test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lending Club Case Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style \n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading loan data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan=pd.read_csv('loan.csv', encoding='utf-8', low_memory=False)\n",
    "loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "loan.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identifying the percentage of missing values in rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                  0.00\n",
       "member_id                           0.00\n",
       "loan_amnt                           0.00\n",
       "funded_amnt                         0.00\n",
       "funded_amnt_inv                     0.00\n",
       "term                                0.00\n",
       "int_rate                            0.00\n",
       "installment                         0.00\n",
       "grade                               0.00\n",
       "sub_grade                           0.00\n",
       "emp_title                           6.19\n",
       "emp_length                          2.71\n",
       "home_ownership                      0.00\n",
       "annual_inc                          0.00\n",
       "verification_status                 0.00\n",
       "issue_d                             0.00\n",
       "loan_status                         0.00\n",
       "pymnt_plan                          0.00\n",
       "url                                 0.00\n",
       "desc                               32.58\n",
       "purpose                             0.00\n",
       "title                               0.03\n",
       "zip_code                            0.00\n",
       "addr_state                          0.00\n",
       "dti                                 0.00\n",
       "delinq_2yrs                         0.00\n",
       "earliest_cr_line                    0.00\n",
       "inq_last_6mths                      0.00\n",
       "mths_since_last_delinq             64.66\n",
       "mths_since_last_record             92.99\n",
       "                                   ...  \n",
       "mo_sin_old_rev_tl_op              100.00\n",
       "mo_sin_rcnt_rev_tl_op             100.00\n",
       "mo_sin_rcnt_tl                    100.00\n",
       "mort_acc                          100.00\n",
       "mths_since_recent_bc              100.00\n",
       "mths_since_recent_bc_dlq          100.00\n",
       "mths_since_recent_inq             100.00\n",
       "mths_since_recent_revol_delinq    100.00\n",
       "num_accts_ever_120_pd             100.00\n",
       "num_actv_bc_tl                    100.00\n",
       "num_actv_rev_tl                   100.00\n",
       "num_bc_sats                       100.00\n",
       "num_bc_tl                         100.00\n",
       "num_il_tl                         100.00\n",
       "num_op_rev_tl                     100.00\n",
       "num_rev_accts                     100.00\n",
       "num_rev_tl_bal_gt_0               100.00\n",
       "num_sats                          100.00\n",
       "num_tl_120dpd_2m                  100.00\n",
       "num_tl_30dpd                      100.00\n",
       "num_tl_90g_dpd_24m                100.00\n",
       "num_tl_op_past_12m                100.00\n",
       "pct_tl_nvr_dlq                    100.00\n",
       "percent_bc_gt_75                  100.00\n",
       "pub_rec_bankruptcies                1.75\n",
       "tax_liens                           0.10\n",
       "tot_hi_cred_lim                   100.00\n",
       "total_bal_ex_mort                 100.00\n",
       "total_bc_limit                    100.00\n",
       "total_il_high_credit_limit        100.00\n",
       "Length: 111, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cols = round(100*((loan.isnull().sum())/(len(loan.index))),2)\n",
    "missing_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the list of columns having more than 50% values missing.\n",
    "It's better to drop these rows as it won't contribute much in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mths_since_last_delinq',\n",
       " 'mths_since_last_record',\n",
       " 'next_pymnt_d',\n",
       " 'mths_since_last_major_derog',\n",
       " 'annual_inc_joint',\n",
       " 'dti_joint',\n",
       " 'verification_status_joint',\n",
       " 'tot_coll_amt',\n",
       " 'tot_cur_bal',\n",
       " 'open_acc_6m',\n",
       " 'open_il_6m',\n",
       " 'open_il_12m',\n",
       " 'open_il_24m',\n",
       " 'mths_since_rcnt_il',\n",
       " 'total_bal_il',\n",
       " 'il_util',\n",
       " 'open_rv_12m',\n",
       " 'open_rv_24m',\n",
       " 'max_bal_bc',\n",
       " 'all_util',\n",
       " 'total_rev_hi_lim',\n",
       " 'inq_fi',\n",
       " 'total_cu_tl',\n",
       " 'inq_last_12m',\n",
       " 'acc_open_past_24mths',\n",
       " 'avg_cur_bal',\n",
       " 'bc_open_to_buy',\n",
       " 'bc_util',\n",
       " 'mo_sin_old_il_acct',\n",
       " 'mo_sin_old_rev_tl_op',\n",
       " 'mo_sin_rcnt_rev_tl_op',\n",
       " 'mo_sin_rcnt_tl',\n",
       " 'mort_acc',\n",
       " 'mths_since_recent_bc',\n",
       " 'mths_since_recent_bc_dlq',\n",
       " 'mths_since_recent_inq',\n",
       " 'mths_since_recent_revol_delinq',\n",
       " 'num_accts_ever_120_pd',\n",
       " 'num_actv_bc_tl',\n",
       " 'num_actv_rev_tl',\n",
       " 'num_bc_sats',\n",
       " 'num_bc_tl',\n",
       " 'num_il_tl',\n",
       " 'num_op_rev_tl',\n",
       " 'num_rev_accts',\n",
       " 'num_rev_tl_bal_gt_0',\n",
       " 'num_sats',\n",
       " 'num_tl_120dpd_2m',\n",
       " 'num_tl_30dpd',\n",
       " 'num_tl_90g_dpd_24m',\n",
       " 'num_tl_op_past_12m',\n",
       " 'pct_tl_nvr_dlq',\n",
       " 'percent_bc_gt_75',\n",
       " 'tot_hi_cred_lim',\n",
       " 'total_bal_ex_mort',\n",
       " 'total_bc_limit',\n",
       " 'total_il_high_credit_limit']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = list(missing_cols[missing_cols > 50].index)\n",
    "cols_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 111)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 111 columns in total and after dropping rows, we are left with 54 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 54)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.drop(cols_to_drop, axis=1, inplace = True)\n",
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move and check further for missing values. We can see below there are still 11 rows with missing values, let's clean them up step by step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_title                      6.19\n",
       "emp_length                     2.71\n",
       "desc                          32.58\n",
       "title                          0.03\n",
       "revol_util                     0.13\n",
       "last_pymnt_d                   0.18\n",
       "last_credit_pull_d             0.01\n",
       "collections_12_mths_ex_med     0.14\n",
       "chargeoff_within_12_mths       0.14\n",
       "pub_rec_bankruptcies           1.75\n",
       "tax_liens                      0.10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cols=round(100*((loan.isnull().sum())/(len(loan.index))),2)\n",
    "missing_cols[missing_cols > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see desc column has considerably more null values.\n",
    "Desc column has Loan description provided by the borrower. It seems this column wont contribute in our analysis and this information can be deduced by other columns such as Purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 53)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.drop(columns=['desc'], inplace = True)\n",
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move ahead and check the nature of all remaining columns having missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>title</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Computer</td>\n",
       "      <td>83.70%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>May-16</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>1</th>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>bike</td>\n",
       "      <td>9.40%</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>Sep-13</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>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>98.50%</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>May-16</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>3</th>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>personel</td>\n",
       "      <td>21%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>Apr-16</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>4</th>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Personal</td>\n",
       "      <td>53.90%</td>\n",
       "      <td>May-16</td>\n",
       "      <td>May-16</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>5</th>\n",
       "      <td>Veolia Transportaton</td>\n",
       "      <td>3 years</td>\n",
       "      <td>My wedding loan I promise to pay back</td>\n",
       "      <td>28.30%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>Jan-16</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>6</th>\n",
       "      <td>Southern Star Photography</td>\n",
       "      <td>8 years</td>\n",
       "      <td>Loan</td>\n",
       "      <td>85.60%</td>\n",
       "      <td>May-16</td>\n",
       "      <td>May-16</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>7</th>\n",
       "      <td>MKC Accounting</td>\n",
       "      <td>9 years</td>\n",
       "      <td>Car Downpayment</td>\n",
       "      <td>87.50%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>Dec-14</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>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Expand Business &amp; Buy Debt Portfolio</td>\n",
       "      <td>32.60%</td>\n",
       "      <td>Apr-12</td>\n",
       "      <td>Aug-12</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>9</th>\n",
       "      <td>Starbucks</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>Building my credit history.</td>\n",
       "      <td>36.50%</td>\n",
       "      <td>Nov-12</td>\n",
       "      <td>Mar-13</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>10</th>\n",
       "      <td>Southwest Rural metro</td>\n",
       "      <td>5 years</td>\n",
       "      <td>High intrest Consolidation</td>\n",
       "      <td>20.60%</td>\n",
       "      <td>Jun-13</td>\n",
       "      <td>Dec-15</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>11</th>\n",
       "      <td>UCLA</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Consolidation</td>\n",
       "      <td>67.10%</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>Aug-13</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>12</th>\n",
       "      <td>Va. Dept of Conservation/Recreation</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>freedom</td>\n",
       "      <td>91.70%</td>\n",
       "      <td>Jul-12</td>\n",
       "      <td>Nov-12</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>13</th>\n",
       "      <td>Target</td>\n",
       "      <td>3 years</td>\n",
       "      <td>citicard fund</td>\n",
       "      <td>43.10%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>May-16</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>14</th>\n",
       "      <td>SFMTA</td>\n",
       "      <td>3 years</td>\n",
       "      <td>Other Loan</td>\n",
       "      <td>55.50%</td>\n",
       "      <td>Oct-13</td>\n",
       "      <td>Mar-14</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>15</th>\n",
       "      <td>Internal revenue Service</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>Debt Consolidation Loan</td>\n",
       "      <td>81.50%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>May-16</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>16</th>\n",
       "      <td>Chin's Restaurant</td>\n",
       "      <td>4 years</td>\n",
       "      <td>Home</td>\n",
       "      <td>70.20%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>Apr-15</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>17</th>\n",
       "      <td>Duracell</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Holiday</td>\n",
       "      <td>16%</td>\n",
       "      <td>May-13</td>\n",
       "      <td>May-14</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>18</th>\n",
       "      <td>Connection Inspection</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Medical</td>\n",
       "      <td>37.73%</td>\n",
       "      <td>Feb-15</td>\n",
       "      <td>Jul-15</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>19</th>\n",
       "      <td>Network Interpreting Service</td>\n",
       "      <td>6 years</td>\n",
       "      <td>lowerratemeanseasiertogetoutofdebt!</td>\n",
       "      <td>23.10%</td>\n",
       "      <td>Jul-12</td>\n",
       "      <td>Feb-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              emp_title emp_length  \\\n",
       "0                                   NaN  10+ years   \n",
       "1                                 Ryder   < 1 year   \n",
       "2                                   NaN  10+ years   \n",
       "3                   AIR RESOURCES BOARD  10+ years   \n",
       "4              University Medical Group     1 year   \n",
       "5                  Veolia Transportaton    3 years   \n",
       "6             Southern Star Photography    8 years   \n",
       "7                       MKC Accounting     9 years   \n",
       "8                                   NaN    4 years   \n",
       "9                             Starbucks   < 1 year   \n",
       "10                Southwest Rural metro    5 years   \n",
       "11                                 UCLA  10+ years   \n",
       "12  Va. Dept of Conservation/Recreation   < 1 year   \n",
       "13                               Target    3 years   \n",
       "14                                SFMTA    3 years   \n",
       "15             Internal revenue Service   < 1 year   \n",
       "16                    Chin's Restaurant    4 years   \n",
       "17                             Duracell  10+ years   \n",
       "18                Connection Inspection     1 year   \n",
       "19         Network Interpreting Service    6 years   \n",
       "\n",
       "                                    title revol_util last_pymnt_d  \\\n",
       "0                                Computer     83.70%       Jan-15   \n",
       "1                                    bike      9.40%       Apr-13   \n",
       "2                    real estate business     98.50%       Jun-14   \n",
       "3                                personel        21%       Jan-15   \n",
       "4                                Personal     53.90%       May-16   \n",
       "5   My wedding loan I promise to pay back     28.30%       Jan-15   \n",
       "6                                    Loan     85.60%       May-16   \n",
       "7                         Car Downpayment     87.50%       Jan-15   \n",
       "8    Expand Business & Buy Debt Portfolio     32.60%       Apr-12   \n",
       "9             Building my credit history.     36.50%       Nov-12   \n",
       "10             High intrest Consolidation     20.60%       Jun-13   \n",
       "11                          Consolidation     67.10%       Sep-13   \n",
       "12                                freedom     91.70%       Jul-12   \n",
       "13                          citicard fund     43.10%       Jan-15   \n",
       "14                             Other Loan     55.50%       Oct-13   \n",
       "15                Debt Consolidation Loan     81.50%       Jan-15   \n",
       "16                                   Home     70.20%       Jan-15   \n",
       "17                                Holiday        16%       May-13   \n",
       "18                                Medical     37.73%       Feb-15   \n",
       "19    lowerratemeanseasiertogetoutofdebt!     23.10%       Jul-12   \n",
       "\n",
       "   last_credit_pull_d  collections_12_mths_ex_med  chargeoff_within_12_mths  \\\n",
       "0              May-16                         0.0                       0.0   \n",
       "1              Sep-13                         0.0                       0.0   \n",
       "2              May-16                         0.0                       0.0   \n",
       "3              Apr-16                         0.0                       0.0   \n",
       "4              May-16                         0.0                       0.0   \n",
       "5              Jan-16                         0.0                       0.0   \n",
       "6              May-16                         0.0                       0.0   \n",
       "7              Dec-14                         0.0                       0.0   \n",
       "8              Aug-12                         0.0                       0.0   \n",
       "9              Mar-13                         0.0                       0.0   \n",
       "10             Dec-15                         0.0                       0.0   \n",
       "11             Aug-13                         0.0                       0.0   \n",
       "12             Nov-12                         0.0                       0.0   \n",
       "13             May-16                         0.0                       0.0   \n",
       "14             Mar-14                         0.0                       0.0   \n",
       "15             May-16                         0.0                       0.0   \n",
