NP_Lending Club Case Study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Columns: 111 entries, id to total_il_high_credit_limit\n",
      "dtypes: float64(74), int64(13), object(24)\n",
      "memory usage: 33.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#Read data \n",
    "loan = pd.read_csv('loan.csv', engine = 'python')\n",
    "loan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>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>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.971700e+04</td>\n",
       "      <td>3.971700e+04</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>3.971700e+04</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>39717.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39020.000000</td>\n",
       "      <td>39678.0</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",
       "      <th>mean</th>\n",
       "      <td>6.831319e+05</td>\n",
       "      <td>8.504636e+05</td>\n",
       "      <td>11219.443815</td>\n",
       "      <td>10947.713196</td>\n",
       "      <td>10397.448868</td>\n",
       "      <td>324.561922</td>\n",
       "      <td>6.896893e+04</td>\n",
       "      <td>13.315130</td>\n",
       "      <td>0.146512</td>\n",
       "      <td>0.869200</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.106941e+05</td>\n",
       "      <td>2.656783e+05</td>\n",
       "      <td>7456.670694</td>\n",
       "      <td>7187.238670</td>\n",
       "      <td>7128.450439</td>\n",
       "      <td>208.874874</td>\n",
       "      <td>6.379377e+04</td>\n",
       "      <td>6.678594</td>\n",
       "      <td>0.491812</td>\n",
       "      <td>1.070219</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.204324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.473400e+04</td>\n",
       "      <td>7.069900e+04</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.690000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.162210e+05</td>\n",
       "      <td>6.667800e+05</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>167.020000</td>\n",
       "      <td>4.040400e+04</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.656650e+05</td>\n",
       "      <td>8.508120e+05</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>8975.000000</td>\n",
       "      <td>280.220000</td>\n",
       "      <td>5.900000e+04</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.377550e+05</td>\n",
       "      <td>1.047339e+06</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>14400.000000</td>\n",
       "      <td>430.780000</td>\n",
       "      <td>8.230000e+04</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.077501e+06</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>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     member_id     loan_amnt   funded_amnt  \\\n",
       "count  3.971700e+04  3.971700e+04  39717.000000  39717.000000   \n",
       "mean   6.831319e+05  8.504636e+05  11219.443815  10947.713196   \n",
       "std    2.106941e+05  2.656783e+05   7456.670694   7187.238670   \n",
       "min    5.473400e+04  7.069900e+04    500.000000    500.000000   \n",
       "25%    5.162210e+05  6.667800e+05   5500.000000   5400.000000   \n",
       "50%    6.656650e+05  8.508120e+05  10000.000000   9600.000000   \n",
       "75%    8.377550e+05  1.047339e+06  15000.000000  15000.000000   \n",
       "max    1.077501e+06  1.314167e+06  35000.000000  35000.000000   \n",
       "\n",
       "       funded_amnt_inv   installment    annual_inc           dti  \\\n",
       "count     39717.000000  39717.000000  3.971700e+04  39717.000000   \n",
       "mean      10397.448868    324.561922  6.896893e+04     13.315130   \n",
       "std        7128.450439    208.874874  6.379377e+04      6.678594   \n",
       "min           0.000000     15.690000  4.000000e+03      0.000000   \n",
       "25%        5000.000000    167.020000  4.040400e+04      8.170000   \n",
       "50%        8975.000000    280.220000  5.900000e+04     13.400000   \n",
       "75%       14400.000000    430.780000  8.230000e+04     18.600000   \n",
       "max       35000.000000   1305.190000  6.000000e+06     29.990000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths  ...  num_tl_90g_dpd_24m  \\\n",
       "count  39717.000000    39717.000000  ...                 0.0   \n",
       "mean       0.146512        0.869200  ...                 NaN   \n",
       "std        0.491812        1.070219  ...                 NaN   \n",
       "min        0.000000        0.000000  ...                 NaN   \n",
       "25%        0.000000        0.000000  ...                 NaN   \n",
       "50%        0.000000        1.000000  ...                 NaN   \n",
       "75%        0.000000        1.000000  ...                 NaN   \n",
       "max       11.000000        8.000000  ...                 NaN   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "count                 0.0             0.0               0.0   \n",
       "mean                  NaN             NaN               NaN   \n",
       "std                   NaN             NaN               NaN   \n",
       "min                   NaN             NaN               NaN   \n",
       "25%                   NaN             NaN               NaN   \n",
       "50%                   NaN             NaN               NaN   \n",
       "75%                   NaN             NaN               NaN   \n",
       "max                   NaN             NaN               NaN   \n",
       "\n",
       "       pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "count          39020.000000    39678.0              0.0                0.0   \n",
       "mean               0.043260        0.0              NaN                NaN   \n",
       "std                0.204324        0.0              NaN                NaN   \n",
       "min                0.000000        0.0              NaN                NaN   \n",
       "25%                0.000000        0.0              NaN                NaN   \n",
       "50%                0.000000        0.0              NaN                NaN   \n",
       "75%                0.000000        0.0              NaN                NaN   \n",
       "max                2.000000        0.0              NaN                NaN   \n",
       "\n",
       "       total_bc_limit  total_il_high_credit_limit  \n",
       "count             0.0                         0.0  \n",
       "mean              NaN                         NaN  \n",
       "std               NaN                         NaN  \n",
       "min               NaN                         NaN  \n",
       "25%               NaN                         NaN  \n",
       "50%               NaN                         NaN  \n",
       "75%               NaN                         NaN  \n",
       "max               NaN                         NaN  \n",
       "\n",
       "[8 rows x 87 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade  ... num_tl_90g_dpd_24m  \\\n",
       "0   10.65%       162.87     B        B2  ...                NaN   \n",
       "1   15.27%        59.83     C        C4  ...                NaN   \n",
       "2   15.96%        84.33     C        C5  ...                NaN   \n",
       "3   13.49%       339.31     C        C1  ...                NaN   \n",
       "4   12.69%        67.79     B        B5  ...                NaN   \n",
       "\n",
       "  num_tl_op_past_12m pct_tl_nvr_dlq  percent_bc_gt_75 pub_rec_bankruptcies  \\\n",
       "0                NaN            NaN               NaN                  0.0   \n",
       "1                NaN            NaN               NaN                  0.0   \n",
       "2                NaN            NaN               NaN                  0.0   \n",
       "3                NaN            NaN               NaN                  0.0   \n",
       "4                NaN            NaN               NaN                  0.0   \n",
       "\n",
       "  tax_liens tot_hi_cred_lim total_bal_ex_mort total_bc_limit  \\\n",
       "0       0.0             NaN               NaN            NaN   \n",
       "1       0.0             NaN               NaN            NaN   \n",
       "2       0.0             NaN               NaN            NaN   \n",
       "3       0.0             NaN               NaN            NaN   \n",
       "4       0.0             NaN               NaN            NaN   \n",
       "\n",
       "  total_il_high_credit_limit  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the percentage of null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0.000000\n",
       "member_id                           0.000000\n",
       "loan_amnt                           0.000000\n",
       "funded_amnt                         0.000000\n",
       "funded_amnt_inv                     0.000000\n",
