gramener_visual.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "source": [
    "# Gramener Case Study\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Imgur](https://imgur.com/apRdaMm.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3051: DtypeWarning: Columns (47) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "df = pd.read_csv('loan.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39717, 111) \n",
      "\n",
      "<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",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.shape,'\\n')\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                              0.0\n",
       "member_id                       0.0\n",
       "loan_amnt                       0.0\n",
       "funded_amnt                     0.0\n",
       "funded_amnt_inv                 0.0\n",
       "                              ...  \n",
       "tax_liens                       0.1\n",
       "tot_hi_cred_lim               100.0\n",
       "total_bal_ex_mort             100.0\n",
       "total_bc_limit                100.0\n",
       "total_il_high_credit_limit    100.0\n",
       "Length: 111, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "round(df.isnull().mean() * 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we can see columns even have 100 % missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### removing the columns having more than 90% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mths_since_last_record', 'next_pymnt_d', 'mths_since_last_major_derog',\n",
       "       'annual_inc_joint', 'dti_joint', 'verification_status_joint',\n",
       "       'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_6m',\n",
       "       'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il',\n",
       "       'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',\n",
       "       'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m',\n",
       "       'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util',\n",
       "       'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op',\n",
       "       'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc',\n",
       "       'mths_since_recent_bc_dlq', 'mths_since_recent_inq',\n",
       "       'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd',\n",
       "       'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl',\n",
       "       'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0',\n",
       "       'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m',\n",
       "       'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75',\n",
       "       'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit',\n",
       "       'total_il_high_credit_limit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing columns having more than 90% missing values\n",
    "missing_columns = df.columns[round(df.isnull().mean() * 100,2) > 90]\n",
    "missing_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# dropping above columns\n",
    "df = df.drop(missing_columns, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39717, 55)\n"
     ]
    }
   ],
   "source": [
    "# updated shape of dataframe\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking again for percentage of missing values after dropping columns greater than 90% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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",
       "open_acc                       0.00\n",
       "pub_rec                        0.00\n",
       "revol_bal                      0.00\n",
       "revol_util                     0.13\n",
       "total_acc                      0.00\n",
       "initial_list_status            0.00\n",
       "out_prncp                      0.00\n",
       "out_prncp_inv                  0.00\n",
       "total_pymnt                    0.00\n",
       "total_pymnt_inv                0.00\n",
       "total_rec_prncp                0.00\n",
       "total_rec_int                  0.00\n",
       "total_rec_late_fee             0.00\n",
       "recoveries                     0.00\n",
       "collection_recovery_fee        0.00\n",
       "last_pymnt_d                   0.18\n",
       "last_pymnt_amnt                0.00\n",
       "last_credit_pull_d             0.01\n",
       "collections_12_mths_ex_med     0.14\n",
       "policy_code                    0.00\n",
       "application_type               0.00\n",
       "acc_now_delinq                 0.00\n",
       "chargeoff_within_12_mths       0.14\n",
       "delinq_amnt                    0.00\n",
       "pub_rec_bankruptcies           1.75\n",
       "tax_liens                      0.10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df.isnull().mean() * 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Still  two columns i.e. 'desc' and 'mths_since_last_delinq ' now remain that have high percentage of missing values\n",
    "###### lets check them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>desc</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Borrower added on 12/22/11 &gt; I need to upgra...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Borrower added on 12/22/11 &gt; I plan to use t...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Borrower added on 12/21/11 &gt; to pay for prop...</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Borrower added on 12/21/11 &gt; I plan on combi...</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Borrower added on 12/18/11 &gt; I am planning o...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Borrower added on 12/16/11 &gt; Downpayment for...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Borrower added on 12/21/11 &gt; I own a small h...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Borrower added on 12/16/11 &gt; I'm trying to b...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                desc  mths_since_last_delinq\n",
       "0    Borrower added on 12/22/11 > I need to upgra...                     NaN\n",
       "1    Borrower added on 12/22/11 > I plan to use t...                     NaN\n",
       "2                                                NaN                     NaN\n",
       "3    Borrower added on 12/21/11 > to pay for prop...                    35.0\n",
       "4    Borrower added on 12/21/11 > I plan on combi...                    38.0\n",
