen the dataset has 52 features and more 42K instances. There are 7 categorical features, 42 numerical features, and 3 datetime features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 892
    },
    "colab_type": "code",
    "id": "7ePdBz8drrkt",
    "outputId": "07b22d52-fa4c-4fc8-ec8b-9b5cb6b539dd"
   },
   "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>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Apr-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501  1296599.0     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430  1314167.0     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175  1313524.0     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863  1277178.0    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358  1311748.0     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade  ... last_pymnt_amnt  \\\n",
       "0   10.65%       162.87     B        B2  ...          171.62   \n",
       "1   15.27%        59.83     C        C4  ...          119.66   \n",
       "2   15.96%        84.33     C        C5  ...          649.91   \n",
       "3   13.49%       339.31     C        C1  ...          357.48   \n",
       "4   12.69%        67.79     B        B5  ...           67.79   \n",
       "\n",
       "  last_credit_pull_d collections_12_mths_ex_med  policy_code application_type  \\\n",
       "0           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "1           Sep-2013                        0.0          1.0       INDIVIDUAL   \n",
       "2           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "3           Apr-2016                        0.0          1.0       INDIVIDUAL   \n",
       "4           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "\n",
       "  acc_now_delinq chargeoff_within_12_mths delinq_amnt pub_rec_bankruptcies  \\\n",
       "0            0.0                      0.0         0.0                  0.0   \n",
       "1            0.0                      0.0         0.0                  0.0   \n",
       "2            0.0                      0.0         0.0                  0.0   \n",
       "3            0.0                      0.0         0.0                  0.0   \n",
       "4            0.0                      0.0         0.0                  0.0   \n",
       "\n",
       "  tax_liens  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data\n",
    "loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "lcnFk21mxCa2",
    "outputId": "29a7c361-3202-4f13-90ab-03d53327ed93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lenght of the data: 42538\n"
     ]
    }
   ],
   "source": [
    "#check the size of the data\n",
    "print(\"The lenght of the data:\",len(loan))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "colab_type": "code",
    "id": "9-KAxEiqS5hC",
    "outputId": "e6e78243-2d8f-47fa-ce11-15a18acb4c20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
       "       'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
       "       'issue_d', 'loan_status', 'pymnt_plan', 'purpose', 'title', 'zip_code',\n",
       "       'addr_state', 'dti', 'delinq_2yrs', 'earliest_cr_line',\n",
       "       'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util',\n",
       "       'total_acc', 'initial_list_status', 'out_prncp', 'out_prncp_inv',\n",
       "       'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
       "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
       "       'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d',\n",
       "       'collections_12_mths_ex_med', 'policy_code', 'application_type',\n",
       "       'acc_now_delinq', 'chargeoff_within_12_mths', 'delinq_amnt',\n",
       "       'pub_rec_bankruptcies', 'tax_liens'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns present in the data\n",
    "loan.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "colab_type": "code",
    "id": "K3q4_n9eBcYT",
    "outputId": "4bf27658-c8c7-4074-db4f-348f81eb0ade"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>...</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4.253500e+04</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>4.253100e+04</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42506.000000</td>\n",
       "      <td>42506.000000</td>\n",
       "      <td>42506.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42535.000000</td>\n",
       "      <td>42390.0</td>\n",
       "      <td>42535.0</td>\n",
       "      <td>42506.000000</td>\n",
       "      <td>42390.0</td>\n",
       "      <td>42506.000000</td>\n",
       "      <td>41170.000000</td>\n",
       "      <td>42430.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>8.257026e+05</td>\n",
       "      <td>11089.722581</td>\n",
       "      <td>10821.585753</td>\n",
       "      <td>10139.830603</td>\n",
       "      <td>322.623063</td>\n",
       "      <td>6.913656e+04</td>\n",
       "      <td>13.373043</td>\n",
       "      <td>0.152449</td>\n",
       "      <td>1.081424</td>\n",
       "      <td>9.343951</td>\n",
       "      <td>...</td>\n",
       "      <td>99.760718</td>\n",
       "      <td>13.858267</td>\n",
       "      <td>2610.756511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143039</td>\n",
       "      <td>0.045227</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.795409e+05</td>\n",
       "      <td>7410.938391</td>\n",
       "      <td>7146.914675</td>\n",
       "      <td>7131.686446</td>\n",
       "      <td>208.927216</td>\n",
       "      <td>6.409635e+04</td>\n",
       "      <td>6.726315</td>\n",
       "      <td>0.512406</td>\n",
       "      <td>1.527455</td>\n",
       "      <td>4.496274</td>\n",
       "      <td>...</td>\n",
       "      <td>716.220266</td>\n",
       "      <td>159.360372</td>\n",
       "      <td>4385.636417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.359579</td>\n",
       "      <td>0.208737</td>\n",
       "      <td>0.004855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7.047300e+04</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>1.896000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>6.384795e+05</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>4950.000000</td>\n",
       "      <td>165.520000</td>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>211.460000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>8.241780e+05</td>\n",
       "      <td>9700.000000</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>277.690000</td>\n",
       "      <td>5.900000e+04</td>\n",
       "      <td>13.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>526.150000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.033946e+06</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>14000.000000</td>\n",
       "      <td>428.180000</td>\n",
       "      <td>8.250000e+04</td>\n",
       "      <td>18.680000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3165.450000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.314167e+06</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>1305.190000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29623.350000</td>\n",
       "      <td>7002.190000</td>\n",
       "      <td>36115.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6053.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          member_id     loan_amnt   funded_amnt  funded_amnt_inv  \\\n",
       "count  4.253500e+04  42535.000000  42535.000000     42535.000000   \n",
       "mean   8.257026e+05  11089.722581  10821.585753     10139.830603   \n",
       "std    2.795409e+05   7410.938391   7146.914675      7131.686446   \n",
       "min    7.047300e+04    500.000000    500.000000         0.000000   \n",
       "25%    6.384795e+05   5200.000000   5000.000000      4950.000000   \n",
       "50%    8.241780e+05   9700.000000   9600.000000      8500.000000   \n",
       "75%    1.033946e+06  15000.000000  15000.000000     14000.000000   \n",
       "max    1.314167e+06  35000.000000  35000.000000     35000.000000   \n",
       "\n",
       "        installment    annual_inc           dti   delinq_2yrs  inq_last_6mths  \\\n",
       "count  42535.000000  4.253100e+04  42535.000000  42506.000000    42506.000000   \n",
       "mean     322.623063  6.913656e+04     13.373043      0.152449        1.081424   \n",
       "std      208.927216  6.409635e+04      6.726315      0.512406        1.527455   \n",
       "min       15.670000  1.896000e+03      0.000000      0.000000        0.000000   \n",
       "25%      165.520000  4.000000e+04      8.200000      0.000000        0.000000   \n",
       "50%      277.690000  5.900000e+04     13.470000      0.000000        1.000000   \n",
       "75%      428.180000  8.250000e+04     18.680000      0.000000        2.000000   \n",
       "max     1305.190000  6.000000e+06     29.990000     13.000000       33.000000   \n",
       "\n",
       "           open_acc  ...    recoveries  collection_recovery_fee  \\\n",
       "count  42506.000000  ...  42535.000000             42535.000000   \n",
       "mean       9.343951  ...     99.760718                13.858267   \n",
       "std        4.496274  ...    716.220266               159.360372   \n",
       "min        1.000000  ...      0.000000                 0.000000   \n",
       "25%        6.000000  ...      0.000000                 0.000000   \n",
       "50%        9.000000  ...      0.000000                 0.000000   \n",
       "75%       12.000000  ...      0.000000                 0.000000   \n",
       "max       47.000000  ...  29623.350000              7002.190000   \n",
       "\n",
       "       last_pymnt_amnt  collections_12_mths_ex_med  policy_code  \\\n",
       "count     42535.000000                     42390.0      42535.0   \n",
       "mean       2610.756511                         0.0          1.0   \n",
       "std        4385.636417                         0.0          0.0   \n",
       "min           0.000000                         0.0          1.0   \n",
       "25%         211.460000                         0.0          1.0   \n",
       "50%         526.150000                         0.0          1.0   \n",
       "75%        3165.450000                         0.0          1.0   \n",
       "max       36115.200000                         0.0          1.0   \n",
       "\n",
       "       acc_now_delinq  chargeoff_within_12_mths   delinq_amnt  \\\n",
       "count    42506.000000                   42390.0  42506.000000   \n",
       "mean         0.000094                       0.0      0.143039   \n",
       "std          0.009700                       0.0     29.359579   \n",
       "min          0.000000                       0.0      0.000000   \n",
       "25%          0.000000                       0.0      0.000000   \n",
       "50%          0.000000                       0.0      0.000000   \n",
       "75%          0.000000                       0.0      0.000000   \n",
