Garv_Daga.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data into Dataframe and Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39717 entries, 0 to 39716\n",
      "Columns: 111 entries, id to total_il_high_credit_limit\n",
      "dtypes: float64(74), int64(13), object(24)\n",
      "memory usage: 33.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(39717, 111)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\") \n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "# Read in the data\n",
    "data = pd.read_csv('loan\\\\loan.csv')\n",
    "data.info()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Cleaining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1 - Removing Unwanted Rows and Columns \n",
    "* Check Non Null Data in All Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verification_status_joint             0\n",
       "bc_util                               0\n",
       "bc_open_to_buy                        0\n",
       "avg_cur_bal                           0\n",
       "acc_open_past_24mths                  0\n",
       "inq_last_12m                          0\n",
       "total_cu_tl                           0\n",
       "inq_fi                                0\n",
       "total_rev_hi_lim                      0\n",
       "all_util                              0\n",
       "max_bal_bc                            0\n",
       "mo_sin_old_il_acct                    0\n",
       "open_rv_24m                           0\n",
       "il_util                               0\n",
       "total_bal_il                          0\n",
       "mths_since_rcnt_il                    0\n",
       "open_il_24m                           0\n",
       "open_il_12m                           0\n",
       "open_il_6m                            0\n",
       "open_acc_6m                           0\n",
       "tot_cur_bal                           0\n",
       "tot_coll_amt                          0\n",
       "total_bc_limit                        0\n",
       "open_rv_12m                           0\n",
       "mo_sin_old_rev_tl_op                  0\n",
       "mo_sin_rcnt_rev_tl_op                 0\n",
       "mo_sin_rcnt_tl                        0\n",
       "total_bal_ex_mort                     0\n",
       "tot_hi_cred_lim                       0\n",
       "percent_bc_gt_75                      0\n",
       "pct_tl_nvr_dlq                        0\n",
       "num_tl_op_past_12m                    0\n",
       "num_tl_90g_dpd_24m                    0\n",
       "num_tl_30dpd                          0\n",
       "num_tl_120dpd_2m                      0\n",
       "num_sats                              0\n",
       "num_rev_tl_bal_gt_0                   0\n",
       "num_rev_accts                         0\n",
       "num_op_rev_tl                         0\n",
       "num_il_tl                             0\n",
       "num_bc_tl                             0\n",
       "num_bc_sats                           0\n",
       "num_actv_rev_tl                       0\n",
       "num_actv_bc_tl                        0\n",
       "num_accts_ever_120_pd                 0\n",
       "mths_since_recent_revol_delinq        0\n",
       "mths_since_recent_inq                 0\n",
       "mths_since_recent_bc_dlq              0\n",
       "mths_since_recent_bc                  0\n",
       "mort_acc                              0\n",
       "dti_joint                             0\n",
       "annual_inc_joint                      0\n",
       "total_il_high_credit_limit            0\n",
       "mths_since_last_major_derog           0\n",
       "next_pymnt_d                       1140\n",
       "mths_since_last_record             2786\n",
       "mths_since_last_delinq            14035\n",
       "desc                              26777\n",
       "emp_title                         37258\n",
       "emp_length                        38642\n",
       "pub_rec_bankruptcies              39020\n",
       "last_pymnt_d                      39646\n",
       "collections_12_mths_ex_med        39661\n",
       "chargeoff_within_12_mths          39661\n",
       "revol_util                        39667\n",
       "tax_liens                         39678\n",
       "title                             39706\n",
       "last_credit_pull_d                39715\n",
       "verification_status               39717\n",
       "pymnt_plan                        39717\n",
       "loan_status                       39717\n",
       "url                               39717\n",
       "issue_d                           39717\n",
       "annual_inc                        39717\n",
       "policy_code                       39717\n",
       "purpose                           39717\n",
       "grade                             39717\n",
       "installment                       39717\n",
       "int_rate                          39717\n",
       "term                              39717\n",
       "funded_amnt_inv                   39717\n",
       "funded_amnt                       39717\n",
       "loan_amnt                         39717\n",
       "member_id                         39717\n",
       "home_ownership                    39717\n",
       "sub_grade                         39717\n",
       "earliest_cr_line                  39717\n",
       "addr_state                        39717\n",
       "acc_now_delinq                    39717\n",
       "last_pymnt_amnt                   39717\n",
       "collection_recovery_fee           39717\n",
       "recoveries                        39717\n",
       "total_rec_late_fee                39717\n",
       "total_rec_int                     39717\n",
       "total_rec_prncp                   39717\n",
       "total_pymnt_inv                   39717\n",
       "total_pymnt                       39717\n",
       "out_prncp_inv                     39717\n",
       "out_prncp                         39717\n",
       "initial_list_status               39717\n",
       "total_acc                         39717\n",