       "16             Apr-15                         0.0                       0.0   \n",
       "17             May-14                         0.0                       0.0   \n",
       "18             Jul-15                         0.0                       0.0   \n",
       "19             Feb-16                         0.0                       0.0   \n",
       "\n",
       "    pub_rec_bankruptcies  tax_liens  \n",
       "0                    0.0        0.0  \n",
       "1                    0.0        0.0  \n",
       "2                    0.0        0.0  \n",
       "3                    0.0        0.0  \n",
       "4                    0.0        0.0  \n",
       "5                    0.0        0.0  \n",
       "6                    0.0        0.0  \n",
       "7                    0.0        0.0  \n",
       "8                    0.0        0.0  \n",
       "9                    0.0        0.0  \n",
       "10                   0.0        0.0  \n",
       "11                   0.0        0.0  \n",
       "12                   0.0        0.0  \n",
       "13                   0.0        0.0  \n",
       "14                   0.0        0.0  \n",
       "15                   0.0        0.0  \n",
       "16                   0.0        0.0  \n",
       "17                   0.0        0.0  \n",
       "18                   0.0        0.0  \n",
       "19                   0.0        0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cols=round(100*((loan.isnull().sum())/(len(loan.index))),2)\n",
    "loan[list(missing_cols[missing_cols > 0].index)].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above result we can see that collections_12_mths_ex_med, chargeoff_within_12_mths, pub_rec_bankruptcies and tax_liens mostly contains values as '0.'. Let's see the unique values they have to understand their spread and contribution in our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collections_12_mths_ex_med info\n",
      "[ 0. nan]\n",
      "2\n",
      "\n",
      "chargeoff_within_12_mths info\n",
      "[ 0. nan]\n",
      "2\n",
      "\n",
      "pub_rec_bankruptcies info\n",
      "[ 0.  1.  2. nan]\n",
      "4\n",
      "\n",
      "tax_liens info\n",
      "[ 0. nan]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "print('collections_12_mths_ex_med info')\n",
    "print(loan['collections_12_mths_ex_med'].unique())\n",
    "print(len(loan['collections_12_mths_ex_med'].unique()))\n",
    "print()\n",
    "\n",
    "print('chargeoff_within_12_mths info')\n",
    "print(loan['chargeoff_within_12_mths'].unique())\n",
    "print(len(loan['chargeoff_within_12_mths'].unique()))\n",
    "print()\n",
    "\n",
    "print('pub_rec_bankruptcies info')\n",
    "print(loan['pub_rec_bankruptcies'].unique())\n",
    "print(len(loan['pub_rec_bankruptcies'].unique()))\n",
    "print()\n",
    "\n",
    "print('tax_liens info')\n",
    "print(loan['tax_liens'].unique())\n",
    "print(len(loan['tax_liens'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see collections_12_mths_ex_med, chargeoff_within_12_mths and tax_liens does not contain much information to contribute and it will be better to narrow down the data set by dropping them.pub_rec_bankruptcies can be relevant to our analysis so not dropping it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 50)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.drop(columns=['collections_12_mths_ex_med','chargeoff_within_12_mths','tax_liens'], inplace = True)\n",
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are left with 6 columns with missing values, notice there are only small percentage of missing values present in these columns. Let's move ahead and see how can we deal with missing values in these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_title               6.19\n",
       "emp_length              2.71\n",
       "title                   0.03\n",
       "revol_util              0.13\n",
       "last_pymnt_d            0.18\n",
       "last_credit_pull_d      0.01\n",
       "pub_rec_bankruptcies    1.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cols=round(100*((loan.isnull().sum())/(len(loan.index))),2)\n",
    "missing_cols[missing_cols > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we can see that only pub_rec_bankruptcies is of type float and rest are objects and contain information in string format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 7 columns):\n",
      "emp_title               37258 non-null object\n",
      "emp_length              38642 non-null object\n",
      "title                   39706 non-null object\n",
      "revol_util              39667 non-null object\n",
      "last_pymnt_d            39646 non-null object\n",
      "last_credit_pull_d      39715 non-null object\n",
      "pub_rec_bankruptcies    39020 non-null float64\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "loan[list(missing_cols[missing_cols > 0].index)].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>title</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>Computer</td>\n",
       "      <td>83.70%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>bike</td>\n",
       "      <td>9.40%</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>98.50%</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>personel</td>\n",
       "      <td>21%</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>Apr-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Personal</td>\n",
       "      <td>53.90%</td>\n",
       "      <td>May-16</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  emp_title emp_length                 title revol_util  \\\n",
       "0                       NaN  10+ years              Computer     83.70%   \n",
       "1                     Ryder   < 1 year                  bike      9.40%   \n",
       "2                       NaN  10+ years  real estate business     98.50%   \n",
       "3       AIR RESOURCES BOARD  10+ years              personel        21%   \n",
       "4  University Medical Group     1 year              Personal     53.90%   \n",
       "\n",
       "  last_pymnt_d last_credit_pull_d  pub_rec_bankruptcies  \n",
       "0       Jan-15             May-16                   0.0  \n",
       "1       Apr-13             Sep-13                   0.0  \n",
       "2       Jun-14             May-16                   0.0  \n",
       "3       Jan-15             Apr-16                   0.0  \n",
       "4       May-16             May-16                   0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[list(missing_cols[missing_cols > 0].index)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can either impute values or either remove missing values, it does not make sense to impute values in other columns as they are non numeric so we will go for removing null rows and also for pub_rec_bankruptcies as imputing values may lead to bias and we will try to avoid it. Also percentage of missing value is less so won't affect our dataset much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36433, 50)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan=loan[~loan.pub_rec_bankruptcies.isnull()]\n",
    "loan=loan[~loan.emp_title.isnull()]\n",
    "loan=loan[~loan.emp_length.isnull()]\n",
    "loan=loan[~loan.title.isnull()]\n",
    "loan=loan[~loan.revol_util.isnull()]\n",
    "loan=loan[~loan.last_pymnt_d.isnull()]\n",
    "loan=loan[~loan.last_credit_pull_d.isnull()]\n",
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, below we can see our data set is cleaned and it does not have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_cols=round(100*((loan.isnull().sum())/(len(loan.index))),2)\n",
    "missing_cols[missing_cols > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are left with 50 columns and we can understand the purpose of each column and try to find out whether they will be usefull in our analysis or not. Also we should check the nature of values they contain so to get a idea of their usefullness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "acc_now_delinq                 1\n",
       "application_type               1\n",
       "policy_code                    1\n",
       "initial_list_status            1\n",
       "delinq_amnt                    1\n",
       "pymnt_plan                     1\n",
       "term                           2\n",
       "pub_rec_bankruptcies           3\n",
       "loan_status                    3\n",
       "verification_status            3\n",
       "home_ownership                 4\n",
       "pub_rec                        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                      38\n",
       "addr_state                    49\n",
       "issue_d                       52\n",
       "total_acc                     79\n",
       "last_credit_pull_d            96\n",
       "last_pymnt_d                  97\n",
       "int_rate                     334\n",
       "earliest_cr_line             510\n",
       "zip_code                     806\n",
       "loan_amnt                    863\n",
       "funded_amnt                 1024\n",
       "out_prncp                   1064\n",
       "out_prncp_inv               1065\n",
       "revol_util                  1082\n",
       "total_rec_late_fee          1194\n",
       "collection_recovery_fee     2386\n",
       "dti                         2857\n",
       "recoveries                  3585\n",
       "annual_inc                  4924\n",
       "total_rec_prncp             7254\n",
       "funded_amnt_inv             7474\n",
       "installment                14367\n",
       "title                      17954\n",
       "revol_bal                  20790\n",
       "emp_title                  28207\n",
       "last_pymnt_amnt            32467\n",
       "total_rec_int              32523\n",
       "total_pymnt_inv            34524\n",
       "total_pymnt                34802\n",
       "url                        36433\n",
       "member_id                  36433\n",
       "id                         36433\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.nunique().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-We can see that there are some columns which has only single values, clearly these columns wont contribute in our analysis and can be dropped for cleare dataset. \n",
    "* acc_now_delinq                 \n",
    "* application_type\n",
    "* policy_code\n",
    "* initial_list_status\n",
    "* delinq_amnt\n",
    "* pymnt_plan\n",
    "\n",
    "-Also, by looking at the data we can see that some columns contain irrelevant information as per our analysis.  \n",
    "These columns are  \n",
    "* id (it's assigned for loan listing and we have one more column member_id which we can use for id column)\n",
    "* url (we dont need urls for our analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_drop=['acc_now_delinq', 'application_type', 'policy_code', 'initial_list_status', 'delinq_amnt', \n",
    "              'pymnt_plan', 'id', 'url']\n",
    "loan.drop(columns=cols_to_drop, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>emp_title</th>\n",
       "      <th>...</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>Ryder</td>\n",
       "      <td>...</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>AIR RESOURCES BOARD</td>\n",
       "      <td>...</td>\n",
       "      <td>12231.89</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2214.92</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Apr-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>University Medical Group</td>\n",
       "      <td>...</td>\n",
       "      <td>3513.33</td>\n",
       "      <td>2475.94</td>\n",
       "      <td>1037.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>May-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1311441</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90%</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Veolia Transportaton</td>\n",
       "      <td>...</td>\n",
       "      <td>5632.21</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>632.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>161.03</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1304742</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>170.08</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Southern Star Photography</td>\n",
       "      <td>...</td>\n",
       "      <td>10110.84</td>\n",
       "      <td>6985.61</td>\n",
       "      <td>3125.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>May-16</td>\n",
       "      <td>1313.76</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   member_id  loan_amnt  funded_amnt  funded_amnt_inv        term int_rate  \\\n",
       "1    1314167       2500         2500           2500.0   60 months   15.27%   \n",
       "3    1277178      10000        10000          10000.0   36 months   13.49%   \n",
       "4    1311748       3000         3000           3000.0   60 months   12.69%   \n",
       "5    1311441       5000         5000           5000.0   36 months    7.90%   \n",
       "6    1304742       7000         7000           7000.0   60 months   15.96%   \n",
       "\n",
       "   installment grade sub_grade                  emp_title  ...  \\\n",
       "1        59.83     C        C4                      Ryder  ...   \n",
       "3       339.31     C        C1        AIR RESOURCES BOARD  ...   \n",
       "4        67.79     B        B5   University Medical Group  ...   \n",
       "5       156.46     A        A4       Veolia Transportaton  ...   \n",
       "6       170.08     C        C5  Southern Star Photography  ...   \n",
       "\n",
       "  total_pymnt_inv total_rec_prncp  total_rec_int total_rec_late_fee  \\\n",
       "1         1008.71          456.46         435.17               0.00   \n",
       "3        12231.89        10000.00        2214.92              16.97   \n",
       "4         3513.33         2475.94        1037.39               0.00   \n",
       "5         5632.21         5000.00         632.21               0.00   \n",
       "6        10110.84         6985.61        3125.23               0.00   \n",
       "\n",
       "  recoveries collection_recovery_fee last_pymnt_d last_pymnt_amnt  \\\n",
       "1     117.08                    1.11       Apr-13          119.66   \n",
       "3       0.00                    0.00       Jan-15          357.48   \n",
       "4       0.00                    0.00       May-16           67.79   \n",
       "5       0.00                    0.00       Jan-15          161.03   \n",
       "6       0.00                    0.00       May-16         1313.76   \n",
       "\n",
       "  last_credit_pull_d pub_rec_bankruptcies  \n",
       "1             Sep-13                  0.0  \n",
       "3             Apr-16                  0.0  \n",
       "4             May-16                  0.0  \n",
       "5             Jan-16                  0.0  \n",
       "6             May-16                  0.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also one interesting observation here is that loan amount funded_amnt and funded_amt_invested have similar values. Information is redundant here. loan_amount is relevant to our analysis and funded_amnt_inv and funded_amnt can be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan.drop(columns = ['funded_amnt', 'funded_amnt_inv'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-Now lets move to convert the columns in to their specific for analysis.  \n",
    "* last_pymnt_d, last_credit_pull_d, issue_d, earliest_cr_line are of object type which needs to be converted in to date type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan.last_pymnt_d = pd.to_datetime(loan.last_pymnt_d, format='%b-%y')\n",
    "loan.last_credit_pull_d = pd.to_datetime(loan.last_credit_pull_d, format='%b-%y')\n",
    "loan.issue_d = pd.to_datetime(loan.issue_d, format='%b-%y')\n",
    "loan.earliest_cr_line = pd.to_datetime(loan.earliest_cr_line, format='%b-%y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-There are some columns which can be converted to numeric columns for analysis.\n",
    "* int-rate, term and revol-util are some probable columns as per our observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 36433 entries, 1 to 39680\n",
      "Data columns (total 3 columns):\n",
      "int_rate      36433 non-null object\n",
      "term          36433 non-null object\n",
      "revol_util    36433 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>term</th>\n",
       "      <th>revol_util</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27%</td>\n",
       "      <td>60 months</td>\n",
       "      <td>9.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69%</td>\n",
       "      <td>60 months</td>\n",
       "      <td>53.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>28.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96%</td>\n",
       "      <td>60 months</td>\n",
       "      <td>85.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.64%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>87.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.69%</td>\n",
       "      <td>60 months</td>\n",
       "      <td>36.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.65%</td>\n",
       "      <td>60 months</td>\n",
       "      <td>20.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.69%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>67.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.49%</td>\n",
       "      <td>36 months</td>\n",
       "      <td>91.70%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate        term revol_util\n",