       "term                                0.000000\n",
       "int_rate                            0.000000\n",
       "installment                         0.000000\n",
       "grade                               0.000000\n",
       "sub_grade                           0.000000\n",
       "emp_title                           6.191303\n",
       "emp_length                          2.706650\n",
       "home_ownership                      0.000000\n",
       "annual_inc                          0.000000\n",
       "verification_status                 0.000000\n",
       "issue_d                             0.000000\n",
       "loan_status                         0.000000\n",
       "pymnt_plan                          0.000000\n",
       "url                                 0.000000\n",
       "desc                               32.580507\n",
       "purpose                             0.000000\n",
       "title                               0.027696\n",
       "zip_code                            0.000000\n",
       "addr_state                          0.000000\n",
       "dti                                 0.000000\n",
       "delinq_2yrs                         0.000000\n",
       "earliest_cr_line                    0.000000\n",
       "inq_last_6mths                      0.000000\n",
       "mths_since_last_delinq             64.662487\n",
       "mths_since_last_record             92.985372\n",
       "open_acc                            0.000000\n",
       "pub_rec                             0.000000\n",
       "revol_bal                           0.000000\n",
       "revol_util                          0.125891\n",
       "total_acc                           0.000000\n",
       "initial_list_status                 0.000000\n",
       "out_prncp                           0.000000\n",
       "out_prncp_inv                       0.000000\n",
       "total_pymnt                         0.000000\n",
       "total_pymnt_inv                     0.000000\n",
       "total_rec_prncp                     0.000000\n",
       "total_rec_int                       0.000000\n",
       "total_rec_late_fee                  0.000000\n",
       "recoveries                          0.000000\n",
       "collection_recovery_fee             0.000000\n",
       "last_pymnt_d                        0.178765\n",
       "last_pymnt_amnt                     0.000000\n",
       "next_pymnt_d                       97.129693\n",
       "last_credit_pull_d                  0.005036\n",
       "collections_12_mths_ex_med          0.140998\n",
       "mths_since_last_major_derog       100.000000\n",
       "policy_code                         0.000000\n",
       "application_type                    0.000000\n",
       "annual_inc_joint                  100.000000\n",
       "dti_joint                         100.000000\n",
       "verification_status_joint         100.000000\n",
       "acc_now_delinq                      0.000000\n",
       "tot_coll_amt                      100.000000\n",
       "tot_cur_bal                       100.000000\n",
       "open_acc_6m                       100.000000\n",
       "open_il_6m                        100.000000\n",
       "open_il_12m                       100.000000\n",
       "open_il_24m                       100.000000\n",
       "mths_since_rcnt_il                100.000000\n",
       "total_bal_il                      100.000000\n",
       "il_util                           100.000000\n",
       "open_rv_12m                       100.000000\n",
       "open_rv_24m                       100.000000\n",
       "max_bal_bc                        100.000000\n",
       "all_util                          100.000000\n",
       "total_rev_hi_lim                  100.000000\n",
       "inq_fi                            100.000000\n",
       "total_cu_tl                       100.000000\n",
       "inq_last_12m                      100.000000\n",
       "acc_open_past_24mths              100.000000\n",
       "avg_cur_bal                       100.000000\n",
       "bc_open_to_buy                    100.000000\n",
       "bc_util                           100.000000\n",
       "chargeoff_within_12_mths            0.140998\n",
       "delinq_amnt                         0.000000\n",
       "mo_sin_old_il_acct                100.000000\n",
       "mo_sin_old_rev_tl_op              100.000000\n",
       "mo_sin_rcnt_rev_tl_op             100.000000\n",
       "mo_sin_rcnt_tl                    100.000000\n",
       "mort_acc                          100.000000\n",
       "mths_since_recent_bc              100.000000\n",
       "mths_since_recent_bc_dlq          100.000000\n",
       "mths_since_recent_inq             100.000000\n",
       "mths_since_recent_revol_delinq    100.000000\n",
       "num_accts_ever_120_pd             100.000000\n",
       "num_actv_bc_tl                    100.000000\n",
       "num_actv_rev_tl                   100.000000\n",
       "num_bc_sats                       100.000000\n",
       "num_bc_tl                         100.000000\n",
       "num_il_tl                         100.000000\n",
       "num_op_rev_tl                     100.000000\n",
       "num_rev_accts                     100.000000\n",
       "num_rev_tl_bal_gt_0               100.000000\n",
       "num_sats                          100.000000\n",
       "num_tl_120dpd_2m                  100.000000\n",
       "num_tl_30dpd                      100.000000\n",
       "num_tl_90g_dpd_24m                100.000000\n",
       "num_tl_op_past_12m                100.000000\n",
       "pct_tl_nvr_dlq                    100.000000\n",
       "percent_bc_gt_75                  100.000000\n",
       "pub_rec_bankruptcies                1.754916\n",
       "tax_liens                           0.098195\n",
       "tot_hi_cred_lim                   100.000000\n",
       "total_bal_ex_mort                 100.000000\n",
       "total_bc_limit                    100.000000\n",
       "total_il_high_credit_limit        100.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Percentage of missing values in each column\n",
    "#Display larger set of values of a dataframe\n",
    "pd.set_option('display.max_rows', 200)\n",
    "(loan.isnull().sum()* 100)/len(loan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are many columns with 100% missing values, let's keep only those columns which have alteast 50% values. Drop all columns with more than 50% missing values ass they will not be of any use in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop columns with more than 50% missing values\n",
    "#thresh controls the numer of non-null values. Here if any column has atleast 50% non-null values, it survives \n",
    "loan_not_null = loan.dropna(axis = 'columns',  thresh=(0.50 *len(loan)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 111)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check loan shape \n",
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets observe the % of missing values in each column again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0.000000\n",
       "member_id                      0.000000\n",
       "loan_amnt                      0.000000\n",
       "funded_amnt                    0.000000\n",
       "funded_amnt_inv                0.000000\n",
       "term                           0.000000\n",
       "int_rate                       0.000000\n",
       "installment                    0.000000\n",
       "grade                          0.000000\n",
       "sub_grade                      0.000000\n",
       "emp_title                      6.191303\n",
       "emp_length                     2.706650\n",
       "home_ownership                 0.000000\n",
       "annual_inc                     0.000000\n",
       "verification_status            0.000000\n",
       "issue_d                        0.000000\n",
       "loan_status                    0.000000\n",
       "pymnt_plan                     0.000000\n",
       "url                            0.000000\n",
       "desc                          32.580507\n",
       "purpose                        0.000000\n",
       "title                          0.027696\n",
       "zip_code                       0.000000\n",
       "addr_state                     0.000000\n",
       "dti                            0.000000\n",
       "delinq_2yrs                    0.000000\n",
       "earliest_cr_line               0.000000\n",
       "inq_last_6mths                 0.000000\n",
       "open_acc                       0.000000\n",
       "pub_rec                        0.000000\n",
       "revol_bal                      0.000000\n",
       "revol_util                     0.125891\n",
       "total_acc                      0.000000\n",
       "initial_list_status            0.000000\n",
       "out_prncp                      0.000000\n",