       "revol_bal                         39717\n",
       "pub_rec                           39717\n",
       "open_acc                          39717\n",
       "delinq_amnt                       39717\n",
       "inq_last_6mths                    39717\n",
       "application_type                  39717\n",
       "delinq_2yrs                       39717\n",
       "dti                               39717\n",
       "zip_code                          39717\n",
       "id                                39717\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.notnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Check Types of Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off', 'Current'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Drop Current loan , as it is not be required for our analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38577 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: 33.0+ MB\n"
     ]
    }
   ],
   "source": [
    "data = data[data['loan_status'] != 'Current']\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Drop All Columns with All Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(axis=1, how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mths_since_last_record         2740\n",
       "mths_since_last_delinq        13672\n",
       "desc                          26050\n",
       "emp_title                     36191\n",
       "emp_length                    37544\n",
       "pub_rec_bankruptcies          37880\n",
       "last_pymnt_d                  38506\n",
       "chargeoff_within_12_mths      38521\n",
       "collections_12_mths_ex_med    38521\n",
       "revol_util                    38527\n",
       "tax_liens                     38538\n",
       "title                         38566\n",
       "last_credit_pull_d            38575\n",
       "initial_list_status           38577\n",
       "out_prncp                     38577\n",
       "revol_bal                     38577\n",
       "out_prncp_inv                 38577\n",
       "total_acc                     38577\n",
       "total_rec_int                 38577\n",
       "total_pymnt_inv               38577\n",
       "total_rec_prncp               38577\n",
       "total_rec_late_fee            38577\n",
       "recoveries                    38577\n",
       "collection_recovery_fee       38577\n",
       "last_pymnt_amnt               38577\n",
       "policy_code                   38577\n",
       "application_type              38577\n",
       "acc_now_delinq                38577\n",
       "delinq_amnt                   38577\n",
       "total_pymnt                   38577\n",
       "pub_rec                       38577\n",
       "id                            38577\n",
       "earliest_cr_line              38577\n",
       "member_id                     38577\n",
       "loan_amnt                     38577\n",
       "funded_amnt                   38577\n",
       "funded_amnt_inv               38577\n",
       "term                          38577\n",
       "int_rate                      38577\n",
       "installment                   38577\n",
       "grade                         38577\n",
       "sub_grade                     38577\n",
       "home_ownership                38577\n",
       "annual_inc                    38577\n",
       "verification_status           38577\n",
       "issue_d                       38577\n",
       "loan_status                   38577\n",
       "pymnt_plan                    38577\n",
       "url                           38577\n",
       "purpose                       38577\n",
       "zip_code                      38577\n",
       "addr_state                    38577\n",
       "dti                           38577\n",
       "delinq_2yrs                   38577\n",
       "open_acc                      38577\n",
       "inq_last_6mths                38577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.notnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Remove Columns having 1 unique or all unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns having all unique :  ['id', 'member_id', 'url']\n",
      "Columns having only 1 unique values:  ['pymnt_plan', 'initial_list_status', 'out_prncp', 'out_prncp_inv', 'collections_12_mths_ex_med', 'policy_code', 'application_type', 'acc_now_delinq', 'chargeoff_within_12_mths', 'delinq_amnt', 'tax_liens']\n"
     ]
    }
   ],
   "source": [
    "# Determines and drops columns which have all unique values\n",
    "def drop_unique_cols():\n",
    "    uniq_cols = [col for col in data.columns if data[col].nunique() == len(data.index)]\n",
    "    print(\"Columns having all unique : \", uniq_cols)\n",
    "    for cols in uniq_cols:\n",
    "        del data[cols]\n",
    "\n",
    "# Determines and drops columns having only 1 unique value\n",
    "def drop_cols_having_only_1_unqiue_value():\n",
    "    cols_having_only_1_unqiue_value = [col for col in data.columns if data[col].nunique() == 1]\n",
    "    print(\"Columns having only 1 unique values: \", cols_having_only_1_unqiue_value)\n",
    "    for cols in cols_having_only_1_unqiue_value:\n",
    "        del data[cols]\n",
    "\n",
    "drop_unique_cols()\n",
    "drop_cols_having_only_1_unqiue_value()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Drop Columns which require much more processing \n",
    " 1. 'desc', emp_title' and 'title' to bring out insights - They will not be required for this EDA\n",
    " 2. Some Columns dont add much importance to our problem statement - 'last_pymnt_d', 'earliest_cr_line','total_rec_int', total_rec_late_fee' and 'total_rec_prncp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38577 entries, 0 to 39716\n",
      "Data columns (total 34 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   loan_amnt                38577 non-null  int64  \n",
      " 1   funded_amnt              38577 non-null  int64  \n",
      " 2   funded_amnt_inv          38577 non-null  float64\n",
      " 3   term                     38577 non-null  object \n",