       "5                                                NaN                     NaN\n",
       "6    Borrower added on 12/18/11 > I am planning o...                     NaN\n",
       "7    Borrower added on 12/16/11 > Downpayment for...                     NaN\n",
       "8    Borrower added on 12/21/11 > I own a small h...                     NaN\n",
       "9    Borrower added on 12/16/11 > I'm trying to b...                     NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking what desc and mths_since_last_delinq contain\n",
    "df.loc[:, ['desc', 'mths_since_last_delinq']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since 'desc' is a column of type text and 'mths_since_last_delinq' data is unknown at the time of loan application, it cannot be used as a predictor of default at the time of loan approval.\n",
    "\n",
    "###### Therefore we drop these two columns as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "df = df.drop(['desc', 'mths_since_last_delinq'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### analysing missing values in rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([], dtype='int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[round(df.isnull().sum(axis=1))>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### i.e. no rows have greater than 5 missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 53)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new shape after data cleani=sing\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 53 columns):\n",
      "id                            39717 non-null int64\n",
      "member_id                     39717 non-null int64\n",
      "loan_amnt                     39717 non-null int64\n",
      "funded_amnt                   39717 non-null int64\n",
      "funded_amnt_inv               39717 non-null float64\n",
      "term                          39717 non-null object\n",
      "int_rate                      39717 non-null object\n",
      "installment                   39717 non-null float64\n",
      "grade                         39717 non-null object\n",
      "sub_grade                     39717 non-null object\n",
      "emp_title                     37258 non-null object\n",
      "emp_length                    38642 non-null object\n",
      "home_ownership                39717 non-null object\n",
      "annual_inc                    39717 non-null float64\n",
      "verification_status           39717 non-null object\n",
      "issue_d                       39717 non-null object\n",
      "loan_status                   39717 non-null object\n",
      "pymnt_plan                    39717 non-null object\n",
      "url                           39717 non-null object\n",
      "purpose                       39717 non-null object\n",
      "title                         39706 non-null object\n",
      "zip_code                      39717 non-null object\n",
      "addr_state                    39717 non-null object\n",
      "dti                           39717 non-null float64\n",
      "delinq_2yrs                   39717 non-null int64\n",
      "earliest_cr_line              39717 non-null object\n",
      "inq_last_6mths                39717 non-null int64\n",
      "open_acc                      39717 non-null int64\n",
      "pub_rec                       39717 non-null int64\n",
      "revol_bal                     39717 non-null int64\n",
      "revol_util                    39667 non-null object\n",
      "total_acc                     39717 non-null int64\n",
      "initial_list_status           39717 non-null object\n",
      "out_prncp                     39717 non-null float64\n",
      "out_prncp_inv                 39717 non-null float64\n",
      "total_pymnt                   39717 non-null float64\n",
      "total_pymnt_inv               39717 non-null float64\n",
      "total_rec_prncp               39717 non-null float64\n",
      "total_rec_int                 39717 non-null float64\n",
      "total_rec_late_fee            39717 non-null float64\n",
      "recoveries                    39717 non-null float64\n",
      "collection_recovery_fee       39717 non-null float64\n",
      "last_pymnt_d                  39646 non-null object\n",
      "last_pymnt_amnt               39717 non-null float64\n",
      "last_credit_pull_d            39715 non-null object\n",
      "collections_12_mths_ex_med    39661 non-null float64\n",
      "policy_code                   39717 non-null int64\n",
      "application_type              39717 non-null object\n",
      "acc_now_delinq                39717 non-null int64\n",
      "chargeoff_within_12_mths      39661 non-null float64\n",
      "delinq_amnt                   39717 non-null int64\n",
      "pub_rec_bankruptcies          39020 non-null float64\n",
      "tax_liens                     39678 non-null float64\n",
      "dtypes: float64(18), int64(13), object(22)\n",
      "memory usage: 16.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking data type of remaining colunmns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65%</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27%</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96%</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49%</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69%</td>\n",
       "      <td>1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90%</td>\n",
       "      <td>3 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96%</td>\n",
       "      <td>8 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.64%</td>\n",
       "      <td>9 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.28%</td>\n",
       "      <td>4 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.69%</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  int_rate emp_length\n",
       "0   10.65%  10+ years\n",
       "1   15.27%   < 1 year\n",
       "2   15.96%  10+ years\n",
       "3   13.49%  10+ years\n",
       "4   12.69%     1 year\n",
       "5    7.90%    3 years\n",
       "6   15.96%    8 years\n",
       "7   18.64%    9 years\n",
       "8   21.28%    4 years\n",
       "9   12.69%   < 1 year"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking what int_rate and emp_length contain\n",