       "out_prncp_inv                  0.000000\n",
       "total_pymnt                    0.000000\n",
       "total_pymnt_inv                0.000000\n",
       "total_rec_prncp                0.000000\n",
       "total_rec_int                  0.000000\n",
       "total_rec_late_fee             0.000000\n",
       "recoveries                     0.000000\n",
       "collection_recovery_fee        0.000000\n",
       "last_pymnt_d                   0.178765\n",
       "last_pymnt_amnt                0.000000\n",
       "last_credit_pull_d             0.005036\n",
       "collections_12_mths_ex_med     0.140998\n",
       "policy_code                    0.000000\n",
       "application_type               0.000000\n",
       "acc_now_delinq                 0.000000\n",
       "chargeoff_within_12_mths       0.140998\n",
       "delinq_amnt                    0.000000\n",
       "pub_rec_bankruptcies           1.754916\n",
       "tax_liens                      0.098195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of missing values in each column\n",
    "(loan_not_null.isnull().sum()* 100)/len(loan_not_null)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'desc' column has 32% null values. If we check the information in the 'desc' column then similar data ia available in 'title' and 'purpose' columns in a concise format. Hence we can drop 'desc' column as corressponding information can be analysed from 'title' and 'purpose' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 53 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   id                          39717 non-null  int64  \n",
      " 1   member_id                   39717 non-null  int64  \n",
      " 2   loan_amnt                   39717 non-null  int64  \n",
      " 3   funded_amnt                 39717 non-null  int64  \n",
      " 4   funded_amnt_inv             39717 non-null  float64\n",
      " 5   term                        39717 non-null  object \n",
      " 6   int_rate                    39717 non-null  object \n",
      " 7   installment                 39717 non-null  float64\n",
      " 8   grade                       39717 non-null  object \n",
      " 9   sub_grade                   39717 non-null  object \n",
      " 10  emp_title                   37258 non-null  object \n",
      " 11  emp_length                  38642 non-null  object \n",
      " 12  home_ownership              39717 non-null  object \n",
      " 13  annual_inc                  39717 non-null  float64\n",
      " 14  verification_status         39717 non-null  object \n",
      " 15  issue_d                     39717 non-null  object \n",
      " 16  loan_status                 39717 non-null  object \n",
      " 17  pymnt_plan                  39717 non-null  object \n",
      " 18  url                         39717 non-null  object \n",
      " 19  purpose                     39717 non-null  object \n",
      " 20  title                       39706 non-null  object \n",
      " 21  zip_code                    39717 non-null  object \n",
      " 22  addr_state                  39717 non-null  object \n",
      " 23  dti                         39717 non-null  float64\n",
      " 24  delinq_2yrs                 39717 non-null  int64  \n",
      " 25  earliest_cr_line            39717 non-null  object \n",
      " 26  inq_last_6mths              39717 non-null  int64  \n",
      " 27  open_acc                    39717 non-null  int64  \n",
      " 28  pub_rec                     39717 non-null  int64  \n",
      " 29  revol_bal                   39717 non-null  int64  \n",
      " 30  revol_util                  39667 non-null  object \n",
      " 31  total_acc                   39717 non-null  int64  \n",
      " 32  initial_list_status         39717 non-null  object \n",
      " 33  out_prncp                   39717 non-null  float64\n",
      " 34  out_prncp_inv               39717 non-null  float64\n",
      " 35  total_pymnt                 39717 non-null  float64\n",
      " 36  total_pymnt_inv             39717 non-null  float64\n",
      " 37  total_rec_prncp             39717 non-null  float64\n",
      " 38  total_rec_int               39717 non-null  float64\n",
      " 39  total_rec_late_fee          39717 non-null  float64\n",
      " 40  recoveries                  39717 non-null  float64\n",
      " 41  collection_recovery_fee     39717 non-null  float64\n",
      " 42  last_pymnt_d                39646 non-null  object \n",
      " 43  last_pymnt_amnt             39717 non-null  float64\n",
      " 44  last_credit_pull_d          39715 non-null  object \n",
      " 45  collections_12_mths_ex_med  39661 non-null  float64\n",
      " 46  policy_code                 39717 non-null  int64  \n",
      " 47  application_type            39717 non-null  object \n",
      " 48  acc_now_delinq              39717 non-null  int64  \n",
      " 49  chargeoff_within_12_mths    39661 non-null  float64\n",
      " 50  delinq_amnt                 39717 non-null  int64  \n",
      " 51  pub_rec_bankruptcies        39020 non-null  float64\n",
      " 52  tax_liens                   39678 non-null  float64\n",
      "dtypes: float64(18), int64(13), object(22)\n",
      "memory usage: 16.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#Dropping desc column\n",
    "loan_not_null = loan_not_null.drop('desc', axis = 'columns')\n",
    "loan_not_null.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check if any columns contain all duplicate/same values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tax_liens                         1\n",
       "delinq_amnt                       1\n",
       "chargeoff_within_12_mths          1\n",
       "acc_now_delinq                    1\n",
       "application_type                  1\n",
       "policy_code                       1\n",
       "collections_12_mths_ex_med        1\n",
       "initial_list_status               1\n",
       "pymnt_plan                        1\n",
       "term                              2\n",
       "pub_rec_bankruptcies              3\n",
       "verification_status               3\n",
       "loan_status                       3\n",
       "pub_rec                           5\n",
       "home_ownership                    5\n",
       "grade                             7\n",
       "inq_last_6mths                    9\n",
       "delinq_2yrs                      11\n",
       "emp_length                       11\n",
       "purpose                          14\n",
       "sub_grade                        35\n",
       "open_acc                         40\n",
       "addr_state                       50\n",
       "issue_d                          55\n",
       "total_acc                        82\n",
       "last_pymnt_d                    101\n",
       "last_credit_pull_d              106\n",
       "int_rate                        371\n",
       "earliest_cr_line                526\n",
       "zip_code                        823\n",
       "loan_amnt                       885\n",
       "funded_amnt                    1041\n",
       "revol_util                     1089\n",
       "out_prncp                      1137\n",
       "out_prncp_inv                  1138\n",
       "total_rec_late_fee             1356\n",
       "collection_recovery_fee        2616\n",
       "dti                            2868\n",
       "recoveries                     4040\n",
       "annual_inc                     5318\n",
       "total_rec_prncp                7976\n",
       "funded_amnt_inv                8205\n",
       "installment                   15383\n",
       "title                         19615\n",
       "revol_bal                     21711\n",
       "emp_title                     28820\n",
       "last_pymnt_amnt               34930\n",
       "total_rec_int                 35148\n",
       "total_pymnt_inv               37518\n",
       "total_pymnt                   37850\n",
       "url                           39717\n",
       "member_id                     39717\n",
       "id                            39717\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the value in each row to get the unique values in each column\n",
    "loan_not_null.nunique(axis=0).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As observed above, there are many column which have duplicate values. These columns will not provide any value addition to the analysis. Hence we will drop them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting columns having all duplicate values\n",
    "loan_nonull_and_duplicates = loan_not_null.drop(['tax_liens', 'delinq_amnt', 'chargeoff_within_12_mths', \n",
    "                  'acc_now_delinq', 'application_type', 'policy_code',\n",
    "                 'collections_12_mths_ex_med', 'initial_list_status', 'pymnt_plan'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 44)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check the shape of loan again\n",