      " 4   int_rate                 38577 non-null  object \n",
      " 5   installment              38577 non-null  float64\n",
      " 6   grade                    38577 non-null  object \n",
      " 7   sub_grade                38577 non-null  object \n",
      " 8   emp_length               37544 non-null  object \n",
      " 9   home_ownership           38577 non-null  object \n",
      " 10  annual_inc               38577 non-null  float64\n",
      " 11  verification_status      38577 non-null  object \n",
      " 12  issue_d                  38577 non-null  object \n",
      " 13  loan_status              38577 non-null  object \n",
      " 14  purpose                  38577 non-null  object \n",
      " 15  zip_code                 38577 non-null  object \n",
      " 16  addr_state               38577 non-null  object \n",
      " 17  dti                      38577 non-null  float64\n",
      " 18  delinq_2yrs              38577 non-null  int64  \n",
      " 19  inq_last_6mths           38577 non-null  int64  \n",
      " 20  mths_since_last_delinq   13672 non-null  float64\n",
      " 21  mths_since_last_record   2740 non-null   float64\n",
      " 22  open_acc                 38577 non-null  int64  \n",
      " 23  pub_rec                  38577 non-null  int64  \n",
      " 24  revol_bal                38577 non-null  int64  \n",
      " 25  revol_util               38527 non-null  object \n",
      " 26  total_acc                38577 non-null  int64  \n",
      " 27  total_pymnt              38577 non-null  float64\n",
      " 28  total_pymnt_inv          38577 non-null  float64\n",
      " 29  recoveries               38577 non-null  float64\n",
      " 30  collection_recovery_fee  38577 non-null  float64\n",
      " 31  last_pymnt_amnt          38577 non-null  float64\n",
      " 32  last_credit_pull_d       38575 non-null  object \n",
      " 33  pub_rec_bankruptcies     37880 non-null  float64\n",
      "dtypes: float64(12), int64(8), object(14)\n",
      "memory usage: 10.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data = data.drop(['desc', 'emp_title','title'], axis=1)\n",
    "data = data.drop(['last_pymnt_d', 'earliest_cr_line', 'total_rec_int', 'total_rec_late_fee', 'total_rec_prncp'], axis=1)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Check mths_since_last_delinq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    13672.000000\n",
       "mean        35.882534\n",
       "std         22.028093\n",
       "min          0.000000\n",
       "25%         18.000000\n",
       "50%         34.000000\n",
       "75%         52.000000\n",
       "max        120.000000\n",
       "Name: mths_since_last_delinq, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['mths_since_last_delinq'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - As the Number of non null rows are quile less less than 36% and the data is numerical , Imputing it with mean will make the property baised . So we are dropping it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mths_since_last_record      2740\n",
       "emp_length                 37544\n",
       "pub_rec_bankruptcies       37880\n",
       "revol_util                 38527\n",
       "last_credit_pull_d         38575\n",
       "recoveries                 38577\n",
       "total_pymnt_inv            38577\n",
       "total_pymnt                38577\n",
       "total_acc                  38577\n",
       "dti                        38577\n",
       "revol_bal                  38577\n",
       "pub_rec                    38577\n",
       "open_acc                   38577\n",
       "last_pymnt_amnt            38577\n",
       "inq_last_6mths             38577\n",
       "delinq_2yrs                38577\n",
       "collection_recovery_fee    38577\n",
       "loan_amnt                  38577\n",
       "purpose                    38577\n",
       "loan_status                38577\n",
       "issue_d                    38577\n",
       "verification_status        38577\n",
       "annual_inc                 38577\n",
       "home_ownership             38577\n",
       "sub_grade                  38577\n",
       "grade                      38577\n",
       "installment                38577\n",
       "int_rate                   38577\n",
       "term                       38577\n",
       "funded_amnt_inv            38577\n",
       "funded_amnt                38577\n",
       "zip_code                   38577\n",
       "addr_state                 38577\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['mths_since_last_delinq'], axis=1, inplace=True)\n",
    "data.notnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   - As the Number of null rows are quile less for all of the columns having null value , we are dropping the rows instead of inputing the values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mths_since_last_record      1929\n",
       "loan_amnt                  36799\n",
       "last_pymnt_amnt            36799\n",
       "collection_recovery_fee    36799\n",
       "recoveries                 36799\n",
       "total_pymnt_inv            36799\n",
       "total_pymnt                36799\n",
       "total_acc                  36799\n",
       "revol_util                 36799\n",
       "revol_bal                  36799\n",
       "pub_rec                    36799\n",
       "open_acc                   36799\n",
       "inq_last_6mths             36799\n",
       "delinq_2yrs                36799\n",
       "dti                        36799\n",
       "last_credit_pull_d         36799\n",
       "addr_state                 36799\n",
       "purpose                    36799\n",
       "loan_status                36799\n",
       "issue_d                    36799\n",
       "verification_status        36799\n",
       "annual_inc                 36799\n",
       "home_ownership             36799\n",
       "emp_length                 36799\n",
       "sub_grade                  36799\n",
       "grade                      36799\n",
