edafinal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad6718a3-8dc1-4ee3-b6f3-44ab2a2dd0a9",
   "metadata": {},
   "source": [
    "### Gramener Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d51a436-3584-4278-ad27-47c9b752b45e",
   "metadata": {},
   "source": [
    "In this case study, we will  develop a basic understanding of risk analytics in banking and financial services and understand how data minimises the risk of losing money while lending it to customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247e70ad-dc62-4ae4-9058-b0a49b06dae3",
   "metadata": {},
   "source": [
    "### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0db9d381-8536-4872-ab98-fabcd6e2281c",
   "metadata": {},
   "source": [
    "I work for a consumer finance company that specialises in providing various types of loans to urban customers. When the company receives a loan application, it has to decide whether to approve or reject it based on the applicant’s profile. \n",
    "\n",
    "Two types of risks are\n",
    "associated with the bank’s decision:\n",
    "\n",
    "If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company.\n",
    "If the applicant is not likely to repay the loan, i.e., they are likely to default, then approving the loan may lead to financial loss for the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdae017-9602-4cda-ae27-f6de4c6d249e",
   "metadata": {},
   "source": [
    "The data given below contains information about past loan applicants and whether they ‘defaulted’.\n",
    "\n",
    "\n",
    "The aim is to identify patterns indicating that a person is likely to default, which may be used to deny the loan, reduce the loan amount, lend (to risky applicants) at a higher interest rate, etc.\n",
    "\n",
    "\n",
    "When a person applies for a loan, there are two types of decisions that could be taken by the company:\n",
    "\n",
    "\n",
    "1. Loan accepted: If the company approves the loan, there are three possible scenarios, as described below:\n",
    "\n",
    "2. Fully paid: The applicant has fully paid the loan (the principal and the interest amount).\n",
    "   Current: The applicant is in the process of paying the instalments, i.e., the tenure of the loan is not yet completed. These candidates are not          labelled as ‘defaulted’.\n",
    "\n",
    "3. Charged-off: The applicant has not paid the instalments in due time for a long period, i.e., they have defaulted on the loan.\n",
    "   Loan rejected: The company had rejected the loan (because the candidate does not meet their requirements, etc.). Since the loan was rejected, there      is no transactional history of those applicants with the company; so, this data is not available with the company (and thus, in this data set)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c478efa5-d721-4ae8-8a3b-f04d17e90d55",
   "metadata": {},
   "source": [
    "### Importing the libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48f565c1-83ba-402a-84ef-5f251842ec36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "# import the useful libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2423c0-8b88-445d-9eda-44f29e878f78",
   "metadata": {},
   "source": [
    "### The analysis is divided into four main parts:\n",
    "1. Data understanding\n",
    "2. Data cleaning (cleaning missing values, removing redundant columns etc.)\n",
    "3. Data Analysis\n",
    "4. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05f0eb4-2eea-443c-a0f5-7869cdaeb18e",
   "metadata": {},
   "source": [
    "### Data understanding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3fd19fc-7388-462d-949d-75880c65e7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data set of \"Loan\" in loan.\n",
    "loan = pd.read_csv('loan.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35d7e45e-2fce-40fd-9641-9069ba67a1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96%</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599       5000         5000           4975.0   36 months   \n",
       "1  1077430    1314167       2500         2500           2500.0   60 months   \n",
       "2  1077175    1313524       2400         2400           2400.0   36 months   \n",
       "3  1076863    1277178      10000        10000          10000.0   36 months   \n",
       "4  1075358    1311748       3000         3000           3000.0   60 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade  ... num_tl_90g_dpd_24m  \\\n",
       "0   10.65%       162.87     B        B2  ...                NaN   \n",
       "1   15.27%        59.83     C        C4  ...                NaN   \n",
       "2   15.96%        84.33     C        C5  ...                NaN   \n",
       "3   13.49%       339.31     C        C1  ...                NaN   \n",
       "4   12.69%        67.79     B        B5  ...                NaN   \n",
       "\n",
       "  num_tl_op_past_12m pct_tl_nvr_dlq  percent_bc_gt_75 pub_rec_bankruptcies  \\\n",
       "0                NaN            NaN               NaN                  0.0   \n",
       "1                NaN            NaN               NaN                  0.0   \n",
       "2                NaN            NaN               NaN                  0.0   \n",
       "3                NaN            NaN               NaN                  0.0   \n",
       "4                NaN            NaN               NaN                  0.0   \n",
       "\n",
       "  tax_liens tot_hi_cred_lim total_bal_ex_mort total_bc_limit  \\\n",
       "0       0.0             NaN               NaN            NaN   \n",
       "1       0.0             NaN               NaN            NaN   \n",
       "2       0.0             NaN               NaN            NaN   \n",
       "3       0.0             NaN               NaN            NaN   \n",
       "4       0.0             NaN               NaN            NaN   \n",
       "\n",
       "  total_il_high_credit_limit  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the head of the data frame.\n",
    "loan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3452189-f1c8-4c55-910b-8b74385cc5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Columns: 111 entries, id to total_il_high_credit_limit\n",
      "dtypes: float64(74), int64(13), object(24)\n",
      "memory usage: 33.6+ MB\n"
     ]
    }
   ],
   "source": [
    "loan.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a4edfc2-22b4-4a2d-b535-373abba90f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 111)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98db45f-9f1f-46fb-8983-865a834a3ba8",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b91d4d9-4cd5-4954-b2d2-24d560e92a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
       "       'term', 'int_rate', 'installment', 'grade', 'sub_grade',\n",
       "       ...\n",
       "       'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq',\n",
       "       'percent_bc_gt_75', 'pub_rec_bankruptcies', 'tax_liens',\n",
       "       'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit',\n",
       "       'total_il_high_credit_limit'],\n",
       "      dtype='object', length=111)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd254ae8-50a2-4f64-b94b-3ed0de654365",
   "metadata": {},
   "source": [
    "### Data Cleaning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a252885c-ada9-4b5c-84f6-12d96d1e269a",
   "metadata": {},
   "source": [
    "### Finding the Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30c7dfa1-5fbf-40b6-a16f-e210bef63012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                0\n",
       "member_id                         0\n",
       "loan_amnt                         0\n",
       "funded_amnt                       0\n",
       "funded_amnt_inv                   0\n",
       "                              ...  \n",
       "tax_liens                        39\n",
       "tot_hi_cred_lim               39717\n",
       "total_bal_ex_mort             39717\n",