       "max          1.000000                       0.0   6053.000000   \n",
       "\n",
       "       pub_rec_bankruptcies     tax_liens  \n",
       "count          41170.000000  42430.000000  \n",
       "mean               0.045227      0.000024  \n",
       "std                0.208737      0.004855  \n",
       "min                0.000000      0.000000  \n",
       "25%                0.000000      0.000000  \n",
       "50%                0.000000      0.000000  \n",
       "75%                0.000000      0.000000  \n",
       "max                2.000000      1.000000  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistics of numerical data\n",
    "loan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SrXs_xb3_zi4"
   },
   "outputs": [],
   "source": [
    "#Remove the % from the interest rate column to convert it to numerical data type.\n",
    "loan['int_rate'] = loan['int_rate'].astype(str).map(lambda x: x.rstrip('%')).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Apr-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jun-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501  1296599.0     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430  1314167.0     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175  1313524.0     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863  1277178.0    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358  1311748.0     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade  ... last_pymnt_amnt  \\\n",
       "0     10.65       162.87     B        B2  ...          171.62   \n",
       "1     15.27        59.83     C        C4  ...          119.66   \n",
       "2     15.96        84.33     C        C5  ...          649.91   \n",
       "3     13.49       339.31     C        C1  ...          357.48   \n",
       "4     12.69        67.79     B        B5  ...           67.79   \n",
       "\n",
       "  last_credit_pull_d collections_12_mths_ex_med  policy_code application_type  \\\n",
       "0           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "1           Sep-2013                        0.0          1.0       INDIVIDUAL   \n",
       "2           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "3           Apr-2016                        0.0          1.0       INDIVIDUAL   \n",
       "4           Jun-2016                        0.0          1.0       INDIVIDUAL   \n",
       "\n",
       "  acc_now_delinq chargeoff_within_12_mths delinq_amnt pub_rec_bankruptcies  \\\n",
       "0            0.0                      0.0         0.0                  0.0   \n",
       "1            0.0                      0.0         0.0                  0.0   \n",
       "2            0.0                      0.0         0.0                  0.0   \n",
       "3            0.0                      0.0         0.0                  0.0   \n",
       "4            0.0                      0.0         0.0                  0.0   \n",
       "\n",
       "  tax_liens  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185
    },
    "colab_type": "code",
    "id": "EqlHRckyTT0S",
    "outputId": "c155b1a1-4051-4b85-b223-d668272c741e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid                                             33136\n",
       "Charged Off                                             5634\n",
       "Does not meet the credit policy. Status:Fully Paid      1988\n",
       "Current                                                  961\n",
       "Does not meet the credit policy. Status:Charged Off      761\n",
       "Late (31-120 days)                                        24\n",
       "In Grace Period                                           20\n",
       "Late (16-30 days)                                          8\n",
       "Default                                                    3\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the different loan status available in the dataset\n",
    "loan[\"loan_status\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9tMPeF6tZCWs"
   },
   "source": [
    "I am concentrating on predicting loans that are either fully paid/current and charged off/default. Also, the loan statuses with Late (16-30 days), In Grace Period, Late (31-120 days) have less instances and the values have been dropped from the dataset. Rest of the values are Current and Does not meet the credit policy. Status:Fully Paid are combined to Fully Paid and Default and Does not meet the credit policy. Status:Charged off are combined to Charged off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X-BI8TvpTVRF"
   },
   "outputs": [],
   "source": [
    "#drop rows with Late (16-30 days), In Grace Period, Late (31-120 days) status\n",
    "loan_stat = [\"Late (16-30 days)\",\"In Grace Period\",\"Late (31-120 days)\"]\n",
    "for stat in loan_stat:\n",
    "    loan.drop(loan[loan[\"loan_status\"] == stat].index,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Svs8T6zTVVC"
   },
   "outputs": [],
   "source": [
    "#map the status to fully paid and charged off\n",
    "loan[\"loan_status\"] = loan[\"loan_status\"].map({\"Default\":\"Charged Off\", \"Does not meet the credit policy. Status:Charged Off\": \n",
    "                                               \"Charged Off\", \"Current\":\"Fully Paid\",\"Does not meet the credit policy. Status:Fully Paid\"\n",
    "                                               :\"Fully Paid\", \"Fully Paid\":\"Fully Paid\",\"Charged Off\":\"Charged Off\" })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YTFVJ1K8rM-k"
   },
   "source": [
    "**Distribution for the features**\n",
    "\n",
    "Lets check the distribution of the features: Loan amount, funded amount, interest rate and DTI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "HvqZvXrZ1w6h",
    "outputId": "b39f78dd-e7d3-4fc9-fcb4-6b4dee632039"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of loan amount\n",
    "fig, axs = plt.subplots(ncols = 4)\n",
    "loan_amnt_ax = sns.distplot(loan[\"loan_amnt\"].dropna(), ax = axs[0], color = \"red\")\n",
    "loan_amnt_ax.set_title('Loan amount distribution', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "loan_amnt_ax.set(xlabel = 'Loan amount')\n",
    "loan_amnt_ax.figure.set_size_inches(10,4)\n",
    "\n",
    "#distribution of funded amount\n",
    "int_rate_ax = sns.distplot(loan[\"funded_amnt\"].dropna(), ax = axs[1], color = \"red\")\n",
    "int_rate_ax.set_title('Funded Amount distribution', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "int_rate_ax.set(xlabel = 'Funded Amount')\n",
    "int_rate_ax.figure.set_size_inches(13,4)\n",
    "\n",
    "#distribution of interest rate\n",
    "int_rate_ax = sns.distplot(loan[\"int_rate\"].dropna(), ax = axs[2], color = \"green\")\n",
    "int_rate_ax.set_title('Interest Rate distribution', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "int_rate_ax.set(xlabel = 'Interest Rate')\n",
    "int_rate_ax.figure.set_size_inches(13,4)\n",
    "\n",
    "#distribution of DTI\n",
    "annual_inc_ax = sns.distplot(loan[\"dti\"].dropna(), ax = axs[3], color = \"green\")\n",
    "annual_inc_ax.set_title('Debt to income ratio distribution', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "annual_inc_ax.set(xlabel = 'dti')\n",
    "annual_inc_ax.figure.set_size_inches(13,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above distribution:\n",
    "\n",
    "1. The loan amount is ranged between 5,000 - 20,000 dollars.\n",
    "2. The loan amount and funded amount distribution are similar concluding that most of the borrowers who are qualified recieve the applied loan amount.\n",
    "3. The interest rates for the loans is mainly from 5-15%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y7OkXJYRcovO"
   },
   "source": [
    "**Loans approved and Recoveries made per year based on term**\n",
    "\n",
    "1. An interesting point to note is from 2007-2009 all loans approved were for 36 months only. \n",
    "2. The graph shows the loans approved for 36 months are higher compared to 60 months from 2010 onwards. \n",
    "3. However the number of loans approved has increased about two times every year compared to its previous year.\n",
    "4. The recoveries made during 2007 was the highest which reduced greatly for 36 months term over the years where as for 60months the recoveries increased from 2010 to 2011."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 276
    },
    "colab_type": "code",
    "id": "0b1aozg8yBOE",
    "outputId": "ddf11062-20dd-4153-e3b8-25a985fead01"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#extract year from date\n",
    "loan[\"year\"] = pd.to_datetime(loan[\"issue_d\"]).dt.year.astype(\"Int64\")\n",
    "#groupby year and term\n",
    "term_per_year = loan.groupby(['year',\"term\"]).size()\n",
    "\n",
    "#calculate the mean recoveries per year and term\n",
    "recovery_by_year = loan.groupby(['year',\"term\"])[\"recoveries\"].mean()\n",
    "\n",
    "#plot the graph\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "\n",
    "#year/term v/s average loan amount\n",
    "ax_loan = term_per_year.unstack().plot(kind = \"bar\",stacked = True, rot = 0, ax = ax1, legend = True, figsize = (20,8))\n",
    "ax1.title.set_text(\"Loan terms count per year\")\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Count of Loans')\n",
    "\n",
    "#bar toppers\n",
    "for p in ax_loan.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ax_loan.text(x + width/2, \n",
    "                 y + height/2, \n",
    "                 '{:.0f}'.format(height), \n",
    "                 horizontalalignment = 'center',\n",
    "                 verticalalignment = 'bottom')\n",
    "\n",
    "#year/term v/s recoveries\n",
    "ax_recovry = recovery_by_year.unstack().plot(kind = \"bar\",stacked = True, rot = 0, ax = ax2, legend = True, figsize = (20,8))\n",
    "ax2.title.set_text(\"Average Recovery for the Loans\")\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Average Recoveries in $')\n",
    "\n",
    "#bar toppers\n",
    "for p in ax_recovry.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ax_recovry.text(x + width/2, \n",
    "                    y + height/2, \n",
    "                    '{:.0f}'.format(height), \n",
    "                    horizontalalignment = 'center',\n",
    "                    verticalalignment = 'bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l3MAHI_SdzXr"
   },
   "source": [
    "**Loans approved and Interest rates per grade**\n",
    "\n",
    "LendingClub assigns a grades (A–C) and sub-grades (1-5) to each member, which reflects LendingClub’s assessment of the credit risk of the corresponding loan. For example, if the borrower has a weaker credit profile they would get a lower grade and sub-grade, like C5. If the borrower has a stronger credit history, they’d get a higher grade and sub-grade, like A1 or B1. \n",