    "loan_nonull_and_duplicates.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's observe the dataset again to check if there are any collumns which will be redundant in the anallysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 44 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       39717 non-null  int64  \n",
      " 1   member_id                39717 non-null  int64  \n",
      " 2   loan_amnt                39717 non-null  int64  \n",
      " 3   funded_amnt              39717 non-null  int64  \n",
      " 4   funded_amnt_inv          39717 non-null  float64\n",
      " 5   term                     39717 non-null  object \n",
      " 6   int_rate                 39717 non-null  object \n",
      " 7   installment              39717 non-null  float64\n",
      " 8   grade                    39717 non-null  object \n",
      " 9   sub_grade                39717 non-null  object \n",
      " 10  emp_title                37258 non-null  object \n",
      " 11  emp_length               38642 non-null  object \n",
      " 12  home_ownership           39717 non-null  object \n",
      " 13  annual_inc               39717 non-null  float64\n",
      " 14  verification_status      39717 non-null  object \n",
      " 15  issue_d                  39717 non-null  object \n",
      " 16  loan_status              39717 non-null  object \n",
      " 17  url                      39717 non-null  object \n",
      " 18  purpose                  39717 non-null  object \n",
      " 19  title                    39706 non-null  object \n",
      " 20  zip_code                 39717 non-null  object \n",
      " 21  addr_state               39717 non-null  object \n",
      " 22  dti                      39717 non-null  float64\n",
      " 23  delinq_2yrs              39717 non-null  int64  \n",
      " 24  earliest_cr_line         39717 non-null  object \n",
      " 25  inq_last_6mths           39717 non-null  int64  \n",
      " 26  open_acc                 39717 non-null  int64  \n",
      " 27  pub_rec                  39717 non-null  int64  \n",
      " 28  revol_bal                39717 non-null  int64  \n",
      " 29  revol_util               39667 non-null  object \n",
      " 30  total_acc                39717 non-null  int64  \n",
      " 31  out_prncp                39717 non-null  float64\n",
      " 32  out_prncp_inv            39717 non-null  float64\n",
      " 33  total_pymnt              39717 non-null  float64\n",
      " 34  total_pymnt_inv          39717 non-null  float64\n",
      " 35  total_rec_prncp          39717 non-null  float64\n",
      " 36  total_rec_int            39717 non-null  float64\n",
      " 37  total_rec_late_fee       39717 non-null  float64\n",
      " 38  recoveries               39717 non-null  float64\n",
      " 39  collection_recovery_fee  39717 non-null  float64\n",
      " 40  last_pymnt_d             39646 non-null  object \n",
      " 41  last_pymnt_amnt          39717 non-null  float64\n",
      " 42  last_credit_pull_d       39715 non-null  object \n",
      " 43  pub_rec_bankruptcies     39020 non-null  float64\n",
      "dtypes: float64(15), int64(10), object(19)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_nonull_and_duplicates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As observed above we have reduced our dataset to 39717 rows and 44 columns. If we observe the data dictionary provided to us we can see that there are furthermore columns which have the similar meaning. Consider the columns below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__loan_amnt__ = The listed amount of the loan applied for by the borrower\n",
    "<br>__funded_amnt__ = The total amount committed to that loan at that point in time i.e the amount recommended/approved by Lending Club\n",
    "<br>__funded_amnt_inv__ = the amount funded by Lending club\n",
    "\n",
    "Potentially these values will always be same. Hence for simplicity we can consider __loan_amt__ and drop remaining 2 columns from our analyis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_nonull_and_duplicates.drop(['funded_amnt', 'funded_amnt_inv'], axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__title__ : The loan title provided by the borrower\n",
    "<br>__purpose__ : A category provided by the borrower for the loan request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Debt Consolidation           2184\n",
       "Debt Consolidation Loan      1729\n",
       "Personal Loan                 659\n",
       "Consolidation                 517\n",
       "debt consolidation            505\n",
       "Home Improvement              356\n",
       "Credit Card Consolidation     356\n",
       "Debt consolidation            334\n",
       "Small Business Loan           328\n",
       "Credit Card Loan              317\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check values of title and purpose\n",
    "loan_nonull_and_duplicates.title.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    18641\n",
       "credit_card            5130\n",
       "other                  3993\n",
       "home_improvement       2976\n",
       "major_purchase         2187\n",
       "small_business         1828\n",
       "car                    1549\n",
       "wedding                 947\n",
       "medical                 693\n",
       "moving                  583\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_nonull_and_duplicates.purpose.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As observed above, title and purpose appear to have the same meaning and values. Hence we will drop title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_nonull_and_duplicates.drop(['title'], axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 41)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_nonull_and_duplicates.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, the columns __id__, __member_id__, __url__, __zip_code__ will not have any impact on the analysis and can be safely dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_clean = loan_nonull_and_duplicates.drop(['id', 'member_id', 'url', 'zip_code'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 37)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of loan\n",
    "loan_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the remaining columns, we can further categorise them into 3 borad categories:\n",
    "<br>__Demographic variables__ = Variables related to the applicant such as employment length, emp title etc.\n",
    "<br> __Loan characteristics__ = Variables such as amount of loan, interest rate, purpose of loan etc.\n",
    "<br> __Customer behaviour__  = Variables such as revolving balance, next payment date etc which are generated after the loan is approved.\n",
    "\n",
    "Out of the above variables, we can safely drop __Customer behaviour__ variables as they will not be present with the investor when the persson applies for a loan and hence they won't help in taking a decision on accepting or rejecting the loan application "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_behaviour_var = ['delinq_2yrs', 'earliest_cr_line',\n",
    "       'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util',\n",
    "       'total_acc', 'out_prncp', 'out_prncp_inv','total_pymnt', \n",
    "        '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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             39717 non-null  int64  \n",
      " 1   term                  39717 non-null  object \n",
      " 2   int_rate              39717 non-null  object \n",
      " 3   installment           39717 non-null  float64\n",
      " 4   grade                 39717 non-null  object \n",
      " 5   sub_grade             39717 non-null  object \n",
      " 6   emp_title             37258 non-null  object \n",
      " 7   emp_length            38642 non-null  object \n",
      " 8   home_ownership        39717 non-null  object \n",
      " 9   annual_inc            39717 non-null  float64\n",
      " 10  verification_status   39717 non-null  object \n",
      " 11  issue_d               39717 non-null  object \n",
      " 12  loan_status           39717 non-null  object \n",
      " 13  purpose               39717 non-null  object \n",
      " 14  addr_state            39717 non-null  object \n",
      " 15  dti                   39717 non-null  float64\n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64\n",
      "dtypes: float64(4), int64(1), object(12)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_clean = loan_clean.drop(customer_behaviour_var, axis=1)\n",
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             39717 non-null  int64  \n",