       "total_bc_limit                39717\n",
       "total_il_high_credit_limit    39717\n",
       "Length: 111, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f4fe4e8-b68b-4823-b650-416bf4d4718a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            0.000000\n",
       "member_id                     0.000000\n",
       "loan_amnt                     0.000000\n",
       "funded_amnt                   0.000000\n",
       "funded_amnt_inv               0.000000\n",
       "                                ...   \n",
       "tax_liens                     0.000982\n",
       "tot_hi_cred_lim               1.000000\n",
       "total_bal_ex_mort             1.000000\n",
       "total_bc_limit                1.000000\n",
       "total_il_high_credit_limit    1.000000\n",
       "Length: 111, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_percentage = loan.isnull().mean()\n",
    "null_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae88c27-d570-44d6-a403-c830a5300277",
   "metadata": {},
   "source": [
    "### Impute/Remove missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "608e93ee-6bef-4019-b65f-8812f4818082",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the columns which contain null values more than or equal to 75%.\n",
    "columns_to_drop = null_percentage[null_percentage >= 0.75].index\n",
    "loan.drop(columns = columns_to_drop, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cba3a1b4-4a93-485a-ab4f-b1b75edf53c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                             0.000000\n",
       "member_id                      0.000000\n",
       "loan_amnt                      0.000000\n",
       "funded_amnt                    0.000000\n",
       "funded_amnt_inv                0.000000\n",
       "term                           0.000000\n",
       "int_rate                       0.000000\n",
       "installment                    0.000000\n",
       "grade                          0.000000\n",
       "sub_grade                      0.000000\n",
       "emp_title                      6.191303\n",
       "emp_length                     2.706650\n",
       "home_ownership                 0.000000\n",
       "annual_inc                     0.000000\n",
       "verification_status            0.000000\n",
       "issue_d                        0.000000\n",
       "loan_status                    0.000000\n",
       "pymnt_plan                     0.000000\n",
       "url                            0.000000\n",
       "desc                          32.585543\n",
       "purpose                        0.000000\n",
       "title                          0.027696\n",
       "zip_code                       0.000000\n",
       "addr_state                     0.000000\n",
       "dti                            0.000000\n",
       "delinq_2yrs                    0.000000\n",
       "earliest_cr_line               0.000000\n",
       "inq_last_6mths                 0.000000\n",
       "mths_since_last_delinq        64.662487\n",
       "open_acc                       0.000000\n",
       "pub_rec                        0.000000\n",
       "revol_bal                      0.000000\n",
       "revol_util                     0.125891\n",
       "total_acc                      0.000000\n",
       "initial_list_status            0.000000\n",
       "out_prncp                      0.000000\n",
       "out_prncp_inv                  0.000000\n",
       "total_pymnt                    0.000000\n",
       "total_pymnt_inv                0.000000\n",
       "total_rec_prncp                0.000000\n",
       "total_rec_int                  0.000000\n",
       "total_rec_late_fee             0.000000\n",
       "recoveries                     0.000000\n",
       "collection_recovery_fee        0.000000\n",
       "last_pymnt_d                   0.178765\n",
       "last_pymnt_amnt                0.000000\n",
       "last_credit_pull_d             0.005036\n",
       "collections_12_mths_ex_med     0.140998\n",
       "policy_code                    0.000000\n",
       "application_type               0.000000\n",
       "acc_now_delinq                 0.000000\n",
       "chargeoff_within_12_mths       0.140998\n",
       "delinq_amnt                    0.000000\n",
       "pub_rec_bankruptcies           1.754916\n",
       "tax_liens                      0.098195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum()/loan.shape[0] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc483823-852e-4ff3-8392-d5f77ffa0e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 55)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f226f33-2a6f-4937-a2a0-35ea426c08f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping desc and 'mths_since_last_delinq' variable as they contain good amount of null values.\n",
    "loan.drop([\"desc\", \"mths_since_last_delinq\"], axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "184460ec-74bc-4316-82e1-45701a1806df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39717, 53)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "043dc247-47cf-41c7-9a48-4b877b58a893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Data columns (total 53 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   id                          39717 non-null  int64  \n",
      " 1   member_id                   39717 non-null  int64  \n",
      " 2   loan_amnt                   39717 non-null  int64  \n",
      " 3   funded_amnt                 39717 non-null  int64  \n",
      " 4   funded_amnt_inv             39717 non-null  float64\n",
      " 5   term                        39717 non-null  object \n",
      " 6   int_rate                    39717 non-null  object \n",
      " 7   installment                 39717 non-null  float64\n",
      " 8   grade                       39717 non-null  object \n",
      " 9   sub_grade                   39717 non-null  object \n",
      " 10  emp_title                   37258 non-null  object \n",
      " 11  emp_length                  38642 non-null  object \n",
      " 12  home_ownership              39717 non-null  object \n",
      " 13  annual_inc                  39717 non-null  float64\n",
      " 14  verification_status         39717 non-null  object \n",
      " 15  issue_d                     39717 non-null  object \n",
      " 16  loan_status                 39717 non-null  object \n",
      " 17  pymnt_plan                  39717 non-null  object \n",
      " 18  url                         39717 non-null  object \n",
      " 19  purpose                     39717 non-null  object \n",
      " 20  title                       39706 non-null  object \n",
      " 21  zip_code                    39717 non-null  object \n",
      " 22  addr_state                  39717 non-null  object \n",
      " 23  dti                         39717 non-null  float64\n",
      " 24  delinq_2yrs                 39717 non-null  int64  \n",
      " 25  earliest_cr_line            39717 non-null  object \n",
      " 26  inq_last_6mths              39717 non-null  int64  \n",
      " 27  open_acc                    39717 non-null  int64  \n",
      " 28  pub_rec                     39717 non-null  int64  \n",
      " 29  revol_bal                   39717 non-null  int64  \n",
      " 30  revol_util                  39667 non-null  object \n",
      " 31  total_acc                   39717 non-null  int64  \n",
      " 32  initial_list_status         39717 non-null  object \n",
      " 33  out_prncp                   39717 non-null  float64\n",
      " 34  out_prncp_inv               39717 non-null  float64\n",
      " 35  total_pymnt                 39717 non-null  float64\n",
      " 36  total_pymnt_inv             39717 non-null  float64\n",
      " 37  total_rec_prncp             39717 non-null  float64\n",
      " 38  total_rec_int               39717 non-null  float64\n",
      " 39  total_rec_late_fee          39717 non-null  float64\n",
      " 40  recoveries                  39717 non-null  float64\n",