    "df.loc[:, ['int_rate','emp_length']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# The column int_rate is object type,\n",
    "#converting to float\n",
    "df['int_rate'] = df['int_rate'].apply(lambda x: pd.to_numeric(x.split(\"%\")[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10+ years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>1 year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90</td>\n",
       "      <td>3 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96</td>\n",
       "      <td>8 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.64</td>\n",
       "      <td>9 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.28</td>\n",
       "      <td>4 years</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.69</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate emp_length\n",
       "0     10.65  10+ years\n",
       "1     15.27   < 1 year\n",
       "2     15.96  10+ years\n",
       "3     13.49  10+ years\n",
       "4     12.69     1 year\n",
       "5      7.90    3 years\n",
       "6     15.96    8 years\n",
       "7     18.64    9 years\n",
       "8     21.28    4 years\n",
       "9     12.69   < 1 year"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['int_rate','emp_length']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.71"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "round(df['emp_length'].isnull().mean() * 100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# removing missing values from column 'emp_length'\n",
    "df = df[~df['emp_length'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A regular expression in a programming language is a special text string used for describing a search pattern.\n",
    "using regular expression to extract numeric values from string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "df['emp_length'] = df['emp_length'].apply(lambda x : re.findall('\\d+',str(x))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# The column emp_length is object type, converting it to integer type\n",
    "df['emp_length'] = df['emp_length'].apply(lambda x : pd.to_numeric(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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>emp_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.65</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.96</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.49</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.96</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.64</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.28</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_rate  emp_length\n",
       "0     10.65          10\n",
       "1     15.27           1\n",
       "2     15.96          10\n",
       "3     13.49          10\n",
       "4     12.69           1\n",
       "5      7.90           3\n",
       "6     15.96           8\n",
       "7     18.64           9\n",
       "8     21.28           4\n",
       "9     12.69           1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['int_rate','emp_length']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, the customer behaviour variables are not available at the time of loan application, and thus they cannot be used as predictors for credit approval. So, removing those columns is a better idea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "non_pred = [\"delinq_2yrs\", \"earliest_cr_line\", \"inq_last_6mths\", \"open_acc\", \"pub_rec\", \n",
    "                 \"revol_bal\", \"revol_util\", \"total_acc\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\", \n",
    "                 \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\", \n",
    "                 \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_d\", \"last_pymnt_amnt\",\n",
    "                 \"last_credit_pull_d\",  \"application_type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# removing behaviour variables\n",
    "df = df.drop(non_pred,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38642, 32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# updated shape of dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38642 entries, 0 to 39716\n",
      "Data columns (total 32 columns):\n",
      "id                            38642 non-null int64\n",
      "member_id                     38642 non-null int64\n",
      "loan_amnt                     38642 non-null int64\n",
      "funded_amnt                   38642 non-null int64\n",
      "funded_amnt_inv               38642 non-null float64\n",
      "term                          38642 non-null object\n",
      "int_rate                      38642 non-null float64\n",
      "installment                   38642 non-null float64\n",
      "grade                         38642 non-null object\n",
      "sub_grade                     38642 non-null object\n",
      "emp_title                     37202 non-null object\n",
      "emp_length                    38642 non-null int64\n",
      "home_ownership                38642 non-null object\n",
      "annual_inc                    38642 non-null float64\n",
      "verification_status           38642 non-null object\n",
      "issue_d                       38642 non-null object\n",
      "loan_status                   38642 non-null object\n",
      "pymnt_plan                    38642 non-null object\n",
      "url                           38642 non-null object\n",
      "purpose                       38642 non-null object\n",
      "title                         38632 non-null object\n",
      "zip_code                      38642 non-null object\n",
      "addr_state                    38642 non-null object\n",
      "dti                           38642 non-null float64\n",
      "initial_list_status           38642 non-null object\n",
      "collections_12_mths_ex_med    38586 non-null float64\n",
      "policy_code                   38642 non-null int64\n",
      "acc_now_delinq                38642 non-null int64\n",
      "chargeoff_within_12_mths      38586 non-null float64\n",
      "delinq_amnt                   38642 non-null int64\n",
      "pub_rec_bankruptcies          37945 non-null float64\n",
      "tax_liens                     38603 non-null float64\n",
      "dtypes: float64(9), int64(8), object(15)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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>title</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Computer</td>\n",
       "      <td>credit_card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bike</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>real estate business</td>\n",
       "      <td>small_business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>personel</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Personal</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>My wedding loan I promise to pay back</td>\n",
       "      <td>wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Loan</td>\n",
       "      <td>debt_consolidation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Car Downpayment</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Expand Business &amp; Buy Debt Portfolio</td>\n",
       "      <td>small_business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Building my credit history.</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   title             purpose\n",
       "0                               Computer         credit_card\n",
       "1                                   bike                 car\n",
       "2                   real estate business      small_business\n",
       "3                               personel               other\n",
       "4                               Personal               other\n",
       "5  My wedding loan I promise to pay back             wedding\n",
       "6                                   Loan  debt_consolidation\n",
       "7                        Car Downpayment                 car\n",
       "8   Expand Business & Buy Debt Portfolio      small_business\n",
       "9            Building my credit history.               other"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['title','purpose']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "columns title and purpose serve similar purpose so we can drop title column.\n",
    "Also columns like zip_code, address, state, etc. are useless for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# dropping columns\n",
    "df = df.drop(['title', 'url', 'zip_code', 'addr_state'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38642, 28)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# updated shape\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target variable: LOAN STATUS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The target variable, which we want to compare across the independent variables, is loan status. \n",
    "#### The strategy is to figure out compare the average default rates across various independent variables and identify the ones that affect default rate the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off', 'Current'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assessing values associated with target variable\n",
    "df.loan_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     32145\n",
       "Charged Off     5399\n",
       "Current         1098\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of various values associated with loan status\n",
    "df.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# removing 'current' values from column loan status\n",
    "df = df[df['loan_status']!='Current']\n",
    "\n",
    "#Labelling Fully paid as 0 and charged off as 1\n",
    "df['loan_status'] = df['loan_status'].apply(lambda x: 0 if x=='Fully Paid' else 1)\n",
    "\n",
    "#Converting to numeric type\n",
    "df['loan_status'] = df['loan_status'].apply(lambda x: pd.to_numeric(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    32145\n",
       "1     5399\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summarising the values\n",
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.38"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#default percentage \n",
    "round(df['loan_status'].mean() * 100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# function for plotting categorical variable\n",
    "def cat_var(var):\n",
    "    sns.barplot(x=var, y='loan_status', data = df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_var('grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from A to G, the default rate increases which indicates riskiness of the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_var('term')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "60 months loan default rate is more than 36 months loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,4))\n",
    "cat_var('sub_grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A1 is better than A2 better than A3 and so on "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#purpose\n",
    "plt.figure(figsize=(18,4))\n",
    "cat_var('purpose')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "small business loans defualt the most!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#verification status\n",
    "cat_var('verification_status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verified loan defaults are greater than the one which are not verified!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#home-ownership\n",
    "cat_var('home_ownership')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "home_ownership is not of much importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "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>issue_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Dec-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   issue_d\n",
       "0   Dec-11\n",
       "1   Dec-11\n",
       "2   Dec-11\n",
       "3   Dec-11\n",
       "5   Dec-11\n",
       "6   Dec-11\n",
       "7   Dec-11\n",
       "8   Dec-11\n",
       "9   Dec-11\n",
       "10  Dec-11"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['issue_d']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['issue_d'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "converting issue_d column into date and time format and then extracting year and month from it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "df['issue_d'] = df['issue_d'].apply(lambda x: datetime.strptime(x, '%b-%y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "#extracting month and year from issue_date\n",
    "df['month'] = df['issue_d'].apply(lambda x: x.month)\n",
    "df['year'] = df['issue_d'].apply(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    4120\n",