       "installment                36799\n",
       "int_rate                   36799\n",
       "term                       36799\n",
       "funded_amnt_inv            36799\n",
       "funded_amnt                36799\n",
       "zip_code                   36799\n",
       "pub_rec_bankruptcies       36799\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(subset=['pub_rec_bankruptcies','revol_util','last_credit_pull_d','emp_length'], axis=0, inplace=True)\n",
    "data.notnull().sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Fixing Column Values and Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term                      2\n",
       "int_rate                336\n",
       "grade                     7\n",
       "sub_grade                35\n",
       "emp_length               11\n",
       "home_ownership            4\n",
       "verification_status       3\n",
       "issue_d                  52\n",
       "loan_status               2\n",
       "purpose                  14\n",
       "zip_code                808\n",
       "addr_state               49\n",
       "revol_util             1086\n",
       "last_credit_pull_d       97\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.select_dtypes('object').apply(pd.Series.nunique, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Correct columns datatypes\n",
    "     * Change int_rate, revol_util, funded_amnt, loan_amnt , funded_amnt_inv, annual_inc to float\n",
    "     * Change issue_d to DateTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "term                     2\n",
       "grade                    7\n",
       "sub_grade               35\n",
       "emp_length              11\n",
       "home_ownership           4\n",
       "verification_status      3\n",
       "loan_status              2\n",
       "purpose                 14\n",
       "zip_code               808\n",
       "addr_state              49\n",
       "last_credit_pull_d      97\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['int_rate'] = data['int_rate'].str.rstrip('%').astype('float')\n",
    "data['revol_util'] = data['revol_util'].str.rstrip('%').astype('float')\n",
    "\n",
    "data = data.astype({ \"loan_amnt\": float, \"funded_amnt\": float, \"funded_amnt_inv\": float, \"annual_inc\": float})\n",
    "\n",
    "data['issue_d'] = pd.to_datetime(data['issue_d'], format='%b-%y')\n",
    "\n",
    "data.select_dtypes('object').apply(pd.Series.nunique, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note we are not converting term to numerical as the data has only two possible values and is catagorical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Drop duplicates if any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36799, 33)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Drop duplicates if there are any\n",
    "data.drop_duplicates()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Remove Outliers for loan_amnt , funded_amnt_inv and annual_inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33826, 33)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove outliers using IQR for loan amount\n",
    "loan_amnt_iqr = data.loan_amnt.quantile(0.75) - data.loan_amnt.quantile(0.25)\n",
    "loan_amnt_lower_bound = data.loan_amnt.quantile(0.25) - 1.5*loan_amnt_iqr\n",
    "loan_amnt_upper_bound = data.loan_amnt.quantile(0.75) + 1.5*loan_amnt_iqr\n",
    "\n",
    "data = data[(data.loan_amnt >= loan_amnt_lower_bound) & (data.loan_amnt <= loan_amnt_upper_bound)]\n",
    "\n",
    "# Remove outliers using IQR for funded amount invested\n",
    "funded_amnt_inv_iqr = data.funded_amnt_inv.quantile(0.75) - data.funded_amnt_inv.quantile(0.25)\n",
    "funded_amnt_inv_lower_bound = data.funded_amnt_inv.quantile(0.25) - 1.5*funded_amnt_inv_iqr\n",
    "funded_amnt_inv_upper_bound = data.funded_amnt_inv.quantile(0.75) + 1.5*funded_amnt_inv_iqr\n",
    "\n",
    "data = data[(data.funded_amnt_inv >= funded_amnt_inv_lower_bound) & (data.funded_amnt_inv <= funded_amnt_inv_upper_bound)]\n",
    "\n",
    "# Remove outliers using IQR for funded amount invested\n",
    "annual_inc_iqr = data.annual_inc.quantile(0.75) - data.annual_inc.quantile(0.25)\n",
    "annual_inc_lower_bound = data.annual_inc.quantile(0.25) - 1.5*annual_inc_iqr\n",
    "annual_inc_upper_bound = data.annual_inc.quantile(0.75) + 1.5*annual_inc_iqr\n",
    "\n",
    "data = data[(data.annual_inc >= annual_inc_lower_bound) & (data.annual_inc <= annual_inc_upper_bound)]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Derived Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Converting dates to Month and Year will help in analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>issue_m</th>\n",
       "      <th>issue_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>5863.155187</td>\n",
       "      <td>5833.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>171.62</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>car</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>3005.666844</td>\n",
       "      <td>3005.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>649.91</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>12231.890000</td>\n",
       "      <td>12231.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Apr-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>3 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>wedding</td>\n",
       "      <td>852xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7963</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12</td>\n",
       "      <td>5632.210000</td>\n",
       "      <td>5632.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.03</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  installment  \\\n",
       "0     5000.0       5000.0           4975.0   36 months     10.65       162.87   \n",
       "1     2500.0       2500.0           2500.0   60 months     15.27        59.83   \n",
       "2     2400.0       2400.0           2400.0   36 months     15.96        84.33   \n",