      " 41  collection_recovery_fee     39717 non-null  float64\n",
      " 42  last_pymnt_d                39646 non-null  object \n",
      " 43  last_pymnt_amnt             39717 non-null  float64\n",
      " 44  last_credit_pull_d          39715 non-null  object \n",
      " 45  collections_12_mths_ex_med  39661 non-null  float64\n",
      " 46  policy_code                 39717 non-null  int64  \n",
      " 47  application_type            39717 non-null  object \n",
      " 48  acc_now_delinq              39717 non-null  int64  \n",
      " 49  chargeoff_within_12_mths    39661 non-null  float64\n",
      " 50  delinq_amnt                 39717 non-null  int64  \n",
      " 51  pub_rec_bankruptcies        39020 non-null  float64\n",
      " 52  tax_liens                   39678 non-null  float64\n",
      "dtypes: float64(18), int64(13), object(22)\n",
      "memory usage: 16.1+ MB\n"
     ]
    }
   ],
   "source": [
    "loan.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0dc97970-b8e0-488a-92f5-fde11e841602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_title                     6.191303\n",
       "emp_length                    2.706650\n",
       "title                         0.027696\n",
       "revol_util                    0.125891\n",
       "last_pymnt_d                  0.178765\n",
       "last_credit_pull_d            0.005036\n",
       "collections_12_mths_ex_med    0.140998\n",
       "chargeoff_within_12_mths      0.140998\n",
       "pub_rec_bankruptcies          1.754916\n",
       "tax_liens                     0.098195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_null_values = loan.isnull().sum()/loan.shape[0] *100\n",
    "loan_null_values[loan_null_values!=0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a892e0a-5bd3-4b9b-95f1-af2cad693ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill null values in 'emp_title' with 'Unknown'\n",
    "loan['emp_title'].fillna('Unknown', inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b9f6964-c257-476f-a126-310d259b6e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find rows with more than 1 null values.\n",
    "rows_with_more_than_1_nulls = loan[loan.isnull().sum(axis = 1) > 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0348757-222c-4485-9659-21e7cca36ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4714</th>\n",
       "      <td>997734</td>\n",
       "      <td>1222858</td>\n",
       "      <td>6100</td>\n",
       "      <td>6100</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.27%</td>\n",
       "      <td>209.29</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>...</td>\n",
       "      <td>209.29</td>\n",
       "      <td>Nov-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11282</th>\n",
       "      <td>817195</td>\n",
       "      <td>1024992</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>21.36%</td>\n",
       "      <td>189.31</td>\n",
       "      <td>F</td>\n",
       "      <td>F4</td>\n",
       "      <td>...</td>\n",
       "      <td>204.01</td>\n",
       "      <td>Feb-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13891</th>\n",
       "      <td>772732</td>\n",
       "      <td>974723</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>18.39%</td>\n",
       "      <td>72.70</td>\n",
       "      <td>E</td>\n",
       "      <td>E2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Nov-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14839</th>\n",
       "      <td>759372</td>\n",
       "      <td>932652</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.43%</td>\n",
       "      <td>33.91</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16719</th>\n",
       "      <td>727583</td>\n",
       "      <td>923109</td>\n",
       "      <td>5550</td>\n",
       "      <td>5550</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.06%</td>\n",
       "      <td>126.46</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>May-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39712</th>\n",
       "      <td>92187</td>\n",
       "      <td>92174</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>78.42</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</td>\n",
       "      <td>80.90</td>\n",
       "      <td>Jun-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39713</th>\n",
       "      <td>90665</td>\n",
       "      <td>90607</td>\n",
       "      <td>8500</td>\n",
       "      <td>8500</td>\n",
       "      <td>875.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>275.38</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>281.94</td>\n",
       "      <td>Jul-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39714</th>\n",
       "      <td>90395</td>\n",
       "      <td>90390</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.07%</td>\n",
       "      <td>156.84</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39715</th>\n",
       "      <td>90376</td>\n",
       "      <td>89243</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>650.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.43%</td>\n",
       "      <td>155.38</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39716</th>\n",
       "      <td>87023</td>\n",
       "      <td>86999</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>800.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.75%</td>\n",
       "      <td>255.43</td>\n",
       "      <td>E</td>\n",
       "      <td>E2</td>\n",
       "      <td>...</td>\n",
       "      <td>256.59</td>\n",
       "      <td>Jun-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "4714   997734    1222858       6100         6100           6100.0   36 months   \n",
       "11282  817195    1024992       5000         5000           5000.0   36 months   \n",
       "13891  772732     974723       2000         2000           1900.0   36 months   \n",
       "14839  759372     932652       1000         1000           1000.0   36 months   \n",
       "16719  727583     923109       5550         5550           5550.0   60 months   \n",
       "...       ...        ...        ...          ...              ...         ...   \n",
       "39712   92187      92174       2500         2500           1075.0   36 months   \n",
       "39713   90665      90607       8500         8500            875.0   36 months   \n",
       "39714   90395      90390       5000         5000           1325.0   36 months   \n",
       "39715   90376      89243       5000         5000            650.0   36 months   \n",
       "39716   87023      86999       7500         7500            800.0   36 months   \n",
       "\n",
       "      int_rate  installment grade sub_grade  ... last_pymnt_amnt  \\\n",
       "4714    14.27%       209.29     C        C2  ...          209.29   \n",
       "11282   21.36%       189.31     F        F4  ...          204.01   \n",
       "13891   18.39%        72.70     E        E2  ...            0.00   \n",
       "14839   13.43%        33.91     C        C3  ...            0.00   \n",
       "16719   13.06%       126.46     C        C2  ...            0.00   \n",
       "...        ...          ...   ...       ...  ...             ...   \n",
       "39712    8.07%        78.42     A        A4  ...           80.90   \n",
       "39713   10.28%       275.38     C        C1  ...          281.94   \n",
       "39714    8.07%       156.84     A        A4  ...            0.00   \n",
       "39715    7.43%       155.38     A        A2  ...            0.00   \n",
       "39716   13.75%       255.43     E        E2  ...          256.59   \n",
       "\n",
       "      last_credit_pull_d collections_12_mths_ex_med  policy_code  \\\n",
       "4714              Nov-12                        0.0            1   \n",
       "11282             Feb-15                        0.0            1   \n",
       "13891             Nov-11                        0.0            1   \n",