       "1    15.27%   60 months      9.40%\n",
       "3    13.49%   36 months        21%\n",
       "4    12.69%   60 months     53.90%\n",
       "5     7.90%   36 months     28.30%\n",
       "6    15.96%   60 months     85.60%\n",
       "7    18.64%   36 months     87.50%\n",
       "9    12.69%   60 months     36.50%\n",
       "10   14.65%   60 months     20.60%\n",
       "11   12.69%   36 months     67.10%\n",
       "12   13.49%   36 months     91.70%"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(loan[['int_rate', 'term', 'revol_util']].info())\n",
    "loan[['int_rate', 'term', 'revol_util']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'int_rate', 'term' ,'emp_length'and 'revol_util' are object type which can be converted in to numeric type, let's convert them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    26377\n",
       " 60 months    10056\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making sure there are only 36 and 60 months as values in term column\n",
    "loan.term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking the nature of emp_length, this column can also be converted in to numeric by truncating year and \n",
    "#considering < 1 year as 0 and 10+ year as 10\n",
    "loan.emp_length.value_counts()\n",
    "\n",
    "# using map function on dataframe column is a good way to map emp_length values,let's create a dictionary for this purpose\n",
    "emp_length_dict = {\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",
    "    '10+ years' : 10\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan.int_rate=loan['int_rate'].str.replace('%','').astype('float')\n",
    "loan.term = loan['term'].str.replace(' months','').astype('float')\n",
    "loan.revol_util = loan['revol_util'].str.replace('%', '').astype('float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan.emp_length = loan['emp_length'].map(emp_length_dict).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 36433 entries, 1 to 39680\n",
      "Data columns (total 4 columns):\n",
      "int_rate      36433 non-null float64\n",
      "term          36433 non-null float64\n",
      "revol_util    36433 non-null float64\n",
      "emp_length    36433 non-null int32\n",
      "dtypes: float64(3), int32(1)\n",
      "memory usage: 1.3 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>term</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>36.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>60.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90</td>\n",
       "      <td>36.0</td>\n",
       "      <td>28.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85.6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.64</td>\n",
       "      <td>36.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.69</td>\n",
       "      <td>60.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.65</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.69</td>\n",
       "      <td>36.0</td>\n",
       "      <td>67.1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.49</td>\n",
       "      <td>36.0</td>\n",
       "      <td>91.7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    int_rate  term  revol_util  emp_length\n",
       "1      15.27  60.0         9.4           0\n",
       "3      13.49  36.0        21.0          10\n",
       "4      12.69  60.0        53.9           1\n",
       "5       7.90  36.0        28.3           3\n",
       "6      15.96  60.0        85.6           8\n",
       "7      18.64  36.0        87.5           9\n",
       "9      12.69  60.0        36.5           0\n",
       "10     14.65  60.0        20.6           5\n",
       "11     12.69  36.0        67.1          10\n",
       "12     13.49  36.0        91.7           0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(loan[['int_rate', 'term', 'revol_util','emp_length']].info())\n",
    "loan[['int_rate', 'term', 'revol_util', 'emp_length']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the precision of float columns, can be round off to 2 digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fetching all float columns for checking precision, total_pymnt is the oly column with precision more than 2\n",
    "loan.loc[:,loan.dtypes=='float'].tail()\n",
    "loan.total_pymnt=round(loan['total_pymnt'], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During going through the dataset i feel two more columns can also be dropped which won't contribute in our analysis.\n",
    "* member_id\n",
    "* zip_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's drop these columns\n",
    "loan.drop(columns=['member_id', 'zip_code'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are done with Data cleaning and let's see at our clean data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36433, 38)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>...</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>3.643300e+04</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.00000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "      <td>36433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11307.679851</td>\n",
       "      <td>42.624324</td>\n",
       "      <td>12.073862</td>\n",
       "      <td>326.186394</td>\n",
       "      <td>5.015645</td>\n",
       "      <td>6.930522e+04</td>\n",
       "      <td>13.444131</td>\n",
       "      <td>0.146131</td>\n",
       "      <td>0.86493</td>\n",
       "      <td>9.351604</td>\n",
       "      <td>...</td>\n",
       "      <td>52.213313</td>\n",
       "      <td>12310.968665</td>\n",
       "      <td>11841.070656</td>\n",
       "      <td>9912.408471</td>\n",
       "      <td>2304.404937</td>\n",
       "      <td>1.299840</td>\n",
       "      <td>92.855490</td>\n",
       "      <td>11.677065</td>\n",
       "      <td>2742.955476</td>\n",
       "      <td>0.041418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7433.859357</td>\n",
       "      <td>10.728712</td>\n",
       "      <td>3.734758</td>\n",
       "      <td>207.389943</td>\n",
       "      <td>3.542914</td>\n",
       "      <td>6.354244e+04</td>\n",
       "      <td>6.643308</td>\n",
       "      <td>0.492199</td>\n",
       "      <td>1.06343</td>\n",
       "      <td>4.373772</td>\n",
       "      <td>...</td>\n",
       "      <td>378.443825</td>\n",
       "      <td>9048.420882</td>\n",
       "      <td>8911.319070</td>\n",
       "      <td>7053.212865</td>\n",
       "      <td>2629.542911</td>\n",
       "      <td>7.065629</td>\n",
       "      <td>682.047187</td>\n",
       "      <td>140.880327</td>\n",
       "      <td>4473.528922</td>\n",
       "      <td>0.200221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>5.420000</td>\n",
       "      <td>16.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.730000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.220000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5600.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>9.320000</td>\n",
       "      <td>168.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.200000e+04</td>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5672.560000</td>\n",
       "      <td>5413.410000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>678.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>226.280000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>11.860000</td>\n",
       "      <td>283.280000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>13.540000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10101.590000</td>\n",
       "      <td>9632.040000</td>\n",
       "      <td>8123.600000</td>\n",
       "      <td>1379.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>571.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>14.650000</td>\n",
       "      <td>432.220000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.300000e+04</td>\n",
       "      <td>18.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16700.720000</td>\n",
       "      <td>16094.490000</td>\n",
       "      <td>14000.000000</td>\n",
       "      <td>2891.860000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3457.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35000.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>1305.190000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6307.370000</td>\n",
       "      <td>58563.680000</td>\n",
       "      <td>58563.680000</td>\n",
       "      <td>35000.020000</td>\n",
       "      <td>23563.680000</td>\n",
       "      <td>180.200000</td>\n",
       "      <td>29623.350000</td>\n",
       "      <td>7002.190000</td>\n",
       "      <td>36115.200000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt          term      int_rate   installment    emp_length  \\\n",
       "count  36433.000000  36433.000000  36433.000000  36433.000000  36433.000000   \n",
       "mean   11307.679851     42.624324     12.073862    326.186394      5.015645   \n",
       "std     7433.859357     10.728712      3.734758    207.389943      3.542914   \n",
       "min      500.000000     36.000000      5.420000     16.080000      0.000000   \n",
       "25%     5600.000000     36.000000      9.320000    168.900000      2.000000   \n",
       "50%    10000.000000     36.000000     11.860000    283.280000      4.000000   \n",
       "75%    15000.000000     60.000000     14.650000    432.220000      9.000000   \n",
       "max    35000.000000     60.000000     24.400000   1305.190000     10.000000   \n",
       "\n",
       "         annual_inc           dti   delinq_2yrs  inq_last_6mths      open_acc  \\\n",
       "count  3.643300e+04  36433.000000  36433.000000     36433.00000  36433.000000   \n",
       "mean   6.930522e+04     13.444131      0.146131         0.86493      9.351604   \n",
       "std    6.354244e+04      6.643308      0.492199         1.06343      4.373772   \n",
       "min    4.000000e+03      0.000000      0.000000         0.00000      2.000000   \n",
       "25%    4.200000e+04      8.360000      0.000000         0.00000      6.000000   \n",
       "50%    6.000000e+04     13.540000      0.000000         1.00000      9.000000   \n",
       "75%    8.300000e+04     18.690000      0.000000         1.00000     12.000000   \n",
       "max    6.000000e+06     29.990000     11.000000         8.00000     44.000000   \n",
       "\n",
       "       ...  out_prncp_inv   total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "count  ...   36433.000000  36433.000000     36433.000000     36433.000000   \n",
       "mean   ...      52.213313  12310.968665     11841.070656      9912.408471   \n",
       "std    ...     378.443825   9048.420882      8911.319070      7053.212865   \n",
       "min    ...       0.000000     33.730000         0.000000         0.000000   \n",
       "25%    ...       0.000000   5672.560000      5413.410000      4800.000000   \n",
       "50%    ...       0.000000  10101.590000      9632.040000      8123.600000   \n",
       "75%    ...       0.000000  16700.720000     16094.490000     14000.000000   \n",
       "max    ...    6307.370000  58563.680000     58563.680000     35000.020000   \n",
       "\n",
       "       total_rec_int  total_rec_late_fee    recoveries  \\\n",
       "count   36433.000000        36433.000000  36433.000000   \n",
       "mean     2304.404937            1.299840     92.855490   \n",
       "std      2629.542911            7.065629    682.047187   \n",
       "min         6.220000            0.000000      0.000000   \n",
       "25%       678.010000            0.000000      0.000000   \n",
       "50%      1379.820000            0.000000      0.000000   \n",
       "75%      2891.860000            0.000000      0.000000   \n",
       "max     23563.680000          180.200000  29623.350000   \n",
       "\n",
       "       collection_recovery_fee  last_pymnt_amnt  pub_rec_bankruptcies  \n",
       "count             36433.000000     36433.000000          36433.000000  \n",
       "mean                 11.677065      2742.955476              0.041418  \n",
       "std                 140.880327      4473.528922              0.200221  \n",
       "min                   0.000000         0.010000              0.000000  \n",
       "25%                   0.000000       226.280000              0.000000  \n",
       "50%                   0.000000       571.700000              0.000000  \n",
       "75%                   0.000000      3457.580000              0.000000  \n",
       "max                7002.190000     36115.200000              2.000000  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</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>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1999-04-01</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>119.66</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12231.89</td>\n",
       "      <td>12231.89</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2214.92</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>357.48</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Current</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>524.06</td>\n",
       "      <td>524.06</td>\n",
       "      <td>3513.33</td>\n",
       "      <td>3513.33</td>\n",
       "      <td>2475.94</td>\n",
       "      <td>1037.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>67.79</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Veolia Transportaton</td>\n",
       "      <td>3</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>wedding</td>\n",
       "      <td>My wedding loan I promise to pay back</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2004-11-01</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7963</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5632.21</td>\n",
       "      <td>5632.21</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>632.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>161.03</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.96</td>\n",
       "      <td>170.08</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Southern Star Photography</td>\n",
       "      <td>8</td>\n",
       "      <td>RENT</td>\n",
       "      <td>47004.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Loan</td>\n",
       "      <td>NC</td>\n",
       "      <td>23.51</td>\n",
       "      <td>0</td>\n",
       "      <td>2005-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>17726</td>\n",
       "      <td>85.6</td>\n",
       "      <td>11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10110.84</td>\n",
       "      <td>10110.84</td>\n",
       "      <td>6985.61</td>\n",
       "      <td>3125.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>1313.76</td>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  term  int_rate  installment grade sub_grade  \\\n",
       "1       2500  60.0     15.27        59.83     C        C4   \n",
       "3      10000  36.0     13.49       339.31     C        C1   \n",
       "4       3000  60.0     12.69        67.79     B        B5   \n",
       "5       5000  36.0      7.90       156.46     A        A4   \n",
       "6       7000  60.0     15.96       170.08     C        C5   \n",
       "\n",
       "                   emp_title  emp_length home_ownership  annual_inc  \\\n",
       "1                      Ryder           0           RENT     30000.0   \n",
       "3        AIR RESOURCES BOARD          10           RENT     49200.0   \n",
       "4   University Medical Group           1           RENT     80000.0   \n",
       "5       Veolia Transportaton           3           RENT     36000.0   \n",
       "6  Southern Star Photography           8           RENT     47004.0   \n",
       "\n",
       "  verification_status    issue_d  loan_status             purpose  \\\n",
       "1     Source Verified 2011-12-01  Charged Off                 car   \n",
       "3     Source Verified 2011-12-01   Fully Paid               other   \n",
       "4     Source Verified 2011-12-01      Current               other   \n",
       "5     Source Verified 2011-12-01   Fully Paid             wedding   \n",
       "6        Not Verified 2011-12-01   Fully Paid  debt_consolidation   \n",
       "\n",
       "                                   title addr_state    dti  delinq_2yrs  \\\n",
       "1                                   bike         GA   1.00            0   \n",
       "3                               personel         CA  20.00            0   \n",
       "4                               Personal         OR  17.94            0   \n",
       "5  My wedding loan I promise to pay back         AZ  11.20            0   \n",
       "6                                   Loan         NC  23.51            0   \n",
       "\n",
       "  earliest_cr_line  inq_last_6mths  open_acc  pub_rec  revol_bal  revol_util  \\\n",
       "1       1999-04-01               5         3        0       1687         9.4   \n",
       "3       1996-02-01               1        10        0       5598        21.0   \n",
       "4       1996-01-01               0        15        0      27783        53.9   \n",
       "5       2004-11-01               3         9        0       7963        28.3   \n",
       "6       2005-07-01               1         7        0      17726        85.6   \n",
       "\n",
       "   total_acc  out_prncp  out_prncp_inv  total_pymnt  total_pymnt_inv  \\\n",
       "1          4       0.00           0.00      1008.71          1008.71   \n",
       "3         37       0.00           0.00     12231.89         12231.89   \n",
       "4         38     524.06         524.06      3513.33          3513.33   \n",