       "3    10000.0      10000.0          10000.0   36 months     13.49       339.31   \n",
       "5     5000.0       5000.0           5000.0   36 months      7.90       156.46   \n",
       "\n",
       "  grade sub_grade emp_length home_ownership  annual_inc verification_status  \\\n",
       "0     B        B2  10+ years           RENT     24000.0            Verified   \n",
       "1     C        C4   < 1 year           RENT     30000.0     Source Verified   \n",
       "2     C        C5  10+ years           RENT     12252.0        Not Verified   \n",
       "3     C        C1  10+ years           RENT     49200.0     Source Verified   \n",
       "5     A        A4    3 years           RENT     36000.0     Source Verified   \n",
       "\n",
       "     issue_d  loan_status         purpose zip_code addr_state    dti  \\\n",
       "0 2011-12-01   Fully Paid     credit_card    860xx         AZ  27.65   \n",
       "1 2011-12-01  Charged Off             car    309xx         GA   1.00   \n",
       "2 2011-12-01   Fully Paid  small_business    606xx         IL   8.72   \n",
       "3 2011-12-01   Fully Paid           other    917xx         CA  20.00   \n",
       "5 2011-12-01   Fully Paid         wedding    852xx         AZ  11.20   \n",
       "\n",
       "   delinq_2yrs  inq_last_6mths  mths_since_last_record  open_acc  pub_rec  \\\n",
       "0            0               1                     NaN         3        0   \n",
       "1            0               5                     NaN         3        0   \n",
       "2            0               2                     NaN         2        0   \n",
       "3            0               1                     NaN        10        0   \n",
       "5            0               3                     NaN         9        0   \n",
       "\n",
       "   revol_bal  revol_util  total_acc   total_pymnt  total_pymnt_inv  \\\n",
       "0      13648        83.7          9   5863.155187          5833.84   \n",
       "1       1687         9.4          4   1008.710000          1008.71   \n",
       "2       2956        98.5         10   3005.666844          3005.67   \n",
       "3       5598        21.0         37  12231.890000         12231.89   \n",
       "5       7963        28.3         12   5632.210000          5632.21   \n",
       "\n",
       "   recoveries  collection_recovery_fee  last_pymnt_amnt last_credit_pull_d  \\\n",
       "0        0.00                     0.00           171.62             May-16   \n",
       "1      117.08                     1.11           119.66             Sep-13   \n",
       "2        0.00                     0.00           649.91             May-16   \n",
       "3        0.00                     0.00           357.48             Apr-16   \n",
       "5        0.00                     0.00           161.03             Jan-16   \n",
       "\n",
       "   pub_rec_bankruptcies  issue_m  issue_y  \n",
       "0                   0.0       12     2011  \n",
       "1                   0.0       12     2011  \n",
       "2                   0.0       12     2011  \n",
       "3                   0.0       12     2011  \n",
       "5                   0.0       12     2011  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['issue_m'] = pd.to_datetime(data['issue_d'], format='%b-%y').dt.month\n",
    "data['issue_y'] = pd.to_datetime(data['issue_d'], format='%b-%y').dt.year\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* % of Amount funded by Investor to the Amount Asked by Borrower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
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       "      <th>int_rate</th>\n",
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       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
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       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
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       "      <th>revol_bal</th>\n",
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       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>issue_m</th>\n",
       "      <th>issue_y</th>\n",
       "      <th>funded_amnt_%</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>5863.155187</td>\n",
       "      <td>5833.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>171.62</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>car</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>3005.666844</td>\n",
       "      <td>3005.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>649.91</td>\n",
       "      <td>May-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>12231.890000</td>\n",
       "      <td>12231.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Apr-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>3 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>wedding</td>\n",
       "      <td>852xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>11.20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7963</td>\n",
       "      <td>28.3</td>\n",
       "      <td>12</td>\n",
       "      <td>5632.210000</td>\n",
       "      <td>5632.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>161.03</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  installment  \\\n",
       "0     5000.0       5000.0           4975.0   36 months     10.65       162.87   \n",
       "1     2500.0       2500.0           2500.0   60 months     15.27        59.83   \n",
       "2     2400.0       2400.0           2400.0   36 months     15.96        84.33   \n",
       "3    10000.0      10000.0          10000.0   36 months     13.49       339.31   \n",
       "5     5000.0       5000.0           5000.0   36 months      7.90       156.46   \n",
       "\n",
       "  grade sub_grade emp_length home_ownership  annual_inc verification_status  \\\n",
       "0     B        B2  10+ years           RENT     24000.0            Verified   \n",
       "1     C        C4   < 1 year           RENT     30000.0     Source Verified   \n",
       "2     C        C5  10+ years           RENT     12252.0        Not Verified   \n",