    "\n",
    "The interest rate varies depending on the credit risk of the corresponding loan. So higher grades (higher credit quality) come with lower interest rates. Lower grades come with higher interest rates.\n",
    "\n",
    "Lets plot the loan amount and interest rate for grades and subgrades to see if the above points holds good for the given data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 674
    },
    "colab_type": "code",
    "id": "O757HJHh4LJv",
    "outputId": "2a11f63e-9a98-4400-cd24-e39fe3ab4290"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group grade and loan amount\n",
    "grade_loan_amt = loan.groupby(['grade'])[[\"loan_amnt\"]].mean()\n",
    "\n",
    "#group grade and interest rate\n",
    "grade_int_rate = loan.groupby(['grade'])[[\"int_rate\"]].mean()\n",
    "\n",
    "#plot the figure\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "width = 0.2\n",
    "\n",
    "plt.title(\"Loan Amount and Interest Rate by Grade\")\n",
    "\n",
    "x_color = \"black\"\n",
    "ax1.set_xlabel('Grade', color = x_color)\n",
    "ax1.tick_params(axis = 'x', labelcolor = x_color)\n",
    "\n",
    "color = 'tab:brown'\n",
    "ax1.set_ylabel('Loan amount', color = color)\n",
    "grade_loan_amt.plot(kind = \"bar\", color = color,width = width, ax = ax1, position = 1, figsize = (10,11))\n",
    "ax1.tick_params(axis = 'y', labelcolor = color)\n",
    "ax1.legend(loc = 2)\n",
    "\n",
    "#create a twin axis\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "color1 = \"tab:blue\"\n",
    "ax2.set_ylabel('Interest Rate', color = color1) \n",
    "ax1.set_xlabel('Grade', color = x_color)\n",
    "grade_int_rate.plot(kind = \"bar\", color = color1, width = width, ax = ax2, position = 0,figsize = (10,11))\n",
    "ax2.tick_params(axis = 'y', labelcolor = color1)\n",
    "ax2.legend(loc = 1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This above graph shows how grade affects the loan amount and interest rates for the approved loans. \n",
    "\n",
    "1. The average loan amount is highest for Grade F and lowest for Grade A.\n",
    "2. The interest rate is least at 7.5% for Grade A with highest of about 20% for the lowest grade G. The interest rate varies as given in the Lending Club website."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aZk1i5B-exER"
   },
   "source": [
    "**Number of loans fully paid based on grade and sub grade**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "TFoZMic_64A5",
    "outputId": "739c0cdb-55a4-460c-8d8f-9525ed6a8065"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z5u5fA5hZNjDMzBYTrMDyi7DOJGACwdK/IiIitd4LL7xATk4OH3/8MWvXrqVv374MHDiQ1q1bM23aNJo2bcratWsZMGAAQ4cOBWDp0qU888wzPPLII5x77rk8//zznH/++TGPf+utt/LNN9+QlpbGhg0baN68OZdeeikZGRlcffXVAKxfv54PPvgAM+PRRx/l9ttv529/+9tO9R577LGY53j66ac5+eSTufHGGykpKaGgoCAOV0qqyt0/AXrGKP+aGKvSuXshcE4Fx7oFuKW62ygiIpJoisGqXyInF78YmBK+b0+QiCoVvTpK+VVT+hPcjatoBRYREZFa75133mHkyJEkJyez//77c9xxxzFv3jxOPfVUbrjhBubMmUNSUhIrV65k9erVAHTs2JGsrCwAevfuzfLlyys8/hFHHMGoUaMYPnw4w4cPj1knNzeX8847j1WrVrFt2zY6duy4W33o27cvF198MUVFRQwfPrysbSIiIiL7KsVg1S8hk4ub2Y1AMTC5tChGtcpWTanSaipR5xtnZvPNbH5eXt7uNldERKTGBVPp7Gzy5Mnk5eWxYMECcnJy2H///SksLAQgLS2trF5ycjLFxcUxjwHwyiuvcPnll7NgwQJ69+4ds+6vf/1rxo8fz6effspDDz1Udp7yUlJSiEQiZe3etm0bAAMHDmTOnDm0b9+e0aNH88QTT1St8yL7mgnNKn6JiEidohis+tV44snMxgBDgFG+/RutaHWUisrXUvEKLDvRqioiIlLbDBw4kClTplBSUkJeXh5z5syhX79+bNy4kTZt2pCamsqsWbP49ttvd/vYkUiEFStW8LOf/Yzbb7+dDRs2kJ+fT5MmTdi0aVNZvY0bN9K+fTCgeNKkSWXl5etlZmayYMECAKZPn05RUREA3377LW3atGHs2LFccsklLFy4cI+uhYiIiEhNUQxW/Wo08WRmpxBMYDnU3aMfMqxodZR5QKdwBbsGBBOQzwgTVhWtwCIiIlLrnXnmmRxxxBEceeSRDBo0iNtvv50DDjiAUaNGMX/+fPr06cPkyZPp3Lnzbh+7pKSE888/nx49etCzZ09++9vf0rx5c8444wymTZtWNmHlhAkTOOecczj22GNp1apV2f7l640dO5a33nqLfv368eGHH9K4cWMgWHklKyuLnj178vzzz3PFFVdU2/URERERiQfFYNXPKhpGttcHNnsGOB5oBawGbiJYxS4NWBdW+8DdLw3r30gw71MxcKW7/yssPw24G0gGJoaTWWJmPwGygZbAR8D54eTklerTp4/Pnz+/mnopFdoXh55P2JjoFohIHbN48WK6dOmS6GbUa7G+AzNb4O59EtQkiaHWx1+VxTV1Kb6oL/0UkVpPMVji7U4MFs9V7UbGKI495ToVr47i7q8Cr8Yoj7kCi4iIiIiIiIiI7BsSuaqdiIiIxNnll1/Ou+++u0PZFVdcwUUXXZSgFomIiIjUfYrBtlPiSUREpA67//77E90EEZHYdjU1gx7vE5FaTDHYdko8iYiIiIjEg+ZMEhERqdlV7UREREREREREpP5Q4klEREREREREROJCiScREZFa7L///S8jRozgkEMOoWvXrpx22ml8+eWXzJ49myFDhiS0bRMmTOCOO+6Iue3hhx+mc+fOdO7cmX79+vHOO++UbXv77bfp1q0bWVlZbNmyhWuuuYZu3bpxzTXX1FTTRURERCqlGKzqNMeTiIhINcm87pVqPd7yW0+vdLu7c+aZZzJmzBiys7MByMnJYfXq1Xt97uLiYlJS4hMmvPzyyzz00EO88847tGrVioULFzJ8+HDmzp3LAQccwOTJk7n66qvLVn156KGHyMvLIy0tLS7tERERkdpNMVjVJCoG04gnERGRWmrWrFmkpqZy6aWXlpVlZWVx7LHHApCfn8/ZZ59N586dGTVqFO4OwM0330zfvn3p3r0748aNKys//vjjueGGGzjuuOO45557+OqrrxgwYAB9+/blD3/4AxkZGWXn+etf/0rfvn054ogjuOmmm8rKb7nlFg4//HBOOOEElixZErPdt912G3/9619p1aoVAL169WLMmDHcf//9PProozz77LPcfPPNjBo1iqFDh7J582b69+/PlClTqvcCioiIiOwBxWC7RyOeREREaqnPPvuM3r17V7j9o48+YtGiRbRr146jjz6ad999l2OOOYbx48fzhz/8AYDRo0fz8ssvc8YZZwCwYcMG3nrrLQCGDBnCFVdcwciRI3nwwQfLjvv666+zdOlS5s6di7szdOhQ5syZQ+PGjcnOzuajjz6iuLiYXr16xWzfokWLdirv06cPkyZN4o9//CPvvPMOQ4YM4eyzzwYgIyODnJycvbtYIiIiItVEMdju0YgnERGROqpfv3506NCBpKQksrKyWL58ORDcpevfvz89evTgzTffZNGiRWX7nHfeeWXv33//fc455xwAfvGLX5SVv/7667z++uv07NmTXr168cUXX7B06VLefvttzjzzTBo1akTTpk0ZOnRoldvq7pjZXvZYREREJPEUg+1IiScREZFaqlu3bixYsKDC7dHP4ycnJ1NcXExhYSGXXXYZU6dO5dNPP2Xs2LEUFhaW1WvcuPEuz+vuXH/99eTk5JCTk8OyZcu45JJLAKoUuHTt2nWndi9cuJCuXbvucl8RERGRRFMMtnuUeBIREamlBg0axNatW3nkkUfKyubNm1c2TDuW0gCnVatW5OfnM3Xq1ArrDhgwgOeffx6gbOJMgJNPPpmJEyeSn58PwMqVK1mzZg0DBw5k2rRpbNmyhU2bNvHSSy/FPO7//u//cu2117Ju3TogmIzz8ccf57LLLqtiz0VEREQSRzHY7tEcTyIiIrWUmTFt2jSuvPJKbr31VtLT08nMzOTuu+9m5cqVMfdp3rw5Y8eOpUePHmRmZtK3b98Kj3/33Xdz/vnn87e//Y3TTz+dZs2aAXDSSSexePFifvrTnwLB8/9PPfUUvXr14rzzziMrK4uDDz64bILN8oYOHcrKlSs56qijMDOaNGnCU089Rdu2bffyioiIiIjEn2Kw3WOls6jXF3369PH58+cnuhl134RmiW7BziZsTHQLRKSOWbx4MV26dEl0M+KmoKCAhg0bYmZkZ2fzzDPPMH369EQ3awexvgMzW+DufRLUJImh1sdflcU1lcUXe7pfotR0e3cVL+6L10hE9gmKwRJvd2IwjXgSERGRmBYsWMD48eNxd5o3b87EiRMT3SQRERGROq+uxWBKPImIiEhMxx57LB9//HGimyEi+7raNrJLRGQfV9diME0uLiIiIiIiIiIicaHEk4iIiEg9YGYHmtksM1tsZovM7IqwfIKZrTSznPB1WtQ+15vZMjNbYmYnR5WfEpYtM7PrEtEfERERqR30qJ2IiIhI/VAM/M7dF5pZE2CBmb0RbrvL3e+IrmxmXYERQDegHfAfMzss3Hw/cCKQC8wzsxnu/nmN9EJERERqFSWeREREROoBd18FrArfbzKzxUD7SnYZBmS7+1bgGzNbBvQLty1z968BzCw7rKvEk4iIiOxEj9qJiIjUYsnJyWRlZZW9li9fXmn9zMxM1q5dC0BGRkaVz7N8+XIaNmxIVlYWXbt25dJLLyUSiVS6z1FHHRWz/MILL2Tq1KlVPrdUPzPLBHoCH4ZF483sEzObaGYtwrL2wIqo3XLDsorKy59jnJnNN7P5eXl51dwDERGRxFIMVnUa8SQiIlJdKlvZaY+Ot+vVoBo2bEhOTk71nrcChxxyCDk5ORQXFzNo0CBefPFFzjrrrArrv/feezXSLtk9ZpYBPA9c6e4/mtkDwB8BD3/+DbgYsBi7O7FvXPpOBe4PAw8D9OnTZ6ftIiIi1UYx2A72tRgsbiOewjtma8zss6iylmb2hpktDX+2CMvNzO4NJ6j8xMx6Re0zJqy/1MzGRJX3NrNPw33uNbNYwZGIiEi98/jjjzN+/Piyz0OGDGH27NkV1h89ejTTp08v+zxq1ChmzJhRYf2UlBSOOuooli1bRn5+PoMHD6ZXr1706NFjh+OU3s1zd8aPH0/Xrl05/fTTWbNmzV70TvaGmaUSJJ0mu/sLAO6+2t1L3D0CPML2x+lygQOjdu8AfF9JuYiISL2mGKyCdsfx2I8D9wFPRJVdB8x091vDFVCuA64FTgU6ha/+wANAfzNrCdwE9CG4k7YgnLxyfVhnHPAB8CpwCvCvOPZnR9WdUd1bVcjIiohI3bNlyxaysrIA6NixI9OmTdvtY/zyl7/krrvuYtiwYWzcuJH33nuPSZMmVVi/oKCAmTNncvPNN5Oens60adNo2rQpa9euZcCAAQwdOpTo+0HTpk1jyZIlfPrpp6xevZquXbty8cUX735nZa+EN+keAxa7+51R5W3D+Z8AzgRKbxrOAJ42szsJJhfvBMwlGAnVycw6AisJJiD/Rc30QkREZN+gGKzq4pZ4cvc54fwB0YYBx4fvJwGzCRJPw4An3N2BD8ysuZm1Deu+4e4/AIQrr5xiZrOBpu7+flj+BDCcmkw8iYiI7AOqY5j3cccdx+WXX86aNWt44YUX+PnPf05Kys4hwldffUVWVhZmxrBhwzj11FMpKirihhtuYM6cOSQlJbFy5UpWr17NAQccULbfnDlzGDlyJMnJybRr145BgwbtVXtljx0NjAY+NbPSfzQ3ACPNLIvgJt9y4FcA7r7IzJ4lmDS8GLjc3UsAzGw88BqQDEx090U12REREZFEUwxWdTU9x9P+pXfU3H2VmbUJy3d38sr24fvy5SIiIvVeSkrKDpNOFhYW7nKf0aNHM3nyZLKzs5k4cWLMOqXzC0SbPHkyeXl5LFiwgNTUVDIzM2OeT0/EJ567v0PseZterWSfW4BbYpS/Wtl+IiIi9ZFisNj2lVXtKpq8cnfLYx9cq6qIiEg9kpmZSU5ODpFIhBUrVjB37txd7nPhhRdy9913A9CtW7cqn2vjxo20adOG1NRUZs2axbfffrtTnYEDB5KdnU1JSQmrVq1i1qxZVe+MiIiISC2hGCy2mh7xtLp0HoHwUbrSma0qm7zy+HLls8PyDjHqx6RVVUREpD45+uij6dixIz169KB79+706tVrl/vsv//+dOnSheHDh+/WuUaNGsUZZ5xBnz59yMrKonPnzjvVOfPMM3nzzTfp0aMHhx12GMcdd9xunUNERESkNlAMFltNJ55mAGOAW8Of06PKx5tZNsHk4hvD5NRrwJ9LV78DTgKud/cfzGyTmQ0APgQuAP5ekx0RERHZSQIWesjPz9+pzMyYPHlyzPrLly+PuW9BQQFLly5l5MiRMffLzMzks88+26m8VatWvP/++5W2zcy47777KuyDiIiIyF5RDBazbftKDBa3R+3M7BngfeBwM8s1s0sIEk4nmtlS4MTwMwRzBHwNLCNYxvcygHBS8T8C88LXzaUTjQP/Azwa7vMVmlhcRERkj/znP/+hc+fO/PrXv6ZZs31s1VYRERGROqq+xGDxXNUudroOBseo68DlFRxnIrDTDFvuPh/ovjdtFBERETjhhBP47rvvEt0MERERkXqlvsRg+8rk4iIiIiIiIiIiUsco8SQiIrIXgkG7kgi69iIiIvWX4oDE2d1rr8STiIjIHkpPT2fdunUKfBLA3Vm3bh3p6emJboqIiIjUMMVgibMnMVhNr2onIiJSZ3To0IHc3Fzy8vIS3ZR6KT09nQ4dOiS6GSIiIlLDFIMl1u7GYEo8iYiI7KHU1FQ6duyY6GaIiIiI1CuKwWoXJZ5ERERERKR2mVDJsuMTNtZcO0REZJc0x5OIiIiIiIiIiMSFEk8iIiIiIiIiIhIXSjyJiIiIiIiIiEhcKPEkIiIiIiIiIiJxocSTiIiIiIiIiIjEhVa1ExERERGR+qGy1fBAK+KJiMSBRjyJiIiIiIiIiEhcKPEkIiIiIiIiIiJxoUftRBJlV0O9a5qGlouIiNR+lcUX+lsvIiIJoBFPIiIiIiIiIiISF0o8iYiIiIiIiIhIXCjxJCIiIlIPmNmBZjbLzBab2SIzuyIsb2lmb5jZ0vBni7DczOxeM1tmZp+YWa+oY40J6y81szGJ6pOIiIjs+5R4EhEREakfioHfuXsXYAy/hZwAACAASURBVABwuZl1Ba4DZrp7J2Bm+BngVKBT+BoHPABBogq4CegP9ANuKk1WiYiIiJSnxJOIiIhIPeDuq9x9Yfh+E7AYaA8MAyaF1SYBw8P3w4AnPPAB0NzM2gInA2+4+w/uvh54AzilBrsiIiIitYgSTyIiIiL1jJllAj2BD4H93X0VBMkpoE1YrT2wImq33LCsonIRERGRnSQk8WRmvw3nFvjMzJ4xs3Qz62hmH4ZzBUwxswZh3bTw87Jwe2bUca4Py5eY2cmJ6IuIiIhIbWJmGcDzwJXu/mNlVWOUeSXl5c8zzszmm9n8vLy8PWusiIiI1Ho1nngys/bAb4A+7t4dSAZGALcBd4XzC6wHLgl3uQRY7+6HAneF9QjnJBgBdCMY3v0PM0uuyb6IiIiI1CZmlkqQdJrs7i+ExavDR+gIf64Jy3OBA6N27wB8X0n5Dtz9YXfv4+59WrduXb0dERERkVojUY/apQANzSwFaASsAgYBU8Pt5ecXKJ13YCow2MwsLM92963u/g2wjGCCSxEREREpJ4yfHgMWu/udUZtmAKUr040BpkeVXxCubjcA2Bg+ivcacJKZtQgnFT8pLBMRERHZSUpNn9DdV5rZHcB3wBbgdWABsMHdi8Nq0XMFlM0j4O7FZrYR2C8s/yDq0JpfQERERKRiRwOjgU/NLCcsuwG4FXjWzC4hiM/OCbe9CpxGcHOvALgIwN1/MLM/AvPCeje7+w810wURERGpbWo88RTeGRsGdAQ2AM8RLNdbXulcAXs1v0B4znEEywBz0EEH7WaLRURERGo/d3+H2PETwOAY9R24vIJjTQQmVl/rREREpK5KxKN2JwDfuHueuxcBLwBHESzRW5oIi54roGwegXB7M+AHqji/AGiOARERERERERGRREhE4uk7YICZNQrnGhgMfA7MAs4O65SfX6B03oGzgTfDO3AzgBHhqncdgU7A3Brqg4iIiIiIiIiI7EIi5nj60MymAguBYuAj4GHgFSDbzP4Ulj0W7vIY8KSZLSMY6TQiPM4iM3uWIGlVDFzu7iU12hkRERERkXiY0KySbRtrrh0iIiJ7qcYTTwDufhNwU7nir4mxKp27F7J9ksvy224Bbqn2BoqIiIiIiIiIyF5LxKN2IiIiIiIiIiJSDyRkxJPUPxdP38LLXxbTprHx2WUZAPywxTlvagHLNziZzY1nz25Ei4bGxkLn/Glb+G5jhOIIXP3TBlzUswEA322M8MsZW1jxo2PAq6Makdk8iWP/uZlNW4NFDddsdvq1T+bFEY0S1V0RERERERERQYknqSEXZqUyvl8DLpi2pazs1ne2MrhjCtcdk8at72zl1ne2ctuJ6dw/bxtdWyXx0shG5G2OcPh9+Yw6IpUGycYF07Zw47FpnHhICvnbnKRwUei3L2pcdtyfP1vAsMP1T1tERESiaM4kERGRhNCjdlIjBh6cQsuGtkPZ9CXFjDkyFYAxR6by4pJiAAzYtM1xd/K3QcuGRkoSfJ5XQnEETjwkSCplNDAape54zE1bnTe/KWZ459T4d0pEREREREREKqVhIZIwq/MjtG0S5D7bNklizeYIAOP7NWBodgHt7sxn01ZnytkNSTLjy3URmqcbZ00p4JsNEU7omMKtJ6SRnLQ9+TTtiyIGd0yhaZrFPKeIiIiIiIiI1JwqjXgys6OrUiZSHV77qpis/ZP5/qoMci7NYPy/Cvlxq1Mcgbe/K+aOk9KZN7YxX2+I8HhO0Q77PvNZESO7a7STiIjUbYrNREREpLao6qN2f69imUiV7Z+RxKpNwSinVZsitGkc/HP8Z04RZ3VJwcw4tGUSHZsn8cXaCB2aGj0PSOYnLZJISTKGH57CwlUlZcdbVxBh7soIpx+mgXwiIlLnKTYTERGRWqHS/0M3s58CRwGtzeyqqE1NgeR4NkzqvqGHpTDp4yKuOyaNSR8XlU0IflBTY+Y3xRx7cAqr8yMsWRfhJy2MFulJrC908jZHaN04iTeXl9Cn7fZ/hs99XsyQw1JIT9FjdiIiUjcpNhMREZHaZldDQxoAGWG9JlHlPwJnx6tRUveMfL6A2ctLWFvgdLhzE/93fBrXHdOAc6du4bGPijiomfHcOY0A+P1xaVz44hZ6PJCPO9x2QhqtGgWjoe44MZ3BTxTgQO+2yYztvf2xuuzPgiSWiIhIHabYTERERGqVShNP7v4W8JaZPe7u39ZQm6QOeubnjWKWz7yg8U5l7Zok8froncshWNHuk//JiLlt9oWx9xEREakrFJuJiIhIbVPVyXDSzOxhIDN6H3cfFI9GiYiIiEilFJuJiIhIrVDVxNNzwIPAo0DJLuqKiIiISHwpNhMREZFaoaqJp2J3fyCuLRERERGRqlJsJlLTJjSrZNvGmmuHiEgtk1TFei+Z2WVm1tbMWpa+4toyEREREamIYjMRERGpFao64mlM+POaqDIHflK9zRERERGRKlBsJiIiIrVClRJP7t4x3g0RERERkapRbCYiIiK1RZUST2Z2Qaxyd3+iepsjIiIiIrui2ExERERqi6o+atc36n06MBhYCCi4EREREal5is1ERESkVqjqo3a/jv5sZs2AJ+PSIhERERGplGIzERERqS2quqpdeQVAp+psiIiIiIjssV3GZmY20czWmNlnUWUTzGylmeWEr9Oitl1vZsvMbImZnRxVfkpYtszMrotLb0RERKTOqOocTy8RrJQCkAx0AZ6NV6NEREREpGJ7GJs9DtzHzo/j3eXud5Q7fldgBNANaAf8x8wOCzffD5wI5ALzzGyGu3++h10RERGROq6qczxFByPFwLfunhuH9oiIiIjIru12bObuc8wss4rHHwZku/tW4BszWwb0C7ctc/evAcwsO6xbc4mnCc0q2baxxpohIiIiVVOlR+3c/S3gC6AJ0ALYtjcnNbPmZjbVzL4ws8Vm9lMza2lmb5jZ0vBni7Cumdm94XDuT8ysV9RxxoT1l5rZmL1pk4iIiEhtUc2x2fgwxppYGn8B7YEVUXVyw7KKyndiZuPMbL6Zzc/Ly9uL5omIiEhtVqXEk5mdC8wFzgHOBT40s7P34rz3AP92987AkcBi4Dpgprt3AmaGnwFOJZizoBMwDnggbFNL4CagP8EduJuigiURERGROqsaY7MHgEOALGAV8LfSU8So65WU71zo/rC793H3Pq1bt96DpolIQkxoVvFLRGQPVPVRuxuBvu6+BsDMWgP/Aabu7gnNrCkwELgQwN23AdvMbBhwfFhtEjAbuJZg+PYT7u7AB+FoqbZh3Tfc/YfwuG8ApwDP7G6bRERERGqZaonN3H116XszewR4OfyYCxwYVbUD8H34vqJyERERkZ1UNfGUVBrYhNax5yvi/QTIA/5pZkcCC4ArgP3dfRWAu68yszZh/WoZ6k0wWoqDDjpoD5tddfd8sJVHFhbhwNheqVw5II0Jswt5ZGERrRsFNwr/PDiN0zqlMvmTIv763tayfT9ZHWHhrxqTdUAypzy1mVX5TnEEjj0omftPSyc5KdaNRpE6aF+7q6Z5Q0Rk31ItsZmZtS2Nv4AzgdIV72YAT5vZnQSTi3ciGGFlQCcz6wisJJiA/Bd71gURERGpD6qaePq3mb3G9tFE5wGv7sU5ewG/dvcPzewetj9WF0u1DPUGHgbo06dPzDrV5bM1JTyysIi5YxvTIBlOeaqA0zsFl/m3Axpw9VFpO9QfdUQqo45IBeDT1SUMyy4g64BkAJ49pxFN0wx35+zntvDc58WM6J4az+aLiIhI7bDbsZmZPUMwYryVmeUSTFlwvJllEcRQy4FfAbj7IjN7lmDS8GLgcncvCY8zHniNYDW9ie6+qHq7JiIiInVJpYknMzuUYCTSNWZ2FnAMQcLnfWDyHp4zF8h19w/Dz1MJEk+rS++6hY/SrYmqH2tIdy7bH80rLZ+9h22qNovzIgzokEyj1CAvdtzBKUz7orhK+z7zWREjoxJLTdOCYxRHYFtJ7EybiIiI1B97E5u5+8gYxY9VUv8W4JYY5a+y5zcgRUREpJ7Z1ZDsu4FNAO7+grtf5e6/JQg27t6TE7r7f4EVZnZ4WDSY4G7aDKB0ZboxwPTw/QzggnB1uwHAxnBI+GvASWbWIpxU/KSwLKG6t0lizrclrCuIUFDkvLqsmBUbIwDcN3cbRzyQz8XTt7B+y84Dr6YsKmJkjx1HNJ381Gba3LGJJg3g7K5VHaAmIiIidVS1x2YiIiIi8bSrTEamu39SvtDd55tZ5l6c99fAZDNrAHwNXESQBHvWzC4BviNYpQWCQOo0YBlQENbF3X8wsz8C88J6N5dONJ5IXVonc+3RDTjxyQIyGhhH7p9ESpLxP30a8PuBaZjB79/cyu9eL2TisIZl+32YW0yjVKN7m+Qdjvfa+Y0pLHZGvbCFN78p4cRDlHyq65asLeG8qVvKPn+9PsLNP0tj5Y/OS18W0yAZDmmZxD+HNaR5ujF3ZQnjXgrqOzDhuDTO7BIkMDPv3kSTNCPZICUJ5o/LSESXRESk+sQrNhMRERGJi11lMdIr2dawkm2VcvccoE+MTYNj1HXg8gqOMxGYuKftiJdLejXgkl4NALhhZiEdmiaxf8b2wWVjezdgyNMFO+yT/VnxDo/ZRUtPMYYelsL0JUVKPNUDh7dKJufSIEFUEnHa35nPmZ1TWbIuwl9OSCMlybj2jUL+8vZWbjsxne5tkpg/rjEpScaqTRGOfHAzZxyeQko4Ef2sMY1o1WhP1wIQEZF9TFxiMxEREZF42VUWY56ZjXX3R6ILw1FJC+LXrNptzeYIbRon8d3GCC8sLub9SxqzalOEtk2C//mftriI7m22JwIi7jz3eRFzLmpcVpa/zdm01WnbJIniSPDI3rEHKelU38z8poRDWiZxcPPgVWpAh2SmLi4CKJtPDKCwGEyTgYmI1GWKzURqm12t1KuVc0WkjttVJuNKYJqZjWJ7MNMHaECw5K7E8PNnt7CuwElNhvtPS6dFQ2P0tEJy/luCAZnNk3hoyPYblnO+LaFD0yR+0mJ7YmHzNmdodgFbi6HEYVBmMpf20Yp29U12uQnnS03MKeK8btv/8/0wt5iLZxTy7YYIT57ZsGy0kxmc9GQBZvCr3g0Y17tBjbVdRETiQrGZiIiI1CqVJp7cfTVwlJn9DOgeFr/i7m/GvWW12NtRI5dKPXlmxaPfj89M4YNf7vhV7J+RxLyxmo+nPttW4sxYUsxfBqftUH7LnK2kJMGoqIno+3dIYdFlGSzOK2HMi1s4tVMK6SnGuxc3pl2TJNZsjnDikwV0bpXEwIM1ck5EpLZSbCYiIiK1TZX+D9TdZwGz4twWEYnyr6XF9Gq74/xgk3K28fLSYmZe0AiL8Uxdl9bJNG5gfLYmQp92ybQLH+9s0ziJMzunMHdliRJPIiJ1gGIzEdnn6JFCEamAZhwW2Uc9U+4xu38vK+a2d7cxY0TDHeZ1+mZ9hOKIA/DthghL1kbIbG5sDucJg+DRzde/Ktlp1UQRERERERGReNLQB5F9UEGR88bXJTw0ZPsjmuNf3cLWEjjxyWBFxAEdknlwSEPe+a6YW9/dRmoSJBn84/R0WjVK4uv1Ec6cEtQtjsAvuqdyyqH6T15ERESk1qhsFJFGEIlILaH/CxXZBzVKNdb9b5Mdypb9pknMuqOPbMDoI3eeNPwnLZL4+FLNEyYiIiIiIiKJo0ftREREREREREQkLpR4EhERERERERGRuFDiSURERERERERE4kKJJxERERERERERiQslnkREREREREREJC6UeBIRERERERERkbhQ4klEREREREREROIiJdENEBEREZF6ZkKzSrZtrLl2iMi+Qb8TROo0jXgSEREREREREZG4UOJJRERERERERETiQoknERERERERERGJCyWeREREREREREQkLjS5uIiIiEg9YGYTgSHAGnfvHpa1BKYAmcBy4Fx3X29mBtwDnAYUABe6+8JwnzHA/wsP+yd3n1ST/RCRKqhssm7QhN0iUqM04klEaqWSiNPzoXyGPF0AgLtz48xCDvt7Pl3uz+feD7cCsLHQOeOZAo58MJ9u/8jnnx9tKzvGdxsjnPTkZrrcn0/X+/NZviGSkL6IiNSQx4FTypVdB8x0907AzPAzwKlAp/A1DngAyhJVNwH9gX7ATWbWIu4tFxERkVorYSOezCwZmA+sdPchZtYRyAZaAguB0e6+zczSgCeA3sA64Dx3Xx4e43rgEqAE+I27v1bzPRGRRLjnw210aZXEj0F+icdziljxo/PF+MYkmbFmc5BEun/eNrq2SuKlkY3I2xzh8PvyGXVEKg2SjQumbeHGY9M48ZAU8rc5SZbADomIxJm7zzGzzHLFw4Djw/eTgNnAtWH5E+7uwAdm1tzM2oZ133D3HwDM7A2CZNYzcW6+iIiI1FKJfNTuCmAx0DT8fBtwl7tnm9mDBAmlB8Kf6939UDMbEdY7z8y6AiOAbkA74D9mdpi7l9R0R0SkZuX+GOGVpcXceGwad74fjGB6YP42nv55I5IsyB61aRwM6DRg0zbH3cnfBi0bGilJ8HleCcUROPGQ4NdgRgNlnUSkXtrf3VcBuPsqM2sTlrcHVkTVyw3LKioXEal5eqRQpFZIyKN2ZtYBOB14NPxswCBgalhlEjA8fD8s/Ey4fXBYfxiQ7e5b3f0bYBnBkG8RqeOu/Hcht5+QvsMIpa/WO1M+K6LPw/mcOnkzS9cFOejx/RqweG2Ednfm0+OBfO45JZ0kM75cF6F5unHWlAJ6PpTPNa8XUhLxBPVIRGSfEysb75WU73wAs3FmNt/M5ufl5VVr40RERKT2SNQcT3cD/wuUTqiyH7DB3YvDz9F3z8rurIXbN4b1dcdNpB56+csi2jQ2erdL3qF8a7GTngLzx2UwtlcDLp5RCMBrXxWTtX8y31+VQc6lGYz/VyE/bnWKI/D2d8XccVI688Y25usNER7PKUpEl0REEml1+Agd4c81YXkucGBUvQ7A95WU78TdH3b3Pu7ep3Xr1tXecBEREakdajzxZGalq6ksiC6OUdV3sU133ETqoXe/K2HGkmIy797EiKlbePObYs5/YQsdmibx866pAJzZOYVPVgcjnv6ZU8RZXVIwMw5tmUTH5kl8sTZCh6ZGzwOS+UmLJFKSjOGHp7BwlZ7UFZF6ZwYwJnw/BpgeVX6BBQYAG8NH8l4DTjKzFuGk4ieFZSIiIiIxJWLE09HAUDNbTjCZ+CCCEVDNzax0zqnou2dld9bC7c2AH9AdN5F66S8npJN7VROWX9mE7LMbMqhjCk+d1ZDhnVN485tg0ORb35Zw2H7Br7eDmhozw/LV+RGWrIvwkxZG33bJrC908sJJyN9cXkLX1smxTyoiUgeY2TPA+8DhZpZrZpcAtwInmtlS4MTwM8CrwNcEUxk8AlwGEE4q/kdgXvi6uXSicREREZFYanxycXe/HrgewMyOB65291Fm9hxwNkEyqvwdtzEEgdLZwJvu7mY2A3jazO4kmFy8EzC3JvsiIvuO645JY9QLW7jrg21kNDAePaMhAL8/Lo0LX9xCjwfycYfbTkijVaMgKXXHiekMfqIAB3q3TWZs79QE9kBEJL7cfWQFmwbHqOvA5RUcZyIwsRqbJiIiInVYIle1K+9aINvM/gR8BDwWlj8GPGlmywhGOo0AcPdFZvYs8DlQDFyuFe1E6pfjM1M4PjP4NdY83XjlF412qtOuSRKvj24cc/8TD0nhk//JiGsbRURERERE6rOEJp7cfTYwO3z/NTFWpXP3QuCcCva/Bbglfi0UEREREREREZE9tS+NeBIRERERERGJvwnNKtm2sebaIVIPJGJycRERERERERERqQeUeBIRERERERERkbhQ4klEREREREREROJCczyJiNRWlc1NkAiaD0FERERERMrRiCcREREREREREYkLjXgSERERERERqYpdjTjXCHCRnWjEk4iIiIiIiIiIxIUSTyIiIiIiIiIiEhdKPImIiIiIiIiISFwo8SQiIiIiIiIiInGhxJOIiIiIiIiIiMSFEk8iIiIiIiIiIhIXSjyJiIiIiIiIiEhcpCS6ASIiUr1WbIxwwYtb+G++k2QwrlcqVwxIY8LsQh5ZWETrRgbAnwencVqnVAD+8vZWHvtoG8lJxr2npHPyoSkUFjsD/7mZrSVQHIGzu6Twfz9LT2TXRERERGqvCc0q2bax5tohUsOUeBIRqWNSkuBvJ6XTq20ym7Y6vR/ezImHBL/ufzugAVcflbZD/c/zSsheVMSiyzL4fpNzwpOb+XJ8BmnJ8OaYxmQ0MIpKnGP+uZlTOxUzoIP+dIiIiIiISNXoUTsRkTqmbZMkerVNBqBJmtGldRIrf/QK60//opgR3VJJSzE6tkji0JZJzF1ZgpmR0SAYHVUUgaISsBrpgYiIiIiI1BVKPImI1GHLN0T4aFUJ/TsEiaj75m7jiAfyuXj6FtZvCZJRKzdFOLDZ9pRShyZJrNwUbCuJOFkP5tPmr5s48Scp9NdoJxERERER2Q1KPImI1FH525yfP1vA3aek0zTN+J8+DfjqNxnkXNqYthnG714vBMBjDIYqTUMlJxk5l2aQe1UT5n5fwmdrSmquAyIiIiIiUusp8SQiUgcVlQRJp1E9UjmrSzCB+P4ZSSQnGUlmjO3dgLkrgyRSh6ZJrNi4PfuUuylCuyY7PlTXPN04/uAU/r2suOY6ISIiIiIitZ4STyIidYy7c8mMQrq0Suaqn26fSHzVpkjZ+2mLi+jeJvgTMPTwFLIXFbG12PlmfYSl6yL0a59M3uYIGwqDhNSWIuc/3xTTuZX+bIjURWa23Mw+NbMcM5sflrU0szfMbGn4s0VYbmZ2r5ktM7NPzKxXYlsvIiIi+7Ian6zDzA4EngAOACLAw+5+j5m1BKYAmcBy4Fx3X29mBtwDnAYUABe6+8LwWGOA/xce+k/uPqkm+yIisi96d0UJT35SRI82SWQ9mA/Anwen8cxnxeT8twQDMpsn8dCQdAC6tUnm3K6pdP1HPilJxv2npZOcZKzKjzDmxQJKIhBxOLdbKkMOS01gz0Qkzn7m7mujPl8HzHT3W83suvDztcCpQKfw1R94IPwpIiLxMKHZLrZvrJl2iOyhRMwSWwz8//buO0yqKtv7+Hd1dabJORsQRR0FQTEgZkUdRR3UUUeMo6PiHdTR650ZA1cvzuvriFcddYyoM6OYZZTXHFFJgigGTIAiSM4d6LDeP85pKJruqqa6u0Lz+zxPPVbtc9bZq3Yfi1O79tn7KnefaWYtgY/N7HXgXLbh4ibsqLoBGAR4eJyJ7r4q6e9IRCSNDOmVjd/Qaqvy43apu9PoT0Pz+NPQvC3K9uocYdbFRY2en4hkjOHAoeHzR4F3CK7NhgOPubsDU8ysjZl1dffFKclSRERE0lrS75lw98XVI5bcfR3wJdCd4CKmesTSo8BJ4fNNFzfuPgVoY2ZdgWOA1919ZdjZ9DowLIlvRURERKS5cOA1M/vYzC4KyzpXdyaF/+0UlncHfoyKXRiWiYiIiGwlpetim9kOwABgKjUubsws3sWNLnpEREREGsdB7r4ovP563cy+irGv1VK21fqYYQfWRQC9evVqnCxFREQk46Ss48nMioBngdHuvjaYyqn2XWsp8xjltdWlCx8RERGROrj7ovC/S83seWA/YEn1LXThaPOl4e4LgZ5R4T2ARbUc837gfoBBgwbVeo0mIiJNLNb8UJobSpIkJcsTmVkOQafTP939ubB4SXhRQz0vbup10QPBhY+7D3L3QR07dmy8NyIiIiKS4cysRTjvJmbWAjgamANMBM4JdzsHeDF8PhEYGa5utz+wRvM7iYiISF2S3vEUrlL3EPClu98etWlbL25eBY42s7bh8r5Hh2UiIiIiUn+dgclmNhuYBrzs7q8AfwGOMrNvgKPC1wCTgO+Bb4EHgEuTn7KIiIhkilTcancQcDbwmZl9Epb9keBi5ikzuwD4ATg13DYJOI7g4qYYOA/A3Vea2U3A9HC//3b3lcl5CyIiIiLNg7t/D+xdS/kK4Ihayh24LAmpiYiISDOQ9I4nd59M7fMzwTZe3Lj7w8DDjZediIiIiIiIiIg0lpTM8SQiIiIiIiIiIs1fyla1ExEREREREZE0FGs1PNCKeLJN1PEkIiIiIiJJt0Ppv2Jun5+cNEREpImp40maRLwLiVSYn+oEaki3Npqf6gREUmjcuHE8+OCDmBm/+MUveOSRR/jggw+4+uqrqaqqoqioiPHjx9OnT59NMc888wynnnoq06dPZ9CgQSnMXkSkccS6NpmfvDTqJdFc1dklIpJ86niSpFs+6Q5KvptOpLA13S64B4DVk//J+tmvklUYDOlsO3QkBTvvS2XJWpa9cAsbF39D0S+OoN1Rl2w6zpKnrqdy/UqoqiKv5+60O+oSLCuSkvckIpnrp59+4s477+SLL76goKCA0047jSeffJKxY8fy4osv0q9fP+655x5uvvlmxo8fD8C6deu48847GTx4cGqTFxFJA5nUYSUiIsmnjidJuqJfHEnLfX7Jipdv36K85aCTaD34lC3KLJJLm4N/Q/myBZQvX7DFto7DryUrrxB3Z/kLt1D81WRa7H5Ik+ff1GrrmKu2ZupzrH7nYXpc/k8iha2pLF3Pikl3ULH6Zyw7h/bH/p7cjjsAsPDe88nKLYCsLCwrQtdz7kjBuxHJDBUVFZSUlJCTk0NxcTHdunXDzFi7di0Aa9asoVu3bpv2v+6667jmmmu47bbbUpWyiIiIiEhGUMeTJF1+zz2pWLOkXvtm5eaT32MPKlYt3npbXmHwpKoSrywHs8ZMM2Xq6pirWLuM0vmziLTquKls7UdPkdtpJzqd8mfKV/zIytfvpfOvx27a3vmMsUQK40wMmKZi/Xpa66i59x6n+NupYEaksA3tjxtNdsv2lK/4keWT4HYfMwAAIABJREFU7mDjku9oc/DILTo31854kfWzXwWHor2PodW+w+usc36jvTNJmniTYoa6A3/Yo4xeXdpTkGMcvXOEoz98nwcPrOC4ww6gIBta5RlTLmwBN/6NWYsr+fH9Mn7Z5hFum78B7j8UXqrHaEtNwikiIhlAI9ikwWJdg+l6aLukjidJG+tmvsSGz98it0sf2h5+IZH8orgxSyZcx8bFX5O/0yAKdz0oCVk2vbo65la9+QBtDzuPpc/evKmsfPkPtDrgVABy2vekYs1SKjesItKibdLyTYXaOudaDf4VbYaeDcDaGRNZ8+ETtD9mFFn5LWl35MUUfzNli2NsXDaf9bNfpcvI27FIDkufup6CnQeR0657Ut+LpN6qEufFuRXM+30RbfKNU58u4R+fbuS5LyuYdGYBg3tk838/KOPKV0u5/4R8rni1lPEnFaQ6bRERSTJ1yMSm+bNEpC5ZqU5ABKDlgOPofvEDdD3vTiJF7Vj11oP1iut8+k30GPU4VJZTuuDTJs4ydYq/mUqkZXtyO+20RXlOpx0pnvshAGWL5lKxZikV61YEG81Y+tT1LB7/e9Z98kqyU25S+T33JFLQcouyTSPgAC8vBYIRcJEWbcjr2ner+b/KVywkr9tuZOXkY1kR8nruSfE3HzV57pJ+3vi+gh3bZNGxRRY5EeOUftl88EMls5dUMrhH8PvM6Xvm8OGPlawrgzlLqzh0/AZ2uGMdUxZWcuITxcxYVJnidyEiIiIikp404knSQvQInZZ7H8PSZ8bUO9aycynoM5iSb6dQsOOApkgvparKS1nz0QQ6n37TVtta738qK9+4n0WPXE5uxx3I7bwzlhX0J3c561ayW7ancsNqlkz4Mznte5Dfc89kp59Uq957jA1z3iIrr5DOZ9wSc9/cDr1Z/d5jVJasxbJzKfl+BnlddklSpsk17qMyHpxVjgG/6JzFI8MLOOrxYtaVOQBLNzj7dY/wwq8LeWd+BcOfLGbHNsF5dEq/HK4/JC+F2Te9Xq2NKT9VUlzuFGTDm/MqGdQ1wtNfVPD1ikr6to/w+ncV9OuYRet8Y/k1mzs9Dx2/gduOzmdQNy1sICIikiiNJhNp3tTxJGmhYv1KsovaAVD89UfkdOgdc/+qjSVUbSwhu6gdXlUZdBr02D0ZqSZdxeqfqVizhEUPXw5A5brlLB4/mq4jbydS1JYOx48GwN356b4LyG7dBYDslu2BYMRPYd8DKFv0dbPveGo7dCRth45kzUdPse7jl2hz8Fl17pvToSetBo9g6YTrsJx8cjvtCM1wVcSf1lZx57SNfHFpEQU5xmlPF/PknHLeP6/Fpn1+9VQxw3fd/M/Bwb2yeenMwtoO1ywN7pHNiH7Z7PP3DWRnwYCuES4amEOPVsavniohy6BtvvHwcN1eJyIikk50e59IZlDHUxNbXepcOLGEOUurMIOHT8xn4VrnxnfL+HJZFdN+22KLX8pveb+Mh2ZtJJJl3Dksn2P6NL8/0bKJt1L2w2dUlqxl4d/OofWQsyj78TM2LvkezMhu3Yl2x4zatP/Ce8/HNxbjlRUUfz2FTqffRKSgJcuevSmYVLyqivzee9FywHEpfFdNJ7fjDvS8/J+bXi+893y6njOOSGFrqkrXYzl5WCSH9bNfJb/nHmTlFVK1sRS8atPz0nmzaH3QGSl8F8nVYvdDWfrMjTE7ngBa7n00Lfc+GoBV7z5KdssOyUgv6SqqoKQCciJOcTl0a7n5Lut1Zc5b8yp4ZDvvVBlzWD5jDtuy7OR+OZzcLydm3Dvntoi5vTkoLS1l6NChlJWVUVFRwYgRIxgzZgwXXHABM2bMwN3p27cv48ePp6ioiCuuuIK3334bgOLiYpYuXcrq1atT/C5Eth8aOZJ+GtI5or9n01HbpqF4C8NoYvKM1fx6NdLM718pZVifbJ45LZeNlcGXvjb5VTx3WgEXv1S6xb5fLKvkyc/L+fzSIhatc458fANfjyoiktU8Vmur1vHEa7Yqq/7yX5selzxca3nXc8Y1Wk7ppLaOubrap3zFjyx/+XawCDkdetL+2N8DUFm8mmXPhZOQV1XRYvdDKNhpYLLeQkqUr/xp08Tgxd9OJaddj7gxlRtWE2nRhoq1Syn++iO6nH1bU6fZqOJdyAKQCxv2fZEO4x7HsnPJ3/Eg5nS/GsKPn/Vz3mRjr6ns5X+EUijd+CnLfryF3HvaESlqT9vDzie3Y+wRiNXmJ/5WJI3l5eXx1ltvUVRURHl5OUOGDOHYY49l3LhxtGrVCoArr7ySu+++m2uvvZZx4zZ/Nt91113MmjUrVamLpJy+2IqIJIlW0ktr6nhqQmvLnPcWVDB+eD4AuREjNwJt8mu/nefFryr49R455GUbO7Y1+rTLYtpPlRzQU3+m7UltHXPRojvi8rr3o/tFD2y1T06bLnQ7/+5Gzy1d1NY5V/r9DMpXLgTLIrtVR9odcxkAletXsfjR0VRtLAbLYt2MF+l24b1k5RWy7IWxVJWsg6wI7Y76Xb1WUsw0laXrKf5mKt1/9xBZeS1Y9uJfWP/52xTtEQzv2fDFexRFdWzmdu5D90seJiu3gJLvprPs+ZtrPceam8oqZ9ADG+jeMouXzizk7mkbuWNKGd+tcpZdXUSHwmCU2ItflXPd22VkGWRnwR3D8hnSq3l/RpsZRUXB/xvl5eWUl5djZps6ndydkpISzLb+keSJJ55gzJj6z9knIiKNQ7egNR21rci2a95Xyyn2/aoqOhYa571YyuwllQzsGuF/h+XTIrf2EUw/rati/x6bO6V6tMzip3WerHRFMsa2jJqLFLWlx2WP1rqty1m3Nmpe6ah0/idkt+5MpDD4Faiw7wGU/fQlRXscRmXJWjYu/prCU/60af/o1QELdt4Xf+1eKovXbIpvrv536kb6dchibVnw+qCeEX7ZtwWHjt+wxX5H7JTNibtmY2Z8uqSS054u4atRza/DsqbKykoGDhzIt99+y2WXXcbgwYMBOO+885g0aRK77747f/3rX7eIWbBgAfPmzePwww9PRcoijSoVI5c0WkpERJqLrPi7SKIqqmDm4iouGZTDrIuLaJFj/GVyWZ37ey19TM3rJjsRSbbsVh3ZuGguVeWluDulC2aT074nAMVfTaagz75Ydu6m/SvXr8LDD6OyRXPBnayCVinJPVkWrq3i5W8quHCfze0woGuEHdps/U9kUa5tGtmzYaNTyyCfZuf888+na9euVFRUsHDhQqZNm8awYcPo378/s2bNIj8/n88++4wJEyYwbdo0+vfvT//+/dl///0ZMGAAkUjzm7RfREREROpPI54SVJ+5VSpzV0HLqzi9w8PB3Cl95rB2yjM8XnojAD9XXcsJZReQVxos4b6m8Cn+tQL+XHoaAEtWX8ekvDO5qrRf3LrmJ/xORKQ5y+u2K4W7HsTi8aOxrCxyO+9My72HAbDhy/dovf+pW+y/Ye5k1s/6f5CVhWXn0eHEa2q9hao5Gf1KKbcemc+6jfUbYfr8l+X815tlLN1Qxcvbwep/5557LqNGjWLkyJG0adOGQw89lBYtWvCHP/wBgKuuuopVq1bx7LPPcvrppzNjxgyys7PZc889+eijj6ioqCA7W5cbIiIiiY5k1O19TUgTmieFrgSbUKSoLdmtOlC+YiE57XsEIw069Kpz/4I+g1n+7/9Lq31PpmL9CipWLSK3a98kZiwizVGbg8+qdYW/Lmf+ZauyVgNPoNXAE5KRVlp46etyOrUwBnaL8M78inrFVK92996CCq57u4w3Rjbvf0r79evH4sWLASgpKeGNN97gmmuu4dtvv2XnnXdmwoQJHHPMMey2224UFgYdcXPnzmX16tXk5MReFVBEREQkY2lC83pr3lfLaaDdkb9j+Uu34ZUVZLfpQvvjRlP89YesfP3vVJasYekzY8jttCOdT7+J3I69abHbwSx66JJwsuNLsCzdoiAi0lQ++KGSiXMrmPTNOkorgkUhfvNcCf84pSBu7NDe2Xy3qoTlxVWbJh/POPF+5QMWL6nkjGdL+G5lFfv2LuK0PXI4fsZZHHxZMYvXVbFivVM6/TGuP74AbnyAqQsr+OUTxawphQkjCsi+uf025KOLNBERkcak+eIkHajjqYnldt6JrufcsUVZYd8DKex7YK37tz7wdFofeHoyUhMRadbqc0s0QyB7SPj8h09h2vNMPu4GdigNihb6+QwoHUckK+igKV+1iOw2XTEzyn7+lmUV/81AexQrjX874vzE3kbK7dU5wstnFvLLfxUz59LNE6l/cH4LLnmphD7tsrjqwLxN5YN7ZLPs6lZ8uaySc14o4dhdssnPbt63a0pm0JcvEZH60+19TSgVt/el+JZCdTyJiIjUsHbGRNZOfZbKDatY/MjlFOw0iPbH/gfFcz9kw5y3IBLBsnPpMPw/m/0cWHWpqHKe+6qCjy9qUev2fh0jtMg15iytYlA3jd4VERHZXqijv5lphFsK1fEkIiIC5Pfai/xeewHQatCJtBp04lb7tN5/BK33H5Hs1NLSG99XsluHLHq02nyb4bxVVfRsbWRnGQtWVzF3eRU7tNk+O+ak6egLjYhI89SQUVbbzcTtGTqvVIZOSrGZmQ0zs7lm9q2ZXZvqfERERJqTM54t5oCHNjB3RRU9bl/HQzM3AvDknHLO2HPLycMn/1DB3vdtoP996zl5QjH3HJ+fufNfSVy6BhMREZH6yOgRT2YWAf4GHAUsBKab2UR3/yK1mYmIiKS/es2DdTzkAd3DlzcBN5UCw+Ad4C+lUfvuGj5Co4HR0dvjmF//XSXFdA0mIiLbu+1mlFUjyOiOJ2A/4Ft3/x7AzJ4EhgO66BERERFpOg26BtPtciIiItsuFZ1djfFvdqaPf+8O/Bj1eiGbf5QVERERkaahazARERGpF3P3VOeQMDM7FTjG3S8MX58N7Oful9fY7yLgovDlrsDcpCYaWwdgeaqTSGNqn/jURrGpfeJTG8Wm9okv3dqot7t3THUSzVl9rsG28for0XOoIeee6lSdqlN1qs7mWWcm5drc6qz1GizTb7VbCPSMet0DWFRzJ3e/H7g/WUltCzOb4e6DUp1HulL7xKc2ik3tE5/aKDa1T3xqo+1S3Guwbbn+SvQcasi5pzpVp+pUnaqzedaZSbluL3Vm+q1204FdzGxHM8sFfg1MTHFOIiIiIs2drsFERESkXjJ6xJO7V5jZKOBVIAI87O6fpzgtERERkWZN12AiIiJSXxnd8QTg7pOASanOowHS8hbANKL2iU9tFJvaJz61UWxqn/jURtuhRr4GS/Qcasi5pzpVp+pUnaqzedaZSbluF3Vm9OTiIiIiIiIiIiKSvjJ9jicREREREREREUlT6nhKITM72czczHZLdS7pxswqzewTM5ttZjPN7MBU55RuzKyLmT1pZt+Z2RdmNsnM+qY6r3QQdf58Hp5DV5qZPu9qiGqn6se1qc4pndTSPjukOqd0YmadzexfZva9mX1sZh+Z2cmpzktERERE0otutUshM3sK6Aq86e43pjidtGJm6929KHx+DPBHdz8kxWmlDTMz4EPgUXe/LyzrD7R09/dTmlwaqHH+dAL+BXzg7jekNrP0Et1OsjW1T93q+AzqDZzo7nelNDkRERERSSsaAZAiZlYEHARcQLAEsdStFbAq1UmkmcOA8uovfADu/ok6nbbm7kuBi4BR4ZdlEWm4w4GNNT6DFqjTSRrCzI6Ks72Vme1cS/le9Th2FzPrEj7vaGanmNkeCeQ4dltjwrgdwzobNMo9XhuF+yTUTo3VRmH8NrdTY7SR2qdex6lXvJnl1FLWIU5MVvUIczPLNbN9zKxdgnlemkBMUVhnm0TqjDpOnZMnm1nEzC42s5vM7KAa2/4c57iFZnaNmV1tZvlmdq6ZTTSzW8PvhtuS49f13G+vqOc5ZvbnsM6xZla4LXVGHSftz6FEzp8wrsHnUH3aJ5G2CffJ2PbRiKcUMbPfAIe5+wVm9iEwyt1npjqvdGFmlcBnQD7BqLDD3f3j1GaVPszsP4Ad3f2KVOeSjmobqWJmq4Dd3H1JitJKO1H/n1W7xd0npCqfdFOjfea5u24jC+kzSJqCmf3g7r3q2HYacAewFMgBznX36eG2me6+T4zjXgxcCxjwf4Bzgc8JfgC81d0fqiPuzppFwNnAYwDu/h8x6nzB3U8Knw8Pc38HOJDgs3Z8XbGxxGqjcHtC7ZRoG4WxCbVTU7SR2ie+erTRYcDjQB4wC7jI3eeH22K10UnA34Eq4HfAH4ENQF/gEnf/d4w6r6xZBPwXMBbA3W+vI+4ed780fD6EYIT7d0Af4OJw5c266qzry7oBs929Rx1xDwKFwDSCv+O77n5luC3eZ9FTwI9AAbAr8CXwFHAC0MXdz64jbh1Q/aW9+kfUQqAYcHdvFaPOTTmZ2V+B9sAjwElAe3cfWVdsjGOm1TmU6PkTxiZ8DsU4Zqx/yxJqm3B7RrdPdn13lEZ3BsE/IABPhq/V8bRZibv3BzCzA4DHzGxPV0+pJE6jnba26f8zqZXap57M7G/AEIJRUPumOh9JX2Y2sa5NBF+I6vJHYKC7Lzaz/YDHzeyP7v4c8T/fRwF7EHzZWwD0cfefzawt8DZQV6fBKQRf9F+LquPXQH1+COsd9fw/CX5Amxf+ov0mML6uwAa0ESTeTom2ESTeTgm1kdqnXudQzc6uTZuAeCMVbgWOcffPzWwE8LqZne3uU4jdRjcAexO00WxgX3efa8Ft2M8CdXY8AWOASQSdedV1RICWcXLdP+r5TcBJ7j7TzHYi6NCJ9aV4GcHfMvo9efi6U4y4/dx9LwAzuxu4x8yeI/guF++zqK+7n2ZmBiwGjnR3N7P3CdqsLuOB1sDV1T+gmtk8d98xTn3UyOkIgr9LuZm9F6vODDuHEj1/IMFzqAHtk2jbQAa1T23U8ZQCZtae4DaFPc3MCf7wbmbXqGNla+7+UfiPbEeCX6gk+OAYkeokMkX44ViJzh+RxvI58KvqF+5+Wfg5PSN1KUmGOBj4DbC+RrkB+8WIi7j7YgB3nxb+avySmfVg80iAupS7ezFQbGbfufvP4XFWWew7sPsRXGgPI/jC95OZ3eDuj8apjxo5Zbv7vLDO5eG1XyyJthEk3k6x2ihevom2U6w2qooR15D2yU6wfSoyqH0AzgOuAspq2XZGnNhcd/88rOsZM/sSeM6CBUhivtfqdglHfMwNyxZY/AVe9gBuB1oAY9y92MzOcfcxceKitfLw7hF3/97MInH2/x44wt1/qLnBzH6MEZdb/cTdK4CLzOx64C2gXrfLhZ1Nk6q/94Wv62xbd7/czAYCT5jZC8DdxP/cq9bagoU/soA8dy+vT51k1jnUGOcPbNs5lGj7JNw2YUymtM9W1PGUGiOAx9z94uoCM3uX4NdizdFTgwX3yUaAFanOJY28BYw1s9+6+wMAZrYvUOju76Y2tfRiZh2B+4C71bEr0miqP4Mucfd7w7KE5oqQ7c4UoLi2f6vMbG6MuHVmtrO7fwcQjlg5DHiO4KI6liozywm/cB0fVV9+rCB3XweMDr/w/cPMXqb+86PubWZrCTpD8sysSzhCJpf4v2on2kZQezsdCrxA7HaK1UYx33MD2ilWG8X6QtOQ9llbR/s8T+z2qcyg9gGYDsxx9w9rbjCzG+PEllfXFeb/uZkdAbwEbDU3Vo1jZ7l7FXB+VFmEqM6a2oSdPyMsuKXwdTMbFyfHaruZ2acEbbSDmbUNOwOzCG6ljOUOoC2wVccTwaiUuswws2Hu/kpU/v9tZouAe2PEVccWuft6d49uo52BdbEC3f1jMzuSYPTduwTTkdTHu8CJ4fMpZtbZ3ZdYMF/Z8hhxGXMONeD8gcTPoUTbJ+G2CY+dKe2zde76HpZ8ZvYO8JfoDywL5svo5+6XpCyxNGJbzq1iBKvavZzClNKOmXUj+EdzIFAKzAdGu/s3qcwrHUSdPzlABcG91LeHH9QSsq3neHrF3a9NVT7pxrSqXUxm1hUYBwwmuGVhA3Cfa54wSYAFk/Se6e6X1bF9b4LOhm9qlA8Fxrv7TjGO3QtYFI5OiC4fAvzd3eNOEG1mBlxKcNvBfbFyjXOcIQS/OB+xrbH1PH5d7ZQDnObu/6wjrhewuHo0RFR5d4Lr0zfqWX91Ox3g7r9J8D20Cev8qI7tuwCd3P2DGuUHE/ydv4tx7H2BfK+xGEt4Hg1195vriBtKMEiktrhdPPYcT32Azu7+QXT7EMzVUme+4fvs7O6T65NLjX3aAaUejNLaJmHnxjJ3n12jvA1wmbv/Tx1x+wKfuXtpjfIdgCHu/o961l9IcGvQYHcfGmff3jWKFrv7RgtG4A714BbKjGBmVt8fSMN/fwd4AvMPbUM+TXEOtSaY17jJziEzawHcSD3On3D/mufQIg9uRYx5DoXtU+LuJfHqqBGXUNuE+9TVPr2Bg+vZPtX/f+3n9VgxPkb7tAcO2ab/x9xdDz300EMPPfTQQw89kvoA+hOMLJhPME/OqATjLm9AnfWKbeQ66/U+axyjA+EPxsmKTdc6CUYG7FVL+SDg300Rm2l11ti/I9Axwb9jQrGZUCdwTdTzU2tsG9vYcRlYZ69E/n4NiU12XAPrHE7QEVv9eirB7ZvfAyMSjDu1AXXWGVtL3DRgXrxcG5rvVsdK9I+khx566KGHHnrooYce2/IgWH3neoLVnCYDlwMLmiou0+oMY/cnmIz6OWAAMAf4mWCewmFNEZuiOg9IMG5OjG2fxck1odgMrNMIRn0sJ5iqYhXByNTr63kO3rCtsQ2ps0bsyvDR1HXOrO15ba8bIy7D63y2PudNQ2OTHdfAOj8Aeka9/oRgcYNewJuNHdcEdbZryjpre9T3PnURERERkYb6imBlpRPcfYi730Ww+ENTxWVanRBMHDwWeIJgPrUL3b0LMBS4pYliU1HnXQnGxZrbpiBOronGZlqdo4GDCFa9au/ubQluiz7IzK6IFRhuH5JAbMJ11oht5+7tklCn1fG8tteNEZfJddZ5K3MjxyY7riGxue4ePQn9ZHdf4cF8Si2aIK6x61zZxHVuRR1PIiIiIpIsvyIY1fK2mT0QTqoa74tTQ+IyrU4IVjB7zd2fBn72YJlt3P2rJozNpDqnm9lvaxaa2QXAx00Um2l1jgTO8HAlPAhWoSJYDXBkE8VmWp1ex/PaXjdGXHOqM55EY5Md15DYtlscxH1U1MuOTRCXaXVuRavaiYiIiEhSuPvzwPPhBLAnAVcAnc3sXuB5d3+tMeMyrc5Q9EIYNSeujffFKNHYTKpzNEHbnsXmDphBBKs6nRwjriGxmVZnjrtvtWKZuy8LJ5pvithMqzN61cCC8Dnh61ijzRKNa051uru3aoLYZMc1JHaqRa0uXs3MLiaYQ6kuicZlWp1b0ap2IiIiIpIy4epApwKnu/vhTR2X7nVasOLoBsIvQkD1qlJGsCJbnV+oE43NtDrD+MOAPcOXn7v7W7H2b4zYTKnTzGa6+z7buq0hsZlWp0hDmFkn4AWgDJgZFg8E8oCT3H1JY8ZlWp21HksdTyKSKcysM8Hy7fsTTCC5Ebg1/GU5kePdCKx399saLUkRERGRFIrq1NtqE/XvSNym2EyrU6QxmNnhwB7hy23pUE4oLtPq3OIY6ngSkUxgZgZ8CDzq7veFZb2BEz2YtLV6v2x3r6jnMW9EHU8iIiIiIiJNRpOLi0imOBzYWN3pBODuC9z9LjM718yeNrN/A6+ZWZGZvWlmM83sMzMbXh1jZn8ys7lm9gawa1T5zmb2ipl9bGbvm9luSX13IiIiIiIizZAmFxeRTLEHm+8trs0BwF7uvtLMsoGT3X2tmXUAppjZRGAf4NfAAILPv5lsnrDzfuB37v6NmQ0G7iHo7BIREREREZEEqeNJRDKSmf0NGEIwz9PfgNfdfWX1ZmCsmQ0lWDWnO9AZOJhgNaHi8BgTw/8WAQcCTwd39AHBpHkiIiIizZKZ/Qk4E6gkuF662N2nxtj/RlIwRYGZzQcG1baCnYhkBnU8iUim+Bz4VfULd78sHM00IyyKnlzyLKAjMNDdy8MLluolY2ub2C4LWO3u/Rs9axEREZE0Y2YHAL8E9nH3svCaKjeJ9dd7Tk4RyXya40lEMsVbQL6ZXRJVVljHvq2BpWGn02FA77D8PeBkMysws5bACQDuvhaYZ2anQjCRuZnt3STvQkRERCT1ugLL3b0MwN2Xu/siCEYYhR1RmNkgM3snKm5vM3vLzL4xs9/WdmAzu87MvjKz183sCTP7Q1j+jpmNNbN3gd+b2QlmNtXMZpnZG+HqxZhZezN7LSz/O8FI9upj/8bMppnZJ2b2dzOLNEHbiEgjU8eTiGQED5bgPAk4xMzmmdk04FHgP2vZ/Z/AIDObQTD66avwGDOBCcAnwLPA+1ExZwEXmNlsgtFVwxERERFpnl4DeprZ12Z2j5kdUs+4vYDjCebWvN7MukVvNLNBBCPUBwCnAINqxLdx90Pc/a/AZGB/dx8APAlcE+5zAzA5LJ8I9AqP3Q84HTgoHKVeSXD9JiJpTrfaiUjGcPfFBJOD12Z81H7LCS6IajvG/wD/U0v5PGBYw7MUERERSW/uvt7MBhLMf3kYMMHMrnX38XFCX3T3EqDEzN4G9gNeiNo+JGofwhWHo02Iet4jrLcrwW1+88LyoQSdVrj7y2a2Kiw/AhgITA/n5CwAltbzLYtICqnjSUREREREZDvj7pXAO8A7ZvYZcA7BD3kVbL4zJr9mWJzXRmzRc3LeBdzu7hPN7FDgxhjHrT72o+7+X3HqEJE0o1vtREREREREtiNmtquZ7RJV1B9YED6fTzCyCKIWdgkNN7N8M2sPHApMr7F9MnBCuE8RwW15dWkN/BQ+Pyeq/D3CW+jM7FigbVj+JjDCzDqF29qZWW9EJO2p40lERERERGT7UgSKER85AAAA6ElEQVQ8amZfmNmnwO5sHnE0BvhfM3ufYB6laNOAl4EpwE3VE5JXc/fpBPMyzQaeI1h9eE0dOdwIPB3WszyqfAww1MxmAkcDP4TH/gL4M/BamPPrBJOki0ias2C+XhEREREREZGGMbOicA6pQoLRSxeFC7yIyHZKczyJiIiIiIhIY7nfzHYnmB/qUXU6iYhGPImIiIiIiIiISJPQHE8iIiIiIiIiItIk1PEkIiIiIiIiIiJNQh1PIiIiIiIiIiLSJNTxJCIiIiIiIiIiTUIdTyIiIiIiIiIi0iTU8SQiIiIiIiIiIk3i/wMH2sPsQJi80gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group grade and loan status\n",
    "loan_stat_grade = loan.groupby(['grade',\"loan_status\"]).size()\n",
    "\n",
    "#group sub grade and loan status\n",
    "loan_stat_sub_g = loan.groupby(['sub_grade',\"loan_status\"]).size()\n",
    "\n",
    "#plot the figure\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(222)\n",
    "\n",
    "#plot grade and loan status\n",
    "grade_plt = loan_stat_grade.unstack().plot(kind = \"bar\",stacked = True, rot = 0, ax = ax1,figsize = (20,8))\n",
    "ax1.title.set_text(\"Loan Status based on Grade\")\n",
    "ax1.set_xlabel('Grade')\n",
    "ax1.set_ylabel('Count')\n",
    "\n",
    "#bar toppers for grade and loan status\n",
    "for p in grade_plt.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    grade_plt.text(x + width/2, \n",
    "                  y + height/2, \n",
    "                  '{:.0f}'.format(height), \n",
    "                  horizontalalignment = 'center',\n",
    "                  verticalalignment = 'bottom')\n",
    "\n",
    "#plot sub grade and loan status\n",
    "loan_stat_sub_g.unstack().plot(kind = \"bar\",stacked = True,ax = ax2,figsize = (20,8))\n",
    "ax2.title.set_text(\"Loan Status based on sub grade\")\n",
    "ax2.set_xlabel('Sub grade')\n",
    "ax2.set_ylabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows the number of loans approved is high if the borrower has a good grade. From the grades graph the percentage of fully paid loans for each grade are: \n",
    "\n",
    "Grade A: 94%,\n",
    "Grade B: 87%,\n",
    "Grade C: 82%,\n",
    "Grade D: 78%,\n",
    "Grade E: 74%,\n",
    "Grade F: 68%,\n",
    "Grade G: 66%\n",
    "\n",
    "This shows the higher grade borrowers tend to pay their loans fully compared to low level grades. This shows that low level graders are subjected to risk when approving their loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vQruYmVgJ05"
   },