       "14839             May-16                        0.0            1   \n",
       "16719             May-11                        0.0            1   \n",
       "...                  ...                        ...          ...   \n",
       "39712             Jun-10                        NaN            1   \n",
       "39713             Jul-10                        NaN            1   \n",
       "39714             Jun-07                        NaN            1   \n",
       "39715             Jun-07                        NaN            1   \n",
       "39716             Jun-10                        NaN            1   \n",
       "\n",
       "      application_type acc_now_delinq chargeoff_within_12_mths delinq_amnt  \\\n",
       "4714        INDIVIDUAL              0                      0.0           0   \n",
       "11282       INDIVIDUAL              0                      0.0           0   \n",
       "13891       INDIVIDUAL              0                      0.0           0   \n",
       "14839       INDIVIDUAL              0                      0.0           0   \n",
       "16719       INDIVIDUAL              0                      0.0           0   \n",
       "...                ...            ...                      ...         ...   \n",
       "39712       INDIVIDUAL              0                      NaN           0   \n",
       "39713       INDIVIDUAL              0                      NaN           0   \n",
       "39714       INDIVIDUAL              0                      NaN           0   \n",
       "39715       INDIVIDUAL              0                      NaN           0   \n",
       "39716       INDIVIDUAL              0                      NaN           0   \n",
       "\n",
       "      pub_rec_bankruptcies tax_liens  \n",
       "4714                   0.0       0.0  \n",
       "11282                  0.0       0.0  \n",
       "13891                  0.0       0.0  \n",
       "14839                  0.0       0.0  \n",
       "16719                  0.0       0.0  \n",
       "...                    ...       ...  \n",
       "39712                  NaN       NaN  \n",
       "39713                  NaN       NaN  \n",
       "39714                  NaN       NaN  \n",
       "39715                  NaN       NaN  \n",
       "39716                  NaN       NaN  \n",
       "\n",
       "[70 rows x 53 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we filter rows where the count of null values is greater than 1.\n",
    "rows_with_more_than_1_nulls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5c7b9798-8c39-4793-836e-db87f2e85e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with more than 1 null values\n",
    "loan.dropna(thresh = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ca77f24-56d0-478a-af55-b94b9390b44a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2057"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06fe7629-8cfe-4860-b38f-8572bcd8e1f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with any null values.\n",
    "# Now, all rows with any null values have been dropped from the dataframe 'loan'.\n",
    "loan.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c518a04-a35a-4786-82b4-63f4a91b5be4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40d63efe-e2c7-43bb-a8b8-8c7b45fbe158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37823, 53)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b4345b-3557-4a8d-a3c9-bdab2f080112",
   "metadata": {},
   "source": [
    "### Hnadling Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74341f06-a64a-472f-ab58-ef1e95aaeb67",
   "metadata": {},
   "source": [
    "### Annual Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "baab48c7-d9f5-422a-bc9b-86be882f9c63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.782300e+04\n",
       "mean     6.967727e+04\n",
       "std      6.365142e+04\n",
       "min      4.000000e+03\n",
       "25%      4.190200e+04\n",
       "50%      6.000000e+04\n",
       "75%      8.355000e+04\n",
       "max      6.000000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the annual_inc variable in loan.\n",
    "loan.annual_inc.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "505b6daf-567c-4213-89d3-f3f943ec630e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the boxplot of annual_inc variable.\n",
    "plt.figure(figsize = [6,4])\n",
    "sns.boxplot(loan.annual_inc)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5dade7d6-01cb-4704-8f0c-27f085fc0f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50     60000.0\n",
       "0.70     77000.0\n",
       "0.90    117242.8\n",
       "0.95    143950.0\n",
       "0.99    235000.0\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the quantile (0.5, 0.7, 0.9, 0.95 and 0.99) of annual_inc variable\n",
    "loan.annual_inc.quantile([0.5, 0.7, 0.9, 0.95, 0.99])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "773de0f4-226b-451c-a920-7552440d42ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.520000e+02\n",
       "mean     4.199938e+05\n",
       "std      4.217453e+05\n",
       "min      2.407340e+05\n",
       "25%      2.700000e+05\n",
       "50%      3.000000e+05\n",
       "75%      4.000000e+05\n",
       "max      6.000000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan[\"annual_inc\"][loan.annual_inc > 240000].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "537aeb9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f76c7d7d-0294-4f64-9060-350bcaff86a7",
   "metadata": {},
   "source": [
    "### Loan Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9ec11018-dbab-4e88-82bc-ebb7cf2b9d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    37823.000000\n",
       "mean     11328.891151\n",
       "std       7477.323047\n",
       "min        500.000000\n",
       "25%       5600.000000\n",
       "50%      10000.000000\n",
       "75%      15000.000000\n",
       "max      35000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the loan amount variable in loan.\n",
    "loan.loan_amnt.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d40252e-3171-4ef1-a4ba-949e60c22231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the boxplot of loan amount variable.\n",
    "plt.figure(figsize = [6,4])\n",
    "sns.boxplot(loan.loan_amnt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b1baf27-102d-41cd-b98a-550379208658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>...</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.782300e+04</td>\n",
       "      <td>3.782300e+04</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>3.782300e+04</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.0</td>\n",
       "      <td>37823.0</td>\n",
       "      <td>37823.0</td>\n",
       "      <td>37823.0</td>\n",
       "      <td>37823.0</td>\n",
       "      <td>37823.000000</td>\n",
       "      <td>37823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.900628e+05</td>\n",
       "      <td>8.598469e+05</td>\n",
       "      <td>11328.891151</td>\n",
       "      <td>11050.996748</td>\n",
       "      <td>10619.465271</td>\n",
       "      <td>327.161001</td>\n",
       "      <td>6.967727e+04</td>\n",
       "      <td>13.377966</td>\n",
       "      <td>0.145811</td>\n",
       "      <td>0.866642</td>\n",
       "      <td>...</td>\n",
       "      <td>94.574677</td>\n",
       "      <td>12.136955</td>\n",
       "      <td>2731.019060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041456</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.028915e+05</td>\n",
       "      <td>2.542415e+05</td>\n",
       "      <td>7477.323047</td>\n",
       "      <td>7205.152593</td>\n",
       "      <td>7113.100590</td>\n",
       "      <td>209.098263</td>\n",
       "      <td>6.365142e+04</td>\n",
       "      <td>6.655886</td>\n",
       "      <td>0.490534</td>\n",
       "      <td>1.066497</td>\n",
       "      <td>...</td>\n",
       "      <td>690.484884</td>\n",
       "      <td>148.132518</td>\n",