       "3     C        C1  10+ years           RENT     49200.0     Source Verified   \n",
       "5     A        A4    3 years           RENT     36000.0     Source Verified   \n",
       "\n",
       "     issue_d  loan_status         purpose zip_code addr_state    dti  \\\n",
       "0 2011-12-01   Fully Paid     credit_card    860xx         AZ  27.65   \n",
       "1 2011-12-01  Charged Off             car    309xx         GA   1.00   \n",
       "2 2011-12-01   Fully Paid  small_business    606xx         IL   8.72   \n",
       "3 2011-12-01   Fully Paid           other    917xx         CA  20.00   \n",
       "5 2011-12-01   Fully Paid         wedding    852xx         AZ  11.20   \n",
       "\n",
       "   delinq_2yrs  inq_last_6mths  mths_since_last_record  open_acc  pub_rec  \\\n",
       "0            0               1                     NaN         3        0   \n",
       "1            0               5                     NaN         3        0   \n",
       "2            0               2                     NaN         2        0   \n",
       "3            0               1                     NaN        10        0   \n",
       "5            0               3                     NaN         9        0   \n",
       "\n",
       "   revol_bal  revol_util  total_acc   total_pymnt  total_pymnt_inv  \\\n",
       "0      13648        83.7          9   5863.155187          5833.84   \n",
       "1       1687         9.4          4   1008.710000          1008.71   \n",
       "2       2956        98.5         10   3005.666844          3005.67   \n",
       "3       5598        21.0         37  12231.890000         12231.89   \n",
       "5       7963        28.3         12   5632.210000          5632.21   \n",
       "\n",
       "   recoveries  collection_recovery_fee  last_pymnt_amnt last_credit_pull_d  \\\n",
       "0        0.00                     0.00           171.62             May-16   \n",
       "1      117.08                     1.11           119.66             Sep-13   \n",
       "2        0.00                     0.00           649.91             May-16   \n",
       "3        0.00                     0.00           357.48             Apr-16   \n",
       "5        0.00                     0.00           161.03             Jan-16   \n",
       "\n",
       "   pub_rec_bankruptcies  issue_m  issue_y  funded_amnt_%  \n",
       "0                   0.0       12     2011           99.5  \n",
       "1                   0.0       12     2011          100.0  \n",
       "2                   0.0       12     2011          100.0  \n",
       "3                   0.0       12     2011          100.0  \n",
       "5                   0.0       12     2011          100.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['funded_amnt_%'] = data['funded_amnt_inv']/data['loan_amnt'] *100\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create a defaulted column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    29005\n",
       "1     4821\n",
       "Name: defaulted, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['defaulted'] = data.loan_status.apply(lambda x: 1 if (x == \"Charged Off\") else 0) \n",
    "data.defaulted.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Derieve Columns using Buckets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert annual income into a categorical variable for further analysis\n",
    "annual_inc_25_quantile = int(data.annual_inc.quantile(0.25))\n",
    "annual_inc_50_quantile = int(data.annual_inc.quantile(0.5))\n",
    "annual_inc_75_quantile = int(data.annual_inc.quantile(0.75))  \n",
    "\n",
    "def binning_annual_inc(annual_inc):  \n",
    "    if (annual_inc <= annual_inc_25_quantile):\n",
    "        return \"0-{}\".format(annual_inc_25_quantile)\n",
    "    if (annual_inc > annual_inc_25_quantile and annual_inc <= annual_inc_50_quantile):\n",
    "        return \"{}-{}\".format(annual_inc_25_quantile, annual_inc_50_quantile)\n",
    "    if (annual_inc > annual_inc_50_quantile and annual_inc <= annual_inc_75_quantile):\n",
    "        return \"{}-{}\".format(annual_inc_50_quantile, annual_inc_75_quantile)\n",
    "    return \">{}\".format(annual_inc_75_quantile)\n",
    "\n",
    "data['annual_inc_category'] = data.annual_inc.apply(binning_annual_inc)\n",
    "\n",
    "# Convert funded amount invested into a categorical variable for further analysis\n",
    "funded_amnt_inv_25_quantile = int(data.funded_amnt_inv.quantile(0.25))\n",
    "funded_amnt_inv_50_quantile = int(data.funded_amnt_inv.quantile(0.5))\n",
    "funded_amnt_inv_75_quantile = int(data.funded_amnt_inv.quantile(0.75)) \n",
    "\n",
    "def binning_funded_amnt_inv(funded_amnt_inv):  \n",
    "    if (funded_amnt_inv <= funded_amnt_inv_25_quantile):\n",
    "        return \"0-{}\".format(funded_amnt_inv_25_quantile)\n",
    "    if (funded_amnt_inv > funded_amnt_inv_25_quantile and funded_amnt_inv <= funded_amnt_inv_50_quantile):\n",
    "        return \"{}-{}\".format(funded_amnt_inv_25_quantile, funded_amnt_inv_50_quantile)\n",
    "    if (funded_amnt_inv > funded_amnt_inv_50_quantile and funded_amnt_inv <= funded_amnt_inv_75_quantile):\n",
    "        return \"{}-{}\".format(funded_amnt_inv_50_quantile, funded_amnt_inv_75_quantile)\n",
    "    return \">{}\".format(funded_amnt_inv_75_quantile)\n",
    "\n",
    "data['funded_amnt_inv_category'] = data.funded_amnt_inv.apply(binning_funded_amnt_inv)\n",
    "\n",
    "# Convert loan amount into a categorical variable for further analysis\n",
    "loan_amt_25_quantile = int(data.loan_amnt.quantile(0.25))\n",
    "loan_amt_50_quantile = int(data.loan_amnt.quantile(0.5))\n",
    "loan_amt_75_quantile = int(data.loan_amnt.quantile(0.75))  \n",
    "\n",
    "def binning_loan_amnt(loan_amnt):  \n",
    "    if (loan_amnt <= loan_amt_25_quantile):\n",
    "        return \"0-{}\".format(loan_amt_25_quantile)\n",
    "    if (loan_amnt > loan_amt_25_quantile and loan_amnt <= loan_amt_50_quantile):\n",