      " 1   term                  39717 non-null  object \n",
      " 2   int_rate              39717 non-null  object \n",
      " 3   installment           39717 non-null  float64\n",
      " 4   grade                 39717 non-null  object \n",
      " 5   sub_grade             39717 non-null  object \n",
      " 6   emp_title             37258 non-null  object \n",
      " 7   emp_length            38642 non-null  object \n",
      " 8   home_ownership        39717 non-null  object \n",
      " 9   annual_inc            39717 non-null  float64\n",
      " 10  verification_status   39717 non-null  object \n",
      " 11  issue_d               39717 non-null  object \n",
      " 12  loan_status           39717 non-null  object \n",
      " 13  purpose               39717 non-null  object \n",
      " 14  addr_state            39717 non-null  object \n",
      " 15  dti                   39717 non-null  float64\n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64\n",
      "dtypes: float64(4), int64(1), object(12)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 17)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape again\n",
    "loan_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             39717 non-null  int64  \n",
      " 1   term                  39717 non-null  object \n",
      " 2   int_rate              39717 non-null  object \n",
      " 3   installment           39717 non-null  float64\n",
      " 4   grade                 39717 non-null  object \n",
      " 5   sub_grade             39717 non-null  object \n",
      " 6   emp_title             37258 non-null  object \n",
      " 7   emp_length            38642 non-null  object \n",
      " 8   home_ownership        39717 non-null  object \n",
      " 9   annual_inc            39717 non-null  float64\n",
      " 10  verification_status   39717 non-null  object \n",
      " 11  issue_d               39717 non-null  object \n",
      " 12  loan_status           39717 non-null  object \n",
      " 13  purpose               39717 non-null  object \n",
      " 14  addr_state            39717 non-null  object \n",
      " 15  dti                   39717 non-null  float64\n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64\n",
      "dtypes: float64(4), int64(1), object(12)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rectifying the incorrect datatypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.99%    956\n",
       "13.49%    826\n",
       "11.49%    825\n",
       "7.51%     787\n",
       "7.88%     725\n",
       "         ... \n",
       "16.71%      1\n",
       "16.96%      1\n",
       "17.34%      1\n",
       "18.72%      1\n",
       "21.48%      1\n",
       "Name: int_rate, Length: 371, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us observe the int_rate column\n",
    "loan_clean.int_rate.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean interest rate to remove %\n",
    "loan_clean['int_rate']= loan_clean['int_rate'].apply(lambda x: pd.to_numeric(x.split('%')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10+ years    8879\n",
       "< 1 year     4583\n",
       "2 years      4388\n",
       "3 years      4095\n",
       "4 years      3436\n",
       "5 years      3282\n",
       "1 year       3240\n",
       "6 years      2229\n",
       "7 years      1773\n",
       "8 years      1479\n",
       "9 years      1258\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us observe emp_length column\n",
    "loan_clean.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_length_dict = {\n",
    "    '10+ years': 10,\n",
    "    '< 1 year' : 0,\n",
    "    '1 year'  : 1,\n",
    "    '2 years' : 2,\n",
    "    '3 years' : 3,\n",
    "    '4 years' : 4,\n",
    "    '5 years' : 5,\n",
    "    '6 years' : 6,\n",
    "    '7 years' : 7,\n",
    "    '8 years' : 8,\n",
    "    '9 years' : 9\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0    8879\n",
       "0.0     4583\n",
       "2.0     4388\n",
       "3.0     4095\n",
       "4.0     3436\n",
       "5.0     3282\n",
       "1.0     3240\n",
       "6.0     2229\n",
       "7.0     1773\n",
       "8.0     1479\n",
       "9.0     1258\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean emp_length to replace years with numbers\n",
    "loan_clean = loan_clean.replace({\"emp_length\": emp_length_dict})\n",
    "loan_clean.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    29096\n",
       " 60 months    10621\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets observe term column\n",
    "loan_clean.term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36    29096\n",
       "60    10621\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean term to remove months\n",
    "loan_clean.term = loan_clean.term.apply(lambda x: x.split()[0])\n",
    "loan_clean.term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             39717 non-null  int64  \n",
      " 1   term                  39717 non-null  object \n",
      " 2   int_rate              39717 non-null  float64\n",
      " 3   installment           39717 non-null  float64\n",
      " 4   grade                 39717 non-null  object \n",
      " 5   sub_grade             39717 non-null  object \n",
      " 6   emp_title             37258 non-null  object \n",
      " 7   emp_length            38642 non-null  float64\n",
      " 8   home_ownership        39717 non-null  object \n",
      " 9   annual_inc            39717 non-null  float64\n",
      " 10  verification_status   39717 non-null  object \n",
      " 11  issue_d               39717 non-null  object \n",
      " 12  loan_status           39717 non-null  object \n",
      " 13  purpose               39717 non-null  object \n",
      " 14  addr_state            39717 non-null  object \n",
      " 15  dti                   39717 non-null  float64\n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   loan_amnt             39717 non-null  int64  \n",
      " 1   term                  39717 non-null  int64  \n",
      " 2   int_rate              39717 non-null  float64\n",
      " 3   installment           39717 non-null  float64\n",
      " 4   grade                 39717 non-null  object \n",
      " 5   sub_grade             39717 non-null  object \n",
      " 6   emp_title             37258 non-null  object \n",
      " 7   emp_length            38642 non-null  float64\n",
      " 8   home_ownership        39717 non-null  object \n",
      " 9   annual_inc            39717 non-null  float64\n",
      " 10  verification_status   39717 non-null  object \n",
      " 11  issue_d               39717 non-null  object \n",
      " 12  loan_status           39717 non-null  object \n",
      " 13  purpose               39717 non-null  object \n",
      " 14  addr_state            39717 non-null  object \n",
      " 15  dti                   39717 non-null  float64\n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(9)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#Converting term to int datatype\n",
    "loan_clean.term = loan_clean.term.apply(pd.to_numeric)\n",
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's\n",
    "split issue_d i.e month in which the loan was funded into month and year column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting issue_d from object to datetime format for ease of splitting\n",
    "loan_clean.issue_d = pd.to_datetime(loan_clean.issue_d, format='%b-%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_clean['issue_d_month'] = loan_clean['issue_d'].dt.month\n",
    "loan_clean['issue_d_year'] = loan_clean['issue_d'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   loan_amnt             39717 non-null  int64         \n",
      " 1   term                  39717 non-null  int64         \n",
      " 2   int_rate              39717 non-null  float64       \n",
      " 3   installment           39717 non-null  float64       \n",
      " 4   grade                 39717 non-null  object        \n",
      " 5   sub_grade             39717 non-null  object        \n",
      " 6   emp_title             37258 non-null  object        \n",
      " 7   emp_length            38642 non-null  float64       \n",
      " 8   home_ownership        39717 non-null  object        \n",
      " 9   annual_inc            39717 non-null  float64       \n",
      " 10  verification_status   39717 non-null  object        \n",
      " 11  issue_d               39717 non-null  datetime64[ns]\n",
      " 12  loan_status           39717 non-null  object        \n",
      " 13  purpose               39717 non-null  object        \n",
      " 14  addr_state            39717 non-null  object        \n",
      " 15  dti                   39717 non-null  float64       \n",
      " 16  pub_rec_bankruptcies  39020 non-null  float64       \n",
      " 17  issue_d_month         39717 non-null  int64         \n",