   "source": [
    "**Percentage of fully paid and charged off loans in the data set**\n",
    "\n",
    "The Fully paid loans are around 85% while the charged off loans are 15%. This make the dataset highly imbalanced with respect to the target feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "MW_EvDK9rtj8",
    "outputId": "9370f0fa-d22e-4c3f-dd9f-1885ae642fc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loan Status percentage in the Lending Club Data')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group loan status by id\n",
    "loan_stat = loan.groupby(['loan_status'])[\"id\"].count()\n",
    "\n",
    "#plot the percentage of loan status\n",
    "loan_stat.plot(kind = \"pie\", subplots= True, colors = [\"royalblue\",\"orange\"],autopct = '%1.1f%%', legend = False)\n",
    "plt.axis('off')\n",
    "plt.title(\"Loan Status percentage in the Lending Club Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2NQTZlBggc12"
   },
   "source": [
    "**Loan status based on verification status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "BiYpE6vJD926",
    "outputId": "0e9c874e-89df-4d32-f5d7-064b574cfcd2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group by verification status and loan status\n",
    "loan_stat_ver = loan.groupby(['verification_status',\"loan_status\"]).size()\n",
    "\n",
    "#plot the figure\n",
    "ver_plt = loan_stat_ver.unstack().plot(kind = \"bar\",stacked = True, rot = 0)\n",
    "plt.title(\"Loan Status based on verfication status\")\n",
    "plt.xlabel('Verfication Status')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "#bar toppers\n",
    "for p in ver_plt.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    ver_plt.text(x + width/2, \n",
    "                  y + height/2, \n",
    "                  '{:.0f}'.format(height), \n",
    "                  horizontalalignment = 'center',\n",
    "                  verticalalignment = 'bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph, suprisingly most of the loans approved are not verified and the number of charged off loans are more for non-verified status. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W8a-wgExh4J4"
   },
   "source": [
    "**Loan status based on term**\n",
    "\n",
    "Lending Club approves its loan in either of the to terms: 36 months or 60 months. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "dbbRU65y4vOx",
    "outputId": "83c769d8-3347-4b2c-bdd5-b266914b1322"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group term and loan status\n",
    "loan_stat_ver = loan.groupby(['term',\"loan_status\"]).size()\n",
    "\n",
    "#plot the figure\n",
    "term_plt = loan_stat_ver.unstack().plot(kind = \"bar\",stacked = True, rot = 0)\n",
    "plt.title(\"Loan Status based on term\")\n",
    "plt.xlabel('Term')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "#bar toppers\n",
    "for p in term_plt.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    term_plt.text(x + width/2, \n",
    "                  y + height/2, \n",
    "                  '{:.0f}'.format(height), \n",
    "                  horizontalalignment = 'center',\n",
    "                  verticalalignment = 'bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph:\n",
    "\n",
    "1. Most of the loans are 36months term compared to 60 months.\n",
    "2. The number of charged off loans are more in 36 months than 60 months term. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jxdi1iPnifdi"
   },
   "source": [
    "**Loan status per year**\n",
    "\n",
    "The data used in this project has data from 2007-2011. Lets plot the loan statues for each year and study the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "3NCYr-ryvpNx",
    "outputId": "821cbd7e-cea0-457c-b5ee-9620e397142d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group year and loan status\n",
    "loan_stat_ver = loan.groupby(['year',\"loan_status\"]).size()\n",
    "\n",
    "#plot the figure\n",
    "year_plt = loan_stat_ver.unstack().plot(kind = \"bar\",stacked = True, rot = 0)\n",
    "plt.title(\"Loan Status every year\")\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "#bar toppers\n",
    "for p in year_plt.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy() \n",
    "    year_plt.text(x + width/2, \n",
    "                  y + height/2, \n",
    "                  '{:.0f}'.format(height), \n",
    "                  horizontalalignment = 'center',\n",
    "                  verticalalignment = 'bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The number of loans that are fully paid and charged off increases year by year.\n",
    "2. From the graph,the percentage of loans charged off per year are: \n",
    "    2007: 26%, 2008: 20%, 2009: 13%, 2010: 14%, 2011: 15%.\n",
    "    This shows there is decrease in charged off loans though the number of loans has increased.\n",
    "3. As the year passes, the number of loans approved increases and it is seen that there is an increase in number of charged off loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SvBoHSFRjNOa"
   },
   "source": [
    "**How does term affect loan amount and interest rate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "colab_type": "code",
    "id": "WlargI6c4vSP",
    "outputId": "ed26df0e-e527-41a2-9d6f-cb7aaac271d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Interest Rate')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols = 2)\n",
    "\n",
    "#box plot for loan amount and term\n",
    "boxplot = sns.boxplot(y = 'loan_amnt', x = 'term', data = loan, ax = axs[0])\n",
    "boxplot.figure.set_size_inches(12,5)\n",
    "boxplot.set_title('Loan amount given per term', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "boxplot.set_xlabel('Term')\n",
    "boxplot.set_ylabel('Loan amount')\n",
    "\n",
    "#box plot for interest rate and term\n",
    "boxplot1 = sns.boxplot(y = 'int_rate', x = 'term', data = loan, ax = axs[1])\n",
    "boxplot1.figure.set_size_inches(12,5)\n",
    "boxplot1.set_title('Interest Rate based on loan term', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "boxplot1.set_xlabel('Term')\n",
    "boxplot1.set_ylabel('Interest Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The 36 months term have a loan amount approved between 5,000 - 12,000 with average of about 7,000 where as 60 months term has 10,000 - 20,000 loan amount with 15,000 of average.\n",
    "2. The interest rates are less for 36 months with an average of 11% and 15% for 60 months term.\n",
    "2. This shows lower the term, lower is the loan amount approved and interest rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VN-my-lLxRmd"
   },
   "source": [
    "**How does the loan approval process effect based on purpose of the loan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 906
    },
    "colab_type": "code",
    "id": "6vPZHx_n1w11",
    "outputId": "62201182-1a8a-4152-c54a-ffb3514b7d22"
   },
   "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></th>\n",
       "      <th>installment</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">car</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>206.08</td>\n",
       "      <td>12.40</td>\n",
       "      <td>7361.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>188.79</td>\n",
       "      <td>10.44</td>\n",
       "      <td>6688.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">credit_card</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>357.06</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12754.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>344.06</td>\n",
       "      <td>11.63</td>\n",
       "      <td>11460.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">debt_consolidation</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>366.59</td>\n",
       "      <td>14.33</td>\n",
       "      <td>13165.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>363.57</td>\n",
       "      <td>12.28</td>\n",
       "      <td>12437.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">educational</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>233.14</td>\n",
       "      <td>13.33</td>\n",
       "      <td>7337.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>209.78</td>\n",
       "      <td>11.80</td>\n",
       "      <td>6439.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">home_improvement</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>327.95</td>\n",
       "      <td>13.45</td>\n",
       "      <td>11505.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>319.42</td>\n",
       "      <td>11.31</td>\n",
       "      <td>11359.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">house</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>334.23</td>\n",
       "      <td>15.26</td>\n",
       "      <td>11910.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>364.31</td>\n",
       "      <td>11.83</td>\n",
       "      <td>12653.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">major_purchase</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>256.18</td>\n",
       "      <td>13.02</td>\n",
       "      <td>9059.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>232.53</td>\n",
       "      <td>10.77</td>\n",
       "      <td>7964.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">medical</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>236.00</td>\n",
       "      <td>13.42</td>\n",
       "      <td>8208.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>233.96</td>\n",
       "      <td>11.32</td>\n",
       "      <td>8111.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">moving</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>178.62</td>\n",
       "      <td>12.63</td>\n",
       "      <td>5969.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>199.14</td>\n",
       "      <td>11.57</td>\n",
       "      <td>6723.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">other</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>236.80</td>\n",
       "      <td>13.60</td>\n",
       "      <td>8184.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>234.81</td>\n",
       "      <td>11.65</td>\n",
       "      <td>7893.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">renewable_energy</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>248.97</td>\n",
       "      <td>12.39</td>\n",
       "      <td>8250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>248.13</td>\n",
       "      <td>11.33</td>\n",
       "      <td>8719.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">small_business</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>419.50</td>\n",
       "      <td>14.34</td>\n",
       "      <td>14699.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>383.60</td>\n",
       "      <td>12.70</td>\n",
       "      <td>13243.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">vacation</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>161.60</td>\n",