    "        return \"{}-{}\".format(loan_amt_25_quantile, loan_amt_50_quantile)\n",
    "    if (loan_amnt > loan_amt_50_quantile and loan_amnt <= loan_amt_75_quantile):\n",
    "        return \"{}-{}\".format(loan_amt_50_quantile, loan_amt_75_quantile)\n",
    "    return \">{}\".format(loan_amt_75_quantile)\n",
    "\n",
    "data['loan_amnt_category'] = data.loan_amnt.apply(binning_loan_amnt)\n",
    "\n",
    "# Convert dti into a categorical variable\n",
    "def dti_bucketing(dti):\n",
    "    if (dti <= 8):\n",
    "        return 'VERY LOW'\n",
    "    if (dti > 8 and dti <=13):\n",
    "        return 'LOW'\n",
    "    if (dti > 13 and dti <=17):\n",
    "        return 'MEDIUM'\n",
    "    if (dti >= 17 and dti < 19):\n",
    "        return 'HIGH'\n",
    "    return 'VERY HIGH'\n",
    "data['dti_bucket'] = data.dti.apply(dti_bucketing)\n",
    "\n",
    "# Convert interest rate into categorical variable:\n",
    "int_rate_25_quantile = int(data.int_rate.quantile(0.25))\n",
    "int_rate_50_quantile = int(data.int_rate.quantile(0.5))\n",
    "int_rate_75_quantile = int(data.int_rate.quantile(0.75))\n",
    "\n",
    "def bin_int_rate(int_rate):\n",
    "    if(int_rate <= int_rate_25_quantile):\n",
    "        return \"LOW\"\n",
    "    if(int_rate > int_rate_25_quantile and int_rate <= int_rate_50_quantile):\n",
    "        return \"MEDIUM\"\n",
    "    if(int_rate > int_rate_50_quantile and int_rate <= int_rate_75_quantile):\n",
    "        return \"HIGH\"\n",
    "    return \"VERY HIGH\"\n",
    "\n",
    "data['int_rates_bucket'] = data.int_rate.apply(bin_int_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>int_rate</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>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>issue_m</th>\n",
       "      <th>issue_y</th>\n",
       "      <th>funded_amnt_%</th>\n",
       "      <th>defaulted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>4821.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10966.842979</td>\n",
       "      <td>10686.921800</td>\n",
       "      <td>10051.897856</td>\n",
       "      <td>13.706055</td>\n",
       "      <td>307.282883</td>\n",
       "      <td>55453.135422</td>\n",
       "      <td>14.120095</td>\n",
       "      <td>0.164696</td>\n",
       "      <td>1.046463</td>\n",
       "      <td>92.095823</td>\n",
       "      <td>9.096660</td>\n",
       "      <td>0.086497</td>\n",
       "      <td>12402.644472</td>\n",
       "      <td>55.710552</td>\n",
       "      <td>20.953329</td>\n",
       "      <td>6158.208880</td>\n",
       "      <td>5833.187164</td>\n",
       "      <td>577.959681</td>\n",
       "      <td>76.236336</td>\n",
       "      <td>302.918009</td>\n",
       "      <td>0.066791</td>\n",
       "      <td>7.349927</td>\n",
       "      <td>2010.386434</td>\n",
       "      <td>93.407807</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6608.436899</td>\n",
       "      <td>6354.118399</td>\n",
       "      <td>6244.033511</td>\n",
       "      <td>3.564856</td>\n",
       "      <td>182.049923</td>\n",
       "      <td>25292.785857</td>\n",
       "      <td>6.570151</td>\n",
       "      <td>0.528293</td>\n",
       "      <td>1.139228</td>\n",
       "      <td>21.928975</td>\n",
       "      <td>4.443909</td>\n",
       "      <td>0.288411</td>\n",
       "      <td>13333.580204</td>\n",
       "      <td>27.802625</td>\n",
       "      <td>11.228174</td>\n",
       "      <td>5538.057975</td>\n",
       "      <td>5411.604097</td>\n",
       "      <td>1449.073642</td>\n",
       "      <td>356.761177</td>\n",
       "      <td>506.500168</td>\n",
       "      <td>0.251342</td>\n",
       "      <td>3.337846</td>\n",
       "      <td>0.821782</td>\n",
       "      <td>17.924716</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.420000</td>\n",
       "      <td>22.790000</td>\n",
       "      <td>4080.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5400.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>11.260000</td>\n",
       "      <td>166.580000</td>\n",
       "      <td>36400.000000</td>\n",
       "      <td>9.230000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3894.000000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2110.920000</td>\n",
       "      <td>1889.100000</td>\n",
       "      <td>9.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.970000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>98.333245</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>13.490000</td>\n",
       "      <td>277.360000</td>\n",
       "      <td>50400.000000</td>\n",
       "      <td>14.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8783.000000</td>\n",
       "      <td>58.700000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>4553.570000</td>\n",
       "      <td>4201.680000</td>\n",
       "      <td>169.550000</td>\n",
       "      <td>2.390000</td>\n",
       "      <td>227.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>14501.912530</td>\n",
       "      <td>16.290000</td>\n",
       "      <td>410.750000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16339.000000</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>8472.770000</td>\n",
       "      <td>8027.530000</td>\n",
       "      <td>534.440000</td>\n",
       "      <td>8.091000</td>\n",
       "      <td>381.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29000.000000</td>\n",
       "      <td>29000.000000</td>\n",
       "      <td>26475.000000</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>934.550000</td>\n",
       "      <td>139000.000000</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>137112.000000</td>\n",
       "      <td>99.900000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>34765.350000</td>\n",
       "      <td>34730.840000</td>\n",
       "      <td>21810.310000</td>\n",
       "      <td>7002.190000</td>\n",
       "      <td>12818.380000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt   funded_amnt  funded_amnt_inv     int_rate  installment  \\\n",