      " 18  issue_d_year          39717 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(6), int64(4), object(8)\n",
      "memory usage: 5.8+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data set is ready for analyis. It has a 39717 rows and 19 columns. Let us copy it's data into a new file for ease of further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_master = loan_clean[:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our objective is to understand the factors which can lead to defaluters. Let us analyze the variables to understand the patterns which result in loan default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us perform analysis on categorical and numeric data and understand the major aspects of each one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     32950\n",
       "Charged Off     5627\n",
       "Current         1140\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_master.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen from above, loan status has 3 values. \n",
    "<br> __Fully Paid__ = Non - defaluters\n",
    "<br> __Charged off__ = Defaulters\n",
    "<br> __Current__ = Nothing can be confirmed yet. In the process of repaying loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are only interested in analyzing the defaulters, current value will be of no use to us. Hence we will ignore that value for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38577, 19)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_master = loan_master[loan_master['loan_status'].isin(['Fully Paid','Charged Off'])]\n",
    "loan_master.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     32950\n",
       "Charged Off     5627\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_master['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to plot count plots of variables\n",
    "def count_plot(var, rotate_val = 0):\n",
    "    sns.countplot(x=var, data=loan_master)\n",
    "    plt.xticks(rotation=rotate_val)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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": [
    "#Plotting a graph for loan status\n",
    "count_plot('loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.17\n"
     ]
    }
   ],
   "source": [
    "#Calculate percentage of defaulters\n",
    "print(\"%.2f\" %(loan_master.loc[loan_master['loan_status'] == 'Charged Off'].loan_status.count() * 100/len(loan)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>From the above plot we can infer that most of the loans have been repaid fully.\n",
    "<br>The percentage of defaluters is 14.17% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check the loan amount variations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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": [
    "#Plotting a graph for loan_amout\n",
    "sns.boxplot(y=loan_master[\"loan_amnt\"] )\n",
    "plt.ylabel('Loan Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    38577.000000\n",
       "mean     11047.025430\n",
       "std       7348.441646\n",
       "min        500.000000\n",
       "25%       5300.000000\n",
       "50%       9600.000000\n",
       "75%      15000.000000\n",
       "max      35000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_master.loan_amnt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>On an average the sanctioned loan amount varies between USD 5300 and USD 9600 with some outliers values having amount sanctioned as USD 35000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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": [
    "#plotting grades\n",
    "count_plot('grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Most of the loans have grade of A and B. Therefore most of the loans are high graded loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "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": [
    "count_plot('purpose', 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum number of loans have been for the category of Debt Consolidation followed by credit cards and others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Emp Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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": [
    "count_plot('emp_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Maximum loans are sanctioned to loan takers having around 10 years of work experience. However, the loans approved don't depend much on the employment length. There is no direct relation between the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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": [
    "count_plot('term')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>More number of loans have been sanctioned for the duration of 36 months as compared to 60 months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "issue_d_year i.e Year of funding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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": [
    "count_plot('issue_d_year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Over the span of 4 years (2007 - 2011) the number of loans approved by Lending Club had witnessed a coniderable rise. This might imply that most of the loans approved by ending Club were recovered successfully"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verification Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "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": [
    "count_plot('verification_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Obervation:**\n",
    "<br>For majority of the loans approved, the income of borrower was unverified by Lending club. This might turn out to be a bad practice "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Segmented Univariate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert loan status to a numeric value and analyze it accross several other categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32950\n",
       "1     5627\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting loan status to numeric from categorical to analyze the default rate of loan status against various other categorical variables\n",
    "loan_master.loan_status = loan_master.loan_status.apply(lambda x: 0 if x == 'Fully Paid' else 1)\n",
    "loan_master.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the Default rate against various categorical variables and draw some observations with regards to loan defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def plot_cat(cat_var):\n",
    "#     sns.barplot( x= cat_var, y= 'loan_status', data=loan_master)\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Default rate vs Loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000    2809\n",
       "12000    2248\n",
       "5000     2028\n",
       "6000     1886\n",
       "15000    1838\n",
       "         ... \n",
       "13950       1\n",
       "29175       1\n",
       "13025       1\n",
       "19150       1\n",
       "28750       1\n",
       "Name: loan_amnt, Length: 870, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Observe loan_amnt\n",
    "loan_master.loan_amnt.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As loan_amnt has wide variations, for ease of analyis we can bin the values in appropriate buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000 - 15000          20675\n",
       "15000 - 25000          7696\n",
       "less than 5000         7444\n",
       "Greater than 25000     2762\n",
       "Name: loan_amnt, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Binning loan_amount\n",
    "def bin_loan_amount(n):\n",
    "    if n < 5000:\n",
    "        return 'less than 5000'\n",
    "    elif n >= 5000 and n <15000:\n",
    "        return '5000 - 15000'\n",
    "    elif n>=15000 and n < 25000:\n",
    "        return '15000 - 25000'\n",
    "    else:\n",
    "        return 'Greater than 25000'\n",
    "loan_master['loan_amnt']= loan_master['loan_amnt'].apply(lambda x: bin_loan_amount(x))\n",
    "loan_master['loan_amnt'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can observe other numerical varialbles and try to bin them for better analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    38577.000000\n",
       "mean        11.932219\n",
       "std          3.691327\n",
       "min          5.420000\n",
       "25%          8.940000\n",
       "50%         11.710000\n",
       "75%         14.380000\n",
       "max         24.400000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_master.int_rate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10% - 15%        18514\n",
       "less than 10%    12067\n",
       "More than 15%     7996\n",
       "Name: int_rate, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Binning int_rate\n",
    "def bin_int_rate(r):\n",