       "      <td>4490.079929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200272</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.473400e+04</td>\n",
       "      <td>8.036400e+04</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.080000</td>\n",
       "      <td>4.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.211055e+05</td>\n",
       "      <td>6.732465e+05</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>168.750000</td>\n",
       "      <td>4.190200e+04</td>\n",
       "      <td>8.270000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>224.810000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.694130e+05</td>\n",
       "      <td>8.556700e+05</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9200.000000</td>\n",
       "      <td>283.200000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>13.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>563.550000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.393200e+05</td>\n",
       "      <td>1.049090e+06</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>14608.725420</td>\n",
       "      <td>434.750000</td>\n",
       "      <td>8.355000e+04</td>\n",
       "      <td>18.630000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3402.630000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.077501e+06</td>\n",
       "      <td>1.314167e+06</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>1305.190000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29623.350000</td>\n",
       "      <td>7002.190000</td>\n",
       "      <td>36115.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id     member_id     loan_amnt   funded_amnt  \\\n",
       "count  3.782300e+04  3.782300e+04  37823.000000  37823.000000   \n",
       "mean   6.900628e+05  8.598469e+05  11328.891151  11050.996748   \n",
       "std    2.028915e+05  2.542415e+05   7477.323047   7205.152593   \n",
       "min    5.473400e+04  8.036400e+04    500.000000    500.000000   \n",
       "25%    5.211055e+05  6.732465e+05   5600.000000   5500.000000   \n",
       "50%    6.694130e+05  8.556700e+05  10000.000000  10000.000000   \n",
       "75%    8.393200e+05  1.049090e+06  15000.000000  15000.000000   \n",
       "max    1.077501e+06  1.314167e+06  35000.000000  35000.000000   \n",
       "\n",
       "       funded_amnt_inv   installment    annual_inc           dti  \\\n",
       "count     37823.000000  37823.000000  3.782300e+04  37823.000000   \n",
       "mean      10619.465271    327.161001  6.967727e+04     13.377966   \n",
       "std        7113.100590    209.098263  6.365142e+04      6.655886   \n",
       "min           0.000000     16.080000  4.000000e+03      0.000000   \n",
       "25%        5000.000000    168.750000  4.190200e+04      8.270000   \n",
       "50%        9200.000000    283.200000  6.000000e+04     13.470000   \n",
       "75%       14608.725420    434.750000  8.355000e+04     18.630000   \n",
       "max       35000.000000   1305.190000  6.000000e+06     29.990000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths  ...    recoveries  \\\n",
       "count  37823.000000    37823.000000  ...  37823.000000   \n",
       "mean       0.145811        0.866642  ...     94.574677   \n",
       "std        0.490534        1.066497  ...    690.484884   \n",
       "min        0.000000        0.000000  ...      0.000000   \n",
       "25%        0.000000        0.000000  ...      0.000000   \n",
       "50%        0.000000        1.000000  ...      0.000000   \n",
       "75%        0.000000        1.000000  ...      0.000000   \n",
       "max       11.000000        8.000000  ...  29623.350000   \n",
       "\n",
       "       collection_recovery_fee  last_pymnt_amnt  collections_12_mths_ex_med  \\\n",
       "count             37823.000000     37823.000000                     37823.0   \n",
       "mean                 12.136955      2731.019060                         0.0   \n",
       "std                 148.132518      4490.079929                         0.0   \n",
       "min                   0.000000         0.010000                         0.0   \n",
       "25%                   0.000000       224.810000                         0.0   \n",
       "50%                   0.000000       563.550000                         0.0   \n",
       "75%                   0.000000      3402.630000                         0.0   \n",
       "max                7002.190000     36115.200000                         0.0   \n",
       "\n",
       "       policy_code  acc_now_delinq  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "count      37823.0         37823.0                   37823.0      37823.0   \n",
       "mean           1.0             0.0                       0.0          0.0   \n",
       "std            0.0             0.0                       0.0          0.0   \n",
       "min            1.0             0.0                       0.0          0.0   \n",
       "25%            1.0             0.0                       0.0          0.0   \n",
       "50%            1.0             0.0                       0.0          0.0   \n",
       "75%            1.0             0.0                       0.0          0.0   \n",
       "max            1.0             0.0                       0.0          0.0   \n",
       "\n",
       "       pub_rec_bankruptcies  tax_liens  \n",
       "count          37823.000000    37823.0  \n",
       "mean               0.041456        0.0  \n",
       "std                0.200272        0.0  \n",
       "min                0.000000        0.0  \n",
       "25%                0.000000        0.0  \n",
       "50%                0.000000        0.0  \n",
       "75%                0.000000        0.0  \n",
       "max                2.000000        0.0  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d577cc-e2a6-4738-940e-120af889a438",
   "metadata": {},
   "source": [
    "### Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2457a59e-6602-4e61-b1e5-f1d4b461a460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "Fully Paid     31524\n",
       "Charged Off     5201\n",
       "Current         1098\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.loan_status.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64a89009-ba1a-4e2f-bb01-5484a2d4b3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_status\n",
       "Fully Paid     83.35\n",
       "Charged Off    13.75\n",
       "Current         2.90\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(100*loan.loan_status.value_counts(normalize = True),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3a788a76-47cc-4b70-a40c-16fb13f06146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the bar chart of percentage loan status categories\n",
    "round(100*loan.loan_status.value_counts(normalize = True),2).plot.bar(color = ['orange','green','maroon'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f5d937ba-e569-4611-98eb-93c226ef5f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "840fd352-9e89-4db1-9fc1-6fddafcaea96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term\n",
       " 36 months    27489\n",
       " 60 months    10334\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.term.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e20fbea1-b84a-4c13-98b5-2d7e13da58f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the bar chart of percentage term categories\n",
    "round(100*loan.term.value_counts(normalize = True),2).plot.bar(color = ['blue','maroon'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2781f54-7f7f-44dc-9655-34baf3285130",
   "metadata": {},
   "source": [
    "### Home Ownership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "16129c52-eb7c-4dff-9e82-6020b0bd3952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "RENT        18026\n",
       "MORTGAGE    16933\n",
       "OWN          2768\n",