       "      <td>11.70</td>\n",
       "      <td>5812.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>161.68</td>\n",
       "      <td>10.79</td>\n",
       "      <td>5360.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">wedding</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>309.62</td>\n",
       "      <td>13.62</td>\n",
       "      <td>10551.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>288.22</td>\n",
       "      <td>11.84</td>\n",
       "      <td>9655.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                installment  int_rate  loan_amnt\n",
       "purpose            loan_status                                  \n",
       "car                Charged Off       206.08     12.40    7361.71\n",
       "                   Fully Paid        188.79     10.44    6688.18\n",
       "credit_card        Charged Off       357.06     13.51   12754.98\n",
       "                   Fully Paid        344.06     11.63   11460.24\n",
       "debt_consolidation Charged Off       366.59     14.33   13165.34\n",
       "                   Fully Paid        363.57     12.28   12437.83\n",
       "educational        Charged Off       233.14     13.33    7337.78\n",
       "                   Fully Paid        209.78     11.80    6439.15\n",
       "home_improvement   Charged Off       327.95     13.45   11505.02\n",
       "                   Fully Paid        319.42     11.31   11359.51\n",
       "house              Charged Off       334.23     15.26   11910.00\n",
       "                   Fully Paid        364.31     11.83   12653.31\n",
       "major_purchase     Charged Off       256.18     13.02    9059.18\n",
       "                   Fully Paid        232.53     10.77    7964.47\n",
       "medical            Charged Off       236.00     13.42    8208.40\n",
       "                   Fully Paid        233.96     11.32    8111.34\n",
       "moving             Charged Off       178.62     12.63    5969.16\n",
       "                   Fully Paid        199.14     11.57    6723.18\n",
       "other              Charged Off       236.80     13.60    8184.75\n",
       "                   Fully Paid        234.81     11.65    7893.53\n",
       "renewable_energy   Charged Off       248.97     12.39    8250.00\n",
       "                   Fully Paid        248.13     11.33    8719.48\n",
       "small_business     Charged Off       419.50     14.34   14699.86\n",
       "                   Fully Paid        383.60     12.70   13243.72\n",
       "vacation           Charged Off       161.60     11.70    5812.29\n",
       "                   Fully Paid        161.68     10.79    5360.44\n",
       "wedding            Charged Off       309.62     13.62   10551.83\n",
       "                   Fully Paid        288.22     11.84    9655.36"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pivot table for purpose/loan statues against installment/int_rate/loan_amnt\n",
    "purpose_loan_stat = np.round(pd.pivot_table(loan, values=[\"installment\",'int_rate',\"loan_amnt\"], index=['purpose', 'loan_status'],\n",
    "                       aggfunc = {\"installment\" : np.mean,\n",
    "                                  \"int_rate\" : np.mean,\n",
    "                                  \"loan_amnt\" : np.mean}),2)\n",
    "purp_loan_stat = pd.DataFrame(data = purpose_loan_stat)\n",
    "purp_loan_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "OXaj9RMyy3pA",
    "outputId": "ecd3f4bc-f2e0-42c8-8712-01096e2fd97f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>purpose</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>other</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>small_business</td>\n",
       "      <td>car</td>\n",
       "      <td>wedding</td>\n",
       "      <td>medical</td>\n",
       "      <td>moving</td>\n",
       "      <td>house</td>\n",
       "      <td>educational</td>\n",
       "      <td>vacation</td>\n",
       "      <td>renewable_energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>19748</td>\n",
       "      <td>5473</td>\n",
       "      <td>4419</td>\n",
       "      <td>3193</td>\n",
       "      <td>2310</td>\n",
       "      <td>1988</td>\n",
       "      <td>1615</td>\n",
       "      <td>1004</td>\n",
       "      <td>752</td>\n",
       "      <td>629</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
       "      <td>399</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0            1      2                 3  \\\n",
       "purpose  debt_consolidation  credit_card  other  home_improvement   \n",
       "0                     19748         5473   4419              3193   \n",
       "\n",
       "                      4               5     6        7        8       9  \\\n",
       "purpose  major_purchase  small_business   car  wedding  medical  moving   \n",
       "0                  2310            1988  1615     1004      752     629   \n",
       "\n",
       "            10           11        12                13  \n",
       "purpose  house  educational  vacation  renewable_energy  \n",
       "0          425          422       399               106  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of loans based on purpose \n",
    "loan.groupby(['purpose']).size().sort_values(ascending = False).reset_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The highest amount of loan approved is for the small business and the least if for vacation. \n",
    "2. The highest interest rate is at 15% for house and least at 10.44% for car loans.\n",
    "3. The highest number of loans approved are for debt_consolidation and the least is for renewable energy.\n",
    "4. Majority of the charged off loans have higher installment, loan amount and interest rate compared to fully paid loans. This shows higher the amount,interest rate and installments higher are the changes that the loan is charged off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fCWQG25w08RD"
   },
   "source": [
    "**How does the loan approval process effect based on home ownership of the loan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 373
    },
    "colab_type": "code",
    "id": "_sNLHkN973HE",
    "outputId": "3ae90d29-13ba-4baf-94c4-765551becc32"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>installment</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>loan_amnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">MORTGAGE</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>377.91</td>\n",
       "      <td>13.91</td>\n",
       "      <td>13747.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>352.49</td>\n",
       "      <td>11.61</td>\n",
       "      <td>12367.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">NONE</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>524.77</td>\n",
       "      <td>15.65</td>\n",
       "      <td>15000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>188.18</td>\n",
       "      <td>8.20</td>\n",
       "      <td>6000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">OTHER</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>350.94</td>\n",
       "      <td>13.79</td>\n",
       "      <td>10735.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>302.63</td>\n",
       "      <td>12.49</td>\n",
       "      <td>9472.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">OWN</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>309.16</td>\n",
       "      <td>13.80</td>\n",
       "      <td>10958.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>297.19</td>\n",
       "      <td>11.58</td>\n",
       "      <td>10042.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">RENT</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>298.07</td>\n",
       "      <td>13.94</td>\n",
       "      <td>10318.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>294.24</td>\n",
       "      <td>12.12</td>\n",
       "      <td>9756.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            installment  int_rate  loan_amnt\n",
       "home_ownership loan_status                                  \n",
       "MORTGAGE       Charged Off       377.91     13.91   13747.19\n",
       "               Fully Paid        352.49     11.61   12367.07\n",
       "NONE           Charged Off       524.77     15.65   15000.00\n",
       "               Fully Paid        188.18      8.20    6000.00\n",
       "OTHER          Charged Off       350.94     13.79   10735.34\n",
       "               Fully Paid        302.63     12.49    9472.90\n",
       "OWN            Charged Off       309.16     13.80   10958.96\n",
       "               Fully Paid        297.19     11.58   10042.37\n",
       "RENT           Charged Off       298.07     13.94   10318.43\n",
       "               Fully Paid        294.24     12.12    9756.98"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pivot table for home_onership/loan statues against installment/int_rate/loan_amnt\n",
    "home_loan_stat = np.round(pd.pivot_table(loan, values=[\"installment\",'int_rate',\"loan_amnt\"], index=['home_ownership', 'loan_status'],\n",
    "                       aggfunc = {\"installment\" : np.mean,\n",
    "                                  \"int_rate\" : np.mean,\n",
    "                                  \"loan_amnt\" : np.mean}),2)\n",
    "home_own_loan_stat = pd.DataFrame(data = home_loan_stat)\n",
    "home_own_loan_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "7DietS9J1Ab_",
    "outputId": "b4fb04c7-441e-41ca-eefd-ab4298ce07ce"
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>home_ownership</td>\n",
       "      <td>RENT</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>OWN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>20160</td>\n",
       "      <td>18931</td>\n",
       "      <td>3248</td>\n",
       "      <td>136</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0         1     2      3     4\n",
       "home_ownership   RENT  MORTGAGE   OWN  OTHER  NONE\n",
       "0               20160     18931  3248    136     8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of loans based on home ownership \n",
    "loan.groupby(['home_ownership']).size().sort_values(ascending = False).reset_index().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The loan amount, interest rate and installements are highest for home ownership = None.\n",
    "2. Just as observed in purpose feature, the higher the amount, interest rate and installment greater are the changes that the loans are charges off.\n",
    "3. The number of people with home ownership of Rent is highest with None having the least number of people."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DyS-QPc7248o"
   },
   "source": [
    "**How does the loan approval process effect based on employement title and length of the loan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "colab_type": "code",