       "5         12       0.00           0.00      5632.21          5632.21   \n",
       "6         11       0.00           0.00     10110.84         10110.84   \n",
       "\n",
       "   total_rec_prncp  total_rec_int  total_rec_late_fee  recoveries  \\\n",
       "1           456.46         435.17                0.00      117.08   \n",
       "3         10000.00        2214.92               16.97        0.00   \n",
       "4          2475.94        1037.39                0.00        0.00   \n",
       "5          5000.00         632.21                0.00        0.00   \n",
       "6          6985.61        3125.23                0.00        0.00   \n",
       "\n",
       "   collection_recovery_fee last_pymnt_d  last_pymnt_amnt last_credit_pull_d  \\\n",
       "1                     1.11   2013-04-01           119.66         2013-09-01   \n",
       "3                     0.00   2015-01-01           357.48         2016-04-01   \n",
       "4                     0.00   2016-05-01            67.79         2016-05-01   \n",
       "5                     0.00   2015-01-01           161.03         2016-01-01   \n",
       "6                     0.00   2016-05-01          1313.76         2016-05-01   \n",
       "\n",
       "   pub_rec_bankruptcies  \n",
       "1                   0.0  \n",
       "3                   0.0  \n",
       "4                   0.0  \n",
       "5                   0.0  \n",
       "6                   0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 50)\n",
    "loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    17470\n",
       "credit_card            4726\n",
       "other                  3575\n",
       "home_improvement       2705\n",
       "major_purchase         2008\n",
       "car                    1443\n",
       "small_business         1414\n",
       "wedding                 886\n",
       "medical                 634\n",
       "moving                  529\n",
       "house                   343\n",
       "vacation                336\n",
       "educational             276\n",
       "renewable_energy         88\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate Analysis\n",
    "-First lets start with plotting of continous variable corelation matrix, it will give us the idea of relation between pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a235b78198>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "cont_var= ['loan_amnt', 'int_rate', 'installment',\n",
    "       'emp_length', 'annual_inc',\n",
    "       'dti', 'delinq_2yrs', 'earliest_cr_line',\n",
    "       'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util',\n",
    "       'total_acc', 'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d',\n",
    "       'pub_rec_bankruptcies']\n",
    "corr = loan[cont_var].corr()\n",
    "sns.heatmap(corr, annot=True, center=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-Observations from above matrix\n",
    "* loan_amnt and int_rate is positively corelated with a factor of 0.31 which means interest rates are higher for higher loan amount, we will verify it more with other plots.\n",
    "* revol_util and int_rate are also corelated with a factor of 0.47, will see it in more details using other plots.\n",
    "* revol_bal is the total amount on your credit that is unpaid, we can see from above matrix it is corelated with loan_amnt with a factor of 0.32 which is significant to tell that loan_amt is more for borrowers having more revol_bal. This can be considered as a risk.\n",
    "\n",
    "Above matrix is just for getting a quick idea of relations between variables. Let's move ahead and compare pairs of variables with other categorical variables also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interest Rates and Loan Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the understanding of loan amount and interest rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#loan amnt distribution plot\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2, figsize=(20,6))\n",
    "sns.distplot(loan['loan_amnt'], axlabel = 'Loan Amount', ax=ax0)\n",
    "sns.boxplot(loan['loan_amnt'], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan amount is mostly in the range of 5000-15000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2, figsize=(20,6))\n",
    "sns.distplot(loan['int_rate'], axlabel = 'Interest Rate', ax=ax0)\n",
    "sns.boxplot(loan['int_rate'], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interest rate are mostly in the range of 8-15%, note some of loans are offered at interest rates higher than 20%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how the loan amnt and interest rates changes over the time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6G7H2AXAqWjv3r1ZXFzMwsJC9uzZM3Y5AAAAMHeEUjADi4uLOXjw4NhlAAAAwNxy+R4AAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAutsxdgGwHq+87uNjl3BCD37t60mSg1/7+tzW+s5dPzB2CQAAAGxhZkoBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALrbMXYBAABsbq21n0jyo0mWknwmyWVV9VfjVgUAzDszpQAAWLfW2jlJLk/y1Kq6KMn2JC8dtyoAYDMQSgEAsFE7kjyitbYjySOT3DVyPQDAJuDyPQAA1q2qvtxa+5UkdyT5epIPVtUHV/Zpre1Osnvon4WFhTWd+8CUa92K1vpe08/0x8QnZRp8VubPtMfk0FTPtnVNe1yEUgAArFtr7cwklyY5P8m9SX6rtfYjVfUbR/pU1b4k+4aHS4uLi/0L3aK81/PHmMwn4zJ/pj0mLhObjrWOy86dO9fUz7gAALAR/zjJF6vqK1X110nel+QZI9cEAGwCZkoBALARdyR5emvtkVm+fO/iJH84bkkAwGZgphTMwsMfnTzysctbADiFVdUnk7w3yW1JPpPl75f7TvgkAICYKQUzsf0pLxy7BADopqquTHLl2HUAAJuLmVIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd6Pffa+19hNJfjTJUpZvI3xZkrOT3JDkrCzfXvjlVXV/a+30JNcn+b4kf57kn1XVl4bzXJHk1UkeTHJ5Vd3S+aUAAAAAsEajzpRqrZ2T5PIkT62qi5JsT/LSJHuTvLWqLkhyOMthU4bt4ar6e0neOvRLa+1Jw/O+O8klSd7eWtve87UAAAAAsHbzcPnejiSPaK3tSPLIJAeSPDvJe4fj1yV50bB/6fA4w/GLW2vbhvYbquobVfXFJLcneVqn+gEAAACY0KihVFV9OcmvJLkjy2HUV5N8Osm9VfXA0G1/knOG/XOS3Dk894Gh/+NWtq/yHAAAAADmzKhrSrXWzszyLKfzk9yb5LeSPG+VrkvDdttxjh2vfbXfuTvJ7iSpqiwsLJy0zgMn7cHJrOV9pq/ZjIlPy0b5rMyfWYzJoamfcevxWQEA2PzGXuj8Hyf5YlV9JUlaa+9L8owkZ7TWdgyzoc5NctfQf3+SJyTZP1zu99gsf7c/0n7Eyuc8RFXtS7JveLi0uLg43VfEqrzP88eYzCfjMn9mMSbzcO38ZrfWcdm5c+eMKwEAYL3G/l58R5Knt9YeOawNdXGSzyb5SJIXD312Jblx2L9peJzh+Ieramlof2lr7fTW2vlJLkjyqU6vAQAAAIAJjb2m1CezvGD5bUk+M9SzL8meJG9ord2e5TWjrhmeck2Sxw3tb0jyxuE8f5ykshxofSDJa6vqwY4vBQAAAIAJjH35XqrqyiRXHtP8haxy97yq+qskLznOed6c5M1TLxAAAACAqRv78j0AAAAAtiChFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdDdRKNVa+85J2gEAAABgNZPOlPqT47R/dqOFAAAAALB1TBpKbTu2obX2mCTfnE45AAAAAGwFO9bSqbV2Z5KlJI9ord1xzOHHJXn3tAsDAAAA4NS1plAqyY9keZbUzUlevqJ9KcndVfX5aRcGAAAAwKlrTaFUVf1ukrTWFqrqvtmWBAAAAMCpbq0zpY54oLW2O8n3JnnUygNV9YqpVQUAAADAKW3SUOr6JN+T5P1J7p5+OQAAAABsBZOGUs9Ncn5V3TuLYgAAAADYGh42Yf87kpw+i0IAAAAA2DrWc/neja21q3LM5XtV9eGpVQUAwMy01n4oyUuTPL6qfri19tQkj/F9DgDoadJQ6seG7b89pn0pyRM3Xg4AALPUWntdktcnuTrJi4fmryd5W5JnjFUXALD1TBRKVdX5syoEAIAufjzJxVX1pdbanqHt/0ty4Yg1AQBb0KRrSgEAsLk9Osmdw/7SsP22JPePUw4AsFVNNFOqtfbkJG9N8r1JHjU0b0uyVFWnTbk2AACm72NJ3pjkzSvaLk/ykXHKAQC2qknXlHp3kv+c5S8uX59+OQAAzNjrkry/tfaaJI9urX0+ydeS/PC4ZQEAW82kodR3Jvn5qlo6aU8AAOZOVR1orf2DJP8gyd/J8qV8n6qqb45bGQCw1UwaSl2X5J8nedcMagEAYMZaazdW1aVJPjX8HGl/X1X90/EqAwC2mklDqbck+Xhr7WeT3L3yQFU9e2pVAQAwKz94nPZn9SwCAGDSUOq9Sb6Y5LdjTSkAgE2jtfYLw+5pK/aPeGKSP+tcEgCwxU0aSn1vksdVlVsGAwBsLk8Ytg9bsZ8kS1leV+pf9y4IANjaJg2lfi/Jk5L80QxqAQBgRqrqsiRprf1+Vf3HsesBAJg0lPpikg+21n4737qm1M9PrSoAAGbiSCDVWnt0koUk21Yc+8JYdQEAW8+kodQjk/xfSU7LQ6d9AwCwCbTWvivJbyZ5cpYv3ds2bJNk+1h1AQBbz0Sh1JFp3wAAbFrvSPKRLN+F74tJzkvyvyX5/RFrAgC2oElnSiUx3RsAYBN7cpIfqqq/bq1tq6qvttZ+Osn/m+Q3Rq4NANhCJgqlWmtPSvKumO4NALBZ/VWSb0vy10kWW2t/O8nhJI8btSoAYMt52IT9357l6d5nJflakjOT/Icku6ZcFwAAs/F7Sdqw/94kv5Pkd5N8eLSKAIAtadLL90z3BgDYxKqqrXj4s1n+HvfoJNePUxEAsFVNGkqZ7g0AsEm11rYn+VCS51bVN6rqm/EfiwDASCa9fM90bwCATaqqHkxyfib/DggAMHUTzZQy3RsAYNP7N0ne0Vq7Msn+/M1NazLMnAIA6GLSy/eOOt5079baZ6rqf9xQVQAAzMrVw/blK9qO3FHZ3ZQBgG7WHUqdwHkzOCcAANNx/tgFAAAkswmllk7eBQCAMVTVn52sj5nvAEAPFrkEAOBY541dAABw6hNKAQBwLDPfAYCZm0UotW0G5wQAAADgFDKLUOpfzOCcAAAAAJxCJlrovLV2VpKfSvK9SR618lhVPXPY/ubUqgMAYAxmvgMAMzfpTKnfTPIDSd6f5JpjfgAAmHOttZ86TvsbVjw08x0AmLmJZkoleUaSb6+qb8yiGAAAZu7nk/zKKu1vSvKriZnvAEAfk4ZS/0+Sc5P86QxqAQBgRlprzx52t7fWfjAPvUTviUn+on9VAMBWNmko9eEkH2it/XqSgysPVNW1U6sKAIBpO7LcwsOTrPzetpTl73Wv614RALClTRpK/c9J9if5oWPal/LQLzcAAMyRqjo/SVpr11fVK8auBwBgolCqqn5wVoUAADB7KwOp1trDjjn2zfWcs7V2RpKrk1yU5f+sfFVVfXwjdQIAp75JZ0od1VrblhVrEaz3SwwAAP201p6S5NeSfE+WL+VLlr/TLSXZvs7TXpXkA1X14tbaaUkeueFCAYBT3kShVGvtnCT/R5JnJjnjmMPr/RIDAEA/1yV5f5JXJblvoydrrT0my98NX5kkVXV/kvs3el4A4NQ36Uyp/zPLX14uTvK7Wf4C8q+T3DzdsgAAmJG/k+TnqmppSud7YpKvJPn11tqTk3w6yeur6i+PdGit7U6yO0mqKgsLC2s68YEpFbiVrfW9pp/pj4lPyjT4rMyfaY/Joamebeua9rhMGko9I8nfrqq/bK0tVdV/a629OsnvJ/mPU60MAIBZ+O0kz0lyy5TOtyPJU5K8rqo+2Vq7Kskbk/yrIx2qal+SfcPDpcXFxSn9ak7Gez1/jMl8Mi7zZ9pj8rCTd2EN1jouO3fuXFO/SUOpB5M8MOzf21r79iRfS3LOhOcBAGAcD0/y2621/5Lk4MoD67wr3/4k+6vqk8Pj92Y5lAIAOKFJw8JPJnn+sH9LkvckeV+SP5xmUQAAzMxnk+xN8l+T/OkxPxOrqoNJ7mytXTg0XTz8DgCAE5p0ptTL8zdB1o8n+akkj0ry76dZFAAA09Nae2ZVfWx4+Hsz+BWvS/Ku4c57X0hy2Qx+BwBwipkolKqqe1fsfz3JL069IgAApu3tSS4a9q85Tp+lLC9aPrGq+qMkT13PcwGArWuiUKq19m1J3pTkFUnOTnJXkv+U5M3D7X8BAJgzVXXRiv3zx6wFAOCISS/f+9+TPC3Jv0jyZ1m+pfC/SvKYJD8x3dIAAAAAOFVNGkq9JMmTq+rPh8efb63dluS/ZZ2hVGvtjCRXZ3lK+VKSVyX5fJYXUT8vyZeStKo63FrbluSqLC+2fl+SV1bVbcN5dmV5FleS/FJVXbeeegAAAACYvUnvvrdtwva1uCrJB6rqf0jy5CSfy/JthD9UVRck+VD+5rbCz0tywfCzO8k7kqS1dlaSK5N8f5Zncl3ZWjtzAzUBAAAAMEOTzpT6rSTvb639myR3ZPnyvTcN7RNrrT0myTOTvDJJhnWp7m+tXZrkWUO365J8NMmeJJcmub6qlpJ8orV2Rmvt7KHvrVV1aDjvrUkuSfLu9dQFAAAAwGxNGkr9TJZDqF9LsjPJl5PckPXfhe+JSb6S5Ndba09O8ukkr0/yHVV1IEmq6kBr7fFD/3OS3Lni+fuHtuO1f4vW2u4sz7JKVWVhYeGkRR6Y4AWxurW8z/Q1mzHxadkon5X5M4sxOTT1M249PisAAJvfSUOp1tqzj2n66PCzLctrQCXJ/5Tkw+v8/U9J8rqq+mRr7ar8zaV6q1ntMsGlE7R/i6ral2TfkT6Li4sTlMt6eZ/njzGZT8Zl/sxiTCa9dp5vtdZx2blz54wrAQBgvdYyU+qa47QfCX2OhFNPXMfv359kf1V9cnj83iyHUne31s4eZkmdneSeFf2fsOL55ya5a2h/1jHtH11HPQAAAAB0cNJQqqrOn9Uvr6qDrbU7W2sXVtXnk1yc5LPDz64kbxm2Nw5PuSnJj7XWbsjyouZfHYKrW5L82xWLmz8nyRWzqhsAAACAjZl0TalZeF2Sd7XWTkvyhSSXZfnKhmqtvTrLC6q/ZOh7c5LnJ7k9yX1D31TVodbaLyb5g6HfLxxZ9BwAAACA+TN6KFVVf5TkqascuniVvktJXnuc81yb5NrpVgcAAADALFhrFQAAAIDuRp8pxcacuT1Jtg1bAAAAgM1BKLXJveas08YuAQAAAGBiLt8DAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC62zF2AQAAbH6tte1J/jDJl6vqBWPXAwDMPzOlAACYhtcn+dzYRQAAm4dQCgCADWmtnZvknyS5euxaAIDNQygFAMBul9YzAAATtUlEQVRG/fskP5Pkm2MXAgBsHtaUAgBg3VprL0hyT1V9urX2rOP02Z1kd5JUVRYWFtZ07gPTKnILW+t7TT/THxOflGnwWZk/0x6TQ1M929Y17XERSgEAsBH/MMkLW2vPT/LwJI9prf1GVf3IkQ5VtS/JvuHh0uLi4ghlbk3e6/ljTOaTcZk/0x4Tl4lNx1rHZefOnWvqJ5QCAGDdquqKJFckyTBT6qdWBlIAAMczF6HUsbcQbq2dn+SGJGcluS3Jy6vq/tba6UmuT/J9Sf48yT+rqi8N57giyauTPJjk8qq6pf8rAQAAAGAt5mUG27G3EN6b5K1VdUGSw1kOmzJsD1fV30vy1qFfWmtPSvLSJN+d5JIkbx+CLgAAOqmqj1bVC8auAwDYHEYPpY69hXBrbVuSZyd579DluiQvGvYvHR5nOH7x0P/SJDdU1Teq6otJbk/ytD6vAAAAAIBJjR5K5VtvIfy4JPdW1QPD4/1Jzhn2z0lyZ5IMx7869D/avspzAAAAAJgzo64pdZxbCG9bpevSSY6d6DnH/s6Jb0nsJqsb5xar82c2Y+LTslE+K/NnFmPilsQb57MCALD5jb3Q+bfcQjjLM6fOaK3tGGZDnZvkrqH//iRPSLK/tbYjyWOz/N3+SPsRK5/zEG5JPA7v8/wxJvPJuMyfWYzJPExT3uymfTtiAAD6G/V7cVVdUVXnVtV5WV6o/MNV9b8k+UiSFw/ddiW5cdi/aXic4fiHq2ppaH9pa+304c59FyT5VKeXAQAAAMCE5vU/a/ckeUNr7fYsrxl1zdB+TZLHDe1vSPLGJKmqP05SST6b5ANJXltVD3avGgAAAIA1GfvyvaOq6qNJPjrsfyGr3D2vqv4qyUuO8/w3J3nz7CoEAAAAYFrmdaYUAAAAAKcwoRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHc7xi4AAIDNq7X2hCTXJ/nOJN9Msq+qrhq3KgBgMzBTCgCAjXggyU9W1XcleXqS17bWnjRyTQDAJiCUAgBg3arqQFXdNuz/RZLPJTln3KoAgM1AKAUAwFS01s5L8veTfHLkUgCATWDUNaWOtwZBa+2sJO9Jcl6SLyVpVXW4tbYtyVVJnp/kviSvPPI/c621XUneNJz6l6rqup6vBQBgK2utPSrJf07y41X1tWOO7U6yO0mqKgsLC2s654FpF7kFrfW9pp/pj4lPyjT4rMyfaY/Joamebeua9riMvdD5kTUIbmutPTrJp1trtyZ5ZZIPVdVbWmtvTPLGJHuSPC/JBcPP9yd5R5LvH0KsK5M8NcnScJ6bqupw91cEALDFtNa+LcuB1Luq6n3HHq+qfUn2DQ+XFhcXe5a3pXmv548xmU/GZf5Me0xcJjYdax2XnTt3rqnfqONygjUILk1yZKbTdUleNOxfmuT6qlqqqk8kOaO1dnaS5ya5taoODUHUrUku6fhSAAC2pGEm+zVJPldVvzp2PQDA5jH2TKmjjlmD4Duq6kCyHFy11h4/dDsnyZ0rnrZ/aDte+2q/Z+Lp4ybEbpzpsPNnNmPi07JRPivzZxZjYvr4xvmszJV/mOTlST7TWvujoe1nq+rmEWsCADaBuQiljl2DoLV2vK7bVmlbOkH7tzB9fBze5/ljTOaTcZk/sxgT08c3btpTx1m/qvovWf27GADACY3+vfg4axDcPVyWl2F7z9C+P8kTVjz93CR3naAdAAAAgDk0aih1gjUIbkqya9jfleTGFe2vaK1ta609PclXh8v8bknynNbama21M5M8Z2gDAAAAYA6NffneqmsQJHlLkmqtvTrJHUleMhy7Ocnzk9ye5L4klyVJVR1qrf1ikj8Y+v1CVVmyAwAAAGBOjRpKnWQNgotX6b+U5LXHOde1Sa6dXnUAAAAAzMroa0oBAAAAsPUIpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHQnlAIAAACgO6EUAAAAAN0JpQAAAADoTigFAAAAQHdCKQAAAAC6E0oBAAAA0J1QCgAAAIDuhFIAAAAAdCeUAgAAAKA7oRQAAAAA3QmlAAAAAOhOKAUAAABAd0IpAAAAALoTSgEAAADQnVAKAAAAgO6EUgAAAAB0J5QCAAAAoDuhFAAAAADdCaUAAAAA6E4oBQAAAEB3QikAAAAAuhNKAQAAANCdUAoAAACA7oRSAAAAAHS3Y+wCpqm1dkmSq5JsT3J1Vb1l5JIAAE55voMBAOtxysyUaq1tT/JrSZ6X5ElJXtba/9/evcfYUZZxHP+u5R5BoIjYUimxCJSCFREaCXJJjC1gQIUnhRSRIA1GIigSuaiIigGDYBMvCXK/8wREEQvlEsAbNQUFSa2GBtEWkIKAlYvSwvrHzMpa2m539+ycl9nvJ5l0z5xz3r7TZ3f213feeU9M7m6vJEmS2s0MJkmShqo1g1LAnsDizHw0M18BrgMO6XKfJEmS2s4MJkmShqRNg1LjgSX9Hi+t90mSJGnkmMEkSdKQtGlNqZ7V7OtddUdEzAZmA2Qm48aNG7DhcVfPHXbn1Fm3n/aJbndBq3HM8QP/PKlZ55zjsi5FGvedbvdA6qQBM9hQ8heYwUpkBiuP+atMZrACmb+K1KaZUkuBCf0ebws8seqLMvPCzNwjM/egClFv+i0iHuh2H9ysy5thsyZlbtalvK2FNdHIGjCDtTF/0c6flVZs1qW8zZqUuVmX8rYW1mRAbZoptQDYISK2Bx4HZgJHdrdLkiRJrWcGkyRJQ9KamVKZuRI4AZgHLKp25cLu9kqSJKndzGCSJGmo2jRTisycC4zGxQcu7HYHtFrWpTzWpEzWpTzWRINiBlNhrEt5rEmZrEt5Rl1Nenp737AWuCRJkiRJkjSiWnP7niRJkiRJkt48WnX7XptExATgCmAb4DXgwsycExFbAtcDE4HHgMjM5yKiB5gDHAi8BHwqM38XEfsDF/RreidgZmb+pLGDaYlO1aRu69vAQVQDw3cAJ2am0xYHqcM1OZeqJgDfyMzrmzyWNhlCXXYCLgV2B87IzPP6tTWdqmZjgIsy089XHoIO1+QS4GBgWWZOafRApBFm/iqTGaw8ZrAymcHKYwYbmDOlyrUSODkzdwamAZ+NiMnAqcBdmbkDcFf9GGAGsEO9zQZ+CJCZd2fm1MycChxA9Uvg9kaPpD06UpOI+CCwN7AbMAX4ALBvg8fRJp2qyUFUJ/6pwF7AKRGxWZMH0jKDrcuzwOeA8/o3EhFjgO9T1W0ycETdjgavIzWpXQZMH/EeS91h/iqTGaw8ZrAymcHKYwYbgINShcrMJ/uuHmTmv6g+zWY8cAhwef2yy4FD668PAa7IzN7MnA9sHhHvXKXZw4BbM/OlET+AFupgTXqBjYANgA2B9YGnGjuQFulgTSYD92bmysx8EXiIFp7wmzLYumTmssxcAKxYpak9gcWZ+WhmvgJcV7ehQepgTcjMX1AFJql1zF9lMoOVxwxWJjNYecxgA3NQ6k0gIiYC7wN+C7wjM5+E6hsc2Lp+2XhgSb+3La339TcTuHZEOztKDKcmmXkfcDfwZL3Ny8xFDXW9tYb5c/IQMCMiNomIrYD9gQkNdb3V1rEua7Iu5zUN0jBrIo0a5q8ymcHKYwYrkxmsPGaw1XNQqnAR8VbgRuCkzFy+lpf2rGbf/+6Pr69E7ArM62wPR5/h1iQiJgE7A9tSndwPiIgPdb6no8dwa5KZt1N9lPlvqP7jcB/VVFsNwyDqsiZrPa9p8DpQE2lUMH+VyQxWHjNYmcxg5TGDrZmDUgWLiPWpvnGvzswf17uf6psWXv+5rN6/lP+/qrAt8ET/5oCbMvMN0wC17jpUk48B8zPzhcx8AbiV6v5iDUGnfk4y8+x6/Y8PU/0ifqSJ/rfVIOuyJgOd1zQIHaqJ1HrmrzKZwcpjBiuTGaw8ZrC1c1CqUPUnVFwMLMrM8/s9dTNwdP310cBP++3/ZET0RMQ04J990wFrR+DU8WHpYE3+BuwbEevVJ6h9qe4t1iB1qiYRMSYixtZt7ka1AKoL0g7REOqyJguAHSJi+4jYgOoWmJs73d/RoIM1kVrN/FUmM1h5zGBlMoOVxww2sPW63QGt0d7AUcDDEfFgve904BwgI+JYql+sh9fPzaX6iNXFVJ/wckxfQ/W9qxOAexvpeXt1qiY3UH0Sz8NU02Bvy8yfNXIE7dOpmqwP/DIiAJYDszLTqeNDN6i6RMQ2wP3AZsBrEXESMDkzl0fECVS3vYwBLsnMhc0eSmt0sibXAvsBW0XEUuDMzLy40aORRo75q0xmsPKYwcpkBiuPGWwAPb293hoqSZIkSZKkZnn7niRJkiRJkhrnoJQkSZIkSZIa56CUJEmSJEmSGueglCRJkiRJkhrnoJQkSZIkSZIa56CUpEZFxMKI2K/b/ZAkSRpNzGCSStTT29vb7T5IkiRJkiRplHGmlCSNsIjoiQjPt5IkSQ0yg0nlW6/bHZA0ukTEY8CngeXAD4D3AC8DV2fmFyJiI+AiYAYwBngEODgzn+p7b2beWbf1NWBSZs6qH08DzgcmA38FTszMe9bSl8OBUzPz/f32nQzsk5mHRsSGwNlAABsCNwGfz8yXI2IL4EpgL6pz6a+B4zNzad3OPfW+/YDdgV2BxUP9d5MkSRoOM5ikEjlqLKlb5gBzMnMz4N1A1vuPBt4GTADGAsdTBaa1iojxwM+BbwJbAl8EboyIt6/lbTcD20fEzv32zaIKOgDnUgW2qcAkYDzw1fq5twCXAtsB76r7+L1V2j8KmA1sShXQJEmSus0MJqkYzpSS1C0rgEkRsVVmPgPM77d/LNXVtz8AD6xje7OAuZk5t358R0TcDxwIXL66N2TmfyLi+vq9Z0TELsBE4JaI6AGOA3bLzGcBIuJbwDXAaZn5D+DGvrYi4mzg7lX+issyc+E69l+SJKkJZjBJxXBQSlK3HAt8HfhTRPwFOCszb6G6QjYBuC4iNgeuAs7IzBUDtLcdcHhEfLTfvvV5Y0hZ1eXAtRHxZaqralkHpa2BTYAHIqLvtT1U09mJiE2AC4DpwBb185tGxJjMfLV+vGSAv1uSJKlpZjBJxXBQSlJXZOYjwBH14pMfB26IiLGZ+SJwFnBWREwE5gJ/Bi4GXqQKKX226ff1EuDKzDxukP2YHxGvAPsAR9YbwDNU08F3yczHV/PWk4Edgb0y8+8RMRX4PVVo6uPHm0qSpKKYwSSVxEEpSV0REbOAeZn5dEQ8X+9+NSL2pwojf6RaiHMF0HfV60FgZkTcCrwXOAy4rX7uKmBBRHwEuJPqCt00YHHfwpdrcQXVWgQrM/NXAJn5WkT8CLggIk7IzGX1mglTMnMe1RoFLwPPR8SWwJnD+geRJElqgBlMUklc6FxSt0wHFkbEC1QLbs7MzH9TXXm7gSoMLQLupQo7AF+hWpDzOaoredf0NZaZS4BDgNOBp6mu2p3Cup3nrgSm8Primn2+RPVpLfMjYjlV0Nqxfu67wMZU4W0+rwczSZKkkpnBJBWjp7fXmY2SRreI2BhYBuxeT2mXJEnSCDODSXKmlCTBZ4AFhiFJkqRGmcGkUc41pSS1Xj09fXVmUE0X7wEOba5HkiRJ7WcGkzQQb9+TJEmSJElS47x9T5IkSZIkSY1zUEqSJEmSJEmNc1BKkiRJkiRJjXNQSpIkSZIkSY1zUEqSJEmSJEmNc1BKkiRJkiRJjfsvDGtULr5SNwYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp=loan[['loan_amnt', 'int_rate', 'issue_d']]\n",
    "tmp['issue_year']=loan['issue_d'].dt.year\n",
    "fig,(ax0,ax1) = plt.subplots(nrows=1, ncols=2, figsize=(20,10))\n",
    "sns.barplot(x='issue_year', y='loan_amnt', data=tmp, ax=ax0)\n",
    "sns.barplot(x='issue_year', y='int_rate', data=tmp, ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see Loan amount has been increasing over the years, in 2008 there's a change in trend and that may be due to the economic crisis, also interest rate has been increasing gradually over the years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how grades are related and try to find out it's impact on our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.countplot(x='grade', data=loan, order=['A','B','C','D','E','F','G'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As visually clear from above plot, most of the loans are of high grade. Let's see their impact on charge-offs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status  grade\n",
       "Charged Off  A         513\n",
       "             B        1253\n",
       "             C        1170\n",
       "             D         980\n",
       "             E         648\n",
       "             F         286\n",
       "             G          93\n",
       "Current      A          35\n",
       "             B         317\n",
       "             C         250\n",
       "             D         212\n",
       "             E         169\n",
       "             F          67\n",
       "             G          16\n",
       "Fully Paid   A        8627\n",
       "             B        9465\n",
       "             C        6011\n",
       "             D        3683\n",
       "             E        1833\n",
       "             F         616\n",