       "OTHER          96\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.home_ownership.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ded65fe-a3f2-49ef-8cf4-18075030de48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the bar chart of percentage home_ownership categories\n",
    "round(100*loan.home_ownership.value_counts(normalize = True),2).plot.bar(color = ['blue','pink','green','black'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42272e1c-949e-4b72-9e49-e4e0dd2dd79d",
   "metadata": {},
   "source": [
    "### Verification Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1b6bb895-80b2-471e-9161-658df05760dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verification_status\n",
       "Not Verified       15735\n",
       "Verified           12325\n",
       "Source Verified     9763\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.verification_status.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb8d699f-f2ab-4a1a-a76c-52fabfe18147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verification_status\n",
       "Not Verified       41.60\n",
       "Verified           32.59\n",
       "Source Verified    25.81\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the percentage of each verification_status category.\n",
    "round(100 * loan.verification_status.value_counts(normalize = True),2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0877d457-05b0-4252-b82e-e91e546afa2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the pie chart of verification_status categories\n",
    "round(100 * loan.verification_status.value_counts(normalize = True),2).plot.pie(colors = ['red','green','yellow'], autopct = '%0.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed442bd-613f-426e-9948-3f825995f9f0",
   "metadata": {},
   "source": [
    "### Employment Length (in years)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aef72c0-2472-4bb4-b5e0-dc32b255c22e",
   "metadata": {},
   "source": [
    " Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3577d1bd-35eb-49f8-be2d-bc199cb39a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "10+ years    8749\n",
       "< 1 year     4396\n",
       "2 years      4291\n",
       "3 years      4023\n",
       "4 years      3375\n",
       "5 years      3233\n",
       "1 year       3132\n",
       "6 years      2192\n",
       "7 years      1746\n",
       "8 years      1449\n",
       "9 years      1237\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan.emp_length.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "baf99158-19f8-407f-981a-e7a07faf85af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_length\n",
       "10+ years    23.13\n",
       "< 1 year     11.62\n",
       "2 years      11.34\n",
       "3 years      10.64\n",
       "4 years       8.92\n",
       "5 years       8.55\n",
       "1 year        8.28\n",
       "6 years       5.80\n",
       "7 years       4.62\n",
       "8 years       3.83\n",
       "9 years       3.27\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the percentage of each verification_status category.\n",
    "round(100 * loan.emp_length.value_counts(normalize = True),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "920adaa8-e7e2-4fcf-9492-9ea6774e7400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the bar chart of percentage home_ownership categories\n",
    "round(100*loan.emp_length.value_counts(normalize = True),2).plot.bar(color = 'g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c666d4a-1358-4ad2-b0e6-09f1a02752c6",
   "metadata": {},
   "source": [
    "### A new dataframe is created where the data of loan status- charged off is present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1e0ce30e-e3a8-411f-9ffd-bb87514324e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27%</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1071795</td>\n",
       "      <td>1306957</td>\n",
       "      <td>5600</td>\n",
       "      <td>5600</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>21.28%</td>\n",
       "      <td>152.39</td>\n",
       "      <td>F</td>\n",
       "      <td>F2</td>\n",
       "      <td>...</td>\n",
       "      <td>152.39</td>\n",
       "      <td>Aug-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1071570</td>\n",
       "      <td>1306721</td>\n",
       "      <td>5375</td>\n",
       "      <td>5375</td>\n",
       "      <td>5350.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69%</td>\n",
       "      <td>121.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>121.45</td>\n",
       "      <td>Mar-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1064687</td>\n",
       "      <td>1298717</td>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>305.38</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>305.38</td>\n",
       "      <td>Nov-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1069057</td>\n",
       "      <td>1303503</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65%</td>\n",
       "      <td>325.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>325.74</td>\n",
       "      <td>Mar-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39251</th>\n",
       "      <td>246276</td>\n",
       "      <td>246273</td>\n",
       "      <td>10100</td>\n",
       "      <td>10100</td>\n",
       "      <td>6425.004533</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.55%</td>\n",
       "      <td>343.00</td>\n",
       "      <td>D</td>\n",
       "      <td>D4</td>\n",
       "      <td>...</td>\n",
       "      <td>343.00</td>\n",
       "      <td>Jan-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39291</th>\n",
       "      <td>239690</td>\n",
       "      <td>239649</td>\n",
       "      <td>15450</td>\n",
       "      <td>15450</td>\n",
       "      <td>5650.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.93%</td>\n",
       "      <td>527.52</td>\n",
       "      <td>E</td>\n",
       "      <td>E1</td>\n",
       "      <td>...</td>\n",
       "      <td>35.74</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39362</th>\n",
       "      <td>218343</td>\n",
       "      <td>218321</td>\n",
       "      <td>2150</td>\n",
       "      <td>2150</td>\n",
       "      <td>1574.996529</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.78%</td>\n",
       "      <td>70.17</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>140.34</td>\n",
       "      <td>Mar-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39425</th>\n",
       "      <td>202270</td>\n",
       "      <td>202156</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>1199.992159</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.36%</td>\n",
       "      <td>133.55</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>133.55</td>\n",
       "      <td>Apr-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>132892</td>\n",
       "      <td>132889</td>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.49%</td>\n",
       "      <td>301.04</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>...</td>\n",
       "      <td>301.05</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5201 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "1      1077430    1314167       2500         2500      2500.000000   \n",
       "8      1071795    1306957       5600         5600      5600.000000   \n",
       "9      1071570    1306721       5375         5375      5350.000000   \n",
       "12     1064687    1298717       9000         9000      9000.000000   \n",
       "14     1069057    1303503      10000        10000     10000.000000   \n",
       "...        ...        ...        ...          ...              ...   \n",
       "39251   246276     246273      10100        10100      6425.004533   \n",
       "39291   239690     239649      15450        15450      5650.000000   \n",
       "39362   218343     218321       2150         2150      1574.996529   \n",
       "39425   202270     202156       4000         4000      1199.992159   \n",
       "39623   132892     132889       9000         9000       700.000000   \n",
       "\n",