       "11    3890\n",
       "10    3637\n",
       "9     3394\n",
       "8     3321\n",
       "7     3253\n",
       "6     3094\n",
       "5     2838\n",
       "4     2756\n",
       "3     2632\n",
       "1     2331\n",
       "2     2278\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011    19801\n",
       "2010    11214\n",
       "2009     4716\n",
       "2008     1562\n",
       "2007      251\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " number of loans has increased steadily across years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Default rate accross years\n",
    "plt.figure(figsize=(12,4))\n",
    "cat_var('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing defaults accross months\n",
    "plt.figure(figsize=(14,4))\n",
    "cat_var('month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variation across continuous variables.\n",
    "plt.figure(figsize=(14,4))\n",
    "sns.distplot(df['loan_amnt'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binning loan amount variable into small, medium, high and  very high discrete categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "#binnig the loan amount\n",
    "def binned(n):\n",
    "    if n<5000:\n",
    "        return 'low'\n",
    "    elif n>5000 and n<15000:\n",
    "        return 'medium'\n",
    "    elif n>15000 and n<25000:\n",
    "        return 'high'\n",
    "    else:\n",
    "        return 'very high'\n",
    "\n",
    "df['loan_amnt'] = df['loan_amnt'].apply(lambda x: binned(x))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium       18198\n",
       "low           7095\n",
       "very high     6484\n",
       "high          5767\n",
       "Name: loan_amnt, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_amnt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# let's also convert funded amount invested to bins\n",
    "df['funded_amnt_inv'] = df['funded_amnt_inv'].apply(lambda x: binned(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAEHCAYAAACZaBYMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAfNklEQVR4nO3de7xdZX3n8c+XQMByUyEKDWDomBkaReIQqBaN443G1hKmgoRihZaXjJ3Sy1hAnBnRpjqt0CnVllpQLmLRSLHWtI3Gtgg4CpgglxCQGsLtANEgF8ELEPKbP/Y6snM4J+vs5OycneTzfr3266z9rOd51rPyOvvkfM/zrLVSVUiSJEmSxrbDZA9AkiRJkgadwUmSJEmSWhicJEmSJKmFwUmSJEmSWhicJEmSJKnFjpM9gC1l7733rhkzZkz2MCRJkiQNqBtuuOGhqpo22r7tJjjNmDGD5cuXT/YwJEmSJA2oJPeMtc+lepIkSZLUwuAkSZIkSS0MTpIkSZLUwuAkSZIkSS0MTpIkSZLUwuAkSZIkSS0MTpIkSZLUwuAkSZIkSS22mwfgSpIkSYPojDPOYM2aNeyzzz6cffbZkz0cjcHgJEmSJE2iNWvWcP/990/2MNTCpXqSJEmS1MLgJEmSJEktDE6SJEmS1MLgJEmSJEktDE6SJEmS1MLgJEmSJEktDE6SJEmS1MLgJEmSJEktfACutgifiC1JkqStWd+DU5J5wEeBKcAnq+pPR+yfC/wF8ApgQVVd0ZS/Hji3q+pBzf5/SHIJ8DrgsWbfSVV1U19PRJvFJ2JLkrR9uHru6yZ7CFudH+84BRJ+PDTkv1+PXnfN1VvsWH0NTkmmAOcBbwaGgGVJFlfVbV3V7gVOAk7rbltVXwVmN/28EFgFfKWryunDIUuSJEmS+qnfM06HA6uqajVAkkXAfOCnwamq7m72rd9IP8cAX6qqH/VvqJIkSZI0un7fHGI6cF/X+6GmrFcLgM+OKPtwkluSnJtk59EaJTklyfIky9euXbsJh5UkSZKk/genjFJWPXWQ7AscDCztKn4fnWueDgNeCLx3tLZVdUFVzamqOdOmTevlsJIkSZL0U/0OTkPA/l3v9wMe6LGPtwNfqKqnhwuq6sHqeBK4mM6SQEmSJEnqi34Hp2XAzCQHJplKZ8nd4h77OJ4Ry/SaWSiSBDgauHUCxipJkiRJo+prcKqqdcCpdJbZ3Q5cXlUrkyxMchRAksOSDAHHAucnWTncPskMOjNWI+8zeFmSFcAKYG/gQ/08D0mSJEnbt74/x6mqlgBLRpSd1bW9jM4SvtHa3s0oN5OoqjdM7CglSZIkaWz9XqonSZIkSVu9vs84bYsOPf3SyR7CVmf3hx5nCnDvQ4/777cJbjjnnZM9BEmSpO2aM06SJEmS1MLgJEmSJEktXKonSZIkTaLnV23wVYPJ4CRJkiRNonc8s36yh6BxcKmeJEmSJLUwOEmSJElSC4OTJEmSJLUwOEmSJElSC4OTJEmSJLUwOEmSJElSC29Hri1i/dRdN/gqSZIkbU0MTtoifjjzyMkegiRJkrTJXKonSZIkSS0MTpIkSZLUwuAkSZIkSS0MTpIkSZLUou/BKcm8JHckWZXkzFH2z03yrSTrkhwzYt8zSW5qXou7yg9Mcn2S7yT5XJKp/T4PSZIkSduvvganJFOA84C3ALOA45PMGlHtXuAk4DOjdPHjqprdvI7qKv8IcG5VzQQeAU6e8MFLkiRJUqPfM06HA6uqanVVPQUsAuZ3V6iqu6vqFmD9eDpMEuANwBVN0aeAoyduyJIkSZK0oX4Hp+nAfV3vh5qy8dolyfIk1yUZDkd7AY9W1bpN7FOSJEmSetLvB+BmlLLqof0BVfVAkp8DrkyyAvjBePtMcgpwCsABBxzQw2ElSZIk6Vn9nnEaAvbver8f8MB4G1fVA83X1cBVwCuBh4DnJxkOfWP2WVUXVNWcqpozbdq03kcvSZIkSfQ/OC0DZjZ3wZsKLAAWt7QBIMkLkuzcbO8NHAHcVlUFfBUYvgPficAXJ3zkkiRJktToa3BqrkM6FVgK3A5cXlUrkyxMchRAksOSDAHHAucnWdk0/3lgeZKb6QSlP62q25p97wXek2QVnWueLuzneUiSJEnavvX7GieqagmwZETZWV3by+gstxvZ7hvAwWP0uZrOHfskSZIkqe/6/gBcSZIkSdraGZwkSZIkqYXBSZIkSZJa9P0aJ0nShs444wzWrFnDPvvsw9lnnz3Zw5EkSeNgcJKkLWzNmjXcf//9kz0MSZLUA5fqSZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILn+MkSZImhQ+DlrQ1MThJ2iz3Ljx4soew1Vn38AuBHVn38D3++/XogLNWTPYQNIF8GLSkrYlL9SRJkiSphcFJkiRJkloYnCRJkiSphcFJkiRJklp4cwhJkibAEX95xGQPYasz9dGp7MAO3Pfoff779ejrv/v1yR6CtN3p+4xTknlJ7kiyKsmZo+yfm+RbSdYlOaarfHaSa5OsTHJLkuO69l2S5K4kNzWv2f0+D0mSJEnbr77OOCWZApwHvBkYApYlWVxVt3VVuxc4CThtRPMfAe+squ8k+VnghiRLq+rRZv/pVXVFP8cvSZIkSdD/pXqHA6uqajVAkkXAfOCnwamq7m72re9uWFX/3rX9QJLvAdOAR5EkSZKkLajfS/WmA/d1vR9qynqS5HBgKnBnV/GHmyV85ybZeYx2pyRZnmT52rVrez2sJEmSJAH9D04Zpax66iDZF/g08JtVNTwr9T7gIOAw4IXAe0drW1UXVNWcqpozbdq0Xg4rSX2z9y7refHz1rH3LuvbK0uSpIHQ76V6Q8D+Xe/3Ax4Yb+MkewD/DPzvqrpuuLyqHmw2n0xyMc+9PkqSBtZpr3DFsSRJW5t+zzgtA2YmOTDJVGABsHg8DZv6XwAuraq/G7Fv3+ZrgKOBWyd01JIkqe/qZ4r1u66nfqanxSiSNCnGHZySHJFk12b7HUn+PMlLNtamqtYBpwJLgduBy6tqZZKFSY5q+josyRBwLHB+kpVN87cDc4GTRrnt+GVJVgArgL2BD437jCVJ0kB4+oineerNT/H0EU9P9lAkqVUvS/U+DhyS5BDgDOBC4FLgdRtrVFVLgCUjys7q2l5GZwnfyHZ/C/ztGH2+oYdxS5IkSdJm6WWp3rqqKjq3E/9oVX0U2L0/w5IkSZKkwdHLjNPjSd4HvAOY2zzcdqf+DEuSJEmSBkcvM07HAU8CJ1fVGjrPYzqnL6OSJEmSpAEy7hmnJiz9edf7e+lc4yRJkiRJ27RxB6ckj/Psw2un0lmm90RV7dmPgUmSJEnSoOhlxmmDG0EkORo4fMJHJEmSJEkDZpMfgFtV/wB4W3BJkiRJ27xelur9WtfbHYA5PLt0T5IkSZK2Wb3cjvxXu7bXAXfTeaaTJEmSJG3TeglOn6yqr3cXJDkC+N7EDkmSJEmSBksv1zj95TjLJEmSJGmb0jrjlOTVwC8C05K8p2vXHsCUfg1MkiRJkgbFeJbqTQV2a+p235L8B8Ax/RiUJEmSJA2S1uBUVVcDVye5pKru2QJjkiRJkqSB0svNIX6U5BzgZcAuw4VV5bOcJEmSJG3Terk5xGXAt4EDgT+iczvyZX0YkyRJkiQNlF6C015VdSHwdFVdXVW/BbyqT+OSJEmSpIHRy1K9p5uvDyb5FeABYL+JH5IkSZIkDZZegtOHkuwJ/CGd5zftAfxBX0YlSZIkSQOkl6V6j1TVY1V1a1W9vqoOBR5ua5RkXpI7kqxKcuYo++cm+VaSdUmOGbHvxCTfaV4ndpUfmmRF0+fHkqSH85AkSZKknvQSnP5ynGU/lWQKcB7wFmAWcHySWSOq3QucBHxmRNsXAh8AfgE4HPhAkhc0uz8OnALMbF7zejgPSZIkSepJ61K9JK8GfhGYluQ9Xbv2AKa0ND8cWFVVq5u+FgHzgduGK1TV3c2+9SPa/hLwL1X1cLP/X4B5Sa4C9qiqa5vyS4GjgS+1nYskSZIkbYrxzDhNBXajE7J273r9ADhmI+0ApgP3db0fasrGY6y205vt1j6TnJJkeZLla9euHedhJUmSJGlDrTNOVXU1cHWSS6rqHoAkOwC7VdUPWpqPdu1RjXNsY7Udd59VdQFwAcCcOXPGe1xJkiRJ2kAv1zj9SZI9kuxKZ6ndHUlOb2kzBOzf9X4/OrcxH4+x2g6x4W3Qe+lTkiRJknrWS3Ca1cwwHQ0sAQ4AfqOlzTJgZpIDk0wFFgCLx3m8pcCRSV7Q3BTiSGBpVT0IPJ7kVc3d9N4JfLGH85AkSZKknvQSnHZKshOd4PTFqnqalmV3VbUOOJVOCLoduLyqViZZmOQogCSHJRkCjgXOT7Kyafsw8Md0wtcyYOHwjSKA3wY+CawC7sQbQ0iSJEnqo14egHs+cDdwM3BNkpfQuUHERlXVEjozVN1lZ3VtL2PDpXfd9S4CLhqlfDnw8h7GLkmSJEmbbNwzTlX1saqaXlW/XFVF5/lLrx/e3/2AWkmSJEnalvSyVG8D1bGuq+j3J2A8kiRJkjRwNjk4jWK024RLkiRJ0lZvIoOTz0mSJEmStE1yxkmSJEmSWkxkcPr6BPYlSZIkSQNj3LcjT7Iz8DZgRne7qlrYfD11ogcnSZIkSYOgl+c4fRF4DLgBeLI/w5EkSZKkwdNLcNqvqub1bSSSJEmSNKB6ucbpG0kO7ttIJEmSJGlA9TLj9BrgpCR30VmqFzrPwX1FX0YmSZIkSQOil+D0lr6NQpIkSZIG2LiDU1XdA5DkRcAufRuRJEmSJA2YcV/jlOSoJN8B7gKuBu4GvtSncUmSJEnSwOjl5hB/DLwK+PeqOhB4Iz70VpIkSdJ2oJfg9HRVfR/YIckOVfVVYHafxiVJkiRJA6OXm0M8mmQ34GvAZUm+B6zrz7AkSZIkaXD0MuM0H/gx8AfAl4E7gV/tx6AkSZIkaZCMOzhV1Q+BvYF5wPeBRc3SvY1KMi/JHUlWJTlzlP07J/lcs//6JDOa8hOS3NT1Wp9kdrPvqqbP4X0vGu95SJIkSVKvermr3tuBbwLHAm8Hrk9yTEubKcB5dJ4BNQs4PsmsEdVOBh6pqpcC5wIfAaiqy6pqdlXNBn4DuLuqbupqd8Lw/qr63njPQ5IkSZJ61cs1Tv8LOGw4pCSZBvwrcMVG2hwOrKqq1U2bRXSW/N3WVWc+8MFm+wrgr5KkqqqrzvHAZ3sYqyRJkiRNmF6ucdphxMzO98fRfjpwX9f7oaZs1DpVtQ54DNhrRJ3jeG5wurhZpvf+JBnH+CVJkiRpk/Qy4/TlJEt5NsAcByxpaTNaoKle6iT5BeBHVXVr1/4Tqur+JLsDn6ezlO/S5xw8OQU4BeCAAw5oGaokSZIkja6Xm0OcDlwAvAI4BLigqt7b0mwI2L/r/X7AA2PVSbIjsCfwcNf+BYyYbaqq+5uvjwOfobMkcLQxX1BVc6pqzrRp01qGKkmSJEmj62XGiar6PJ0ZnvFaBsxMciBwP50Q9Osj6iwGTgSuBY4Brhy+vinJDnRuRjF3uHITrp5fVQ8l2Ql4K51rrSRJkiSpL1qDU5LHee7yOugssauq2mOstlW1LsmpwFJgCnBRVa1MshBYXlWLgQuBTydZRWemaUFXF3OBoeGbSzR2BpY2oWkKndD0ibbzkCRJkqRN1Rqcqmr3zTlAVS1hxLVQVXVW1/ZP6Mwqjdb2KuBVI8p+CBy6OWOSJEmSpF70clc9SZIkSdouGZwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJaGJwkSZIkqYXBSZIkSZJa9D04JZmX5I4kq5KcOcr+nZN8rtl/fZIZTfmMJD9OclPz+puuNocmWdG0+ViS9Ps8JEmSJG2/+hqckkwBzgPeAswCjk8ya0S1k4FHquqlwLnAR7r23VlVs5vXu7vKPw6cAsxsXvP6dQ6SJEmS1O8Zp8OBVVW1uqqeAhYB80fUmQ98qtm+AnjjxmaQkuwL7FFV11ZVAZcCR0/80CVJkiSpo9/BaTpwX9f7oaZs1DpVtQ54DNir2XdgkhuTXJ3ktV31h1r6BCDJKUmWJ1m+du3azTsTSZIkSdutfgen0WaOapx1HgQOqKpXAu8BPpNkj3H22SmsuqCq5lTVnGnTpvUwbEmSJEl6Vr+D0xCwf9f7/YAHxqqTZEdgT+Dhqnqyqr4PUFU3AHcC/7Gpv19Ln5IkSZI0YfodnJYBM5McmGQqsABYPKLOYuDEZvsY4MqqqiTTmptLkOTn6NwEYnVVPQg8nuRVzbVQ7wS+2OfzkCRJkrQd27GfnVfVuiSnAkuBKcBFVbUyyUJgeVUtBi4EPp1kFfAwnXAFMBdYmGQd8Azw7qp6uNn328AlwPOALzUvSZIkSeqLvgYngKpaAiwZUXZW1/ZPgGNHafd54PNj9LkcePnEjlSSJEmSRtf3B+BKkiRJ0tbO4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktTC4CRJkiRJLQxOkiRJktSi78EpybwkdyRZleTMUfbvnORzzf7rk8xoyt+c5IYkK5qvb+hqc1XT503N60X9Pg9JkiRJ268d+9l5kinAecCbgSFgWZLFVXVbV7WTgUeq6qVJFgAfAY4DHgJ+taoeSPJyYCkwvavdCVW1vJ/jlyRJkiTo/4zT4cCqqlpdVU8Bi4D5I+rMBz7VbF8BvDFJqurGqnqgKV8J7JJk5z6PV5IkSZKeo9/BaTpwX9f7ITacNdqgTlWtAx4D9hpR523AjVX1ZFfZxc0yvfcnyWgHT3JKkuVJlq9du3ZzzkOSJEnSdqzfwWm0QFO91EnyMjrL9/5b1/4Tqupg4LXN6zdGO3hVXVBVc6pqzrRp03oauCRJkiQN63dwGgL273q/H/DAWHWS7AjsCTzcvN8P+ALwzqq6c7hBVd3ffH0c+AydJYGSJEmS1Bf9Dk7LgJlJDkwyFVgALB5RZzFwYrN9DHBlVVWS5wP/DLyvqr4+XDnJjkn2brZ3At4K3Nrn85AkSZK0HetrcGquWTqVzh3xbgcur6qVSRYmOaqpdiGwV5JVwHuA4VuWnwq8FHj/iNuO7wwsTXILcBNwP/CJfp6HJEmSpO1bX29HDlBVS4AlI8rO6tr+CXDsKO0+BHxojG4PncgxSpIkSdLG9P0BuJIkSZK0tTM4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIktTA4SZIkSVILg5MkSZIkteh7cEoyL8kdSVYlOXOU/Tsn+Vyz//okM7r2va8pvyPJL423T0mSJEmaSH0NTkmmAOcBbwFmAccnmTWi2snAI1X1UuBc4CNN21nAAuBlwDzgr5NMGWefkiRJkjRh+j3jdDiwqqpWV9VTwCJg/og684FPNdtXAG9MkqZ8UVU9WVV3Aaua/sbTpyRJkiRNmB373P904L6u90PAL4xVp6rWJXkM2Kspv25E2+nNdlufACQ5BTileftEkjs24Rw0cfYGHprsQWyN8mcnTvYQNPH8PGyKD2SyR6CJ52dhE+T3/Cxsg/wsbIpM+GfhJWPt6HdwGu1Mapx1xiofbZZsZJ+dwqoLgAs2NkBtOUmWV9WcyR6HNAj8PEgdfhakDj8Lg6/fS/WGgP273u8HPDBWnSQ7AnsCD2+k7Xj6lCRJkqQJ0+/gtAyYmeTAJFPp3Oxh8Yg6i4HhdUjHAFdWVTXlC5q77h0IzAS+Oc4+JUmSJGnC9HWpXnPN0qnAUmAKcFFVrUyyEFheVYuBC4FPJ1lFZ6ZpQdN2ZZLLgduAdcDvVNUzAKP12c/z0IRx2aT0LD8PUoefBanDz8KAS2dyR5IkSZI0lr4/AFeSJEmStnYGJ0mSJElqYXDSpEjyxBjl707yzpa2JyX5q/6MTJoYY32PS3pWkquSzGm2lyR5/mSPSZpoSWYkuXWU8oVJ3tTS9oNJTuvf6NSLfj/HSduRJKFz3dz6Te2jqv5mAockSdpKVNUvT/YYpC2pqs6a7DGoN844aQNJPpLkv3e9/2CSP2y2T0+yLMktSf6oKZuR5PYkfw18C3h/knO72r8ryZ+PcawPJ7k5yXVJXtx1vNOa7cOaY12b5JwRf6352SRfTvKdJGdP+D+ENEHScU6SW5OsSHJcU/7XSY5qtr+Q5KJm++QkH5rMMUsb0/zc/3aSTzbf15cleVOSrzc/kw9PsmuSi5r/M25MMr9p+7wki5qf7Z8DntfV791J9h751/kkpyX5YLN9VZJzk1zT/N9zWJK/b47r50aDbEqSTyRZmeQrzWfhkiTHACT55eZz9f+SfCzJP3W1ndV8769O8nuTNH5hcNJzLQKO63r/duDvkhxJ51lahwOzgUOTzG3q/Cfg0qp6JfBnwFFJdmr2/SZw8SjH2RW4rqoOAa4B3jVKnYuBd1fVq4FnRuyb3YzzYOC4JPuPbCwNiF+j8/16CPAm4Jwk+9L5vn9tU2c6MKvZfg3wtS09SKlHLwU+CrwCOAj4dTrfu6cB/xP4X3Sey3gY8Ho63/e7Ar8N/KiqXgF8GDh0E479VFXNBf4G+CLwO8DLgZOS7LVZZyX1z0zgvKp6GfAo8LbhHUl2Ac4H3lJVrwGmjWh7EPBLdH4H+0DX71jawgxO2kBV3Qi8KMnPJjkEeKSq7gWObF430plZOojODwGAe6rquqb9D4ErgbcmOQjYqapWjHKop4Dhv6bcAMzo3tmsc9+9qr7RFH1mRPt/q6rHquondJ719ZJNPWepz14DfLaqnqmq7wJXA4fRCUevTTKLzvfwd5tA9WrgG2P2Jg2Gu6pqRbM0eyWdn8kFrKDz8/xI4MwkNwFXAbsABwBzgb8FqKpbgFs24djDD71fAaysqger6klgNeAf0TSo7qqqm5rtkb/3HASsrqq7mvefHdH2n6vqyap6CPge8OK+jlRj8honjeYK4BhgHzozUAAB/qSqzu+umGQG8MMR7T9J5y+O32b02SaAp+vZh4g9w3O/F9Myxie7tkdrLw2KUb+Xq+r+JC8A5tGZfXohnRneJ6rq8S04PmlTdP8MXt/1fj2dn8fPAG+rqju6G3UuhaXtAZLr2PAPu7uMcezu43YfWxpEI39veV7Xe3/n2Uo446TRLAIW0AlPVzRlS4HfSrIbQJLpSV40WuOqup7OX/1+nef+1WRcquoR4PEkr2qKFmxKP9IAuIbOctIpSabR+Yv7N5t91wJ/0NT5Gp1lTi7T07ZgKfC7zU2DSPLKpvwa4ISm7OV0lvqN9F06Kx/2SrIz8NYtMF5pMn0b+Lnmj9Gw4SUTGiAmVj1HVa1Msjtwf1U92JR9JcnPA9c2/w8+AbyD5157NOxyYHYTgDbVycAnkvyQzlKPxzajL2myfIHO8rub6fyl/YyqWtPs+xpwZFWtSnIPnVkng5O2BX8M/AVwSxOe7qYTgD4OXJzkFuAmnv0jwk9V1dNJFgLXA3fR+aVS2mZV1Y+bG3N9OclDjPK50GDIs6ulpInT3A3m3Kr6t83oY7eqeqLZPhPYt6p+f6LGKEmSNAiGf+dp/tBwHvCdqjq3rZ22LJfqaUIleX6Sfwd+vDmhqfErSW5qbkv7WsBbzUqSpG3Ru5qbqawE9qRzlz0NGGecJEmSJKmFM06SJEmS1MLgJEmSJEktDE6SJEmS1MLgJEmSJEktDE6SpIGT5InJHsPmSHJ0klmTPQ5J0sQxOEmSNPGOBgxOkrQNMThJkgZWOs5JcmuSFUmOa8p3S/JvSb7VlM9vymckuT3JJ5KsTPKVJM/bSP/vSrIsyc1JPp/kZ5ryS5J8PMlXk6xO8rokFzV9X9LV/okkH27aX5fkxUl+ETgKOKd5Ft1/6Os/kiRpizA4SZIG2a8Bs4FDgDfRCSP7Aj8B/mtV/Wfg9cD/TZKmzUzgvKp6GfAo8LaN9P/3VXVYVR0C3A6c3LXvBcAbgP8B/CNwLvAy4OAks5s6uwLXNe2vAd5VVd8AFgOnV9Xsqrpz8/4JJEmDwOAkSRpkrwE+W1XPVNV3gauBw4AA/yfJLcC/AtOBFzdt7qqqm5rtG4AZG+n/5Um+lmQFcAKdYDTsH6vzlPgVwHerakVVrQdWdvX5FPBP4zyWJGkrtuNkD0CSpI3IGOUnANOAQ6vq6SR3A7s0+57sqvcMMOZSPeAS4OiqujnJScB/6do33M/6EX2u59n/P59uwtXwsfx/VZK2Uc44SZIG2TXAcUmmJJkGzAW+CewJfK8JTa8HXrKJ/e8OPJhkJzphbKI83vQtSdpGGJwkSYPsC8AtwM3AlcAZVbUGuAyYk2Q5ncDz7U3s//3A9cC/bEYfo1kEnJ7kRm8OIUnbhjy7wkCSJEmSNBpnnCRJkiSphRexSpK2eUnOA44YUfzRqrp4MsYjSdr6uFRPkiRJklq4VE+SJEmSWhicJEmSJKmFwUmSJEmSWhicJEmSJKnF/we/OX+qw8xxxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "cat_var('loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Higher the loan amount, higher the default rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8GFjTTViSJEmSNHUMM+J0LPAI8OfA14AfAa/vIihJkiRJmkqGWVXvZ0meDcwGfgJ8tZm6J0mSJElPa8OsqvdG4FvA8cAbgRuSzO0qMEmSJEmaKoaZqvc/gNlVdXJVnQQcCbyvrVGSo5PclmRZkrMGHN8+yWXN8RuSzGzKT0xyU99rbZLDm2NXN32uO/asIa5DkiRJkoYyTOK0TVX9uG//J23tk0wDzgdeCxwMvCnJwaOqnQrcX1XPB84DPghQVZdU1eFVdTjwVuCOqrqpr92J646PikuSJEmSJtQwidPXkixMckqSU4CvAAta2hwJLKuq5VX1GHApvUUm+h0LfLbZvgJ4dZKMqvMm4ItDxCpJkiRJE2bciVNVvRu4AHghcBhwQVW9p6XZPsDdffsjTdnAOlW1BngQ2GNUnRN4auJ0UTNN730DEi0AkpyWZHGSxatXr24JVZIkSZIGG+Y5TlTVl4AvDdFkUEJTw9RJ8pvAz6vqlr7jJ1bViiS7NPG8FfjcgHgvoJfsMWvWrNHnlSRJkqRxaR1xSvJQkp8OeD2U5KctzUeA/fr29wVWjlUnybbAbsB9fcfnMWq0qapWND8fAr5Ab0qgJEmSJHWidcSpqnbZhP4XAQcmOQBYQS8JevOoOvOBk4HrgLnAVVVVAEm2obf8+Zx1lZvk6hlVdW+S7YDXAf93E2KUJEmSpA0aaqresKpqTZLTgYXANODCqlqa5BxgcVXNBz4DfD7JMnojTfP6upgDjFTV8r6y7YGFTdI0jV7S9Kkur0OSJEnS1q3TxAmgqhYwavW9qjq7b/sX9EaVBrW9GnjxqLKfAUdMeKCSJEmSNIZhliOXJEmSpK2SiZMkSZIktTBxkiRJkqQWJk6SJEmS1MLESZIkSZJamDhJkiRJUgsTJ0mSJElqYeIkSZIkSS1MnCRJkiSphYmTJEmSJLUwcZIkSZKkFiZOkiRJktTCxEmSJEmSWpg4SZIkSVILEydJkiRJamHiJEmSJEktTJwkSZIkqYWJkyRJkiS16DxxSnJ0ktuSLEty1oDj2ye5rDl+Q5KZTfnMJI8kual5faKvzRFJljRtPpokXV+HJEmSpK1Xp4lTkmnA+cBrgYOBNyU5eFS1U4H7q+r5wHnAB/uO/aiqDm9eb+8r/zhwGnBg8zq6q2uQJEmSpK5HnI4EllXV8qp6DLgUOHZUnWOBzzbbVwCv3tAIUpK9gV2r6rqqKuBzwHETH7okSZIk9XSdOO0D3N23P9KUDaxTVWuAB4E9mmMHJPlOkmuSvLyv/khLnwAkOS3J4iSLV69evWlXIkmSJGmr1XXiNGjkqMZZ5x5g/6p6EfAu4AtJdh1nn73CqguqalZVzZoxY8YQYUuSJEnSk7pOnEaA/fr29wVWjlUnybbAbsB9VfVoVf0EoKpuBH4E/GpTf9+WPiVJkiRpwnSdOC0CDkxyQJLpwDxg/qg684GTm+25wFVVVUlmNItLkOR59BaBWF5V9wAPJXlxcy/UScCXO74OSZIkSVuxbbvsvKrWJDkdWAhMAy6sqqVJzgEWV9V84DPA55MsA+6jl1wBzAHOSbIGeAJ4e1Xd1xx7B3AxsCPw1eYlSZIkSZ3oNHECqKoFwIJRZWf3bf8COH5Auy8BXxqjz8XAIRMbqSRJkiQN1vkDcCVJkiRpS2fiJEmSJEktTJwkSZIkqYWJkyRJkiS1MHGSJEmSpBYmTpIkSZLUwsRJkiRJklqYOEmSJElSCxMnSZIkSWph4iRJkiRJLUycJEmSJKmFiZMkSZIktTBxkiRJkqQWJk6SJEmS1MLESZIkSZJamDhJkiRJUgsTJ0mSJElqYeIkSZIkSS06T5ySHJ3ktiTLkpw14Pj2SS5rjt+QZGZT/pokNyZZ0vx8VV+bq5s+b2pez+r6OiRJkiRtvbbtsvMk04DzgdcAI8CiJPOr6ta+aqcC91fV85PMAz4InADcC7y+qlYmOQRYCOzT1+7EqlrcZfySJEmSBN2POB0JLKuq5VX1GHApcOyoOscCn222rwBenSRV9Z2qWtmULwV2SLJ9x/FKkiRJ0lN0nTjtA9zdtz/C+qNG69WpqjXAg8Aeo+q8AfhOVT3aV3ZRM03vfUkysWFLkiRJ0pO6TpwGJTQ1TJ0kL6A3fe+P+o6fWFWHAi9vXm8dePLktCSLkyxevXr1UIFLkiRJ0jpdJ04jwH59+/sCK8eqk2RbYDfgvmZ/X+BK4KSq+tG6BlW1ovn5EPAFelMCn6KqLqiqWVU1a8aMGRNyQZIkSZK2Pl0nTouAA5MckGQ6MA+YP6rOfODkZnsucFVVVZJnAF8B3ltV31xXOcm2SfZstrcDXgfc0vF1SJIkSdqKdZo4NfcsnU5vRbzvAZdX1dIk5yQ5pqn2GWCPJMuAdwHrliw/HXg+8L5Ry45vDyxMcjNwE7AC+FSX1yFJkiRp69bpcuQAVbUAWDCq7Oy+7V8Axw9ody5w7hjdHjGRMUqSJEnShnT+AFxJkiRJ2tKZOEmSJElSCxMnSZIkSWph4iRJkiRJLUycJEmSJKmFiZMkSZIktTBxkiRJkqQWJk6SJEmS1MLESZIkSZJamDhJkiRJUgsTJ0mSJElqYeIkSZIkSS1MnCRJkiSphYmTJEmSJLUwcZIkSZKkFiZOkiRJktTCxEmSJEmSWpg4SZIkSVILEydJkiRJatF54pTk6CS3JVmW5KwBx7dPcllz/IYkM/uOvbcpvy3J74y3T0mSJEmaSJ0mTkmmAecDrwUOBt6U5OBR1U4F7q+q5wPnAR9s2h4MzANeABwNfCzJtHH2KUmSJEkTpusRpyOBZVW1vKoeAy4Fjh1V51jgs832FcCrk6Qpv7SqHq2q24FlTX/j6VOSJEmSJsy2Hfe/D3B33/4I8Jtj1amqNUkeBPZoyq8f1XafZrutTwCSnAac1uw+nOS2jbgGTZw9gXsnO4gtUf725MkOQRPP98PG+MtMdgSaeL4XNkLe6Xvhacj3wsbIhL8XnjvWga4Tp0FXUuOsM1b5oFGy0X32CqsuAC7YUIDafJIsrqpZkx2HNBX4fpB6fC9IPb4Xpr6up+qNAPv17e8LrByrTpJtgd2A+zbQdjx9SpIkSdKE6TpxWgQcmOSAJNPpLfYwf1Sd+cC6eUhzgauqqpryec2qewcABwLfGmefkiRJkjRhOp2q19yzdDqwEJgGXFhVS5OcAyyuqvnAZ4DPJ1lGb6RpXtN2aZLLgVuBNcCfVNUTAIP67PI6NGGcNik9yfeD1ON7QerxvTDFpTe4I0mSJEkaS+cPwJUkSZKkLZ2JkyRJkiS1MHHShEry8GTHIE11Sa5OMqvZXpDkGZMdkzTZxvr8SPL2JCe1tD0lyT90E5m0aZLMTHLLgPJzkvx2S9v3Jzmju+g0jK6f4yRJ2oCq+t3JjkHqSpLQu5967cb2UVWfmMCQpCmjqs6e7Bg0HEec1In0fDjJLUmWJDmhKf9YkmOa7SuTXNhsn5rk3MmMWdqQ5hvD7yf5dPN3fUmS307yzSQ/THJkkp2SXJhkUZLvJDm2abtjkkuT3JzkMmDHvn7vSLLn6G8kk5yR5P3N9tVJzktybZLvJZmd5J+b8/q+UaeSfDDJH/ftvz/JXzTb727+3m9O8ldN2czm7/RjwLeB9yU5r6/925L83Rjn+kCS7ya5Psmz+853RrM9uznXdes+Y/qaPyfJ15r3xYcm/BchbZppST6VZGmSrzefCxcnmQuQ5Hebz5j/l+SjSf6lr+3BzefA8iTvnKT4hYmTuvP7wOHAYcBvAx9OsjdwLfDyps4+wMHN9suAb2zuIKUhPR/4CPBC4CDgzfT+ds8A/jvwP+g9i2428Ep6f/c7Ae8Afl5VLwQ+AByxEed+rKrmAJ8Avgz8CXAIcEqSPTbpqqQNuxQ4oW//jcA/JTmK3jMWj6T37/0RSeY0dX4N+FxVvQj4W+CYJNs1x/4AuGjAeXYCrq+qw+h9VrxtQJ2LgLdX1UuAJ0YdO7yJ81DghCT7DXeZUqcOBM6vqhcADwBvWHcgyQ7AJ4HXVtXLgBmj2h4E/A6999pf9r2XtJmZOKkrLwO+WFVPVNV/AtcAs+klRy9PcjC9Z3T9Z5NQvQT4j0mLVhqf26tqSTPtaCnwb80Du5cAM4GjgLOS3ARcDewA7A/MAf4RoKpuBm7eiHOve9D3EmBpVd1TVY8CywH/B1GdqarvAM9K8pwkhwH3V9Vd9P7ejwK+Q29k6SB6/3MIcGdVXd+0/xlwFfC6JAcB21XVkgGnegxY9y37jfTeU7/U3Au4S1Wt+6z4wqj2/1ZVD1bVL+h9vjx3Y69Z6sDtVXVTsz367/sgYHlV3d7sf3FU269U1aNVdS/wY+DZnUaqMXmPk7qSQYVVtSLJM4Gj6X2juDu9by8frqqHNmN80sZ4tG97bd/+Wnr/nj4BvKGqbutv1LvNg7aH5q1h/S+zdhjj3P3n7T+31KUrgLnAXvRGoKD37/zfVNUn+ysmmQn8bFT7T9Mblf0+g0ebAB6vJx8u+QRP/bse+LnSp/99Mai9NJlG/33u2Lfv3/YWwhEndeVaelMlpiWZQe8b9281x64D/ryp8w1605ycpqeng4XAnzY3xJPkRU35tcCJTdkh9Kb6jfaf9L7V3yPJ9sDrNkO80nhdCsyjlzxd0ZQtBP4wyc4ASfZJ8qxBjavqBnojo2/mqd+mj0tV3Q88lOTFTdG8jelHmoK+Dzyv+dIB1p8aqynEjFVduZLe9Lvv0vum/cyqWtUc+wZwVFUtS3InvVEnEyc9Hfw18L+Bm5vk6Q56CdDHgYuS3AzcxJNfIvxSVT2e5BzgBuB2eh+k0pRQVUuT7AKsqKp7mrKvJ/l14Lrmu4KHgbfw1HuP1rkcOLxJgDbWqcCnkvyM3nTYBzehL2lKqKpHmgVYvpbkXgZ8RmhqyJOj4pIkSd1oVgk7r6r+bRP62LmqHm62zwL2rqo/m6gYpcmy7m+7+dLtfOCHVXVeWzttXk7VkyRJnUnyjCQ/AB7ZlKSp8XtJbmqWIX854HL8erp4W7Ow0FJgN3qr7GmKccRJkiRJklo44iRJkiRJLUycJEmSJKmFiZMkSZIktTBxkiRJkqQWJk6SpAmV5J1Jvpfkkk3sZ2azetowbS5OMndTzrsxkpyS5DktdT6d5ODNFZMkaWL5AFxJ0kT7Y+C1VXX7ZAeyGZ0C3AKsHKtCVf23zRaNJGnCOeIkSZowST4BPA+Yn+TBJGf0HbulGUWa2YxIfSrJ0iRfT7JjU+eIJN9Nch3wJ31tpyX5cJJFSW5O8kdNeZL8Q5Jbk3wFeFZLfGc3fdyS5ILmYZMkuTrJeUmubWKbneSfk/wwyblNnYFxNyNcs4BLmmcM7TjGua9OMqvZfjjJB5prvT7Js5PsluSOJNs0dX4lyd1JttvY/x6SpIlj4iRJmjBV9XZ6oy6vBDb01PsDgfOr6gXAA8AbmvKLgHdW1UtG1T8VeLCqZgOz6T0s8gDgvwK/BhwKvA34rZYQ/6GqZlfVIcCOwOv6jj1WVXOATwBfppe4HQKckmSPseKuqiuAxcCJVXV4VT3SEgPATsD1VXUYcC3wtqp6EPgu8IqmzuuBhVX1+Dj6kyR1zMRJkjQZbq+qm5rtG4GZSXYDnlFV1zTln++rfxRwUpKbgBuAPeglMXOAL1bVE1W1Eriq5byvTHJDkiXAq4AX9B2b3/xcAiytqnuq6lFgObDfWHGP/5LX8xjwLwP6uQw4odme1+xLkqYA73GSJHVlDet/QbdD3/ajfdtP0Bv9CVBj9BXgT6tq4XqFye9uoM36HSQ7AB8DZlXV3UneP0ZMa0fFt5YnPy8Hxb0xHq+qdXE/0df/fOBvkuwOHEF7IihJ2kwccZIkdeUO4DcAkvwGcMCGKlfVA8CDSV7WFJ3Yd3gh8I519/sk+dUkO9Gb5javuQdqb3pTBMeyLkm6N8nOwESuvvcQsMumdlJVDwPfAj4C/EtVPbGpfUqSJoYjTpKkrnyJJ6fXLQJ+MI42fwBcmOTn9JKldT5Nbzrbt5sFHVYDxwFX0ptyt6Tp/xrGUFUPJPlUU/eOJqaJcjHwiSSPAC8Z531OY7kM+Cfgv0xAXJKkCZInZwpIkiRJkgZxqp4kSZIktXCqniTpabdrCuUAAAA/SURBVCfJlTz1nqr3jF5c4ul2bklSd5yqJ0mSJEktnKonSZIkSS1MnCRJkiSphYmTJEmSJLUwcZIkSZKkFv8fzgnqs92OKt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# funded amount invested\n",
    "plt.figure(figsize=(14,4))\n",
    "cat_var('funded_amnt_inv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# Binning interest rate to low, medium, high\n",
    "def rate(n):\n",