    "    if r < 10:\n",
    "        return 'less than 10%'\n",
    "    elif r >= 10 and r < 15:\n",
    "        return '10% - 15%'\n",
    "    else:\n",
    "        return 'More than 15%'\n",
    "loan_master['int_rate'] = loan_master['int_rate'].apply(lambda x: bin_int_rate(x))\n",
    "loan_master['int_rate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000.0    1466\n",
       "50000.0    1029\n",
       "40000.0     855\n",
       "45000.0     811\n",
       "30000.0     808\n",
       "           ... \n",
       "34977.0       1\n",
       "51360.0       1\n",
       "69950.0       1\n",
       "55454.0       1\n",
       "90111.0       1\n",
       "Name: annual_inc, Length: 5215, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Observing annual income\n",
    "loan_master.annual_inc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000 - 100000        18238\n",
       "less than 50000       14360\n",
       "Higher than 100000     5979\n",
       "Name: annual_inc, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Binning annual_inc\n",
    "def bin_annual_inc(x):\n",
    "    if x < 50000:\n",
    "        return 'less than 50000'\n",
    "    elif x >= 50000 and x < 100000:\n",
    "        return '50000 - 100000'\n",
    "    else:\n",
    "        return 'Higher than 100000'\n",
    "loan_master['annual_inc'] = loan_master['annual_inc'].apply(lambda x: bin_annual_inc(x))\n",
    "loan_master['annual_inc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38577 entries, 0 to 39716\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   loan_amnt             38577 non-null  object        \n",
      " 1   term                  38577 non-null  int64         \n",
      " 2   int_rate              38577 non-null  object        \n",
      " 3   installment           38577 non-null  float64       \n",
      " 4   grade                 38577 non-null  object        \n",
      " 5   sub_grade             38577 non-null  object        \n",
      " 6   emp_title             36191 non-null  object        \n",
      " 7   emp_length            37544 non-null  float64       \n",
      " 8   home_ownership        38577 non-null  object        \n",
      " 9   annual_inc            38577 non-null  object        \n",
      " 10  verification_status   38577 non-null  object        \n",
      " 11  issue_d               38577 non-null  datetime64[ns]\n",
      " 12  loan_status           38577 non-null  int64         \n",
      " 13  purpose               38577 non-null  object        \n",
      " 14  addr_state            38577 non-null  object        \n",
      " 15  dti                   38577 non-null  float64       \n",
      " 16  pub_rec_bankruptcies  37880 non-null  float64       \n",
      " 17  issue_d_month         38577 non-null  int64         \n",
      " 18  issue_d_year          38577 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(4), int64(4), object(10)\n",
      "memory usage: 7.1+ MB\n"
     ]
    }
   ],
   "source": [
    "loan_master.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "It can be observed that longer the duration of loan, higher is the probabiity of defaulting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_categories(df, arr):\n",
    "    plt.figure(figsize=(15,20))\n",
    "    rows = int(len(arr)/2)\n",
    "    for idx, val in enumerate(arr, start=1):\n",
    "        plt.subplot(rows, 2, idx)\n",
    "        if val == 'purpose':\n",
    "            plt.xticks(rotation=90)\n",
    "        ax = sns.barplot(x=val, y = 'loan_status', data=loan_master)\n",
    "#         showLabels(ax)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categories(loan_master, ['loan_amnt', 'annual_inc', 'term', 'int_rate', 'grade', 'purpose', 'emp_length', 'verification_status', 'home_ownership', 'pub_rec_bankruptcies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "* **Default rate vs loan_amnt:** If the loan amount is increasing, the probability of defaulting also increases. Higher loan amounts, higher the probability to default\n",
    "* **Default rate vs annual_inc:** The probability to default is higher in lower income groups and lower in higher income groups.\n",
    "* **Default rate vs term:** The longer the duration of loan, higher is the probabiity of defaulting\n",
    "* **Default rate vs int_rate:** The probability to default rises proportionately with the rate of interest. For loans with higher interest rate, the defaullt rate observed is higher\n",
    "* **Default rate vs grade:** For loans with Grade 'A', the default rate is least and it increases proportionately with maximum value for grade 'G'. So lower grade quality loans are more prone to default as compared to those of higher grade.\n",
    "* **Default rate vs purpose:** The probability of defaulting varies considerably with the purpose of applying for a loan.\n",
    "* **Default rate vs emp_length:** There is a marginal difference in the probability of defaulting with respect to the employment length of the loan taker. However it can be observed that those with 10 years of employment length tend to default more as compared to those with lesser years of employment length.\n",
    "* **Default rate vs verification_status:** It appears that for Verified loans the proability of defaulting is higher as compared to Not verified loans.\n",
    "* **Default rate vs home_ownership:** The probaility of defaulting is highest for 'Other'. However there is just a marginal impact of various home ownership statuses on the default rate.\n",
    "* **Default rate vs pub_rec_bankruptcies:** It can be observed that for loan takers with higher record of bankruptcies in the past, the probability of defaulting increases. Those with no bankruptcy records have a very small chance of defaulting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we can observe few factors and patterns which result in loan default "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us analyze Default rate by grouping it over loan characteritc variables and demographic variables to understand and confirm our observations of univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to observe Default Rate over 2 varibales\n",
    "def bar_plot(cat_var1, cat_var2):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    if cat_var1 == 'purpose':\n",
    "            plt.xticks(rotation=90)\n",
    "    sns.barplot(x=cat_var1, y='loan_status', hue=cat_var2, data=loan_master)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('annual_inc','loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>For higher loan amounts the proability to default is higher. However, loan takers with higher annual incomes, even if they have applied for higher loan amounts have lesser probability of defaulting as compared to the ones with medium or lower salaries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('pub_rec_bankruptcies','loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br> Loan takers with loan amounts less than 5000 have lesser chances of defaulting even if they have been bankrupt before. The default rate is directly proportinal to the loan amount and bankruptcy values. Increae in either of the two will increase the tendancy to default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('verification_status','loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Most of the loans of higher values were verified by Lending Club. However, the default rate is higher for verified and source verified loans. From the previous observations, it can be said that the probability to default depends more on the loan amount and annual income and verification status doesn't contribute much to the driving factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('term','loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Most of the loans defaulted have been of a higher duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('term','grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Default rates are higher for loans of lower grades of longer durations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('purpose','term')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Small business has been the most risky loan cateogry. Maximum default rate is observed for it over longer as well as shorter durations.\n",