       "             term int_rate  installment grade sub_grade  ... last_pymnt_amnt  \\\n",
       "1       60 months   15.27%        59.83     C        C4  ...          119.66   \n",
       "8       60 months   21.28%       152.39     F        F2  ...          152.39   \n",
       "9       60 months   12.69%       121.45     B        B5  ...          121.45   \n",
       "12      36 months   13.49%       305.38     C        C1  ...          305.38   \n",
       "14      36 months   10.65%       325.74     B        B2  ...          325.74   \n",
       "...           ...      ...          ...   ...       ...  ...             ...   \n",
       "39251   36 months   13.55%       343.00     D        D4  ...          343.00   \n",
       "39291   36 months   13.93%       527.52     E        E1  ...           35.74   \n",
       "39362   36 months   10.78%        70.17     C        C1  ...          140.34   \n",
       "39425   36 months   12.36%       133.55     D        D1  ...          133.55   \n",
       "39623   36 months   12.49%       301.04     D        D3  ...          301.05   \n",
       "\n",
       "      last_credit_pull_d collections_12_mths_ex_med  policy_code  \\\n",
       "1                 Sep-13                        0.0            1   \n",
       "8                 Aug-12                        0.0            1   \n",
       "9                 Mar-13                        0.0            1   \n",
       "12                Nov-12                        0.0            1   \n",
       "14                Mar-14                        0.0            1   \n",
       "...                  ...                        ...          ...   \n",
       "39251             Jan-09                        0.0            1   \n",
       "39291             May-16                        0.0            1   \n",
       "39362             Mar-10                        0.0            1   \n",
       "39425             Apr-16                        0.0            1   \n",
       "39623             May-16                        0.0            1   \n",
       "\n",
       "      application_type acc_now_delinq chargeoff_within_12_mths delinq_amnt  \\\n",
       "1           INDIVIDUAL              0                      0.0           0   \n",
       "8           INDIVIDUAL              0                      0.0           0   \n",
       "9           INDIVIDUAL              0                      0.0           0   \n",
       "12          INDIVIDUAL              0                      0.0           0   \n",
       "14          INDIVIDUAL              0                      0.0           0   \n",
       "...                ...            ...                      ...         ...   \n",
       "39251       INDIVIDUAL              0                      0.0           0   \n",
       "39291       INDIVIDUAL              0                      0.0           0   \n",
       "39362       INDIVIDUAL              0                      0.0           0   \n",
       "39425       INDIVIDUAL              0                      0.0           0   \n",
       "39623       INDIVIDUAL              0                      0.0           0   \n",
       "\n",
       "      pub_rec_bankruptcies tax_liens  \n",
       "1                      0.0       0.0  \n",
       "8                      0.0       0.0  \n",
       "9                      0.0       0.0  \n",
       "12                     0.0       0.0  \n",
       "14                     0.0       0.0  \n",
       "...                    ...       ...  \n",
       "39251                  1.0       0.0  \n",
       "39291                  1.0       0.0  \n",
       "39362                  1.0       0.0  \n",
       "39425                  1.0       0.0  \n",
       "39623                  1.0       0.0  \n",
       "\n",
       "[5201 rows x 53 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charged_off = loan[loan.loan_status == 'Charged Off']\n",
    "charged_off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dfffd391-462a-4908-a02f-ffc52b131a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5201, 53)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charged_off.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbae9ee1-8ba7-4048-9d06-febb7f6e8efc",
   "metadata": {},
   "source": [
    "### Plotting default rates across grade of the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8dad5f36-0b23-452d-9e81-191bff51db2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "round(100 * charged_off.grade.value_counts(normalize = True),2).plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c108306-6adc-4b58-9b55-acccf7415cee",
   "metadata": {},
   "source": [
    "### Plotting default rates across verification status of the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b3e4dad0-609b-498f-a5e9-a65efafa8b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verification_status\n",
       "Verified           0.371275\n",
       "Not Verified       0.363392\n",
       "Source Verified    0.265334\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "charged_off.verification_status.value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "db07ea6d-4161-4081-b72c-1ab0d2c277e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the pie chart of verification_status categories\n",
    "round(100 * charged_off.verification_status.value_counts(normalize = True),2).plot.pie(colors = ['green','red','yellow'], autopct = '%0.1f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090235dc-407f-4fe0-a6c4-a5ec8576a466",
   "metadata": {},
   "source": [
    "### Plotting default rates across home ownership of the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2ed796bd-6988-4eac-8277-700508444ff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "RENT        2619\n",
       "MORTGAGE    2182\n",
       "OWN          382\n",
       "OTHER         18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charged_off.home_ownership.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "396da392-5e70-4f37-bdc1-46c7f4c95290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the bar chart of percentage home_ownership categories\n",
    "round(100*charged_off.home_ownership.value_counts(normalize = True),2).plot.bar(color = ['blue','maroon','orange','blue'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186a0680-0797-4ff9-87c1-19daacb5f5ee",
   "metadata": {},
   "source": [
    "### Plotting default rates across delinq_2yrs of the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bc1c782a-6c9c-4e64-905b-8bd20c22cb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "delinq_2yrs\n",
       "0    4573\n",
       "1     465\n",
       "2     119\n",
       "3      26\n",
       "4      12\n",
       "5       3\n",
       "7       1\n",
       "6       1\n",
       "8       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charged_off.delinq_2yrs.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fc23df-23fa-40e0-b733-81343dd0360b",
   "metadata": {},
   "source": [
    "### lets Create verification_status of numerical data type where 'Not Verified' = 0, else = 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "936578a6-97af-49af-b9a7-fce4093dddce",
   "metadata": {},
   "outputs": [],
   "source": [
    "charged_off[\"verification_status\"] = charged_off[\"verification_status\"].apply(lambda x: 0 if x=='Not Verified' else 1)\n",
    "charged_off[\"verification_status\"] = charged_off[\"verification_status\"].apply(lambda x:pd.to_numeric(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d78efda8-9507-4034-b49a-c661abf63520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verification_status\n",
       "1    3311\n",
       "0    1890\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing the values\n",
    "charged_off[\"verification_status\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cae03e-236a-4378-9514-97433cf5a35b",
   "metadata": {},
   "source": [