    "    if n <= 10:\n",
    "        return 'low'\n",
    "    elif n > 10 and n <=15:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'\n",
    "    \n",
    "    \n",
    "df['int_rate'] = df['int_rate'].apply(lambda x: rate(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# binnng debt to income ratio\n",
    "def ratio(n):\n",
    "    if n <= 10:\n",
    "        return 'low'\n",
    "    elif n > 10 and n <=20:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'\n",
    "    \n",
    "\n",
    "df['dti'] = df['dti'].apply(lambda x: ratio(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# funded amount\n",
    "def funded_amount(n):\n",
    "    if n <= 5000:\n",
    "        return 'low'\n",
    "    elif n > 5000 and n <=15000:\n",
    "        return 'medium'\n",
    "    else:\n",
    "        return 'high'\n",
    "    \n",
    "df['funded_amnt'] = df['funded_amnt'].apply(lambda x: funded_amount(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# annual income\n",
    "def annual_income(n):\n",
    "    if n <= 50000:\n",
    "        return 'low'\n",
    "    elif n > 50000 and n <=100000:\n",
    "        return 'medium'\n",
    "    elif n > 100000 and n <=150000:\n",
    "        return 'high'\n",
    "    else:\n",
    "        return 'very high'\n",
    "\n",
    "df['annual_inc'] = df['annual_inc'].apply(lambda x: annual_income(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "df['emp_length'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# binning emp_length\n",
    "def emp_length(n):\n",
    "    if n <= 1:\n",
    "        return 'fresher'\n",
    "    elif n > 1 and n <=3:\n",
    "        return 'junior'\n",
    "    elif n > 3 and n <=7:\n",
    "        return 'senior'\n",
    "    else:\n",
    "        return 'expert'\n",
    "\n",
    "df['emp_length'] = df['emp_length'].apply(lambda x: emp_length(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# installment\n",
    "def installment(n):\n",
    "    if n <= 200:\n",
    "        return 'low'\n",
    "    elif n > 200 and n <=400:\n",
    "        return 'medium'\n",
    "    elif n > 400 and n <=600:\n",
    "        return 'high'\n",
    "    else:\n",
    "        return 'very high'\n",
    "    \n",
    "df['installment'] = df['installment'].apply(lambda x: installment(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('int_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High interest rates default more!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('dti')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('funded_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('dti')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('installment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "cat_var('annual_inc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lower the annual income, higher the default rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "cat_var('emp_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "emp_length is not a good predictor of default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEGMENTED UNIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### some of the important predictors are purpose of the loan, interest rate, annual income, grade etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# purpose: \n",
    "plt.figure(figsize=(18, 8))\n",
    "cat_var('purpose')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "small business loans defualt the most, then renewable energy and education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of loans for each type (purpose) of the loan\n",
    "plt.figure(figsize=(18, 8))\n",
    "sns.countplot(x='purpose', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### top 4 types of loans based on purpose: consolidation, credit card, home improvement and major purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    17675\n",
       "credit_card            4899\n",
       "home_improvement       2785\n",
       "major_purchase         2080\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_purp = [\"credit_card\",\"debt_consolidation\",\"home_improvement\",\"major_purchase\"]\n",
    "df = df[df['purpose'].isin(main_purp)]\n",
    "df['purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting number of loans by purpose\n",
    "plt.figure(figsize=(14, 6))\n",
    "sns.countplot(x=df['purpose'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# function which takes a categorical variable and plots the default rate segmented by purpose \n",
    "\n",
    "def seg(cat_var):\n",
    "    plt.figure(figsize=(18, 6))\n",
    "    sns.barplot(x=cat_var, y='loan_status', hue='purpose', data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing the default rates across two categorical variables i.e.\n",
    "# purpose of loan (constant) and another categorical variable (which changes)\n",
    "seg('term')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('home_ownership')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('emp_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('loan_amnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('int_rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('annual_inc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('installment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seg('dti')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking variation of the default rate across the categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annual_inc\n",
      "low          0.16\n",
      "medium       0.13\n",
      "very high    0.10\n",
      "high         0.10\n",
      "Name: loan_status, dtype: float64\n",
      "0.06\n"
     ]
    }
   ],
   "source": [
    "# function takes in a categorical variable and computes the mean  default rate across the categories\n",
    "def diff(cat_var):\n",
    "    default_rates = df.groupby(cat_var).loan_status.mean().sort_values(ascending=False)\n",
    "    return (round(default_rates, 2), round(default_rates[0] - default_rates[-1], 2))\n",
    "\n",
    "default_rates, diff = diff('annual_inc')\n",
    "print(default_rates) \n",
    "print(diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thus, there is a 6% increase in default rate as you go from high to low annual income."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}