    "For al other categories, longer duration have defaulted more as compared to shorter duration ones. Educational loans of longer duration have also resulted in large defaults followed by house, vacation and debt-consolidation loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('int_rate','loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Loans of any values of higher interest rates have defaulted most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vT5Kk1qbNLfV04o9ObNLjLfjWgiY9Xi7mz59Pt27dOOGEE/J6nr59+/Lwww/zgx/8oF77pk2bmDx5MuXl5UQEQ4YMYfTo0axevRqAJUuWcPLJJ7N582a2b9/OwoULufXWW/NaqyRJrYFX0JrZXXfdxdChQykpKeG2224DqpdEOuussxg8eDBHH3107QoAEydOZODAgZSUlDB+/Ph6x1m1ahXTp0/nhz/8IaWlpfzud78Dqq92nXDCCXzxi1+svdq1detWvv71r3PssccyaNCg2qtYq1at4sgjj+Tqq6/mqKOOYtSoUezYsWOvmvv160dJSQnt2tX/1+XZZ59l5MiR9OjRgwMPPJCRI0fyzDPP0KFDB3bs2MHu3bv58MMPKSoqYtKkSUyZMqVp/2FKktRKtbkraIU0d+5cVqxYwcKFC0kpMXr0aJ5//nk2bNhAnz59+NWvfgVUr5O5adMmZs2axfLly4kI3n///XrH6tevH9dccw3dunWrDW8/+9nPePvtt/n973/P8uXLGT16NOeddx6dO3dm1qxZ7L///rz77rsMHz6c0aNHA7BixQoef/xxfvrTn/LNb36TJ598kosvvrhR32ft2rUcfvjhtdvFxcWsXbuWCy+8kL59+3LsscdyySWXsHLlSlJKHHPMMU3wT1GSpNbPgNaM5s6dy9y5c2uDytatW1mxYgUnn3wy48eP58Ybb+Tss8/m5JNPpqqqis6dO3PVVVdx1llncfbZZzfqHOeeey7t2rVj4MCBvPPOOwCklLj55pt5/vnnadeuHWvXrq3d179/f0pLSwEYMmQIq1atavT3aWiZsIgA4J577qltO+ecc/jJT37C3/3d3/HHP/6RkSNHcvXVVzf6PJIktTVOcTajlBI33XQTFRUVVFRUsHLlSq688kq+/OUvs3jxYgYNGsRNN93ElClTaN++PQsXLmTs2LE89dRT9RYc/zSdOnWqdz6Axx57jA0bNrB48WIqKio4+OCD2blz5179i4qKqKqqavT3KS4urr3fDGDNmjX06dOnXp/Zs2dTVlbGtm3bWLp0Kb/85S959NFH2b59e6PPI0lSW2NAa0annXYaDz30EFu3bgWqpwjXr1/PunXr6NKlCxdffDHjx4/n3//939m6dSubN2/mzDPP5J577qGiomKv43Xv3p0tW7bs87ybN2+md+/edOjQgXnz5vHmm2822feZO3cu7733Hu+99x5z587ltNNOq92/a9cu7r33Xm644Qa2b99ee3Xt43vTJElSw5zibEajRo1i2bJlHH/88UD16zf+6Z/+iZUrV3LDDTfQrl07OnTowP3338+WLVsYM2YMO3fuJKXED3/4w72Od84553Deeecxe/ZsfvSjH33ieS+66CLOOeccysrKKC0t5Ygjjsip7kWLFvGNb3yD9957j3/913/ltttu49VXX6VHjx7ceuutDB06FIBJkybRo0eP2nH33Xcfl112GV26dKGkpISUEoMGDeLMM8/kgAMOyKkGSZLakmjoPqKWqqysLJWXl9drW7ZsGUceeWSBKtLn5d+fpE/z1pRBhS6hReg76ZVCl6AGRMTilFJZQ/uc4pQkScoYA5okSVLGGNAkSZIypk0EtNZ0n11b4t+bJKmtavUBrXPnzmzcuNH/2LcwKSU2btxI586dC12KJEnNrtW/ZqO4uJg1a9awYcOGQpeiHHXu3Jni4uJClyFJUrNr9QGtQ4cO9O/fv9BlSJIkNVqrn+KUJElqaQxokiRJGWNAkyRJyhgDmiRJUsYY0CRJkjLGgCZJkpQxBjRJkqSMMaBJkiRljAFNkiQpYwxokiRJGZP3gBYRp0fE6xGxMiImNrD/oohYUvPnhYgYXGffqoh4JSIqIqI837VKkiRlQV7X4oyIIuA+YCSwBlgUEU+nlF6r0+3PwFdTSu9FxBnAA8BxdfafmlJ6N591SpIkZUm+r6ANA1amlN5IKX0IPAGMqdshpfRCSum9ms2XgOI81yRJkpRp+Q5ohwGr62yvqWn7JFcCv66znYC5EbE4IsY1NCAixkVEeUSUb9iw4XMXLEmSVGh5neIEooG21GDHiFOpDmgn1Wk+MaW0LiJ6A89FxPKU0vP1DpbSA1RPi1JWVtbgsSVJklqSfF9BWwMcXme7GFi3Z6eIKAEeBMaklDZ+3J5SWlfzcz0wi+opU0mSpFYt3wFtETAgIvpHREfgAuDpuh0ioi/wL8AlKaX/qNPeNSK6f/wZGAUszXO9kiRJBZfXKc6UUlVEXAs8CxQBD6WUXo2Ia2r2TwcmAQcB/xgRAFUppTLgYGBWTVt74OcppWfyWa8kSVIW5PseNFJKc4A5e7RNr/P5KuCqBsa9AQzes12SJKm1cyUBSZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIypn2hC5AkZd+ECROorKzkkEMOYerUqYUuR2r1DGiSpH2qrKxk7dq1hS5DajOc4pQkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxrU4JSljhtzwSKFL2Ev3d7dQBLz17pZM1Tere6ErkPLDK2iSJEkZY0CTJEnKGAOaJElSxngPmtqkCRMmUFlZySGHHMLUqVMLXY4kSfUY0NQmVVZWsnbt2kKXIUlSg5zilCRJyhgDmiRJUsYY0CRJkjLGgCZJkpQxBjRJkqSMyXtAi4jTI+L1iFgZERMb2H9RRCyp+fNCRAxu7FhJkqTWKK8BLSKKgPuAM4CBwIURMXCPbn8GvppSKgHuAB7IYawkSVKrk+8raMOAlSmlN1JKHwJPAGPqdkgpvZBSeq9m8yWguLFjJUmSWqN8B7TDgNV1ttfUtH2SK4Fff8axkqQ82d2xKx912p/dHbsWuhSpTcj3SgLRQFtqsGPEqVQHtJNyGRsR44BxAH379v1sVUqSPtW2AaMKXYLUpuT7Ctoa4PA628XAuj07RUQJ8CAwJqW0MZexKaUHUkplKaWyXr16NVnhkiRJhZLvgLYIGBAR/SOiI3AB8HTdDhHRF/gX4JKU0n/kMlaSJKk1yusUZ0qpKiKuBZ4FioCHUkqvRsQ1NfunA5OAg4B/jAiAqporYg2OzWe9yp+3pgwqdAn1VG3qAbSnatObmaqt76RXCl2CJCkDGh3QIuJEoCKltC0iLgaOBe5NKb35aeNSSnOAOXu0Ta/z+SrgqsaOlSRJau1ymeK8H9he8yLZCcCbwCN5qUqSJKkNyyWgVaWUEtXvIrs3pXQv0D0/ZUmSJLVdudyDtiUibgIuBkbUvOm/Q37KkiRJartyuYJ2PvAX4MqUUiXVL429Ky9VSZIktWGNvoJWE8r+oc72W3gPmiRJUpPL5SnOLfznm/w7Uj29uTWl9IV8FCZJktRW5XIFrd4DARFxLtULmkuSJKkJfeaVBFJKTwFfa7pSJEmSBLlNcf73OpvtgDI+YeFzSZIkfXa5vGbjnDqfq4BVVL8TTZIkSU0ol4D2YEppQd2GmuWf1jdtSZIkSW1bLveg/aiRbVLm9ey8m4P3q6Jn592FLkWSpL3s8wpaRBwPnAD0ioi/qbNrf6AoX4VJ+TS+5P1ClyBJ0idqzBRnR6BbTd+6r9r4ADgvH0VJkiS1ZfsMaCml3wK/jYiHU0pvNkNNkiRJbVouDwlsj4i7gKOAzh83ppR8F5okSVITyuUhgceA5UB/YDLVr9lYlIeaJEmS2rRcAtpBKaWfAbtSSr9NKV0BDM9TXZIkSW1WLlOcu2p+vh0RZwHrgOKmL0mSJKltyyWgfS8ivgB8l+r3n+0PfDsfRUmSJLVluQS091JKm4HNwKlQu5KAJEmSmpArCUiSJGWMKwlIkiRljCsJSJIkZcxnWkkgItoB3VJKH+S7QEmSpLYml3vQ/k9E7B8RXYHXgNcj4oY81SVJktRm5RLQBtZcMTsXmAP0BS7JR1GSJEltWS4BrUNEdKA6oM1OKe0CUl6qkiRJasNyCWg/oXr9za7A8xHxX6h+UECSJElNqNEBLaU0LaV0WErpzJRSAt6i5oW1ABFxWT4KlCRJamtyuYJWT6pWVafp+iaoR5Ikqc37zAGtAdGEx5IkSWqzmjKg+cCAJElSE/AKmiRJUsY0ZUBb0ITHkiRJarMasxYnABHRCRgL9Ks7LqU0pebntU1dnCRJUlvU6IAGzAY2A4uBv+SnHEmSJOUS0IpTSqfnrRJJkiQBud2D9kJEDMpbJZIkSQJyu4J2EnB5RPyZ6inOoPp9tSV5qUySJKmNyiWgnfFZThARpwP3AkXAgymlO/fYfwTwf4FjgVtSSj+os28VsAX4CKhKKZV9lhokSZJakkYHtJTSmwAR0Rvo3JgxEVEE3AeMBNYAiyLi6ZTSa3W6bQKuA879hMOcmlJ6t7F1SpIktXSNvgctIkZHxArgz8BvgVXAr/cxbBiwMqX0RkrpQ+AJYEzdDiml9SmlRcCuXAqXJElqrXJ5SOAOYDjwHyml/sDX2ffLaQ8DVtfZXlPT1lgJmBsRiyNiXEMdImJcRJRHRPmGDRtyOLQkSVI25RLQdqWUNgLtIqJdSmkeULqPMQ0t/5TLmp0nppSOpfr+t/8dESP2OlhKD6SUylJKZb169crh0JIkSdmUy0MC70dEN+B3wGMRsR6o2seYNcDhdbaLgXWNPWFKaV3Nz/URMYvqKdPnc6hZkiSpxcnlCtoYYAfwbeAZ4E/AOfsYswgYEBH9I6IjcAHwdGNOFhFdI6L7x5+BUcDSHOqVJElqkXJ5inNbRBwMDAU2Ar+umfL8tDFVEXEt8CzVr9l4KKX0akRcU7N/ekQcApQD+wO7I+LbwECgJzArIj6u8+cppWdy/YKSJEktTS6LpX8TuAuYT/W9ZT+KiBtSSjM/bVxKaQ4wZ4+26XU+V1I99bmnD4DBja1PkiSptcjlHrRbgKEppfUAEdEL+A3wqQFNkiRJuckloLX7OJzV2Ehu97BJkiQxYcIEKisrOeSQQ5g6dWqhy8mkXALaMxHxLPB4zfb57DF1KUmStC+VlZWsXbu20GVkWi4PCdwQEWOBE6m+B+2BlNKsvFUmSZLURuVyBY2U0pPAk3mqRZIkSTQioEXEFhp++38AKaW0f5NXJUmS1IbtM6CllLo3RyGSJEmq5lOYkiRJGZPTPWjKPh9dliSp5TOgtTI+uixJUsvnFKckSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYxPcX4OQ254pNAl7KX7u1soAt56d0um6pvl644lSWo0r6BJkiRljAFNkiQpYwxokiRJGWNAkyRJyhgDmiRJUsb4FGcrs7tj13o/JUlSy2NAa2W2DRhV6BIkSdLn5BSnJElSxhjQJEmSMsaAJkmSlDHegyZJUit34o9OLHQJ9XR8vyPtaMfq91dnrrYF31pQ6BIAr6BJkiRljgFNkiQpYwxokiRJGWNAkyRJyhgDmiRJUsYY0CRJkjLGgCZJkpQxBjRJkqSMMaBJkiRljAFNkiQpYwxokiRJGWNAkyRJypi8B7SIOD0iXo+IlRExsYH9R0TEixHxl4gYn8tYSZKk1iivAS0iioD7gDOAgcCFETFwj26bgOuAH3yGsZIkSa1Ovq+gDQNWppTeSCl9CDwBjKnbIaW0PqW0CNiV61hJkqTWKN8B7TBgdZ3tNTVtTTY2IsZFRHlElG/YsOEzFypJkpQV+Q5o0UBbasqxKaUHUkplKaWyXr165VScJElSFuU7oK0BDq+zXQysa4axkiRJLVa+A9oiYEBE9I+IjsAFwNPNMFaSJKnFap/Pg6eUqiLiWuBZoAh4KKX0akRcU7N/ekQcApQD+wO7I+LbwMCU0gcNjc1nvZIkSVmQ14AGkFKaA8zZo216nc+VVE9fNmqsJElSa+dKApIkSRljQJMkScqYvE9xSpIk1ZW6JHazm9SlsW/eansMaJIkqVntOnHPxYO0J6c4JUmSMsaAJkmSlDEGNEmSpIwxoEmSJGWMAU2SJCljDGiSJEkZY0CTJEnKGAOaJElSxhjQJEmSMsaAJkmSlDEGNEmSpIwxoEmSJGWMAU2SJCljDGiSJEkZY0CTJEnKGAOaJElSxhjQJEmSMsaAJkmSlDEGNEmSpIwxoEmSJGWMAU2SJClj2he6AEnZNWHCBCorKznkkEOYOnVqocuRpDbDgCbpE1VWVrJ27dpClyFJbY5TnJIkSRljQJMkScoYA5okSVLGeA+alCEn/ujEQpdQT8f3O9KOdqx+f3XmalvwrQWFLkGS8sYraJIkSRljQJMkScoYA5okSVLGGNAkSZIyxocEJH2i1CWxm92kLqnQpUhSm2JAk/SJdp24q9AlSFKb5BSnJElSxhjQJEmSMibvAS0iTo+I1yNiZURMbGB/RMS0mv1LIuLYOvtWRcQrEVEREeX5rlWSJCkL8noPWkQUAfcBI4E1wKKIeDql9FqdbmcAA2r+HAfcX/PzY6emlN7NZ52SJElZku8raMOAlSmlN1JKHwJPAGP26DMGeCRVewk4ICIOzXNdkiRJmZXvgHYYsLrO9pqatsb2ScDciFgcEePyVqUkSVKG5Ps1G9FA254vVPq0PiemlNZFRG/guYhYnlJ6vt7g6uA2DqBv376ft15JkqSCy/cVtDXA4XW2i4F1je2TUvr453pgFtVTpvWklB5IKZWllMp69erVhKVLkiQVRr4D2iJgQET0j4iOwAXA03v0eRq4tOZpzuHA5pTS2xHRNSK6A0REV2AUsDTP9UqSJBVcXqc4U0pVEXEt8CxQBDyUUno1Iq6p2T8dmAOcCawEtgP/q2b4wcCsiPi4zp+nlJ7JZ72SJElZkPelnlJKc6gOYXXbptf5nID/3cC4N4DB+a5PkiQpa1xJQJIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjDGgSZIkZYwBTZIkKWMMaJIkSRljQJMkScoYA5okSVLGGNAkSZIyxoAmSZKUMQY0SZKkjMl7QIuI0yPi9YhYGRETG9gfETGtZv+SiDi2sWMlSZJao7wGtIgoAu4DzgAGAhdGxMA9up0BDKj5Mw64P4exkiRJrU6+r6ANA1amlN5IKX0IPAGM2aPPGOCRVO0l4ICIOLSRYyVJklqdfAe0w4DVdbbX1LQ1pk9jxkqSJLU67fN8/GigLTWyT2PGEhHjqJ4aBdgaEa/nVKGaxX8pdAEN6wm8W+gi9NnEdQ39ilBb4+8WNbVm/t3yif8K5zugrQEOr7NdDKxrZJ+OjRhLSukB4IGmKFZtS0SUp5TKCl2HpNbF3y1qCvme4lwEDIiI/hHREbgAeHqPPk8Dl9Y8zTkc2JxSeruRYyVJklqdvF5BSylVRcS1wLNAEfBQSunViLimZv90YA5wJrAS2A78r08bm896JUmSsiBS2uu2LqlNiIhxNVPkktRk/N2ipmBAkyRJyhiXepIkScoYA5pavYjoHBELI+KPEfFqREyus+9bNcuJvRoRUwtZp6SWJyIOiIiZEbE8IpZFxPER0SMinouIFTU/Dyx0nWp5nOJUqxcRAXRNKW2NiA7A74Hrgf2AW4CzUkp/iYjeKaX1haxVUssSETOA36WUHqx540AX4GZgU0rpzpp1pA9MKd1Y0ELV4hjQ1KZERBeqA9pfAd8FHkgp/aawVUlqiSJif+CPwBdTnf+Y1rww/ZSU0ts1SxfOTyl9pVB1qmVyilNtQkQURUQFsB54LqX0MvBl4OSIeDkifhsRQwtapKSW5ovABuD/RsQfIuLBiOgKHFzzPk9qfvYuZJFqmQxoahNSSh+llEqpXpFiWEQcTfV7AA8EhgM3AL+smQ6VpMZoDxwL3J9SOgbYBkwsbElqLQxoalNSSu8D84HTqV5m7F9StYXAbqrX0JOkxlgDrKm5Ig8wk+rA9k7N1CY1P723VTkzoKnVi4heEXFAzef9gP8GLAeeAr5W0/5lqtd/dYFjSY2SUqoEVkfEx/eXfR14jeplCS+rabsMmF2A8tTC5XuxdCkLDgVmREQR1f9T8suU0r/VPHH1UEQsBT4ELks+NSMpN98CHqv5ffIG1csVtqP6lokrgbeA/1HA+tRC+RSnJElSxjjFKUmSlDEGNEmSpIwxoEmSJGWMAU2SJCljDGiSJEkZY0CT1CZFxAER8deFrkOSGmJAk9RWHQA0OqBFNX9nSmoW/rKR1FbdCfzXiKiIiLsi4oaIWBQRSyJiMkBE9IuIZRHxj8C/AydHxPKaRbGXRsRjEfHfImJBRKyIiGEF/UaSWg0DmqS2aiLwp5RSKfAcMAAYBpQCQyJiRE2/rwCP1CyG/SbwJeBeoAQ4AvifwEnAeODmZqxfUivmUk+SBKNq/vyhZrsb1YHtLeDNlNJLdfr+OaX0CkBEvAr8v5RSiohXgH7NV7Kk1syAJkkQwP9JKf2kXmNEP2DbHn3/Uufz7jrbu/F3qqQm4hSnpLZqC9C95vOzwBUR0Q0gIg6LiN4Fq0xSm+f/7Ulqk1JKG2tu7l8K/Br4OfBiRABsBS4GPipgiZLasEgpFboGSZIk1eEUpyRJUsYY0CRJkjLGgCZJkpQxBjRJkqSMMaBJkiRljAFNkiQpYwxokiRJGWNAkyRJypj/D/1SGiUQvPu2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('term','int_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "<br>Loans of longer as well as shorter durations of higher interest rates have defaulted more as compared to those with lesser interest rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}