       "             G         189\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','grade'])['grade'].count()\n",
    "grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "A     9175\n",
       "B    11035\n",
       "C     7431\n",
       "D     4875\n",
       "E     2650\n",
       "F      969\n",
       "G      298\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt = loan.groupby('grade')['grade'].count()\n",
    "cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>grade</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>5.591281</td>\n",
       "      <td>11.354780</td>\n",
       "      <td>15.744853</td>\n",
       "      <td>20.102564</td>\n",
       "      <td>24.452830</td>\n",
       "      <td>29.514964</td>\n",
       "      <td>31.208054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current</th>\n",
       "      <td>0.381471</td>\n",
       "      <td>2.872678</td>\n",
       "      <td>3.364285</td>\n",
       "      <td>4.348718</td>\n",
       "      <td>6.377358</td>\n",
       "      <td>6.914345</td>\n",
       "      <td>5.369128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>94.027248</td>\n",
       "      <td>85.772542</td>\n",
       "      <td>80.890863</td>\n",
       "      <td>75.548718</td>\n",
       "      <td>69.169811</td>\n",
       "      <td>63.570691</td>\n",
       "      <td>63.422819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "grade                A          B          C          D          E          F  \\\n",
       "loan_status                                                                     \n",
       "Charged Off   5.591281  11.354780  15.744853  20.102564  24.452830  29.514964   \n",
       "Current       0.381471   2.872678   3.364285   4.348718   6.377358   6.914345   \n",
       "Fully Paid   94.027248  85.772542  80.890863  75.548718  69.169811  63.570691   \n",
       "\n",
       "grade                G  \n",
       "loan_status             \n",
       "Charged Off  31.208054  \n",
       "Current       5.369128  \n",
       "Fully Paid   63.422819  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','grade'])['grade'].count()\n",
    "cnt = loan.groupby('grade')['grade'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly evident from above graph low grades have more chances of charged-offs and we should avoid giving loans to customers in lower grade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how we are charging Interest Rates as per the grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(x='grade', y='int_rate',data=loan,order=['A','B','C','D','E','F','G'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above plot suggests that we are charging more interest rates in lower grade that is good as they are more riskier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loan Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36433.000000\n",
       "mean     11307.679851\n",
       "std       7433.859357\n",
       "min        500.000000\n",
       "25%       5600.000000\n",
       "50%      10000.000000\n",
       "75%      15000.000000\n",
       "max      35000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Frist we need to bin income variable for better visualization.\n",
    "loan['loan_amnt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['loan_amnt_bin'] = pd.cut(loan['loan_amnt'], [x for x in range(0, 36000, 5000)], \n",
    "                                      labels=[str(x)+'-'+str(x+5)+'k' for x in range (0, 35, 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_amnt_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500</td>\n",
       "      <td>0-5k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>5-10k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>0-5k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>0-5k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7000</td>\n",
       "      <td>5-10k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt loan_amnt_bin\n",
       "1       2500          0-5k\n",
       "3      10000         5-10k\n",
       "4       3000          0-5k\n",
       "5       5000          0-5k\n",
       "6       7000         5-10k"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[['loan_amnt','loan_amnt_bin']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(x='loan_amnt_bin', hue='purpose',data=loan)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in detail here, debt consolidation and credit card are always the top purpose in each loan amount segment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count of loans')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot loan amount against percentage of charge-offs to see it's impact on defaults\n",
    "grp=loan.groupby(by=['loan_status', 'loan_amnt_bin'])['loan_amnt_bin'].count()\n",
    "cnt=loan.groupby(by='loan_amnt_bin')['loan_amnt_bin'].count()\n",
    "ax=cnt.plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Count of loans')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see most of the loan_amnt has been taken in the range of 5-10k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perc=grp.unstack() * 100/ cnt.T\n",
    "ax=perc.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This clearly depicts that instead most of the loan amount are in the lower range but charge-offs increases as the loan amount increases. Approving high amount of loans is always risky as it will have more than 20% chances of defaults."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customer Income/ Annual Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.643300e+04\n",
       "mean     6.930522e+04\n",
       "std      6.354244e+04\n",
       "min      4.000000e+03\n",
       "25%      4.200000e+04\n",
       "50%      6.000000e+04\n",
       "75%      8.300000e+04\n",
       "max      6.000000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bin customer income and compare it with loan amount and loan status.\n",
    "loan.annual_inc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#annual income distribution plot\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2, figsize=(20,6))\n",
    "sns.distplot(loan['annual_inc'], axlabel = 'Annual Income of Borrower', ax=ax0)\n",
    "sns.boxplot(loan['annual_inc'], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there are some outliers which are affecting the visualization of this graph, outliers must be avoided to see the existing patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149981.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#excluding outliers in annual income\n",
    "temp=loan[loan['annual_inc'] < 150000]\n",
    "temp.annual_inc.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J15IkObuw20+BIYVFyy8Hvlg4djGQ0lIQzQY+k6Zpc3tjFsb6AnB5YawhhbFJkuTIJEmagA8BP06S5A/7HwQ8kiTJAlrKrG+2egKgtMfEV18i/vIa2H8yYdYHs47TrjBwMJx0ZstT/+7+LXHbtqwjSZIkSZKqWFdu2SNN01uBW1tt+8oOX2+mpShq69grgSu7MmZh+zL+70l8O26fT8stfK23PwBM6fRDSD0sbt9O/iffgVyO3McvJ+Rqso7UoTC4kTjjNLj7Vvj97cQTTyfkujJJUpIkSZKkntWlQkrSzuKtKbzwLOHSvyIMGUp+7uwO98/NmFWkZO0LI8cSp58AD8+FeXOJ02dYSkmSJEmSis5CStoFcd0a4u9uJEyfQe7IE7KO0y1h/4OJG96CxY/Dpo3E408h9KrPOpYkSZIkqYo4NULaBfE3v4IYCR+4MOsou+awo2H6DFi9En53I/HN17NOJEmSJEmqIhZSUjfFl1cR77+D8J5ZhMZhWcfZJSEEwgEHw6lnw5Yt8LsbyT9wJzHfnHU0SZIkSVIVsJCSuine/Euo60U4s811/MtKGDYCzjwPBgwi/vv3yP/dZ4mPPUCMMetokiRJkqQK5hpSUjfEF58nzv894YyE0H9Q1nF6RNirH3HWB8k19Cf/v78g/6/fhLETyH3gQph0KCGErCNKkiRJkiqMhZTUDflf/xf0bSC89/1ZR+lRIQTCtOPIHXo08aF7iLdcR/6fvgr7H0zuAx8jTJiUdURJkiRJUgWxkJK6KD73FDz5KOHciwh9G7KOs0eEmhrCcScTp88g3nc78Te/In/VFwnTjiNOnESoq8s6oiRJkiSpAlhISV2Un30j9BtAmHlW1lH2uFBXR5h5JvHYk4lzbiLefB288Axx5hkVW8ZJkiRJkorHRc2lLojr34CF8wnvOZ1QX591nKIJ9b3JnXU+uT/7W1j/Jtx6I3HdmqxjSZIkSZLKnIWU1BVLFkJtLWHm6VknyUSYMg1mfQBCgNm/Jr6yKutIkiRJkqQyZiEldSJu2QzPP0046j0V82S9XREGNcLp58JeDfD7OcTNb2cdSZIkSZJUpiykpM489xQ0byecck7WSTIX+u4FJ5wGWzbDA3cRY8w6kiRJkiSpDLmoudSB2NwMTy+C4aMIo8bt1lj5ubN7JlTGwuBG4uHHwiP3wTOL4MCpWUeSJEmSJJUZZ0hJHVnxPLy9ESYdknWS0nLgFBg5Fh59kLhubdZpJEmSJEllxkJKakeMEZYsgP4DYcSYrOOUlBACHHsS1NfDfXNaZpJJkiRJktRFFlJSe9a+AuvWwEFTWwoYvUvo3QeOmQlvvg7PPpl1HEmSJElSGbGQktrz7GKoq4Px+2edpHSNGAPDR8GiR4lbt2adRpIkSZJUJiykpDbELZthxVIYvz+hrlfWcUpWCAEOP7rlqXtPPZF1HEmSJElSmfApe6oYnT3FLjdjVtcHW/YMNDfDxMm7maryhSF7E8fuB0ueIB5wMKFP36wjSZIkSZJKnDOkpFZijC236w0dRhjcmHWc8nDoUdCch4WPZJ1EkiRJklQGLKSk1l5ZDevfcHZUN4T+A2HiQfDcU8T1b2YdR5IkSZJU4iykpNaefRJ61cPYCVknKS9Tj4RcDhbMyzqJJEmSJKnEWUhJO4hvb4IXX4D9DiTUusRad4Q+feHAKbBiKXH9G1nHkSRJkiSVMAspaUdLl0DMw8RJWScpTwcd0jJL6snHsk4iSZIkSSphFlJSQYyxpZAaNoIwYFDWccpS6NMXJhwEy54lbnwr6ziSJEmSpBJlISX9wcurYMN6Z0ftrsmHtfy6+Ilsc0iSJEmSSpaFlPQHS5e0LGY+et+sk5S1sFc/2Hd/WPpUy5pckiRJkiS1YiElQcvtZS8ug/H7u5h5T5h8GDQ3w5IFWSeRJEmSJJUgCykJiA/dC/nmlvWPtNvCgEEwdj949kni1i1Zx5EkSZIklRgLKVW9GCPxvtth8FDC4Mas41SOg6fBtm2wZGHWSSRJkiRJJcZCSlqxFJqWw0RnR/WkMLgRRo2Hpxc6S0qSJEmS9C4WUqp68fdzoFcvGDcx6yiV55AjYOsWeHpR1kkkSZIkSSXEQkpVLW7ZTJw/lzDtOEKv+qzjVJwweGjLLKklC4ibNmYdR5IkSZJUIiykVNXiow/A25sIx5+adZTKNbVlllS865ask0iSJEmSSoSFlKpafOBOGDocJk7OOkrFCkOGwqhxxDk3O0tKkiRJkgRYSKmKxTUvwzOLCMeeTAgh6ziVbeqRsGkD8a7fZJ1EkiRJklQCLKRUteKDd0MIhGNOyjpKxQtDhsIh04lz/pf41vqs40iSJEmSMmYhpaoU83nig3fBgVNbyhLtcbkP/BFs2Uz89c+zjiJJkiRJylht1gGkTDy3GNa+Qjjno1kn6TH5ubOzjtChMHIM4aSziHfcTDzhvYTxE7OOJEmSJEnKiIWUqlK8/07o3Ydw2DFZR6kq4X0fJs6bS/6XPyL3pW8Tck7SlCRJklRZ1q9fz2c/+9l3bTvggAN45pln2j1mwoQJrFixgm3btjFkyBBee+01AEIIxBjbPa53795s3ryZcePGsXz5cgCmTJnCokWLAKipqSGfz1NbW8tHP/pRrrvuOvbee28++clP0r9//938pLvHQkpVJ25+m/jYA4TpMwj19VnHqSqhT1/CeX9M/OnVxPvvIJxw2jvvdTbDKzdj1p6OJ0mSJEm77dVXX91pW0dlFMDSpUvf+foPZRTQYRkFsHnzZoB3yijgnTIKoLm5GYBt27bxn//5nzQ3N7Ny5Upmz55NkiQdjr2nOT1BVSc++gBs2Uw49uSso1SlcNSJMGES8X+uJW58K+s4kiRJktRj1q8v3Yc4/aGcAnjooYcyz+oMKVWd+MAdsPcI2O/ArKNUpRACuY98kvzX/4L4q5/AxZ8jhJB1LElSGVuzZg1btmzh+9//ftZRekRdXR3btm3LOkYm6urqaGpqoi76d4NK93oMbGtq4vvf/35Vf8+DP/OV9tnbmh1VirZv3575LClnSKmqxLWvwLOLCcfMtATJUBg9nnBWQnzwbuK9v8s6jiRpD0qS5NIkSR5JkuSRrLNIkqT/88gj2f7R7AwpVZX48L0AhKPek3EShbMuIC5fSrz+J8RR47OOI0naQ9I0vQa4pvCy44UwdtHQoUMBdlpAtlw1Njaydu3arGNkorGxka985Stsfb7jtVZU/gaFSK9Ro/jsZz9b1d/z4M98pX32cvqz6Igjjsj0/M6QUtWIMbYUUhMOIgwdnnWcqhdyOXKXXA6DG8n/6Cri25uyjiRJkiRJu2XvvffOOkKX1NbWMmtWtg+OspBS9Vj5Ary00tlRJSTs1UDu01+CtzfC3NuIOyyyJ0mSJEnlpn///llHaFdNTc07Xx999NGZZ7WQUtWID98LNTWEI47POop2EEaNJ1z0Z/DqS3D3rcQKW9RQkiRJUnVpa5bUAQcc0OExEyZMoK6uDoAhQ4a8s72ztY979+4NwLhx497ZNmXKlHe+rqmpIYRAXV0dF154IfX19YwePTrz2VHgGlKqEjGfh3n3wsHTCA2l21hXq9z0GTQ/+Sg8dA/ceQtx5hmE+t5Zx5IkSZKkbuvfvz9XXHFFh/tktX7W4YcfXvRztsdCStXhldXwxjpCsuu36+Xnzu7BQGotTDiIWNcL7psDc24invw+Qp++WceSJEmSJO0B3rKn6vDCs9C7D2Hq9KyTqANh7H4w80xY/yb87kbiujVZR5IkSZIk7QHOkFLFi83b4cVlhCOOJ9TXZx1HnQgjRhNPOwfunQ2z/4d41HsI+x24x8/b2Qy43Izs77GWJEmSpEphIaXK17QCtm0lHO3T9cpFaBxGPOND8Ps58MBdxLWvEI85mVBY5K8tOxZKmxoayG/Y8K73LZQkSZIkqXR4y54q3/LnoE9fOHBq1knUDaFPXzjlfTD5MHh2Mfnv/A1x/etZx5IkSZIk9QALKVW0uG0brFoBY/Yj5GqyjqNuCrkc4fBjYMZpsHIZ+Ss/T1z5QtaxJEmSJEm7yUJKla1pOTQ3w9j9sk6i3RDGTiD311dBPk/+qi8QH3sw60iSJEmSpN1gIaXKtmJpy+16e++TdRLtpjB2P3J/8x0YMYb8j75J/v47s44kSZIkSdpFFlKqWHHbVlj1YsvteiFkHUc9IAwcTO7zV8JBhxKv/T75h+7OOpIkSZIkaRdYSKlyNa2AfDOMm5B1EvWg0Kue3Ge+DAdMIf7se+QfvjfrSJIkSZKkbrKQUuVasRT67AVDh2edRD0s9Kond9nfwv6TiT+9mvjIfVlHkiRJkiR1Q21XdkqSZBbwPaAG+Emapt9s9X498HNgGvAacH6apssL730JuARoBj6bpultHY2ZJMl44HpgMPAYcGGapluTJJkB/BMwFbggTdMbdjj/RcAVhZffSNP02m5eB1WYuLVwu97+k71dr0KF+npyl11B/nt/R/6nV8OpZxMah2UdS5IkSZLUBZ3OkEqSpAb4IXA6MAn4cJIkk1rtdgnwepqmE4CrgasKx04CLgAmA7OAf0mSpKaTMa8Crk7TdCLwemFsgBeBPwZ+2SrfYOCrwFHAdOCrSZIM6uoFUIVqWt5yu55P16tooXcfcp/+GxgwCO6dTXx7U9aRJEmSJEld0JVb9qYDS9M0XZam6VZaZi+d02qfc4A/zEq6ATg5SZJQ2H59mqZb0jR9AVhaGK/NMQvHnFQYg8KY7wdI03R5mqYLgXyrc78XmJOm6bo0TV8H5tBSfqmarXge+nq7XjUI/fqT+/SXYcuWllKquTnrSJIkSZKkTnSlkBoJrNzhdVNhW5v7pGm6HXgTGNLBse1tHwK8URijvXPtSj5Vkbh1K6z26XrVJIzZF46dCWtehvm/zzqOJEmSJKkTXVlDqq3/oo9d3Ke97W0VYR3t35EuHZMkyaXApQBpmtLY2NjJsKWhtra2bLIWQ0fXY1NDAwDbnnuKzflm+hx4MLWFbQB9O7mOm3bYt5zU5Gpo6IHse/r67O74nR5/8GFs2bCerY8/zPbR42k4YHJRz1/K/H1kZ16Td/N6SJIkqdi6Ukg1AaN3eD0KWN3OPk1JktQCA4B1nRzb1va1wMAkSWoLs6TaOldb+U5sNdY9rXdK0/Qa4JrCy7h27dpOhi0NjY2NlEvWYujoeuQ3bAAgPrcE+vTl7YYBhMI2gE2dXMf8DvuWk4aGBjb0QPY9fX12d/yuHB8nHQarm3h77u3QbyCh/4Cinb+U+fvIzrwm71ap12PEiBFZR5AkSVI7unLL3nxgYpIk45Mk6UXLIuU3t9rnZuCiwtfnAXelaRoL2y9IkqS+8PS8icC89sYsHHN3YQwKY97USb7bgNOSJBlUWMz8tMI2VaHYvL3l6Xqjxnm7XhUKuRwcdxLU1MB9c4h515OSJEmSpFLUaSFVmKl0GS0lz5KWTeniJEm+liTJ2YXdfgoMSZJkKXA58MXCsYuBFHgKmA18Jk3T5vbGLIz1BeDywlhDCmOTJMmRSZI0AR8CfpwkyeLCOdYBX6el5JoPfK2wTdXopSbYvg3G7Jt1EmUk7NWP3u85DV57FRY+knUcSZIkSVIbunLLHmma3grc2mrbV3b4ejMtRVFbx14JXNmVMQvbl9HyFL7W2+fTcjteW+f4GfCzDj+EqsOLy6CuFwxzXftqVrffgWze71l48jHiPqMJw7xtR5IkSZJKSVdu2ZPKQsznoWk5jBxLqKnJOo6yduTx0NAP7r+DuHVL1mkkSZIkSTuwkFLlWPMybNkMY8ZnnUQlINT1guNOhU0b4ZH7s44jSZIkSdqBhZQqx4vLIFcDI8ZmnUQlIgwdBpMPg+efJi6Yl3UcSZIkSVKBhZQqQowRVr4A+4wi1NVlHUelZOqRMGgI+Z//gPjW+qzTSJIkSZKwkFKlWLkMNr7l0/W0k1BTA8edDBs3EH/5o6zjSJIkSZKwkFKFiI8/DCHAqHFZR1EJCoMaCWd/mPjIfeTnzc06jiRJkiRVPQspVYT4xEOw9z6E3n2yjqISFd77Qdj3AOIvfkR8Y13WcSRJkiSpqllIqezFta9A03JnR6lDoaaG3MWfg+1bW9aTijHrSJJkPPToAAAgAElEQVQkSZJUtSykVPbigvktX1hIqRNh+EjCBy+CRY8Q75uTdRxJkiRJqlq1WQeQdldcOA+GjyL0H5h1FJWBMPNM4uMPEX/1U+JBhxAah3XpuPzc2R2+n5sxqyfiSZIkSVJVcIaUylp8exM88yThkCOzjqIyEXI5chf/OQTI/8f3ifl81pEkSZIkqeo4Q0rl7anHoXk7Yep04ssrs06jPaizGUrdEYbsTTj/E8Rr/5l4128Ip5zdY2NLkiRJkjpnIaWyFhfMg736wX4HQieFVE8WGip/4bhTiI89SPyfnxMPPjzrOJIkSZJUVbxlT2Ur5puJix4hTJlGqKnJOo7KTAiB3B9dBr3qyf/sn7x1T5IkSZKKyBlSKl/PPwMb3iIcMj3rJBWhGmeQhYGDCR/9FPGab8OAQTBlWtaRJEmSJKkqOENKZSsumAc1tTDZ262063JHnkA48gRYOJ+4bm3WcSRJkiSpKlhIqWzFhfNh/8mEPn2zjqIyFz7ySajvDfffQWxuzjqOJEmSJFU8CymVpfjqanhppbfrqUeEhv5wzEx4Yx088XDWcSRJkiSp4llIqSzFBfMBCFOPzDiJKkUYORb2nwxPPUF8eVXWcSRJkiSpollIqSzFBfNg5FjC0OFZR1ElOfxY6D8Q7r+TuHVL1mkkSZIkqWJZSKnsxI0b4LnFzo5Sjwt1dXDcyfD2Jnh4btZxJEmSJKliWUip7MQnH4V83vWjtEeExmEw9QhY/hzxheeyjiNJkiRJFclCSuVn4XzoNwDGT8w6iSrVwYfD0GHw8L3EjW9lnUaSJEmSKo6FlMpK3L6d+OSjhClHEHI1WcdRhQq5HBx3CsQI999FjDHrSJIkSZJUUWqzDiB1VX7ubN56cx1s2kisqyM/d3bWkVTBQr8BxCOPhwfvhiULYNKhWUeSJEmSpIphIaWysn35UsjlYJ/RWUdRN5VlgbjfgdC0HB5/iLjPKMKgxqwTSZIkSVJF8JY9lY0YI9tXLIXho1qehibtYSEEOPpE6FUP991BbN6edSRJkiRJqggWUiof698gvvkGjBqXdRJVkdC7Dxx7EryxDh57KOs4kiRJklQRLKRUPpqWt/w6amymMVR9wsixcOAUeHohcdWKrONIkiRJUtlzDSmVj6bl5Br3Ju7VL+skqkaHHwMvr2p56t77zif06dutwztbQys3Y9bupJMkSZKksuIMKZWFuGE9rHmZ2rH7ZR1FVSrU1MIJp8H2rXD/ncQYs44kSZIkSWXLGVIqC3HRoxAjteMmsC3rMKpaYeBg4rTjYN5cWLIAJh2adaR3dDQDa1NDAxx+fBHTSJIkSVLHnCGlshAXPAx9+pIbOjzrKKp2+0+G0ePh8YeIa17JOo0kSZIklSULKZW8uG0bLH4cRo4jhJB1HFW5EAIcMxP67AVzbyNu2Zx1JEmSJEkqOxZSKn3PPgmb34bR47JOIgEQ6nvDe94LmzfB/Xe4npQkSZIkdZOFlEpeXDAPevWC4aOyjiK9IwzZG444Dla9CE8+lnUcSZIkSSorFlIqaTHGlkLqoEMJta7BrxKz/8EwbgIsmEd8emHWaSRJkiSpbFhIqbQ1LYd1awiHTM86ibSTEAIcfSL0H0j+R1cR17ycdSRJkiRJKgsWUippccE8AMLUIzNOIrUt1PWCE8+AGMn/89eJb2/KOpIkSZIklTwLKZW0uGAejN+fMGBQ1lGkdoX+A8h96gvw6mry//aPxHxz1pEkqaqMHDmSkSNHZh1DkiT/TOoGF+VRyYpvrIPlzxHe/7Gso0idCgcdQrjgUuIv/pV4488JH7o460iSVDXOPffcrCNIkgT4Z1J3WEipZOTnzn7X6/jcUy2/bt+203tSKcqdeDr51SuIt/+a/OBGcie/L+tIkiRJklSSLKRUupqWw179YODgrJNIXRbO/xPiG+uI1/8b+b4N5I6ZmXUkSZIkSSo5riGlkhS3b4OXVsKocS1PMpPKRKipIfcnn4cDpxL/43vvLMwvSZIkSfo/FlIqTS+tguZmGDUu6yRSt4W6XuQ+82UYvS/5H3+L+MyTWUeSJEmSpJJiIaXS1PQC1NXBsBFZJ5F2Sejdl9yf/x0M2Zv89/8f8aWmrCNJkiRJUslwDSmVnBgjrFoBI8YQamqyjiPtstCvP7nPX0n+u38Ld/+W+J7TCSPH7NJYLuwvSZIkqZI4Q0ql57VX4e1N3q6nihAGDCL3+b+HAYPgnluJK1/IOpIkSZIkZc5CSqWnaTmEACPHZp1E6hGhX3845WwYPBTunU18/umsI0mSJElSpiykVHqalsPQfQj1vbNOIvWYUN8bTnkfDBsJD9xFXDCv5fZUSZIkSapCFlIqKXHDW/D6azDK2VGqPKGuF5x0Jux3ICx8BO6/k9jcnHUsSZIkSSo6FzVXaWla3vLr6PGZxpD2lFBTQzxmJvTrD0/Mg41vEU84LetYkiRJklRUFlIqLU0vQP+BhP4Ds06iClNKT6kLIcCUI4gNA+DBu+G3KXHsBMJBh2QdTZIkSZKKwlv2VDLi1q3wymqfrqeqEcZPhDPOg/re5K/+CvlbrifmvYVPkiRJUuWzkFLpeOlFyOctpFRVwsDBcPp5hKNOJN78S/Lf/jJx9YtZx5IkSZKkPcpCSqXjxWVQ3weGDs86iVRUoa6O8PHPES7+HLzURP5rnyP/6/8ibt2SdTRJkiRJ2iNcQ0olIW7bCk0rYNwEQs6eVNUnhEA49iTilGnE//534q0pcf5cwjkfJRx5fNbxJEmSJKlH+V/+Kg1LFsD2bTBm36yTSJkK/QaQ+/jnyF3+dehVT/zJd8h/9TLismeI+XzW8SRJkiSpR1hIqSTExx6Eul4wfFTWUaSSEA46hNxXvkfuU1+E2jq4/0646ZfEp54gbtmcdTxJkiRJ2i3esqfMxeZm4oKHYdRYQk1N1nGkkhFyOZh2LLnDjib/yx+1zCR89AF4Yh5x/P5w0NSWRdElSZIkqcxYSCl7zz4JG96CacdmnUQqSSGXI4zZF8bsS1y3Fp55El54FpY+RRw1Hg4+nDB0WNYxJUmSJKnLLKSUufj4g9CrF+wzJusoUskLgxvhmBOJhx8NTy+EpxfB7BeIw0fB9BMIAwZlHVGSJEmSOtWlQipJklnA94Aa4Cdpmn6z1fv1wM+BacBrwPlpmi4vvPcl4BKgGfhsmqa3dTRmkiTjgeuBwcBjwIVpmm5t7xxJkowDlgDPFOI8lKbpp7p/KZSFmM8TH38IDp5GqKvLOo5UNkJ9bzhkOnHSYfDcYlj0KPw2JR56FBw4teKeVpmfO7vD93MzZhUpiSRJkqSe0Ol/sSRJUgP8EDgdmAR8OEmSSa12uwR4PU3TCcDVwFWFYycBFwCTgVnAvyRJUtPJmFcBV6dpOhF4vTB2u+coeD5N00ML/1hGlZMXnoU31hEOOybrJFJZCnV1hEmHwvsugH1Gt6wxdfv/Ete/mXU0SZIkSWpXV/4X+nRgaZqmy9I03UrL7KVzWu1zDnBt4esbgJOTJAmF7denabolTdMXgKWF8docs3DMSYUxKIz5/k7OoTIWH3sQamoJU4/IOopU1kLfveDE0+G4k+HN1+F3NxBfW5N1LEmSJElqU1cKqZHAyh1eNxW2tblPmqbbgTeBIR0c2972IcAbhTFan6u9cwCMT5Lk8SRJ7k2S5IQufCaVgBhjy/pRB00l9G3IOo5U9kIIhH0PgDPOg7o6mHMTcc3LWceSJEmSpJ10ZQ2ptmYhxS7u0972toqwjvbv6BwvAWPSNH0tSZJpwP8mSTI5TdP1O+6YJMmlwKUAaZrS2NjYxnClp7a2tmyydte2pUtYt+Zl+icX06exkU0NnZdSNbkaGrqwXzXxmuwsi2vSt5Of086+v3f3+HdpaCD/gY+x6ebriXfeQjwrKfvfR3b3+rVWyb+37gqvhyRJkoqtK4VUEzB6h9ejgNXt7NOUJEktMABY18mxbW1fCwxMkqS2MAtqx/3bPEeaphHYApCm6aNJkjwP7A88smPANE2vAa4pvIxr167twkfPXmNjI+WStbvyt98MNbVsmDiFjWvXkt+wodNjGhoa2NCF/aqJ12RnWVyTTZ38nHb2/b27x+8k5IinngNzbmLDLb9i08QphImtl/8rH7t7/Vqr5N9bd0WlXo8RI0ZkHUGSJEnt6Mote/OBiUmSjE+SpBcti5Tf3Gqfm4GLCl+fB9xVKIpuBi5IkqS+8PS8icC89sYsHHN3YQwKY97U0TmSJBlaWCSdJEn2LZxjWdcvgbIQ883E+b+Hgw8n7OXsHmlPCH33gtPeT9irH/l//QfiOteUkiRJklQaOi2kCjOVLgNuA5a0bEoXJ0nytSRJzi7s9lNgSJIkS4HLgS8Wjl0MpMBTwGzgM2maNrc3ZmGsLwCXF8YaUhi73XMAM4CFSZIsoGWx80+labpu1y6Hiua5p1qerjd9RtZJpIoW+vSlz+kfhG1byf/LPxC3bsk6kiRJkiR16ZY90jS9Fbi11bav7PD1ZuBD7Rx7JXBlV8YsbF9Gy1P4Wm9v8xxpmt4I3Njph1BJifPmQq96wiE7/auW1MNqBg0h94m/JP+DbxD/84fw8b8gBB9SKkmSJCk7XbllT+pRcfs24qMPEA49ilDfO+s4UlUIh0wnnPMR4kP3EOfc1PkBkiRJkrQHWUip+J56Aja+5e16UpGFMxI4/BjiDf9BfO6prONIkiRJqmIWUiq6OG8u9G2AyYdlHUWqKiGXI/fHfw6Ne5P/t38kbnwr60iSJEmSqpSFlIoqbtlCfOJhwrRjCbV1WceRqk7o05fcpX8F698g/+/fI8aYdSRJkiRJVchCSkUVF86HLZsJR56QdRSpaoVxEwnnXQQL5hHv+m3WcSRJkiRVoS49ZU/qKfHhe2DAYDjg4KyjSD0qP3d21hG6JZx8NnHJQuINPyNOOIgwdr+sI0mSJEmqIs6QUtHEN16DRY8QjplJyNVkHUeqaiGElvWkGgaQ//FVxI0bso4kSZIkqYo4Q0pFk//FjyCfJ9bXl91sEqkShX79yX3qC+S//WXyP/0uucuuIOR27f9TdPYznZsxa5fGlSRJklSZnCGloogxwvNPw94jCP0HZh1HUkHY70DC+Z+ARY8Qf/OrrONIkiRJqhIWUiqOZxfDW2/CxIOyTiKplXDi6YRjZhJvua7lwQOSJEmStIdZSKko4n1zoK4XjNk36yiSWgkhED72aRg9nvxPv0t8qSnrSJIkSZIqnGtIaY+LmzYQH70fxk8k1NZlHUcqSVmvqxZ61ZP70y+R/+Zfk//OFeT++u8Je4/INJMkSZKkyuUMKe1xcd5c2LYVJni7nlTKwtDh5C7/BjRvJ/+dvyW+9mrWkSRJkiRVKAsp7XHxvjtg1HgYPDTrKJI6EUaOIfcXX4PNm8h/5wriurVZR5IkSZJUgSyktEfFF5+HFUsJJ5xKCCHrOJK6IIzZl9znvgZvvUn+H79MfHFZ1pEkSZIkVRjXkNIeFe+4Bep7E44+kfjIfVnHkdSONtewmnkG3Dub/JV/SfjYnxKOt1iWJEmS1DOcIaU9Jr75OnH+XMKxJxP6NmQdR1I3hcZhcGYCw/Yh/vwHxH//J+KWzVnHkiRJklQBnCGlPSbecys0NxNOeV/WUSTtotC7D/Gkswjr3yT+5nriM4sI511MOOJ4Z0tJkiRJ2mXOkNIeEbduId7zO5h6pI+Ol8pcyOXInf1hcn/1D7BXP+I13yb/7S+1rBEnSZIkSbvAQkp7RHz4Xtiwntyp52QdRVIPCRMnkbviu4QLPwMvNZH/xuXkf3o1ce0rWUeTJEmSVGa8ZU89LsZIvONmGD0e9j846ziSelDI1RBmvJd4xHHEW28g3vUb4vzfw8SDYMoRhN59so4oSZIkqQw4Q0o976knYPWLhFPOcY0ZqUKFvg3kzvtjct/4EeHYk+CZJ+HX/0VcMJ+4bWvW8SRJkiSVOGdIqcfl77gJBgwiHHlC1lEk7WFhcCPhjy6jedAQeOJhWDgfnnmSOGUa7D+ZUFOTdURJkiRJJcgZUupR8YXn4MnHCDPPJNTVZR1HUpGEAYMI75kFp58LAwfDI/fBTb8kLnuGmM/3+PlijMTm7XtkbEmSJEl7njOk1KPyt1wHe/UjnHRW1lEkZSA0DiOeeja8tBIefwjuvxMWP04c1NiyxlSu+/8fJG7ZTHxxGaxaAa+shq1bYNtWyOehto64zyjyIUeYMo0wcEibY+Tnzn7X600NDeQ3bHjndW7GrG7nkiRJkrTrLKTUY+KyZ2DRI4QPXEjo0zfrOJIyEkKAEWOI+4yG5UthwcPkf/ANGDqccMJ7CcedTOg/sMMx4mtriAvnExfOg6cXwfZtUNcLho+EPntBXV3L641vwaoXiT//AREI044jJJcQBjcW58NKkiRJ2iUWUuox+Vuug4Z+hJPOzDqKpFZazxAqhhACjJ9IHLsvoW8/4tzbiP9zLfGmX8CEgwj7jILhowl7Dye+tR7WvQprXyWuWApNy1sG2XsfwolnEIkwbB9Cbuc1qWKM5PY7iDj/98Q5NxGffJRw1vmEU84m1HrrsCRJklSKLKTUI+LzT7esHfXBiwi9nR0l6f+EXA256TNg+gziSyuJv7+d+PzTxIfnwtsbiTvuPGAw7DOK8KGLCVOPJAwfBXRcqIUQCKPGEUaNIx5/Kvlf/YR447XEB+4i9yefJ4wev9ufobNCz1v+JEmSpO6xkFKPaJkd1Z8w84yso0gqYWGf0YTkEqBlZhPr34A1L0O/ATC4kVDXa/fGHzqcmsuuIC6cT/7nPyT/D39F+MgneyK6JEmSpB5kIaXdFp9/GhY/Tjj3IkLvPlnHkVQmQggwYBAMGPTODKTYyTFdHnvqkeS+cjX5f/sO8dp/hv0OhOkneAufJEmSVCK6/7gjaQcxRvI3/Dv0G0CY6dpRkkpH6D+I3F/8P8JZ58PzT8PvbiSufyPrWJIkSZKwkNLuevR+WLqE8P6PEup7Z51Gkt4l5GrInfNROOkseHsT3PrfLYumS5IkScqUt+ypy1ov6hubt8NN18GgIcR8PpOneElSV4SRY4hnfgjm3g5zb2fz668RpxxBqNn5qX2SpOy9EnP8Ymt91jF61CuxZS5ApX2uXfVKzDE66xCSMmUhpV23ZCFsfAuOOZuQc7KdpNIW9upHPO398NiDbFv0KKx8gXjsyYQhQ7OOJknawciRI7OOsEf0XrMGgF5D2/5zp66ujm3bthUzUqZGU7n/riV1jYWUdkl8exM8+SiMGkfYZ1TWcSSpS0JNDRx5PL3HT+Dte2a3rCs15fCWYsoFzyWpJJx77rlZR8hEY2Mja9euzTqGJBWNhZR2zRPzYHszTDs26ySSiqDSbsmtHTcB3ncBzL8PFj5C/huXkzv/E4SDDsk6miRJklQVvM9K3RZfWwPPL4EDDib0H5h1HEnaJaG+N+H4U+DE02Hz2+S/+7c0f/9rxJdWZh1NkiRJqnjOkFK3xHweHroHeveBQ47MOo4k7bYwejwhuYR45y3EW/+b/N/9GeGYmYT3nlu0W5I7m4GWmzGrKDkkSZKkYrGQUvc8vRDWrYEZ7yX08gkhkipDqOtFmHUu8bhTib/9FXHubcQH7oJDjiI364NZx5MkSZIqjoWUuixuWN+ydtSocTBm36zj/P/27jxMqurM4/j3NpuI7KgIKKggiruiYjRoXBA1gknMK8q4L9Fxm+gkauJEx8RRM0nUmGjiFtzxdcddRwSJiiK44BIVFFlEBQV3ULru/HFOadF0NV0NXVVd9fs8Tz/ddddzzr117+n3nnOuiMhql3TsRDLqONL9jXT8/aTjHyDz4mRYtxdsvi302oAkSUqdTBERERGRFk8BKWmUNE3h2SchSWDH7+sfMhGpaEnHziQjR5Pu82PSSY+S3j8Wxj8AXbuTDtoG+vYPb+wTEREREZEmUUBKGiWdMgnemw2DdyXp0LHUyRERKYpkjfYke4+ktnVrmPUWvPoCPPU4THuGdOAWMGBzkjXalzqZIiIiIiItjgJSslLpJ4tIb70Kuq8DA7codXJERIouadUKNt6UdKOBITj/r5dDF+bpU0n79ift3Q82GqjWoyIiIiIijaSAlDQoTVMyY/4MS5fAHvuR1NSUOkkiIiWTJAn07gu9+5Iu/hjemA5vv0nmol9C774kQ/chGbwLSaeupU6qiIiIiEhZU0BKGpROeAhemUpy6M9A46WIiHwr6dINdtqNdLudSdq0JZ34COmtV5GOvRoGDCLZ7nskW+0APdZVyykRERERkToUkJK80vlzSe+4DrbYjmT3/UgnPVLqJImIlJ2kTdvwe7d9SBd9BLNnwrtvk469OgSn2q8Jm21DsvGmJH36Qc/e0LWHglQiIiIiUtUUkJJ6pcu+IXPNH6FtO2qOOJUkSUhLnSgRkTKXdO0OXbvD1juSfrII3p8HC+bD7Jmk057+7jrabg1YtzdJz97Qsw/pooXQqQt06kLSWrdmEREREal8qvVKvdK7boDZM6k58azQLUVERAqSdO4KnbvCwC2oGTqc9NNFMH8u6fy58P5c0g/mkc78F0yZBOl3If+0Q8ew3np9oE8/kk5dSpgLEREREZHmoYCUrCDz7ETSx+4l+cH+JNt9r9TJEZEKkHny4VInoeSSTl2hU1eSgVsuNz1dupTMQ7fDp4vh00XwyWJYtBCmPg1Tnybt1IXMRwtIhg4n6b52iVIvIiIiIrJ6KSAly0nnvkN6w+XQfxCJHVPq5IiIVLykXTuSbj2gW4/lpqeffwpzZ8GcWaQP3Un68J2w7RBq9hwB/TfTGFQiIiIi0qIpICXfSr/4jMwVF8Kaa1Fzwpkax0REpISStTrBplvBpluRbL4d6RMPkk56lMzUp6H/ZtT8+AiSAYNKnUwRERERkSZRxEEASGtryfz+bPjoQxh2IOlLz2oQcxFpMSq9S2DSfR2Sg44kPWAU6dPjSR9wMr8/C7bagZofHRbe3iciIiIi0oLUlDoBUnppJkN6/eXw3mzYcSjJ2j1LnSQREalH0m4Nan6wHzUX/J3kx4fDW6+ROf80MjddQfrFZ6VOnoiIiIhIo6mFVJVL05TUryV9ZjxsvaO6f4iItABJu3Yk+x5EOnQf0vvGko6/n3TyBNh2SL3jS9UMHV6ahIqIiIiI5KGAVJVL77+N9PH7SPYaSdqzV6mTIyIiBUg6dCQZdRy1a7SHKZNg8gR46zXSnYaSdF+n1MkTEREREclLAakqlnnkbtJxt5DssieJHQ2THil1kkREKlJzj3GVdOtBOuxAeOdNmPo0PHgH6YBBsO0QknZrNOu+RURERESaQgGpKpRmaklvu5Z0/P0kg3clOexkkiTRIOYiUrUqYVD0JElgo4GkffrBS1Pgjenw7kzSbYeQ7ro3SU2rUidRRERERORbCkhVmXTJV2Su/gO8PIVk2IEkPzmSpEZj24uIlLNCAmZJ23aww66k/TeD5ybBsxPJvD+PmtEnkGy4STOmUkRERESk8RSQqiLpgvfJ/O0imDOLZPQJ1Oy+X6mTJCIizSTp2p102Eh45y14ZSqZC39BsuveJCMOIenSvdTJExEREZEqp4BUFUgztaSP3096z43QqjU1p5xDsuXgUidLRESaWejGtwnJwceS3ndreBvfsxNI9jiAZPhPSDqsVeokioiIiEiVUkCqwqXz3iVz/eVhoNutdqBm9Ikk3XqUOlkiIlJESfs1SewY0t33Ix13C+kjd5FOfJhkrxEku+8LPXRfEBEREZHiUkCqQqUz/0Xm0bvhhcnQoSPJsWeQLvmS9JXnNXi5iEiVStZZL9wPhv+EzD03hVZTD93BJ0OHke46jGT9DUudRBERERGpEo0KSJnZcOAyoBVwjbtfVGd+O+AGYHvgI+Bgd58V550NHAPUAqe6+yMNbdPMNgTGAt2AacBh7v51U/ZRbdLPPiGd/jzppMdgxmuwZgeSfQ8KT8A7dq6It0iJiEjh6rv+J1sNJu27MfxrOkueehzGPwDrb0iy/S7hDazr9ipBSkVERESkWqz09Wpm1gr4K7AvMAg4xMwG1VnsGGCRu/cHLgEujusOAkYBmwPDgSvMrNVKtnkxcIm7DwAWxW0XvI9CC6IlSj/7lPS1F8k8eDu1F59J5ozDSf9xGSxaSHLwsdRcfB01PzqMpGPnUidVRETKUNK5K8lOQ1n7mntIDj4G2rYjvecmMuecQO15p5C5+Uoyk58gXfA+aar2tSIiIiKy+jSmhdSOwAx3fxvAzMYCI4HXcpYZCZwX/74D+IuZJXH6WHdfCrxjZjPi9qhvm2b2OrAHcGhc5vq43SubsI9nGlkGRZOmKazwk4GU8Lt2GSxZAku/gqVLYelXLG3XjsyCD+DLz2HRx7D4I9JFC2H+XFj80Xcb32Ajkv0PJtl6B9hgY5KalcYaRUREAKhZqxM1e42EvUaSfryAdOrTpK9MJZ08ASY8FLp6t18T1ulFss56sPZ60KkLdOxEslYn6LAWtG4DbdpA67bhd5s2YVqr1mFwdRERERGRHI0JSPUG5uR8ngvslG8Zd19mZp8A3eP0yXXW7R3/rm+b3YHF7r6snuWbso+iSt96jcyl5y4fcCKFTAw8NcHi3A9JDXTpBl27k2y6Zeha0WfD8Dt2yUtnz4TZMzVOlIiINEnSbW2SvUfC3iNJM7UwbzbpzNfhvdmkH84nnfUWPP/Ut/e1Rt1vkuS7HxJICPe0hNCid+jwZsyRiIiIiJSjxgSk6nusWbf+mW+ZfNPra77T0PJN2cdyzOx44HgAd6dXr2YYG6NXL9htr9W/3cYadXTp9l1EXUqdgDKkMlmRymR5Ko8VqUyWl/e+2Gd92GmX4iZGKlohdbBmqa+1EMp7dVLeq1c15195r16N6dc1F1g/53Mf4L18y5hZa6Az8HED6+abvhDoErdRd1+F7mM57n6Vuw929w2jwUoAABMjSURBVMHEZ7Mt4cfMppY6DeX0o/JQmahMVB4qE5VHgT9SXnReKu/Ku/KuvCv/ynvl571RGhOQmgIMMLMNzawtYQDxcXWWGQccEf8+CBjv7mmcPsrM2sW35w0Ansu3zbjOE3EbxG3e28R9iIiIiIiIiIhIGVppQCqO53Qy8Ajwepjkr5rZ+WY2Ii52LdA9Dih+OnBWXPdVwAkDoD8MnOTutfm2Gbd1JnB63Fb3uO2C99HUAhERERERERERkebVmDGkcPcHgQfrTPtNzt9LgJ/mWfcC4ILGbDNOf5vv3sSXO73gfVSIq0qdgDKj8liRymRFKpPlqTxWpDJZnspDylE1n5fKe3VS3qtXNedfea9iSZrqfWwiIiIiIiIiIlI8jRlDSkREREREREREZLVpVJc9KT4zGw5cBrQCrnH3i0qcpNXGzNYHbgB6AhngKne/zMzOA44DFsRFfxW7dmJmZwPHALXAqe7+SJxebznFAe7HAt2AacBh7v51cXLYNGY2C/iMkMdl7j7YzLoBtwH9gFmAufsiM0sI+d4P+BI40t2nxe0cAZwTN/s7d78+Tt8eGAO0J3SXPS2+GKAsmdlAQt6zNgJ+A3Shis4TM7sO+CHwobtvEac1+3mRbx/NnN2VylMe/wscAHwNzASOcvfFZtaPME7hG3H1ye5+QlynoHw3VLallqdMzqOZvydm1o5wLd8e+Ag42N1nNXuGpeJVSh2ogfpO1dzbzawV8Dwwz91/2JTrSaHXrHJgZl2Aa4AtgBQ4mnAvqvjjbmY/B44l5Hs6cBSwHhV63Ku5nlZInSzOq5j6R315z5n3n8D/Amu7+8JKO+6rm1pIlaF48/4rsC8wCDjEzAaVNlWr1TLgDHffDBgCnJSTv0vcfZv4k/3naRDhTYybA8OBK8ys1UrK6eK4rQHAIsLFryX4Qcz74Pj5LODxmI/H42cIeR4Qf44HroRvb4DnAjsRxmI718y6xnWujMtm1xve/NlpOnd/I3suEG44XwJ3x9nVdJ6MYcVjVYzzIt8+Sm0MK5bHY8AW7r4V8CZwds68mTnnygk50wvNd71lWybGUP/3ubm/J8cAi9y9P3BJXE5klVRYHShffaea7u2nER4MZBV0PWniNascXAY87O6bAlsTyqDij7uZ9QZOBQbHf9JbEY5fJR/3MVRvPW0MjayTVWD9Ywz1fO/ig4i9gdk5kyvtuK9WCkiVpx2BGe7+dmytMRYYWeI0rTbuPj8bFXb3zwg36d4NrDISGOvuS939HWAGoYzqLacYhd4DuCOufz1wYPPkptmNJKQfls/HSOAGd0/dfTLQxczWA/YBHnP3j2O0/DFgeJzXyd2fiU/QbqBllcmehMDCuw0sU5Hnibs/CXxcZ3Ixzot8+yip+srD3R/18PZWgMlAn4a20cR85yvbkstzjuSzOr8nuWV1B7BnXF5kVVRMHaiB+k5V3NvNrA+wP6GlEE28nhR0zWr+XK2cmXUChhLfFO7uX8cWIlVx3Ak9cNqbWWtgTWA+FXzcq7meVmCdrKLqHw3UvS4BfkloIZhVUcd9dVNAqjz1BubkfJ5LwwGbFit2qdkWeDZOOtnMXjaz63IixPnKI9/07sDinIthSym/FHjUzKaa2fFx2rruPh9CxRZYJ04vtEx6x7/rTm8pRgG35nyu5vMEinNe5NtHuTsaeCjn84Zm9oKZTTSz78dpTcl3S7wuN/f35Nt14vxP4vIiq6IlftdWqk59p1ru7ZcS/jHLxM9NuZ4UWiblYCNCd+l/xPvPNWbWgSo47u4+D/gDoXXIfMJxnEp1HPdcqqcFuXWyiq9/mNkIQvfkl+rMqrbjXhAFpMpTfRHesugXvjqZ2VrAncB/uPunhKaJGwPbEG5if4yL5iuPQqeXu13cfTtCs86TzGxoA8tWS5lgZm2BEcDtcVK1nycNqeoyMLNfE7rI3BwnzQc2cPdtgdOBW+KT66bku6WVVTG+Jy2tTKRlqLjzqp76Tj4Vcw03s+zYKlNzJjfletLi8k5oIbQdcGW8/3xBw91qKibv8eHHSGBDoBfQgVCvrasSj3tjVE1+66mTVXT9w8zWBH5NGO+2rqo57k2hgFR5mgusn/O5D/BeidLSLMysDaFydrO73wXg7h+4e627Z4CrCU04IX955Ju+kNAUsnWd6WXN3d+Lvz8kjJW0I/BBtltQ/P1hXLzQMpnL8t2YWkSZRPsC09z9A9B5EhXjvMi3j7IUB4X8ITA6Nm8mNgv/KP49lTC45iY0Ld8t6rpcpO/Jt+vE+Z1pfNdBkXxa1HdtZeqr71Ad9/ZdgBEWXtgyltD15lIKv54UWiblYC4w192zrf/vIASoquG47wW84+4L3P0b4C7ge1THcc9V1fW0+upkVH79Y2NCIPaleN3rA0wzs55UyXFvKgWkytMUYICZbRhbh4wCxpU4TatN7ON7LfC6u/8pZ3rueCw/Al6Jf48DRplZOwtvWxgAPEeecooXvieAg+L6RwD3NmeeVpWZdTCzjtm/gWGE/I8jpB+Wz8c44HAzS8xsCPBJbLb5CDDMzLrGp1TDgEfivM/MbEgs/8Mp8zLJcQg53fWq+TzJUYzzIt8+yo6Ft7OcCYxw9y9zpq8dB8vEzDYinBNvNzHf+cq2LBXpe5JbVgcB471M3vIkLVrF1IHy1Xeognu7u5/t7n3cvR/hGI5399EUfj0p6JpVhKytlLu/D8yx8LZgCONgvkYVHHdCV70hZrZmTFs27xV/3Ouo2npavjoZFV7/cPfp7r6Ou/eL1725wHbxelDxx31VtF75IlJs7r7MzE4mnKStgOvc/dUSJ2t12gU4DJhuZi/Gab8ivFVhG0KTxFnAzwDc/VUzc8INbRlwkrvXAjRQTmcCY83sd8ALxIEly9i6wN1mBuF7eYu7P2xmUwA3s2MIN/mfxuUfJLw6dAbh7XNHAbj7x2b2W8LFHeB8d88+MTiR714f+hDLj7NTliw0f92beC5Ev6+m88TMbgV2B3qY2VzC2zguovnPi3z7KKk85XE20A54LH6HJnt4o95Q4HwzW0Z4xfAJq5Dvesu2HOQpk92L8D25FrjRzGYQnkyOauasShWosDpQvvpOMa7h5aqg60kTr1nl4BTg5vgP9tuEY1lDhR93d3/WzO4AphGO1wvAVcADVOhxr+Z6WiF1skqrf9SXd3fP939ERR331S1JUz3MFBERERERERGR4lGXPRERERERERERKSoFpEREREREREREpKgUkBIRERERERERkaJSQEpERERERERERIpKASkRERERERERESmq1qVOgIi0XGa2O3CTu/cpdVqKzcwGAmOB/sCv3f3PJU6SiIiIiIhIi6GAlEgLYmYTgK2Bnu6+tMTJWSkzS4EB7j6j1GlpBr8EJrj7tvXNjMdqCLAMqAVeAk5y9+lFS6GIiIhImWpMPdHM/gbMc/ffFi9lIlIs6rIn0kKYWT/g+0AKjChtagToC7y6kmVOdve1gO7ABODGpuzIzFZ4eFDftOZmZomZ6b4hIiIiReHuJygYJVK51EJKpOU4HJgMPAscAdyenWFmY4AvgH7AUOA14FB3nxnnp8CJwBlAD+AWQrAkNbPzgP7u/m9x2X7AO0Abd19mZkcRWgP1ARYAF7v73wtNfNzPIGAJ8CNgNnCEuz8f568PXEYIutUAt7r7yTEA8ivgOKA98DBwirt/kpPWo4HzgbWAs4GpwLXABoQuhSfnpONo4BdAT+A54Hh3fzdPmkcAFwK9gReBE939dTMbD+wG7GpmlwLbufub+fIey3EscFbOttsBFwOWXQw4092XZrtCApcDPwceM7Nr604DDjOz44AzgW7AP4ET3P09M/tvoJu7n2JmbYDFwF/d/Zdm1h5YBKzn7ovMbAjwp3h83gVOc/cJMZ0TgKeA3YHtgC2BSmzxJiIiIiIiRaSAlEjLcTghaPAsMNnM1nX3D3LmHwIMB6YB1wMXAKNy5v8Q2AHoRAjY3EcI7qzMh3HdtwnBrofMbIq7T2tCHkYAPwaOAn4H/AUYYmatgPuB8cBhhC5ug+M6R8afH8S03BDXOyxnuzsBA2L6xsV87QW0AV4ws9vdfaKZHUgIbh0AvEUIEN0KfK9uQs1skzjvQELrpp8D95nZIHffIwZqbnL3a1aWaTNrC4wmBBSzfk3o0rcNodXbvcA5wH/F+T0JQaa+hADdTnWnmdkehIDZMEJrrT8QxrUaCkwkBPggHPf3CUE0gJ2BN2IwqjfwQCzPh4E9gTvNbFN3XxCXPwzYF3gDSFaWXxERESkvZnYW4eHeOsAcwviXd5vZkcCxhDrKMYQHWP/u7g/F9SYAk4A9gK2AZwgPPRfWN5aomc0CjnX3/zOzHQl1kc2Ar4A7gdPd/esC0j0GmOvu5+Q8sLuE8DCuFviVu/8jLtueUL88COgCTAf2dvevCikrESkedb0QaQHMbFdCEMLdfSowEzi0zmJ3uftz7r4MuJkQ6Mh1kbsvdvfZwBP1zK+Xuz/g7jPdPXX3icCjhFZMTfFPd3/Q3WsJ3de2jtN3BHoBv3D3L9x9ibv/M84bDfzJ3d92988JLaBG1emy9tu4zqOElmK3uvuH7j6PUInKjvP0M+BCd389ltP/ANuYWd960now8IC7P+bu3xCCPe2pJ3jVgD+b2WLgc+Bk4L9z5o0Gzo/pXBDn5QbZMsC57r40pyJVd9po4Dp3nxbHFDsb2Dm2HHsGGGBm3QkBqmuB3ma2FiEwNTFu89+AB+Nxybj7Y8DzwH45aRnj7q+6+7JYFiIiItKyzCTU3zoT6hw3mdl6cd5OhIdOPYDfA9eaWe4DqEMJDxPXAdoC/9nIfdYSHuj1IDwM2xP491XLBj1jHnoTAmh/NbOucd4fgO0JdbVuhBb+mVXcn4g0I7WQEmkZjgAedfeF8fMtcdolOcu8n/P3l4TuaxQwv15mti9wLrAJIYi9JuGJU1PUTcMaMbC0PvBuDBLV1YvQjSzrXcK1a92cabktxb6q53M2r32By8zsjznzE0Klpm63veX26+4ZM5sTl22sU939mtjtcBdgnJnt5u4v58lXr5zPC9x9SZ3t1Z3Wi9AiLpvGz83sI6C3u88ys+cJwaehhBZz28R07Ebo+gehTH5qZgfkbLcNIWiZNaeAPIuIiEiZcffbcz7eZmZnEx4IQqiDXQ1gZtcDVxDqWdl62z+yQxOYmdPIsUzjQ9SsWWb2d0Id5NImZwS+ITzQWwY8aGafAwPN7DnCEA5D4gNJgKdXYT8iUgQKSImUudj82IBWZpatGLQDupjZ1u7+0iru4gtCkCmrZ86+2xGaVx8O3Ovu35jZPaz+bltzgA3MrHU9Qan3CEGTrA0Ib677gDCuVaH7ucDdb27Esu8RxksCwoDehMDZvLxr5OHuGWCSmc0gdK97me/ylR0YfYM4LSutZ1N1py1XNmbWgTCAejaNEwlN7LcFpsTP+xAqoE/GZeYAN7r7cQ1kob60iIiISAthZocDpxPGG4XwsK4HoRXTtw8M3f1LM8vOz2rqQ81NCMNNDCbUNVsTho1YFR/VqStm09MDWIPQEkxEWggFpETK34GEysKWQG6feycEis5Yxe2/CJxpZhsAnxC6fWW1JQS/FgDLYmupYcArq7jPup4D5gMXmdm5hPxu7+5PEcZxOtPMHorp+B/gtjhQeKH7+RvwWzN70d1fNbPOwLA6Tw2zHDjLzPYkBG9OA5bSxKdtZrYzYdDwbADqVuAcM5tCCPj8hjAuQiFuAcaa2S3A64SyedbdZ8X5E4E7gCnu/nUcB+JC4J2c8aFuAqaY2T7A/xFaRw0BZrj73IIzKiIiImUlDk1wNaHL3DPuXmtmL7LqDxiXe6gZxwRdO2f+lcALwCHu/pmZ/QdhfKfmsJDw4pyNgVV9WCsiRaIxpETK3xGEptKz3f397A9hYO/RdcZSKlgcM+g2QqudqYTBxbPzPgNOJQRnFhHGEBi3KvvLk4ZawkDj/Qlv35tLGMMJ4DrCeFNPEt6otwQ4pYn7uZvwZruxZvYpIbC2b55l3yCMr3Q5oZJzAHBAIQNxAn8xs89jc/IbgXOyg4QSBt18nlDu0wld735XYH4eJwyCfichoLcxyw9k/zRh3Ktsa6jXCOX3ZM425gAjCYO9LyC0mPoFuj+IiIhUig6Eh18LAOIblLdYDdt9kzD8wv7xjb7nEB5kZnUEPgU+N7NNCW98bhaxNfp1wJ/MrJeZtTKznWNrfxEpU0maqieGiIiIiIhIpTKzCwgBoQzhjcXbEx6WLSO8FW/XnGVTYIC7z6j7VuHsW/myy8fPFwKtCAOin8x3b9kbClxFGGLhBcL4lHvkrPvtfhpI9xjqvGWvgbf6tY9p+SmhG99LwD56y55I+VJASkREREREREREikpdMkREREREREREpKg0qLmIiIiIiIiUhJm9yvJvVM76WSPfjCwiLZS67ImIiIiIiIiISFGpy56IiIiIiIiIiBSVAlIiIiIiIiIiIlJUCkiJiIiIiIiIiEhRKSAlIiIiIiIiIiJFpYCUiIiIiIiIiIgU1f8DkSDacHkQxE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#annual income distribution plot\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1,ncols=2, figsize=(20,6))\n",
    "sns.distplot(temp['annual_inc'], axlabel = 'Annual Income of Borrower', ax=ax0)\n",
    "sns.boxplot(temp['annual_inc'], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above plot we can see the maximun spread is in between 0-150000 and bin size of 25000 seems good for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['annual_inc_bin'] = pd.cut(loan['annual_inc'], [x for x in range(0, 160000, 25000)], \n",
    "                                      labels=[str(x)+'-'+str(x+25)+'k' for x in range (0, 150, 25)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>annual_inc_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>25-50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49200.0</td>\n",
       "      <td>25-50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80000.0</td>\n",
       "      <td>75-100k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36000.0</td>\n",
       "      <td>25-50k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47004.0</td>\n",
       "      <td>25-50k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annual_inc annual_inc_bin\n",
       "1     30000.0         25-50k\n",
       "3     49200.0         25-50k\n",
       "4     80000.0        75-100k\n",
       "5     36000.0         25-50k\n",
       "6     47004.0         25-50k"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[['annual_inc', 'annual_inc_bin']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting annual income against loan amount and int_rate\n",
    "fig,(ax0,ax1)=plt.subplots(nrows=1,ncols=2,figsize=(20,7))\n",
    "sns.barplot(x='annual_inc_bin', y='loan_amnt',data=loan,ax=ax0)\n",
    "sns.barplot(x='annual_inc_bin', y='int_rate',data=loan,ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems obvious, loan amount increases as income increases and a small variance in int rate that is obvious also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','annual_inc_bin'])['annual_inc_bin'].count()\n",
    "cnt = loan.groupby('annual_inc_bin')['annual_inc_bin'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charge-Offs percentage are more for customer in lower income segment, Lending clubs can be more concious when providing loan in lower annual income segment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DTI - Debt To Income ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how DTI ratio is related with Loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#first we will see the DTI variance and data spread to identify the bin size.\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(loan['dti'], axlabel='Debt to Income ratio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36433.000000\n",
       "mean        13.444131\n",
       "std          6.643308\n",
       "min          0.000000\n",
       "25%          8.360000\n",
       "50%         13.540000\n",
       "75%         18.690000\n",
       "max         29.990000\n",
       "Name: dti, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.dti.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets derive new column dti_bin\n",
    "loan['dti_bin'] = pd.cut(loan['dti'], [x for x in range(0, 31, 5)], \n",
    "                                      labels=[str(x)+'-'+str(x+5)+'%' for x in range (0, 30, 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dti</th>\n",
       "      <th>dti_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0-5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.00</td>\n",
       "      <td>15-20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.94</td>\n",
       "      <td>15-20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.20</td>\n",
       "      <td>10-15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.51</td>\n",
       "      <td>20-25%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     dti dti_bin\n",
       "1   1.00    0-5%\n",
       "3  20.00  15-20%\n",
       "4  17.94  15-20%\n",
       "5  11.20  10-15%\n",
       "6  23.51  20-25%"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[['dti', 'dti_bin']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dti_bin\n",
       "0-5%      4376\n",
       "5-10%     7314\n",
       "10-15%    9152\n",
       "15-20%    8428\n",
       "20-25%    6394\n",
       "25-30%     624\n",
       "Name: dti_bin, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's plot dti against the percentage of charged off loans.\n",
    "grp=loan.groupby(by=['loan_status', 'dti_bin'])['dti_bin'].count()\n",
    "cnt=loan.groupby(by='dti_bin')['dti_bin'].count()\n",
    "cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: There are very less customer in DTI range 25-30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>dti_bin</th>\n",
       "      <th>0-5%</th>\n",
       "      <th>5-10%</th>\n",
       "      <th>10-15%</th>\n",
       "      <th>15-20%</th>\n",
       "      <th>20-25%</th>\n",
       "      <th>25-30%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>11.334552</td>\n",