    "id": "0ULcmf1p8wO6",
    "outputId": "2b0b91cf-9c9a-4d22-bffa-844e4d333fb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loan amount in $')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#violinplot for loan amount and emp_length\n",
    "violinplot = sns.violinplot(y = 'loan_amnt', x = 'emp_length', data = loan)\n",
    "violinplot.figure.set_size_inches(14,7)\n",
    "violinplot.set_title('Loan amount given based on employement length', fontsize = 12, color = \"black\", alpha = 0.9)\n",
    "violinplot.set_xlabel('Employement length')\n",
    "violinplot.set_ylabel('Loan amount in $')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The loans approved dont vary a lot with employment length as majority of amount is less than 10,000. \n",
    "2. However more borrowers with employment length of more than 10 years compared to others have loan amounts more than 30,000.\n",
    "3. The interest rate seems to be in the same range irrespective of the employment length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Determine the ratio of fully paid to charged off loans with loan amount and interest rates in different states in the USA.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Renderers configuration\n",
       "-----------------------\n",
       "    Default renderer: 'plotly_mimetype+notebook'\n",
       "    Available renderers:\n",
       "        ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode',\n",
       "         'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab',\n",
       "         'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg',\n",
       "         'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe',\n",
       "         'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.io as pio\n",
    "\n",
    "pio.renderers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "colab_type": "code",
    "id": "bvfb6CFB_SLZ",
    "outputId": "e720b2e1-c53a-4623-99a2-55dd9108afff"
   },
   "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",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot a map for Fully paid loans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "pH5jwIwMkif2",
    "outputId": "cb206340-f3c3-4e3f-efcb-ab032eb4027f"
   },
   "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>addr_state</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>fully_paid</th>\n",
       "      <th>charged_off</th>\n",
       "      <th>fully_paid_text</th>\n",
       "      <th>charged_off_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AK</td>\n",
       "      <td>12913.95</td>\n",
       "      <td>12.86</td>\n",
       "      <td>80.23</td>\n",
       "      <td>19.77</td>\n",
       "      <td>State:AK&lt;br&gt;Average Loan amount:12913.95$&lt;br&gt;A...</td>\n",
       "      <td>State:AK&lt;br&gt;Average Loan amount:12913.95$&lt;br&gt;A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>AL</td>\n",
       "      <td>11173.30</td>\n",
       "      <td>11.96</td>\n",
       "      <td>87.19</td>\n",
       "      <td>12.81</td>\n",
       "      <td>State:AL&lt;br&gt;Average Loan amount:11173.3$&lt;br&gt;Av...</td>\n",
       "      <td>State:AL&lt;br&gt;Average Loan amount:11173.3$&lt;br&gt;Av...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AR</td>\n",
       "      <td>10369.83</td>\n",
       "      <td>11.75</td>\n",
       "      <td>86.97</td>\n",
       "      <td>13.03</td>\n",
       "      <td>State:AR&lt;br&gt;Average Loan amount:10369.83$&lt;br&gt;A...</td>\n",
       "      <td>State:AR&lt;br&gt;Average Loan amount:10369.83$&lt;br&gt;A...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  addr_state  loan_amnt  int_rate  fully_paid  charged_off  \\\n",
       "0         AK   12913.95     12.86       80.23        19.77   \n",
       "1         AL   11173.30     11.96       87.19        12.81   \n",
       "2         AR   10369.83     11.75       86.97        13.03   \n",
       "\n",
       "                                     fully_paid_text  \\\n",
       "0  State:AK<br>Average Loan amount:12913.95$<br>A...   \n",
       "1  State:AL<br>Average Loan amount:11173.3$<br>Av...   \n",
       "2  State:AR<br>Average Loan amount:10369.83$<br>A...   \n",
       "\n",
       "                                    charged_off_text  \n",
       "0  State:AK<br>Average Loan amount:12913.95$<br>A...  \n",
       "1  State:AL<br>Average Loan amount:11173.3$<br>Av...  \n",
       "2  State:AR<br>Average Loan amount:10369.83$<br>A...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the average loan amount and interest rate per state \n",
    "loc_loan = loan.groupby([\"addr_state\"])[\"loan_amnt\",\"int_rate\"].apply(lambda x : round(x.mean(),2))\n",
    "\n",
    "#create a new loan_stat column to find percentage of fully paid loans \n",
    "loc_loan[\"fully_paid\"] =  loan.groupby('addr_state')['loan_status'].apply(lambda x : round(((x == 'Fully Paid').sum()/\n",
    "                                                                                    ((x == 'Fully Paid').sum() + \n",
    "                                                                                    (x == 'Charged Off').sum()))\n",
    "                                                                                   *100,2))\n",
    "\n",
    "#create a new loan_stat column to find percentage of charged off loans \n",
    "loc_loan[\"charged_off\"] =  loan.groupby('addr_state')['loan_status'].apply(lambda x : round(((x == 'Charged Off').sum()/\n",
    "                                                                                    ((x == 'Fully Paid').sum() + \n",
    "                                                                                    (x == 'Charged Off').sum()))\n",
    "                                                                                    *100,2))\n",
    "\n",
    "loc_loan = loc_loan.reset_index()\n",
    "\n",
    "#create a display_text column to display on the graph\n",
    "loc_loan[\"fully_paid_text\"] = \"State:\" + loc_loan[\"addr_state\"].astype(str) + \"<br>\" + 'Average Loan amount:' +\\\n",
    "                            loc_loan['loan_amnt'].apply(str) + \"$\" + \"<br>\" +'Average Interest Rate: ' +\\\n",
    "                            loc_loan['int_rate'].apply(str)\n",
    "\n",
    "#create a display_text column to display on the graph\n",
    "loc_loan[\"charged_off_text\"] = \"State:\" + loc_loan[\"addr_state\"].astype(str) + \"<br>\" + 'Average Loan amount:' +\\\n",
    "                            loc_loan['loan_amnt'].apply(str) + \"$\" + \"<br>\" +'Average Interest Rate: ' +\\\n",
    "                            loc_loan['int_rate'].apply(str)\n",
    "\n",
    "#check the newly created data\n",
    "loc_loan.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K79lPT0VitjD"
   },
   "outputs": [],
   "source": [
    "#create a map dictionary\n",
    "map_data = dict(type = 'choropleth',\n",
    "            locations = loc_loan[\"addr_state\"],\n",
    "            locationmode = 'USA-states',\n",
    "            colorscale = 'Portland',\n",
    "            text = loc_loan[\"fully_paid_text\"],\n",
    "            z = loc_loan[\"fully_paid\"],\n",
    "            colorbar = dict(title = \"USA States\"),\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M0wgCCMwm5vA"
   },
   "outputs": [],
   "source": [
    "#set the map geography to USA\n",
    "map_layout = dict(geo = {'scope':'usa'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PIggCLplm8pY"
   },
   "outputs": [],
   "source": [
    "#set the Figure parameters with data and USA map layout\n",
    "map_actual = go.Figure(data = [map_data], layout = map_layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Renderers configuration\n",
       "-----------------------\n",
       "    Default renderer: 'plotly_mimetype+notebook_connected'\n",
       "    Available renderers:\n",
       "        ['plotly_mimetype', 'jupyterlab', 'nteract', 'vscode',\n",
       "         'notebook', 'notebook_connected', 'kaggle', 'azure', 'colab',\n",
       "         'cocalc', 'databricks', 'json', 'png', 'jpeg', 'jpg', 'svg',\n",
       "         'pdf', 'browser', 'firefox', 'chrome', 'chromium', 'iframe',\n",
       "         'iframe_connected', 'sphinx_gallery', 'sphinx_gallery_png']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for the renderers available\n",
    "import plotly.io as pio\n",
    "pio.renderers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#render the map visual using SVG format\n",
    "map_actual.show(renderer = \"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above map:\n",
    "\n",
    "1. The legend shows that there are states with 100% loans repaid and the least percentage is around 45%.\n",
    "2. This map shows the Nebraska having least number of Fully Paid loans at 45% and Maine having 100% loans that are Fully paid. And majority of the other states have between 80-90% of the loans that are fully paid. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot a map for Charged Off loans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TIBgq4t_AJEI"
   },
   "outputs": [],
   "source": [
    "#create a map dictionary\n",
    "map_data_co = dict(type = 'choropleth',\n",
    "            locations = loc_loan[\"addr_state\"],\n",
    "            locationmode = 'USA-states',\n",
    "            colorscale = 'Portland',\n",
    "            text = loc_loan[\"charged_off_text\"],\n",
    "            z = loc_loan[\"charged_off\"],\n",
    "            colorbar = dict(title = \"USA States\")\n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rO7TnktlASL8"
   },
   "outputs": [],
   "source": [
    "#set the Figure parameters with data and USA map layout\n",
    "map_actual_co = go.Figure(data = [map_data_co], layout = map_layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "colab_type": "code",
    "id": "9wokXQXBATV8",
    "outputId": "93c3bdc4-1a35-436b-939b-ef3865443024"
   },
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#render the map visual using SVG format\n",
    "map_actual_co.show(renderer = \"svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H6_PXUN-AUlg"
   },
   "source": [
    "From the above map:\n",
    "\n",
    "1. The legend shows that there are states with highest of 50% loans charged off and the least percentage is 0.\n",
    "2. This map shows the Nebraska having highest number of Charged Off loans at 54% and Maine having 0% loans that are Charged Off. These states had the lowest and highest fully paid loans respectively. This shows Nebraska borrowers tend to charge off their loans and the lenders need to think before approving the loans.\n",
    "3. Majority of the other states have between 10-20% of the loans that are charged off. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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