    "### Emp_length VS verification_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "428a7f8f-118e-49e8-b8b0-edc14a6ff124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting default rates across emp_length of the loan\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'emp_length', y = 'verification_status', data = charged_off)\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de06baf8-497a-4e5f-9733-cbc5c3be0626",
   "metadata": {},
   "source": [
    "### lets Define a function to plot loan_status across categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3bdf0186-5391-4578-859d-184d99b98e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ver(cat_var):\n",
    " sns.barplot(x = cat_var, y = 'verification_status', data = charged_off)\n",
    " plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6c6faa-9911-4ad2-9fbf-8e015a0e7966",
   "metadata": {},
   "source": [
    "### Home Ownership VS Verification Status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "66537dd1-3f80-4e8d-98f2-fd9fa3519bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "MORTGAGE    0.658570\n",
       "OTHER       0.444444\n",
       "OWN         0.594241\n",
       "RENT        0.625811\n",
       "Name: verification_status, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the mean of verification status with different home ownership.\n",
    "charged_off.groupby(\"home_ownership\")[\"verification_status\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2a4201ce-3e96-4da4-b204-8419f599c9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets plot the bar graph of home_ownership with average value of verification status\n",
    "charged_off.groupby(\"home_ownership\")[\"verification_status\"].mean().plot.bar()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb7c4ad-622e-400e-ae33-4c0450ffda6c",
   "metadata": {},
   "source": [
    "### Term VS Verification Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "da79961d-d97f-409e-a468-621537acbdc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # lets Compare default rates across term of loan\n",
    "plot_ver('term')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac97e246-d747-4002-adb5-c6c606de336d",
   "metadata": {},
   "source": [
    "### Emp_length VS Verification Status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d94f5fdb-f688-4bd1-900c-b0affc3f1382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# lets plot the bar chart of emp_length categories\n",
    "plot_ver('grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76437c33-badf-4e17-a053-56b67a818818",
   "metadata": {},
   "source": [
    "#### Purpose VS Verification Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "39c514f7-b190-4df9-893b-d972340f207c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare default rates across purpose of loan\n",
    "plt.figure(figsize = (20,6))\n",
    "plot_ver('purpose')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48260baf-db9d-4372-814e-0f2acf1bc7ec",
   "metadata": {},
   "source": [
    "### loan amount: the median loan amount is around 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d5ebf9cb-70cf-4e28-9231-820d32c083f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(charged_off['loan_amnt'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fa7450f-7a48-4591-9d13-baafb9749f5b",
   "metadata": {},
   "source": [
    "compare the default rates across loan amount type : small, medium, high, very high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "70af6132-4793-4fda-bbc9-7481f10e0959",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loan_amount(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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bf8fb587-a171-417f-80d8-6403c1f45f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "charged_off['loan_amnt'] = charged_off['loan_amnt'].apply(lambda x: loan_amount(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9a02dac8-ffd5-45dc-bd11-3795d17a3a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt\n",
       "medium       2539\n",
       "high         1226\n",
       "low           889\n",
       "very high     547\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charged_off['loan_amnt'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a065e214-b79c-4324-b2f5-1b0d1089ea75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ver('loan_amnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b5cadb-7ccb-47f2-852d-8631302d12d2",
   "metadata": {},
   "source": [
    "### Funded amount invested VS Verification Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a1594200-1498-471e-99b5-8b6dd3d3d8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert funded amount invested to bins\n",
    "charged_off['funded_amnt_inv'] = charged_off['funded_amnt_inv'].apply(lambda x: loan_amount(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "93413346-aa4b-4729-aa5b-ea13777b96fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ver('funded_amnt_inv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a28863e-65ce-4bbd-9d2d-3a5dace0444c",
   "metadata": {},
   "source": [
    "### Interest Rate VS Verification Status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "beb420bc-114b-4fd0-bd8c-b19e556624a2",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Remove the percentage symbol and convert to numeric\n",
    "charged_off['int_rate'] = charged_off['int_rate'].str.rstrip('%').astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "297a5c07-ba97-4a7a-9ceb-d6316e0ecab2",
   "metadata": {},
   "outputs": [],
   "source": [
    " #lets convert interest rate to low, medium, high\n",
    "def int_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dba01d85-d751-41ad-833d-d63fb3807e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert interest rate invested to bins\n",
    "charged_off['int_rate'] = charged_off['int_rate'].apply(lambda x: int_rate(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a6ca2083-7221-4fa4-bc77-7779d635966f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing default rates across rates of interest\n",
    "plot_ver('int_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e429ba0-2f9e-4e5b-9ea7-bd842129205f",
   "metadata": {},
   "source": [
    "### dti VS Verification Status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b6b54b46-b4fd-4924-a1d9-22167c88fa4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  convert dti to low, medium, high\n",
    "\n",
    "def dti(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",
    "charged_off['dti'] = charged_off['dti'].apply(lambda x: int_rate(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0bd1b289-9375-4920-baed-d91c477015c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing default rates across dti\n",
    "plot_ver('dti')"
   ]
  },
  {
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   "source": [
    "### Conclusion"
   ]
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    "### Driving Factors (or driver variables)\n",
    "\n",
    "(1) Loan Term: Average Interest rate for defaulted applications is very high with 12.38 % for 36 months and 15.75 % for 60 months term.\n",
    "\n",
    "(2) Grade: Default Rate is high in high risk loan applicants. It would be important for LC to thoroughly vet high risk loan applications.\n",
    "\n",
    "(3) Loan Amount: Defaulter rate increases as the requested loan amount increases.\n",
    "\n",
    "(4) Annual Income: Applicants fr90K USD) income group have a greater share of defaulted loans.\n",
    "\n",
    "(5) Employment Length: Maximum number of defaulters have 10/10+ years of experience and 0 to 2 years of experience. Hence, LC should be take this aspect into consideration while lending loans.\n",
    "\n",
    "(6) Loan Purpose: The top two reasons for loans are debt consolidation and credit card. Such applications should be careful\n",
    "\n",
    "(7) Interest Rate:  The higher the installment amount, the higher the default rate. ly assessed.\n"
   ]
  }
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