       "      <td>11.594203</td>\n",
       "      <td>13.767483</td>\n",
       "      <td>14.653536</td>\n",
       "      <td>15.686581</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current</th>\n",
       "      <td>1.919561</td>\n",
       "      <td>2.584085</td>\n",
       "      <td>2.742570</td>\n",
       "      <td>3.061224</td>\n",
       "      <td>3.597122</td>\n",
       "      <td>7.852564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>86.745887</td>\n",
       "      <td>85.821712</td>\n",
       "      <td>83.489948</td>\n",
       "      <td>82.285240</td>\n",
       "      <td>80.716297</td>\n",
       "      <td>79.647436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "dti_bin           0-5%      5-10%     10-15%     15-20%     20-25%     25-30%\n",
       "loan_status                                                                  \n",
       "Charged Off  11.334552  11.594203  13.767483  14.653536  15.686581  12.500000\n",
       "Current       1.919561   2.584085   2.742570   3.061224   3.597122   7.852564\n",
       "Fully Paid   86.745887  85.821712  83.489948  82.285240  80.716297  79.647436"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perc=grp.unstack()*100/cnt.T\n",
    "perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% of loans Charged Off')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k+jW5blB9/W6p9GjV128J1Y7Y73f9VmRI6qbA7sD6wJ3AebZ/0YPj/y1wkO2rJZ0MHE4xrUaL7YXAwvLt0pGRkWketv8GIcbpaHL9mlw3SP0GXbf1Gx4e7mi/Toek7kxxFf9c4H7gOcB15bML07EIWFS2PgDOp0gSERFRg047mo8HdrX9T7aPsL0HsGtZ3jXbdwN3lAv4AOwA/HQ63xkREd3r9PbRBsC3x5R9pyyfroOAcyStAvwK2K8H3xkREV3oNCncALwb+HBb2SFl+bTYvgHYarrfExER09dpUngbcLGkg4E7gA2BB4Hp9ilERMQM0lGfgu2fAZsBbwY+BgjY3HYeNIuIaJCOh6TafpTl+xUiIqJBJkwKku6gmItoUraf0dOIIiKiNpO1FPasLIqIiJgRJkwKtq+qMpCIiKhfpw+vRUTELJCkEBERLUkKERHRMmFSkLS47fXp1YQTERF1mqylsLKk0YV13lhFMBERUa/JhqSeSjGD6QiwuqTbx9spzylERDTHZENS3yvps8A84HJgr6qCioiIekw6zYXtRcAiSTvnuYWIiObrdI3mKyTtR9FaGF2O8/O2z+hncBERUa1Ol+M8kmLt5C8C7yj/PrQsj4iIhuh0ltQDgO1t3zZaIOky4FvAcf0ILCIiqtdpUngScN+Yst8Aq/UiCEkrAdcBd9reqRffGRERK67TpHApxTrKhwO3AxtRtBAu61EcBwM3A2v26PsiIqILnU5z8Xbg98CNwB8o1mZ+EDhougFI2gB4HfC56X5XRERMT6ejj34H7C1pX2AIGLH9WI9iOAk4FHhyj74vIiK61PFynABlIri3VweXtBNwr+3rJW0/yX4LgAVlDAwNDfUqhL4ZhBino8n1a3LdoPr63VLp0aqv3xJum3qnHup3/VYoKfTBi4FdJL0WWBVYU9IXbD9u1TfbC4GF5dulIyMjFYe54gYhxulocv2aXDdI/QZdt/UbHh7uaL9ak4LtI4AjAMqWwnvGJoSIiKhO1lOIiIiWFWopSFqT4sr+r4FfAR+yvXjyT3XG9pXAlb34roiI6M6KthROoRiS+gmKIann9zyiiIiozaRJQdKJktqHij6DonVwOXAs8Nx+BhcREdWaqqVwHXClpDeX778C/FDSF4AfAGf1M7iIiKjWpEnB9jnAK4CXlBPgXQbsBlwE7Gn7Xf0PMSIiqjJlR7Pt3wIHSXohcBrFzKgfsP1Qv4OLiIhqTZoUJK1HMdpoE+AnwK4ULYXvSzrK9kX9DzEiIqoyVZ/C+cBDwCeBOcAnbZ8C7AhI0sV9ji8iIio0VVLYDDjS9mXAUcDmALbvKZ88/lif44uIiApN1adwNvBfkr4DvBQ4s31j+cBZREQ0xFSjj94JvIdiHYW32T6pkqgiIqIWnYw+uha4toJYIiKiZpkQLyIiWpIUIiKiJUkhIiJakhQiIqIlSSEiIlqSFCIioiVJISIiWlZoOc5ek7QhxVPTTwceAxbaPrnOmCIiZrO6WwqPAu+2vRmwDfCvkjavOaaIiFmr1qRg+y7bPyhf/x64GVi/zpgiImazWm8ftZM0D3gBcPU42xYACwBsMzQ0VG1wXRiEGKejyfVrct2g+vrdUunRqq/fEm6r9Hj9rt+MSAqS1qBY//mdtn83drvthcDC8u3SkZGRKsPryiDEOB1Nrl+T6wap36Drtn7Dw8Md7Vd3nwKSVqZICOfYvqDueCIiZrNak4KkORTrPt9s++N1xhIREfXfPnoxsBfwY0k3lGX/bvuSGmOKiJi1ak0Ktr9DsfZzRETMALX3KURExMyRpBARES1JChER0ZKkEBERLUkKERHRkqQQEREtSQoREdGSpBARES1JChER0ZKkEBERLUkKERHRkqQQEREtSQoREdGSpBARES1JChER0ZKkEBERLUkKERHRUvdynEiaD5wMrAR8zvaHag4pImLWqrWlIGkl4BTgNcDmwO6SNq8zpoiI2azu20dbA7fY/pXth4EvArvWHFNExKxV9+2j9YE72t4vAv5u7E6SFgALAGwzPDy8wge6865rugxxMNx72VfrDqGvbjrpf+oOoW8+cux1dYfQV8PHnVh3CH01fPCKn49msrpbCnPGKVs6tsD2Qttb2d6q/ExlfyRdX/UxU7/ULfVL/fr0Z0p1J4VFwIZt7zcAFtcUS0TErFf37aNrgU0lbQzcCewG/FO9IUVEzF61thRsPwq8HbgMuLko8k/qjGkcC+sOoM+aXL8m1w1Sv0E3I+s3Z+nS5W7hR0TELFV3n0JERMwgSQoREdGSpBARES1JChER0VL3kNQZTdKqwCq2f1d3LL1W1m0PYHXgXNu/qTmkiFlD0rOA9wOrASfY/l69ES2TlsIEJB1AMVT2a5KOrzuePjiZ4qLgIeDCmmOJeBxJ20j6pqTvSnp93fFMV3kR1u6DwAeAw4HPVB/RxJIUSpJ2HlP0Stvb2X4p8Lo6YuolSedKemZb0drAOcB5wFr1RNVfTTuxtGta3SQ9fUzRIcAuwHyKE+igu1jSXm3vHwHmlX/+UkdAE8nto2W2LFsHR9m+EfiRpHMo5mKaaQ/UdeO9wLGSFlP8kp0AXASsStGMHXiSnm777rai0RPLHOB/GOAWUZPrVvpsORfQR20/BDxAMbvBY0ATbt/OB94m6VLgOOA9wDsobt/uUWdgY+XhtTbl1coHyrdHAWsAq9v+UX1R9Zakl1AkiK8Bn7Y9o65SpkPShUDrxCJpIXAdxYllP9svrjXAaWhy3UaVrfWDgbOAr1AkhdWB82zfV2dsvSLpKRTnlvWA99n+Zc0hLSdJoY2kJ1M05TaluJq+lmVXLgNN0loUv2SPUKxb8Xpgb+Ak2/9ZZ2y91OQTS5PrNqpceOtAilu2x9n+ds0h9YSkvwP+DXgYOB74E0WLYRHwQdu/rTG8x0mfQknSsRRXz1cAL7e9C3AjRUfzXpN+eDBcCPyZ4nbR522fDewMvFDSRbVG1kO2LwZ2BJ4KXAD8r+1PNOGk2eS6SdpF0neAbwI3UUyO+Q+SzhvTFzaoPgscBnwYONX2L23vBlwMuNbIxkhSWGYn2y8DtqW4gsb2RRS/hGvXGViPrAOcS9G5vD6A7T/ZPgZ4a52B9UqTTyxNrlvpWIrftX8EPmz7AduHUNxqOa7WyHrjLxSdys+gaC0AYPsq2zvWFdR40tG8zE2SPk8xbviq0cJyJteTa4uqd44CvkHxw3l4+wbbd9USUe8dC/w9xf/hJba3Bg6RtCnFiWW3OoObpibXDeC3FHVYDbh3tND2Lxj8ukFxq++tFAlh75pjmVT6FNpI+mvgEds/qzuWWHGSvg2cSXFimW97p3oj6p0m1w1A0hCwO0Wf17lNfGAUQNK6FC31pcBi2/fUHNJykhQmUC788wLgp01IEuWoh8MpOpifVhbfC3wV+JDtB+qKrVeafGJpct0mImlt2/fXHUcvSHo+Rb/CUygWFINipckHgANt/6Cu2MZKUihJutD268vXuwInAVdS9DH8P9tn1hfd9Em6jOJ+9Fmj493LIbj7UDyo96o64+uXhp1Y5gBb03alCVxje+B/iSW91/ax5evNKQZGrEzxHMabbV9dZ3zTJekG4K1j6yFpG4qO5y3riWx56WheZqO214cBr7C9H/Bi4F31hNRT82x/uP0BKNt32/4wRefXwJP03rbXm0v6OXC9pFvLIYEDS9KrgV9QPGj4Woohm8cAvyi3Dbo3tL3+KHCw7Y0BASfWE1JPPWm8xGb7+8CTaohnQuloXqb9amuu7V8D2B6R9FhNMfXSbZIOpWgp3AOt+5v7AnfUGVgPvYGiQxaWnVi+LmlripbftrVFNn0nU7Tobm0vLG9zXgJsVkdQfTJs++sAtq+RtFrdAfXA1yV9DTibZb9vG1J0Ol9aW1TjSFJYZktJv6Norj5xdFoBSasAK9UcWy+8maJP4SpJo30K91BMdaHaouqfpp1Y5lI86DTWnRS3WQbdJuXzMnOADSStbvuP5baBr5/td0h6LcXUJOtT1HMRcIrtS2oNbowkhZLtiU78q9OAcfy2l1DcFjus7lj6qMknltOBayV9kcdfae4GnFZbVL2z65j3T4BWa3ZGzSLarfLkP6MSwHjS0TwJSTs1aQqIiUjaz/YZdccxXZK2G1N0ve0/lCeWN9o+pY64eqXsgB17pXmR7Z/WGlhMqRzUcTTFXFVHAQdR3O78GcVtzhnzrFBaCpP7AND4pEDRYTnwScH2VROU3wMMdEIAKE/+jUwA5ZxHB1AM07zU9nfbtrVGJg2wMymm0XkS8N8UMwvsRNFC+izLt5Rqk6QwuTl1B9Arkiaa6XUOsG6VsdRB0kLbC+qOo1uS1gSOoDhpXmL7vLZtn7Z9YG3B9capFLdqrwE+IemqcpoLePwAgkG1ru1PAkg6sBz1B/BJSW+pMa7lJCm0kfRciow9Og78Akmb2b653sh6Yl2KuWWWjCkfnY9/4EmaaI6qORTDOAfZGRRDUr8C7C/pjcA/2f4zsE2tkfXG1rb/BkDSp4BPS7qA4oG9JlyctQ//P3uSbbVLUihJOoziB/CLFFcrUFyVnSfpi7Y/VFtwvfGfwBq2bxi7QdKV1YfTF/cBt/H4k8jS8v3Txv3E4Him7X8sX18o6Ujgm5J2qTOoHlpl9EU539gCSUdRPHC5Rm1R9c5XJa1h+w+225+neRbw8xrjWk46mkvlg07Ps/3ImPJVgJ/Y3rSeyKJTkn4B7GD79nG23WF7wxrC6glJN1P8fD7WVrYPcChFst9owg8PAElfAL5g+9Ix5QcAn7E96KPHBsaMarbU7DFgeJzy9cptjSNpYO+xT+AkJl5v+iNVBtIHFwOvaC+wfRbwbtqmYh5UtvccmxDK8s81NSFImpGDWNJSKEmaD3yK4r7t6DjwZwDPAt4+3g/soJP0A9t/W3ccEeMZ9MEBU5H0Q9svqDuOsdJSKJUn/WdTDM+8DLicYp6Z5zQxIZSa0IE3qXIt40aaqVeaPbRV3QH02Q/rDmA86WhuU96v/X7dcVRB0kuAr0h6te3L646nj5p8Ylm/7gD67N6pdxlctvevO4bxpKUwS0i6pu31P1PcKlsJOFrS4RN+cPA1+cQyI680e8X2/Lpj6JXy9vTo66dIOk3SjySdWz5xP2OkpTB7tHfWLQBeZfs+SSdQtI4GfcjtuJp0Yhlrpl5pdqNcBOoIikWg/qosbtIiUMezbDbUjwF3ATtTPJh3KkW9Z4QkhdnjCZLWomgdzrF9H4DtByU9Wm9ovdHkE4uk+aN9W2U9Pw68CLgJeNdMXNZxBZnimYTtx1kE6stAkxaB2sr288vXJ5ZDi2eM3D6aPZ4CXA9cB6xd/sIhaQ2a0+Fsiie2t7e9ju11gJeXZV+uNbLpO77tdfuV5rUUV5qDrumLQD1N0iGS3g2sWa6iN2pGnYfTUpglbM+bYNNjwD9UGEo/zWubUwYoTizAhyU15lYLM/xKs0tNXwTq/wNPLl+fBQwB95UXZ8vNMlCnJIVZrlxv4Nd1x9EjTT6xPE3SIRStujUlzWlbm3lGXWl2qX0RqHUppidpzCJQto8p51ZbH7ja9h/K8rslnVtvdI/XhB+miFFvBtahOLHcL+l+4EpgbeBNdQbWA6NXmmuw7EqTmXil2Y1yEagzgLcDG9pe2/Zmtg8Dtq43uumTdBBF39ZBwE2S2qfKPn78T9UjLYVojMlWl5O0HwO8ZoTtYyYov1vSf1cdT69Jegfwr8DNwOckHWz7q+Xm9pE7g2oB8MJy0ad5wPmS5tk+mRnWp5eWQswW455UG6IJdftnipPm64HtgfdJOrjcNqNOml1aqe2W0a0UdXyNpI8zw+qXlkI0RpMXEmpy3UqPO2lK2p7ianojZthJs0t3S3r+6NT1ZYthJ4q1t/+63tAeL0khmqTJCwk1uW4wQCfNLu0NPO55oHLdiL0lzaghxUkK0SRNXkioyXWDATppdsP2okm2fXeibXXI1NkREdGSjuaIiGhJUoiIiJb0KUSMIelMYFH7Autt2/4d2MT2AZN8fh7FU+Irl/fFV/g7IuqSPoWIMUaTAvBfFIvJb7CCn5/HJEkhYibL7aOIiGhJSyFmPUkvAE4DNgUuoZiM7S7gX4AnAn+/LzcCAAABfElEQVQsd302xXQFz7K95yTfN4+ipfBWinW+5wAn2P5Yuf39o9/Rtu++wAeB1YETbR/XwypGdCwthZjVJK0CXAh8nmLivC8D/wg8CLwGWGx7jfLP4hX8+pdTJJpXA4dLeuUk+74EeA6wA3CUpM1W8FgRPZGkELPdNhRLlZ5k+xHb51MsXNMLx9h+0PaPKSbj232Kff9k+0bgRmDLHsUQsUKSFGK2GwbubFubAOC2Hn13+xoOt5XHmsjdba//SDFFdkTlkhRitrsLWH/M8oijyz9Ot8NtwzHfuaK3nyIql6QQs933KObceYekuZLewLJFXe4B1pH0lC6/+32SVpf0PGA/4EvTDzeiv5IUYlaz/TDwBorRP0soVm+7oNz2M+A84FeSHpA02e2f8VwF3AJcQTH66PJexR3RLxmSGhERLWkpRERES+Y+iuiCpD2A8eb5v83286qOJ6JXcvsoIiJacvsoIiJakhQiIqIlSSEiIlqSFCIioiVJISIiWv4PzCKwdMIdXbgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=perc.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('% of loans Charged Off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charge-Offs percentage is increasing as DTI increases that means it is risky to provide loans to customer whose DTI percentage is high. Charge-Offs percentage is more than 14% in DTI range 15-25% but decreases after that which is due to the very less number of customer in that range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Home Ownership Vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     30424\n",
       "Charged Off     4943\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#before comparison, lets filter out our loan dataset to include only fully_paid and charged-off loan status, \n",
    "#current wont add much in our analysis.\n",
    "filter_cond=loan['loan_status'].str.contains('Charged Off') | loan['loan_status'].str.contains('Fully Paid')\n",
    "loan_filtered = loan[filter_cond]\n",
    "loan_filtered.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','home_ownership'])['home_ownership'].count()\n",
    "cnt = loan.groupby('home_ownership')['home_ownership'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that home ownership does not have any significant impact on Loan Status and we can not say that borrower's home ownership with rent or mortgage will tend to default more or vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Purpose Vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (30,15))\n",
    "sns.countplot(x='purpose', data = loan)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More number of loans is taken for debt_consolidation followed by credit_card."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,15))\n",
    "sns.barplot(x='purpose', y='loan_amnt', hue='loan_status',data=loan_filtered, estimator=np.mean)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is one interesting observation, here we can see loan is taken more for small business purpose and also risk is more in when it is taken for the purpose of small business."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Emp Length Vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEPCAYAAACtCNj2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHBxJREFUeJzt3XuYHGWZ9/HvcAgSCSg7EBwCBFQ8wCqHgIi8ERaJuAsYXbgRV4EIZhXksKwgLC8bwdUNIERYfdXhEALh9COg4ruuHETFKCCgsCoHCYFASCDMQggQTiHZP6qm0g6Z7prurq7pnt/nuuaa7qrq5757Mul7nnqqnqdr1apVmJmZAaxVdgJmZjZ8uCiYmVnGRcHMzDIuCmZmlnFRMDOzjIuCmZllXBTMzCzjomBmZhkXBTMzy6xTdgJ18C3YZmb16ap1QDsWBRYtWlTX67q7u+nr62tyNsM79kiLW2Zsv+eREbtd33NPT0+u43z6yMzMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPLuCiYmVmmLW9eMzPrRJPOOrDu19540pym5OCiYGZVLRk1evB9y5ZDlf2bvrq8iJSsQD59ZGZmGRcFMzPLuCiYmVnGRcHMzDItGWiOiIuB/YAlkrYfsO/LwNnAJpLKmY/WzMyA1vUULgH2HbgxIrYA9gEea1EeZmZWRUuKgqRbgWfWsGsGcBJeTc3MbFgo7T6FiDgAeELSvRFR69ipwFQASXR3d9cVc5111qn7tY0qK/ZIi1tm7E59z0uW1X+vQZE/j5H471xNs3IqpShExGjgVGBSnuMl9QK96dNV9S5H167L6Dlue8Tu2Pdc5ea0Wor8eYzEf+dqauU03JfjfDuwNXBvRDwKjAN+FxGblZSPmZlRUk9B0h+ATfufp4Vhgq8+MjMrV0t6ChFxJXAb8K6IWBgRR7QirpmZDU1LegqSDqmxf3wr8jAzs+o8S6qZDUu77Dy57tfeefcPm5jJyOKiYNYG5s8bU2P/K8Caj9nmHc8XkJF1KheFJtlhyhfrfu09M7/bxEysSM9es2DwfQy+D+CtB23V7HTMms4T4pmZWcY9Bavbt/7fAXW/9vijrm9iJmbWLO4pmJlZxkXBzMwyHXf6yJexmZnVzz0FMzPLdFxPwTrfvO+cXX1/jde/4+gTm5eMWYdxT8HMzDIuCmZmlnFRMDOzjMcU2tyksw5s6PU3njSnSZmYWSdwT8HMzDIuCmZmlhny6aOIeDewPTBf0u+an5KZmZVlSD2FiDgauAb4JHBVRJxVSFZmZlaKqkUhInYasOkgYEdJnwZ2ArzWsplZB6l1+ujrEfEwcIqk54EngC9HxF3AXsBDRSdoZmatU7UoSPpYRBwM/DIiZgBHA6cAJwCPALmuh4yIi4H9gCWStk+3nQ3sD7wKPAxMkbS03jdiZtYMjayiCO2/kmLNMQVJVwMfBnYGrgVmSfpbSUdLWpgzziXAvgO23QRsL+l9wJ9Jio2ZmZWo5tVH6bjCNkAvMAr4fkTcBkyT9FKeIJJujYjxA7bdWPH0dnL2OszMrDhVi0J6iucTwJ3AN4BvAxOBfwRuj4jTJV3XhDw+B1xdJY+pwFQASXR3dzch5BsV1e5wjVtm7Ebi1poFtcjYz7KglLjz571SSlyAJcuWlxa73eKWGbtZcWv1FKYAW0t6PiLeAvxc0vnA9yLiOuBsoKGiEBGnAiuAywc7RlIvSU8FYFVfX18jIQdVVLvDNW6Zsf2eh2pMSXGBUaPLi91mccuMXStuT09PrnZqFYWHgEMi4ufAPsCD/TskLQEOyxVlEBFxGMkA9N6SVjXSlpmZNa7WQPNBwPuB84CtgMaG5StExL7AV4ADJNXfPzUzs6apdUnqQpLLUBsSEVcCewLdEbEQmEZytdF6wE0RAXC7pC80GsvMzOrXkqmzJR2yhs0XtSK2mZnl51lSzcws46JgZmYZFwUzM8vkGlOIiI2BLwM7ABtU7pM0sYC8zMysBHkHmq8guVJIgC8fNTPrUHmLwu7AJpLqv9fezMyGvbxjCv8NjCsyETMzK1/ensItwE8jYibwZOUOSRc3PSszMytF3qLwf4CFJPMfVVoFuCiYmXWIXEVB0l5FJ2JmZuUbtChERFf/zKURMejYg6SVRSRmZmatV62n8BywYfp4Bcmpokpd6ba1C8jLzMxKUK0obFfxeOuiEzEzs/INWhQkPV7xuP41CM3MrG147iMzM8u4KJiZWcZFwczMMnUVhYjYJiK2anYyZmZWrlxFISKujIjd08dTgD8B90XEEUUmZ2ZmrZV3mou9gcPSxycAHwGWAj8kx1rLEXExsB+wRNL26baNgauB8cCjQEh6dgi5m5lZk+U9fTRK0qsRsTmwsaRfS/oTMDbn6y8B9h2w7WTgZ5LeCfwsfW5mZiXKWxTuiYhTgNOA/wRIC8SyPC+WdCvwzIDNHwdmpY9nAZNz5mJmZgXJe/roCOBrwGvAiem2DwKXNxB7rKTFAJIWR8Smgx0YEVOBqemxdHd3NxB2cEW1O1zjlhm7kbjzSoz9LPXfx9lI3Pnz6l/fqtF/4yXL6l9ssR1/v9o1drPi5p0l9WHg0wO2zQHmNCWL2vF7gd706aq+vr5C4hTV7nCNW2Zsv+ehGlNSXGDU6PJit1ncMmPXitvT05Ornbw9BSJiErADsEHldkn/mreNAZ6KiLelvYS3AUvqbMfMzJok7yWp3wZmAzsDWwz4qtf1rL6i6TDgRw20ZWZmTZC3p3AIsEPlJHlDERFXAnsC3RGxEJgGTAeU3uvwGHBQPW2bmVnz5C0K/0NyX0JdJB0yyK69623TzMyaL29ROAe4PCL+HXiqcoek+U3PyszMSpG3KHw3/b7fgO1eec3MrIPkvSTVs6mamY0A/rA3M7NMrp5CRKwDHAV8GOgGuvr3SZpYTGpmZtZqeXsKM4B/BG4luVfhWmBT4JaC8jIzsxLkLQqfBD4m6TxgRfp9MrBXYZmZmVnL5S0Ko4H+G9deiojRkh4AdiwmLTMzK0PeS1LvB3YBfgvcBXw1IpYBTxSVmJmZtV7eonAc8Hr6+ASS+xbGkE5nbWZmnSHvfQp3Vjx+iGQ5TjMz6zBDmTp7L+CzwOYkp41mS/LVR2ZmHSTv1NlHAlcDTwLXAYuBKyLi8wXmZmZmLZa3p3ASsI+ke/s3RMTVJPcrXFBEYmZm1np5L0n9K+C+AdseBDZubjpmZlamvEVhLnBuRIwGiIg3A2cDvykqMTMza728ReELwPuA5yLiKZIFd95PMvWFmZl1iLyXpC4GPhwRWwBvAxZJWlhoZmZm1nKDFoWIWFMv4on0K9svaWUxqZmZWatV6ymsIFlZbTBdNGHltYj4J+DItK0/AFMkvdxIm2ZmVp9qYwpbA9tU+erfX7eI2Bw4FpggaXuSAvOpRto0M7P6DdpTkLSghTmsHxGvkczGuqhFcc3MbIDc01wUQdITEfFN4DHgJeBGSTcOPC4ippJOvieJ7u7uQvIpqt3hGrfM2I3EnVdi7Gep/2+lRuLOn/dKKXEBlixbXlrsdotbZuxmxS21KETEW4GPk5yKWgpcExGfkTS78jhJvUBv+nRVX19fIfkU1e5wjVtmbL/noRpTUlxg1OjyYrdZ3DJj14rb09OTq51BxxTSD+yifQR4RNLTkl4jmVdp9xbENTOzNag20Jz1kyPi5oLiPwbsFhGjI6IL2JtkQR8zMytBtdNHyyNie5IP6V3TD+2ugQc1cp+CpDsiYg7wO5JLYH/P6tNEZmbWYtWKwukky2+ulz5fMWB/U+5TkDQNmNZIG2Zm1hyDnj6S9F1gQ2ArkiuDmn6fgpmZDS/Vprm4XdJuwMKI+FEL71swM7OSVBto3jYi3pQ+3r8VyZiZWbmqjSn8CPhzRDxKcsfxrWs6SNLEIhIzM7PWqzbNxZSI2AMYD+wCXNSqpMzMrBxV72iWNBeYGxGjJM1qUU5mZlaSmtNcRMQ6wMqIuBzoBvqAm4HZ6V3IZmbWIaouxxkRG5Gsw3wm8BrJTWavAdOB36T7zcysQ9TqKfw78DSwl6QX+zdGxJsBpfuPKi49MzNrpao9BWAy8MXKggCQPj8a+ERRiZmZWevVKgobka7JvAYLSe54NjOzDlGrKDwM/M0g+/YG5jc3HTMzK1OtMYVzgUsj4kvADyStjIi1gE8C/wH8S9EJmplZ61TtKUi6BPgmcAnwckQsAl4GZgLnSppZdIJmZtY6tU4fIekcoIdk/qMT0++bSzq74NzMzKzFcq3RLOl54IaCczEzs5LV7CmYmdnI4aJgZmYZFwUzM8tUW3ktV8GQtLKRBCLiLcCFwPYkaz5/TtJtjbRpZmb1qfbBv4Jk8rtaX406D/ippHcD7wfub0KbZmZWh2pXH21d8fjvgANJJsBbAGwFfAW4tpHgEbEhMBE4HEDSq8CrjbRpZmb1q7by2oL+xxFxAjBB0tJ0058j4i7gLuC7DcTfhmQW1pkR8X7gbuC4gRPwRcRUYGqaF93d3Q2EHFxR7Q7XuGXGbiTuvBJjP8uC2gcVEHf+vFdKiQuwZNny0mK3W9wyYzcrbq77FEgmxhsNLK3YNjrd3mj8nYBjJN0REecBJwOnVR4kqRfoTZ+u6uvrazDsmhXV7nCNW2Zsv+ehGlNSXGDU6PJit1ncMmPXitvT05OrnbxFYRZwc0R8C3gc2AI4Nt3eiIXAQkl3pM/nkBQFMzMrQd5LUk8CzgcOJpkk71PAt9PtdZP0JPB4RLwr3bQ3cF8jbZqZWf3yTnOxEvhe+tVsxwCXR8Qokqm4pxQQw8zMcshVFCKiCziSpIewiaT3RcREYDNJaiQBSfcAExppw8zMmiPv6aMzgCOAC4At020LSS5LNTOzDpG3KBwO7CfpKpK7jgEeIbmk1MzMOkTeorA28EL6uL8obFCxzczMOkDeovAT4NyIWA+yMYavAT8uKjEzM2u9vEXhBJLV154juWHtBVZPdWFmZh0i7yWpy4DJETGWZKD58fQeAzMz6yB5L0nt71E8nX4REWs1Om22mZkNL3mnuVjB6gHmTESsABYB1wHTJHng2cysjeUdUzgGuAWYBLwH+CjwM5JpLr4I7A58q4gEzcysdfL2FE4AdpL0XPq8f+rsuyW9PSL+QDLttZmZtbG8PYUNSabKrlQ5dfaTwPrNSsrMzMqRt6dwKXBTut7B48A44DhWT509CXiw+emZmVkr5S0KJwIPkUyI1wMsBr5DMhcSwM+BXzQ7OTMza62aRSEi1gamAV+XtMapsyW93OzEzMys9WqOKUh6HTgaeK34dMzMrEx5B5pnAV8oMhEzMytf3jGFXYFjIuIkkoHm7EY2SROLSMzMzFovb1G4gNWDymZm1qHyTog3q/ZRZmbW7vL2FEhnSN0V6Aa6+rdLuriAvMzMrAR5Z0mdDMwmuVdhO+BPwPbAXKDhopBe9noX8ISk/Rptz8zM6pP36qN/A6ZI2hF4Mf0+lebNd3QccH+T2jIzszrlLQpbSrpmwLZZwKGNJhAR44C/Ay5stC0zM2tM3jGFJRExVtJTwKMR8UGgD1i7CTl8i2QK7jGDHRARU0l6Jkiiu7u7CWHfqKh2h2vcMmM3EndeibGfZUEpcefPe6WUuABLli0vLXa7xS0zdrPiDuWS1D2Aa4EZJHMdrQTOaSR4ROwHLJF0d0TsOdhxknqB3vTpqr6+vkbCDqqododr3DJj+z0P1aB/MxUcFxg1cILkFsZus7hlxq4Vt6enJ1c7uU4fSTpT0rXp40uBbYGdJZ2WK8rgPgQcEBGPAlcBfxMRsxts08zM6pR3TCGTrte8EHiwYu3mukg6RdI4SeNJZmC9RdJnGmnTzMzql/eS1J1Ipsp+H/CmdHMXyXQXzRhXMDOzYSDvmMIs4MfA54D6R52qkPQLvCaDmVmp8haFrYBTJa2qeaSZmbWtvGMCPyBZctPMzDrYoD2FiLiM1VNkrwf8ICLmAk9WHiep4RvYzMxseKh2+mjgPUL3FZmImZmVb9CiIOn0ViZiZmblqzqmEBG7R8SZg+ybHhG7FZOWmZmVodZA86nArYPs+2W638zMOkStorAD8NNB9t0E7NzcdMzMrEy1isKGwKhB9q1LI7N0mZnZsFOrKDzA4PcnTEr3m5lZh6h1R/MM4Pvpcpk/lLQynQRvMslcSCcUnaCZmbVO1Z6CpCuAs0jmPno5IhYBLwOXAGdJurLwDM3MrGVqTnMh6Vxgc2B/4Mvp93GSZhScm5mZtViuCfEkLQNuKDgXMzMrWUOL5JiZWWdxUTAzs4yLgpmZZVwUzMws46JgZmaZvMtxFiIitgAuBTYDVgK9ks4rMyczs5Gs7J7CCuCfJb0H2A04OiLeW3JOZmYjVqlFQdJiSb9LHz8P3E9yo5yZmZWg1NNHlSJiPLAjcMca9k0FpgJIoru7u5Acimp3uMYtM3YjcQeuE9vK2M+yoJS48+e9UkpcgCXLlpcWu93ilhm7WXGHRVGIiA2Aa4Hj07un/4KkXqA3fbqqr6+vkDyKane4xi0ztt/zUNU/S33D73fU6PJit1ncMmPXitvT05OrnbLHFIiIdUkKwuWSris7HzOzkazUohARXcBFwP3pxHtmZlaisk8ffQj4LPCHiLgn3fYvkn5SYk5mZiNWqUVB0lygq8wczMxstdLHFMzMbPhwUTAzs4yLgpmZZVwUzMws46JgZmYZFwUzM8u4KJiZWcZFwczMMi4KZmaWcVEwM7OMi4KZmWVcFMzMLOOiYGZmGRcFMzPLuCiYmVnGRcHMzDIuCmZmlnFRMDOzjIuCmZllSl2jGSAi9gXOA9YGLpQ0veSUzMxGrFJ7ChGxNvAd4GPAe4FDIuK9ZeZkZjaSlX36aFdgnqT5kl4FrgI+XnJOZmYjVteqVatKCx4RBwL7Sjoyff5Z4AOSvjTguKnAVABJO7c8UTOzztBV64CyewprSvANVUpSr6QJkiakr6nrKyLubuT17Rh7pMX1ex4Zcf2e6/6qqeyisBDYouL5OGBRSbmYmY14ZV99dCfwzojYGngC+BTw6XJTMjMbuUrtKUhaAXwJuAG4P9mkPxUYsrfAtodr7JEWt8zYfs8jI3ZHv+dSB5rNzGx4KXtMwczMhhEXBTMzy7gomJlZpuyrjwoVEe8muUN6c5L7HxYB10u6v9TEOlBE7AqsknRnOlXJvsADkn7S4jwulXRoK2NasSJiFMmViYsk3RwRnwZ2J7k4pVfSa6Um2GE6dqA5Ir4CHEIydcbCdPM4kl+uqzp14r20EG4O3CHphYrt+0r6aUExp5HMX7UOcBPwAeAXwEeAGyR9vaC41w/Y1AXsBdwCIOmAIuIOksseJNO2/FHSjQXH+gBwv6RlEbE+cDKwE3Af8A1JzxUU91jgB5IeL6L9KnEvJ/ndGg0sBTYArgP2BrokHVZw/LcDnyC5p2oF8BBwZVE/57J18umjI4BdJE2XNDv9mk7yH/eIspKKiCkFtn0s8CPgGOCPEVE5j9Q3iooLHAh8CJgIHA1MlnQG8FHg4ALjjgOWAecC56Rfz1c8LkxE/Lbi8eeBbwNjgGkRcXKRsYGLgeXp4/OAjYAz020zC4z7NeCOiPhVRBwVEZsUGKvSX0s6mOSDeRJwoKTLgCnAjkUGTv9PfQ94E7ALsD5JcbgtIvYsMnZZOvn00UqgB1gwYPvb0n1lOZ3i/uN+HthZ0gsRMR6YExHjJZ1Hzlvc67RC0uvA8oh4WNIyAEkvRUSRP+sJwHHAqcCJku6JiJck/bLAmP3WrXg8FdhH0tMR8U3gdqDInuha6T0+ABMk7ZQ+nhsR9xQYdz6wM0kP8GDg9HTahSuB6yQ9X1DctdJTSG8m6S1sBDwDrMdf/jsU4fPADpJej4hzgZ9I2jMivk/yB1hhRSkiNgJOASYD/QV4SRp3uqSlRcTt5KJwPPCziHgI6O/ubgm8g+SGucJExH8PsqsLGFtg6LX7TxlJejT9S2ZORGxFsUXh1YgYLWk5yYcGkP1SF1YUJK0EZkTENen3p2jd7/RaEfFWkt52l6Sn05xejIgV1V/asD9GxBRJM4F7I2KCpLsiYlugyPPrq9Kf+Y3AjRGxLslpw0OAb7L6g6vZLgIeIFlz5VTgmoiYD+xGcnq4aOsAr5MUoTEAkh5L33+RRHIqdE9JTwJExGbAYcA1wD5FBO3Y00fp+fNtSf4yv4HkF/mrwLuKOrdeYSxwKLD/Gr7+p8C4T0bEDv1P0gKxH9AN/HWBcSemBaH/g7rfuiS/wIWStFDSQcB/AbOLjpfaCLgbuAvYOP3PSkRsQLEFGOBI4MMR8TDJOiS3pR+SF6T7ivIX70vSa5Kul3QIyR9chZA0A9gD+KCk84G/J/k/fYSk04uKm7oQuDMieoHbSE4Tkp46e6bg2OMlndlfEAAkPSnpTAr8eXfsQHOZIuIiYKakuWvYd4WkQuZ3iohxJKdynlzDvg9J+nURcW21iBgNjJX0SAtijQG2IflLdqGkpwqOt62kPxcZYziKiO2A95BcRPBAC+PeCNwMzOr/t42IscDhJKcrP1JEXBcFM7NhKD09eTLJZfWbppufAq4nGVN4toi4LgpmZm2mYkyp6Tp2TMHMrIMVNpbSyVcfmZm1rbKuYnRRMDMbnsaS3AA6cOygC/hNUUFdFMzMhqf/D2wg6Q03JEbEL4oK6oFmMzPLeKDZzMwyLgpmZpbxmIJZA9KJBx8B1q2YpK5VsQ8HjpS0RyvjWmdzUTBrA2UWHxtZfPrIzMwy7ilY24uIHuA/SBb5eQGYIen8iPgqsB3wCsn8MY+SzLD598A/pduP6F8pLb3M7zaSFb3eRbJ63BRJuWfDTKcLPxf4W5Jpw2cC09L5+A8nmcX0dpKFnpYCR0n6r/S1WwOzSObovwN4ENhI0meAW9MQSyMCKqZNTtdweEN7ZvVwT8HaWkSsBfwYuJdkGdK9geMj4qPpIfsDlwFvBX5PMuXyWumxZwDfH9DkocDnSBZoWgGcP8SUZqWvewfJh/sk/nI66w+QfNh3A2cBF0VE/5TUVwC/Bf6KZJr3z1a8bmL6/S2SNpB0W472zIbMRcHa3S7AJpLOkPSqpP51BT6V7v+VpBvS8/DXkCwEMz1d7P0qYHxEvKWivcsk/VHSi8BpQETE2nkSSac1/hhwvKQXJS0BZlTkArBA0gXpSnWzSFYCHBsRW6bv5V/T9zGXZDbMWtbYXp58zdbEp4+s3W0F9ERE5dKEawO/IlmKtXKNgZeAvvQDtP85JAvB97++clH6BSQLBXUPaKdaLusCi9NTPJD84VXZZuWCKcvT4zZIYzzTv1hRRS5b1Ig5WHtmdXFRsHb3OPCIpHcO3JGOKQxV5YfwliTLW/YNIZdXgO46rhBaTLKC2+iKwlCZi6cesJZwUbB291tgWUR8heT8/6skq2StX2d7n4mIS0kGpc8A5lT0LKqStDhdLeuciDiNZNB7a2CcpF/WeO2CiLgL+GpE/F+Sta73JxkvAXiaZOB6G2DErX5mreMxBWtr6Qf2/sAOJNfx95Gsq7tRnU1eBlxCclrmTcCxQ3z9ocAo4D6S2S3nkJznz+MfgA+SrOP9b8DVJD0P0t7D14FfR8TSiNhtiHmZ5eIJ8cxS6SWpsyVdWHYuABFxNfCApGll52Ijh08fmQ0TEbEL8AxJj2cSyb0V00tNykYcFwWzGiLiH3jj/QyQXA66XRNDbQZcR3KfwkLgi5J+38T2zWry6SMzM8t4oNnMzDIuCmZmlnFRMDOzjIuCmZllXBTMzCzzv8vJ2Yofr+3cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','emp_length'])['emp_length'].count()\n",
    "cnt = loan.groupby('emp_length')['emp_length'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experienced borrowers have a slight more tendency of default but the difference is not that much significant to tell that borrowers in certain experience range are more expected to default on loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interest Rate Vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we will see the int_rate variance and data spread to identify the bin size.\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(loan['int_rate'], axlabel='Interest Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36433.000000\n",
       "mean        12.073862\n",
       "std          3.734758\n",
       "min          5.420000\n",
       "25%          9.320000\n",
       "50%         11.860000\n",
       "75%         14.650000\n",
       "max         24.400000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.int_rate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets derive new column int_rate_bin\n",
    "loan['int_rate_bin'] = pd.cut(loan['int_rate'], [x for x in range(0, 26, 5)], \n",
    "                                      labels=[str(x)+'-'+str(x+5)+'%' for x in range (0, 25, 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_rate</th>\n",
       "      <th>int_rate_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>15-20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10-15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>10-15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90</td>\n",
       "      <td>5-10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96</td>\n",
       "      <td>15-20%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate int_rate_bin\n",
       "1     15.27       15-20%\n",
       "3     13.49       10-15%\n",
       "4     12.69       10-15%\n",
       "5      7.90        5-10%\n",
       "6     15.96       15-20%"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[['int_rate', 'int_rate_bin']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEpCAYAAAB8/T7dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHtxJREFUeJzt3XmYJFWd7vFv0wUIIigUIAXI4jB3QJRmkUF0EESRmWET8VVwABFsR3Eb9AoqI4uogMriqGijQKMsviiyiSIqiIwL2wVFUBFssNlLWUUQ6L5/RFRWUlRVRlVXZlRlvp/nqacjT0Rm/PI8XfGrE+fEObMWL15MREQEwFJ1BxAREdNHkkJERDQkKUREREOSQkRENCQpREREQ5JCREQ0JClERERDkkJERDQkKURERENf3QFMQh7BjoiYnFmtDpiJSYG77rqr1vP39/czODhYawzTRepiWOpiWOpi2HSpi4GBgUrH5fZRREQ0JClERERDkkJERDQkKUREREOSQkRENCQpREREQ5JCREQ0JClERETDjHx4LSLa64Qv7VJ3CHzg3RfUHUJPSkshIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiIUkhIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiIUkhIiIakhQiIqKhI1NnS3oOcAWwbHnOb9k+TNJ6wNnAysB1wN62/96JmCIi4tk61VJ4AniN7U2AOcCOkrYCjgGOt70B8ACwf4fiiYiIUXSkpWB7MfBo+XLp8mcx8Bpgr7J8PnA4cFInYoqIiGfr2MprkmYD1wL/AHwRuBV40PZT5SELgTU7FU9ERDxbx5KC7aeBOZKeD3wH2HCUwxaP9l5Jc4G55efQ39/ftjir6Ovrqz2G6SJ1MSx1MbW6pS5n2v+Ljq/RbPtBSZcDWwHPl9RXthbWAu4a4z3zgHnly8WDg4MdiXUs/f391B3DdJG6GJa6mFrdUpfT5f/FwMBApeM60tEsadWyhYCk5YDXAjcDlwF7lIftC5zfiXgiImJ0nRp9tAZwmaRfAVcDl9q+CDgYOEjSH4BVgK91KJ6IiBhFp0Yf/QrYdJTy24AtOxFDRES0lieaIyKiIUkhIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiYcLPKUj6J2Bj4Dbb1019SBERUZcJtRQkHQicA+wOnC3p2LZEFRERtRg3KUjabETRm4BNbe8FbEYWxYmI6Cqtbh99UtKtwEdsPwLcCXxI0jXAdsAt7Q4wIiI6Z9yWgu1/BX4K/ETS3sCBwAuAg4DnMzzDaUREdIGWHc22vynpYuATwNuA99q+qd2BRURE57VMCmW/wvoUi9wsA3xF0s+Bw2z/rc3xRUREB7XqaP4MYOCNwHnANuXPbcAvJO3e9ggjIqJjWg1J3Y9itNGeFOse7Gd7se0vA68Ddm13gBER0Tmtbh/dAuwp6TKKJPC7oR2276NYQjMiIrpEq5bCm4BNgBOBdYB3tT2iiIiozbgtBdsLKYahRkRED8iEeBER0ZCkEBERDUkKERHRkKQQERENldZTkLQy8CFgDrBC8z7b21R4/9rA6cALgUXAPNsnSjoceAdwf3noR21fXDn6iIiYUlUX2TkTWJbi6ebHJnGep4AP2r5O0vOAayVdWu473vZnJ/GZERExxaomha2BVW0/MZmT2L4buLvcfkTSzcCak/msiIhon6pJ4VfAWsCtS3pCSesCmwK/BF4JvEfSPsA1FK2JB0Z5z1xgLoBt+vv7lzSMJdLX11d7DNNF6mJY6mJqdUtdzrT/F1WTwo+B70s6FbineYftU6qeTNIKwLeBD9h+WNJJFFNyLy7//Rzw9pHvsz2PYpZWgMWDg4NVT9kW/f391B3DdJG6GJa6mFrdUpfT5f/FwMBApeOqJoV/ARZSzH/UbDFQKSlIWpoiIZxh+1wA2/c27T8ZuKhiPBER0QaVkoLt7ZbkJJJmAV8DbrZ9XFP5GmV/A8AbgBuX5DwREbFkxkwKkmbZXlxuj/k8g+1FFc7zSmBv4NeSri/LPkoxA+scihbHAuCdFeOOiIg2GK+l8BCwYrn9FMWFu9mssmx2q5PYvrI8fqQ8kxARMY2MlxRe0rS9XrsDiYiI+o2ZFGz/qWn79s6EExERdcrcRxER0ZCkEBERDUkKERHRMKmkIGl9SetMdTAREVGvSklB0lmSti639wN+A9wkaf92BhcREZ1VtaWwPcWEdQAHAa8FtgQOaUdQERFRj6pzHy1j+++S1gRWtv2/AJJWb19oEZ21w7F71B0CP/jwt+oOIXpc1aRwvaSPAOsA3wUoE8TD7QosIiI6r+rto/2BlwLLAYeWZa8AzmhHUBERUY+qs6TeCuw1ouxbQNq6ERFdpOrtIyTtAMwBVmgut/3xqQ4qIiLqUSkpSPoCIOAy4LG2RhQREbWp2lLYE5jTPEleRER0n6odzX8GHmxnIBERUb+qLYXPAWdI+jRwb/MO27dNeVQREVGLqknhpPLfnUaUV1p5LSIiZoaqQ1Izm2pERA/IxT4iIhqqDkntA94NvBroB2YN7bO9TXtCi4iITqvaUjgeeCdwBbA58G1gNeDHbYorIiJqULWjeXfgFbbvkHSE7RMlXQJ8BTi81ZslrQ2cDrwQWATMKz9jZeCbwLrAAkC2H5jwt4iIiClRtaWwPDD04NrfJC1v+7fAphXf/xTwQdsbAlsBB0raiGI9hh/Z3gD4EVmfISKiVlVbCjcDLweuolhs53BJDwN3Vnmz7buBu8vtRyTdDKwJ7ApsWx42H7gcOLhiTBERMcWqthTeT/HXPhQrr20G7AzMnegJJa1L0cL4JbB6mTCGEsdqE/28iIiYOlWfU7i6afsWiuU4J0zSChSd1B+w/bCkqu+bS5mAbNPf3z+Z00+Zvr6+2mOYLlIXUyt1Oaxb6mKm/Y5MZOrs7YC9KW773Al8w3bl0UeSlqZICGfYPrcsvlfSGrbvlrQGcN9o77U9D5hXvlw8ODhY9bRt0d/fT90xTBepi6mVuhzWLXUxXX5HBgYGKh1X6faRpAMoRgndA5xL0T9wpqR3VHz/LOBrwM22j2vadQGwb7m9L3B+pagjIqItqrYUPgy8zvYNQwWSvknxl//JFd7/SopWxq8lXV+WfRQ4GrCk/YE7gDdVDTwiIqZe1aSwCnDTiLLfAStXebPtK2l6CnqE7SvGEBERbVZ19NGVwHGSlgeQ9FzgM8DP2hVYRER0XtWk8J/Ay4CHJN1LseDOJhRTX0RERJeoOiT1buDV5XQVawB32V7Y1sgiIqLjxkwKkkZrRdxZ/jT2217UntAiIqLTxmspPEWxstpYZpGV1yIiusp4SWG9jkURERHTwphJwfbtnQwkIiLql+U4IyKiIUkhIiIaxkwKkl7QyUAiIqJ+47UUGn0Kkn7YgVgiIqJm440+ekzSxhSrrm1ZznT6rPmL8pxCRET3GC8pHEGx/Oay5eunRuzPcwoREV1mzNtHtk8CVgTWAf4GrD/iZ73y34iI6BLjTXPxC9tbAQslnZ/nFiIiut94Hc3/KOk55fbOnQgmIiLqNV6fwvnA7yUtAJaTdMVoB9neph2BRURE5403zcV+kl4FrAu8nGKN5YiI6GLjrqdQLqN5paRlbM/vUEwREVGTlovsSOoDFkk6A+gHBoEfAt+w/WSb44uIiA4ad+4jSStRrMN8DPAkcF3579HAz8r9ERHRJVq1FD4N3A9sZ/uvQ4WSngu43P/u9oUXERGd1GqW1N2AdzUnBIDy9YHAG9oVWEREdF6rlsJKlGsyj2IhxRPPLUk6BdgJuM/2xmXZ4cA7KFoiAB+1fXGVz4uIiPZolRRuBV4DXDrKvu2B2yqe5zTgC8DpI8qPt/3Zip8RERFt1ur20XHA6ZLeKGkpAElLSdqD4kJ/XJWT2L4C+MuSBBoREe3X6jmF0yStQpEAzpI0SDEs9QngSNunLuH53yNpH+Aa4IO2HxjtIElzgbllTPT39y/haZdMX19f7TFMF6mLqZW6HNYtdTHTfkdaPqdg+3OS5gFbM/ycws9tP7yE5z4J+ATF9NufAD4HvH2MGOYB88qXiwcHB5fw1Eumv7+fumOYLlIXUyt1Oaxb6mK6/I4MDAxUOq5lUgCw/QhwyZIENMpn3ju0Lelk4KKp/PyIiJi4Vn0KbSNpjaaXbwBurCuWiIgoVGopLClJZwHbAv2SFgKHAdtKmkNx+2gB8M5OxBIREWPrSFKwvecoxZl1NSJimhlv5bVKt5ZsL5q6cCIiok7jtRSeori108rsKYolIiJqNl5SWK9p+9+BPSgmwLsdWAc4GPh2+0KLiIhOG2/ltduHtiUdBGxh+8Gy6PeSrqF46Oyk9oYY7TRnv3fVHQLXn5r/QhHTRdUhqSsBy48oW74sj4iILlF19NF84IeSTgD+BKwNvK8sj4iILlE1KXwY+APwZmAAuJti1tOT2xRXRETUoOo0F4uAL5c/ERHRpSolBUmzgAOAtwCr2n6ZpG2AF9p2OwOMiIjOqdrRfCSwP8XtoheVZQsphqVGRESXqJoU3gbsZPtshh9o+yOwfjuCioiIelRNCrOBR8vtoaSwQlNZRER0gapJ4WLgOEnLQqOP4RPAhe0KLCIiOq9qUjiIYijqQxQPrD3K8FQXERHRJaoOSX0Y2E3S6hQdzX+yfU9bI4uIiI6rOiR1qEVxf/mDpKUybXZERHep+kTzqNNoS3oKuAs4FzjMdjqeIyJmsKp9Cu8FfgzsAGwIvB74EcX0F+8CtgZOaEeAERHROVVbCgcBm9l+qHw9NHX2tbZfLOnXwLVtiTAiIjqmakthRcafOvseYLmpCioiIupRtaVwOnCppBMpps5eC3g/w1Nn7wD8burDi4iITqqaFP4vcAvFhHhDU2d/keGpsy8DLp/q4CIiorNaJgVJs4HDgE/aHnXqbNuPt/iMU4CdgPtsb1yWrQx8E1gXWADI9gMTCT4iIqZWyz4F208DBwJPLsF5TgN2HFF2CPAj2xtQjGQ6ZAk+PyIipkDVjub5wH9O9iS2rwD+MqJ4V4b7JOYDu0328yMiYmpU7VPYEnivpA9TdDQ3HmSzvc0kz7267bvLz7hb0mqT/JyIiJgiVZPCydS4HrOkucBcANv09/fXFQoAfX19tcfQTVKXw1IXw7qlLmba9aLqhHjzWx81YfdKWqNsJawB3DfO+ecB88qXiwcHB9sQTnX9/f3UHUM3SV0OS10M65a6mC7Xi4GBgUrHVW0pUM6QuiXQD8waKrd9ykSDK10A7AscXf57/iQ/JyIipkjVWVJ3A75B8azCS4DfABsDVwItk4Kks4BtgX5JCymGuB4NWNL+wB3AmyYRf0RETKGqLYWjgP1snyPpAdubStqPIkG0ZHvPMXZtX/H8ERHRAVWHpL7I9jkjyuYD+0xxPBERUaOqSeG+sk8BYIGkVwAvBma3J6yIiKhD1aRwMvCqcvt4irmObgC+1I6gIiKiHlWHpB7TtH26pMuB59q+uV2BRURE51VtKTSU6zUvBH7XtHZzRER0gapDUjejmCr7ZcBzyuJZFNNdpF8hIqJLVB2SOh+4EHg78Fj7womIiDpVTQrrAB+zvbjlkRERMWNV7RP4DsWSmxER0cXGbClI+jrDU2QvC3xH0pXAPc3H2c4DbBERXWK820d/GPH6pnYGEhER9RszKdg+opOBRERE/cbtU5C0taRjxth3tKSt2hNWRETUoVVH88eAK8bY95Nyf0REdIlWSWEO8P0x9l0KbD614URERJ1aJYUVgWXG2Lc08LypDSciIurUKin8lrGfT9ih3B8REV2i1RPNxwNfkTQbOM/2onISvN0o5kI6qN0BRkRE54zbUrB9JnAsxdxHj0u6C3gcOA041vZZbY8wIiI6puXcR7aPk/RV4BXAKsCfgZ/bfrjdwbXLyzffre4QuPra8+oOISLiWaousvMwcEmbY4mIiJplkZyIiGhIUoiIiIaq6ym0jaQFwCPA08BTtreoN6KIiN5Ve1IobWd7sO4gIiJ6XW4fRUREw3RoKSwGfiBpMfAV2/NGHiBpLjAXwDb9/f0dDnHqdcN3mCqpi2Gpi2HdUhd9fX0z6rtMh6TwStt3SVoNuFTSb20/Y2bWMlEMJYvFg4Mz/05TN3yHqZK6GJa6GNYtddHf3z8tvsvAwECl42q/fWT7rvLf+yjWgt6y3ogiInpXrUlB0nMlPW9om2KSvRvrjCkiopfVfftodeA7koZiOdP2WOs3REREm9WaFGzfBmxSZwwRETGs9j6FiIiYPpIUIiKiIUkhIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiIUkhIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiIUkhIiIakhQiIqIhSSEiIhqSFCIioiFJISIiGpIUIiKiIUkhIiIakhQiIqKhr+4AJO0InAjMBr5q++iaQ4qI6Fm1thQkzQa+CPwrsBGwp6SN6owpIqKX1X37aEvgD7Zvs/134Gxg15pjiojoWXXfPloT+FPT64XAP488SNJcYC6AbQYGBpbopHfefdUSvb+b3HfJ+XWHMG3ceMLP6g5h2jj2qGvqDqGrLOk1q5PqbinMGqVs8cgC2/Nsb2F7i/I9tf5IurbuGKbLT+oidZG6mFF10VLdSWEhsHbT67WAu2qKJSKi59V9++hqYANJ6wF3Am8B9qo3pIiI3lVrS8H2U8B7gEuAm4si/6bOmCqaV3cA00jqYljqYljqYtiMqotZixc/6xZ+RET0qLr7FCIiYhpJUoiIiIYkhYiIaEhSiIiIhrqHpM44kp4DvBVYHjjT9p9rDikiprHymrGM7YfrjqWKJIWJOxG4DngcOA/4l3rDiZheJG0FfApYFviM7fNqDqk2kg4A9gaWkvRT2x+tO6ZWkhRakHQm8N+2by2LVgbOKLffX09U00cuAIVergdJL7R9T1PRQcAuFNMq/Izij6eeIGln2xc2Fb3W9qvLfTcASQpd4FDgKEl3AZ8APgtcADwHOLzGuGqRC0Ah9fAMXy7n9/mM7ceBBylmJlgEzIhbJlNok7J18HHbNwC/knQGxZxuM+HB3CSFVmzfBuwl6VXAN4HvAq+z/XS9kdUmF4BC6qFkezdJOwMXSZoPfICiLpYHdqs1uA6zfZSkFwJHSgL4OLACsLztX9UaXEUZfdSCpBdIOpBiESABDwGXSNqp3sjqYXs34HqKC8DeFBeARfTYBSD18EzlLZPXA88HzgV+Z/vztu+vN7Ja/JXi/8MXKaa42BP4fa0RTUCSQmvnAU9Q3C76uu3TgZ2BzSVdUGtkNckFoJB6KEjaRdKVwI+BGykmtnyDpLMkvbje6DpL0lEUdxN+BGxnexfgBuC75R8P016SQmurAGdSdC6vCWD7b7aPAN5ZZ2B1yAWgkHp4hqMokuMbgWNsP2j7IIpbJ5+sNbLO28n2NsDWwD4Ati+gqJ+V6wysqvQptPZx4FLgaeCQ5h22764lonodBbwCWA642PaWwEGSNqC4ALylzuA6KPUw7CGK77sccN9Qoe1b6K16ALhR0tcp6uInQ4XljNAn1hbVBCQptGD7XIpbA1HIBaCQehj2Bor75k/S4+uh2P4PSS8FnrT927rjmYwkhRYkrUTRQtgNWK0svg84Hzja9oN1xVaTXAAKqYeS7UHgf5rLJK1s+y81hVQr278e2i4XENsUuGmmJImsp9CCpEso7hvPHxqXXg4525fiwZTX1RnfdNCrFwBJs4AtKfqaFlMsJXuV7Z76pZJ0qO2jyu2NKAZnLE3xzMabbf+yzvg6SdJ55cg0JO0KnABcTtHH8Gnbp9UXXTXpaG5tXdvHND+oZPse28cAL6oxrlpIOrRpeyNJvweulbRA0j/XGFpHSdoBuIXiAcZ/A/4dOAK4pdzXS3Zv2v4M8H7b61EM4T6+npBqs07T9sHAa2zvB7wS+K96QpqY3D5q7XZJH6ZoKdwLIGl14G3An+oMrCa7U3SywvAF4HuStqT4q2jr2iLrrBMpWooLmgvL2wUXAxvWEdQ0MGD7ewC2r5K0XN0BdVhzK7HP9h+huMUmaVFNMU1IWgqtvZliWOpPJP1F0l8omoMrU/wl1MuecQGg6HTtFX3AwlHK76S4ddJL1pd0gaQLgbUkLd+0r9fqYhNJD0t6BJhT3mpG0jLA7HpDqyYthRZsP0DRDDy47limifXLh/ZmUV4AbD9W7uulC8ApwNWSzma4xbg2xcijr9UWVT12HfF6KWi0qE/qfDj1sT3WhX95ZshzTeloXgKS9rN9at1xdJKkV48outb2o+UFYA/bX6wjrjqUnaq7UHQ0z6JoOVxg+6ZaA4tpRdJOti+qO46qkhSWgKQ7bPdcZ3NEM0mzgQOAtYDv2/7fpn2NkUm9StJ1tjerO46qcvuoBUljzWw4C1i9k7FMd5Lm2Z5bdxydIGlF4CMUF8KLbZ/VtO9Ltt9dW3Cd9xWK2yNXAZ+X9JNymgt45sCEXjWr7gAmIkmhtdUp5i15YET50Lz5PUXSWPO3zKIYmtkrTqUYkvpt4O2S9gD2sv0EsFWtkXXelrZfBiDpC8CXJJ1L8XDfjLogTgVJ/0TRzzL0/Mq5kja0fXO9kVWTpNDaRcAKtq8fuUPS5Z0Pp3b3A7fzzF/2xeXr1UZ9R3d6se03ltvnSfoY8GNJu9QZVE2WGdoo5/iZK+njFA99rlBbVDWQdDBFMjybouUERWvyLEln2z66tuAqSlJowfb+4+zrxekNbgO2t33HyB2Seum5jWUlLWV7EYDtT0paCFxBj10IgWsk7Wj7+0MFto8sVyvsqdFHwP7AS2w/2Vwo6TiKldemfVLIcwqTIKkn7puP4QTgBWPsO7aTgdTsQuA1zQW25wMfBP5eS0Q1sf0fzQmhqfyrtntpmDIUCy0NjFK+Rrlv2svoo0mYaaMJIjqtlwYdNJO0I/AFiv6moZbzi4B/AN4zWvKcbtJSmJye6zwbj6R5dccwHUiaMWPRO2CLugOoQ3nR/0eKebAuAX5AMT/W/5kJCQHSpzBhkl4FfFvSDrZ/UHc800RPXgBGsWbdAUwj97U+pDuV/Uy/qDuOyUpLoQVJVzVtv4OiaTgbOEzSIWO+sbf07AVghP9XdwDThe0d644hJicthdaaO8rmAq+zfb+kz1L8NTDtRxO0Wy4ABdtvrzuGOpQLUX2EYiGqVcviXl6IakZLUmhtKUkvoGhVzbJ9P4Dtv0p6qt7QOi8XgELzEMyyTo4DXg7cCPzX0DTrPcIUzyRsO8pCVOcAPb8Q1UyS20etrQRcC1wDrNw0Fe4K9GaHsyme7t7W9iq2VwG2K8vOqTWyzvpU0/bngLuBnYGrKaZ96CVZiKqLpKXQgu11x9i1iGKd3l6zbvnL3lBeDI6R1JO3T4AtbM8pt4+XtG+t0XReFqLqIkkKk1SuIfDHuuOoQS4AhdUkHUTRWlxR0qymtZl7rQX+ZuAQioWoVqeY9uRe4AKyENWMk6QQE9V8ARia62joAvCm2qLqvJOB55Xb84F+4P7y9uKz5snqZrYfkHQqcCnwC9uPDu0rH+aaEePzo5AnmmPK9OKiQ6PptXqQ9D7gQOBmYA7Fut3nl/vy9P8M02vN3GivI+oOYJrotXp4B7C57d2AbYH/lvT+cl8vDsaY0XL7KCYkiw4VUg/PMHvolpHtBZK2Bb4laR2SFGacJIWYqCw6VEg9DLtH0pyhNUfKNbt3Ak4BXlpvaDFRSQoxUVl0qJB6GLYP8IwHOcvFdvaR1GvPbMx46WiOiIiGdDRHRERDkkJERDQkKcSMJ+k35YiXGU/S4ZK+Mc7+rvmuMT2lozlmPNsvqXKcpAXAAbZ/ONUxSDoNWGj70Kn+7GZVv2vEZKWlENGCpPzxFD0jo49ixhtqAQCvAjYCHqeYwfYOYF/b10j6OvBW4AngaeBI28eO8XnrUkx2eABwGLDA9jaSzgH+BVgOuAF4l+3fSJoLfJFiIri/A5fZ3lnSAPA/wDbAo8Dxtj/f4rscDmxcxvhvFAvA72f7hubvavuH5bGjft/KlRcxQloK0W12Ac4Gnk8xSd8XAGzvTXHR3Nn2CmMlhBFeDWxI8ZAawPeADYDVgOuAM8rPnlduH1t+9s6SlgIupEgeawLbAx+Q9Hpa25VibYqVgTOB8yQtPcaxo37fiMlKszi6zZW2LwYoWwcfWILPOtz2X4de2D5laLv8K/0BSSvZfmiU974cWNX2keXr2ySdDLwFuKTFea+1/a3yPMcBHwS2An46yrFT+X0jkhSi69zTtP0Y8BxJfeUTthPVWB9C0mzgkxTTg69KscgSFFNmj5YU1gEGJDUvTzqb0S/sY57X9iJJC4GBMY6dyu8bkaQQPWWiHWjNx+9FcVvntcACimVaH2B4wreRn/0n4I+2N5h4mKw9tFHehloLuGsSnxMxYUkK0UvuBdaf5HufR9FJ/WdgeZ65RvNon30V8LCkg4HPU3RAbwgsZ/vqFufaXNLuFH0E7yvP+4tJxh0xIelojl7yaeBQSQ9K+tAE33s6cDtwJ3ATz75Ifw3YqPzs82w/DexMsejMH4FB4KsULYxWzqdY4e4BYG9gd9tPTjDeiEnJkNSIiGhISyEiIhrSpxA9SdJbgdHm+r+93VNJSPoexUNwI33K9si+ioiOyu2jiIhoyO2jiIhoSFKIiIiGJIWIiGhIUoiIiIYkhYiIaPj/7i6V+9NhhC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','int_rate_bin'])['int_rate_bin'].count()\n",
    "cnt = loan.groupby('int_rate_bin')['int_rate_bin'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Form above plot we can see that charge-offs are more for loans with higher interest rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Revol_Util vs Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36433.000000\n",
       "mean        49.276251\n",
       "std         28.191811\n",
       "min          0.000000\n",
       "25%         26.100000\n",
       "50%         49.900000\n",
       "75%         72.700000\n",
       "max         99.900000\n",
       "Name: revol_util, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.revol_util.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan['revol_util_bin'] = pd.cut(loan['revol_util'], [x for x in range(0, 101, 10)], \n",
    "                                      labels=[str(x)+'-'+str(x+10)+'%' for x in range (0, 100, 10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_util</th>\n",
       "      <th>revol_util_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.4</td>\n",
       "      <td>0-10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>20-30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.9</td>\n",
       "      <td>50-60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.3</td>\n",
       "      <td>20-30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85.6</td>\n",
       "      <td>80-90%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   revol_util revol_util_bin\n",
       "1         9.4          0-10%\n",
       "3        21.0         20-30%\n",
       "4        53.9         50-60%\n",
       "5        28.3         20-30%\n",
       "6        85.6         80-90%"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[['revol_util', 'revol_util_bin']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Charged Off loans in %')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grp = loan.groupby(['loan_status','revol_util_bin'])['revol_util_bin'].count()\n",
    "cnt = loan.groupby('revol_util_bin')['revol_util_bin'].count()\n",
    "percentages = grp.unstack() * 100 / cnt.T\n",
    "ax = percentages.loc['Charged Off'].plot.bar(color=sns.color_palette('cubehelix', 8))\n",
    "ax.set_ylabel('Charged Off loans in %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Revolving credit utilization is a good indicator of borrowers defaulting on loan, we can see in above plot charge-offs increases linearly with revol_util rate."
   ]
  }
 ],
 "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",
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