       "count   4821.000000   4821.000000      4821.000000  4821.000000  4821.000000   \n",
       "mean   10966.842979  10686.921800     10051.897856    13.706055   307.282883   \n",
       "std     6608.436899   6354.118399      6244.033511     3.564856   182.049923   \n",
       "min     1000.000000   1000.000000         0.000000     5.420000    22.790000   \n",
       "25%     5400.000000   5200.000000      5000.000000    11.260000   166.580000   \n",
       "50%    10000.000000  10000.000000      9000.000000    13.490000   277.360000   \n",
       "75%    15000.000000  15000.000000     14501.912530    16.290000   410.750000   \n",
       "max    29000.000000  29000.000000     26475.000000    24.400000   934.550000   \n",
       "\n",
       "          annual_inc          dti  delinq_2yrs  inq_last_6mths  \\\n",
       "count    4821.000000  4821.000000  4821.000000     4821.000000   \n",
       "mean    55453.135422    14.120095     0.164696        1.046463   \n",
       "std     25292.785857     6.570151     0.528293        1.139228   \n",
       "min      4080.000000     0.000000     0.000000        0.000000   \n",
       "25%     36400.000000     9.230000     0.000000        0.000000   \n",
       "50%     50400.000000    14.440000     0.000000        1.000000   \n",
       "75%     70000.000000    19.400000     0.000000        2.000000   \n",
       "max    139000.000000    29.850000     8.000000        8.000000   \n",
       "\n",
       "       mths_since_last_record     open_acc      pub_rec      revol_bal  \\\n",
       "count              407.000000  4821.000000  4821.000000    4821.000000   \n",
       "mean                92.095823     9.096660     0.086497   12402.644472   \n",
       "std                 21.928975     4.443909     0.288411   13333.580204   \n",
       "min                  7.000000     2.000000     0.000000       0.000000   \n",
       "25%                 87.000000     6.000000     0.000000    3894.000000   \n",
       "50%                 96.000000     8.000000     0.000000    8783.000000   \n",
       "75%                107.500000    11.000000     0.000000   16339.000000   \n",
       "max                119.000000    38.000000     2.000000  137112.000000   \n",
       "\n",
       "        revol_util    total_acc   total_pymnt  total_pymnt_inv    recoveries  \\\n",
       "count  4821.000000  4821.000000   4821.000000      4821.000000   4821.000000   \n",
       "mean     55.710552    20.953329   6158.208880      5833.187164    577.959681   \n",
       "std      27.802625    11.228174   5538.057975      5411.604097   1449.073642   \n",
       "min       0.000000     2.000000      0.000000         0.000000      0.000000   \n",
       "25%      34.400000    12.000000   2110.920000      1889.100000      9.340000   \n",
       "50%      58.700000    19.000000   4553.570000      4201.680000    169.550000   \n",
       "75%      78.900000    28.000000   8472.770000      8027.530000    534.440000   \n",
       "max      99.900000    74.000000  34765.350000     34730.840000  21810.310000   \n",
       "\n",
       "       collection_recovery_fee  last_pymnt_amnt  pub_rec_bankruptcies  \\\n",
       "count              4821.000000      4821.000000           4821.000000   \n",
       "mean                 76.236336       302.918009              0.066791   \n",
       "std                 356.761177       506.500168              0.251342   \n",
       "min                   0.000000         0.000000              0.000000   \n",
       "25%                   0.000000       111.970000              0.000000   \n",
       "50%                   2.390000       227.920000              0.000000   \n",
       "75%                   8.091000       381.040000              0.000000   \n",
       "max                7002.190000     12818.380000              2.000000   \n",
       "\n",
       "           issue_m      issue_y  funded_amnt_%  defaulted  \n",
       "count  4821.000000  4821.000000    4821.000000     4821.0  \n",
       "mean      7.349927  2010.386434      93.407807        1.0  \n",
       "std       3.337846     0.821782      17.924716        0.0  \n",
       "min       1.000000  2007.000000       0.000000        1.0  \n",
       "25%       5.000000  2010.000000      98.333245        1.0  \n",
       "50%       8.000000  2011.000000     100.000000        1.0  \n",
       "75%      10.000000  2011.000000     100.000000        1.0  \n",
       "max      12.000000  2011.000000     100.000000        1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaultedLoan = data[data['loan_status'] == 'Charged Off']\n",
    "defaultedLoan.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grade = round(defaultedLoan['grade'].value_counts()/data['grade'].value_counts()*100, 2).sort_values(ascending=False)\n",
    "grade.plot(kind='bar', figsize=(10, 5))\n",
    "plt.xlabel('Grade')\n",
    "plt.ylabel('Defaulted Loan %')\n",
    "plt.title('Defaulted Loan % by Grade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Risk increases with the increase of Grade .**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "badloan_percent = round(defaultedLoan['purpose'].value_counts()/data['purpose'].value_counts()*100, 2).sort_values(ascending=False)\n",
    "#Plot the above badloan_percent as hist plot\n",
    "badloan_percent.plot(kind='bar', figsize=(15, 5))\n",
    "plt.xlabel('Purpose')\n",
    "plt.ylabel('Defaulted Loan %')\n",
    "plt.title('Defaulted Loan % by Purpose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can say that the chances of bad loans is the most for small business .**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    33826.000000\n",
       "mean     10157.092178\n",
       "std       6149.602700\n",
       "min        500.000000\n",
       "25%       5000.000000\n",
       "50%       9000.000000\n",
       "75%      14000.000000\n",
       "max      29000.000000\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.boxplot(y=\"loan_amnt\", data=data)\n",
    "plt.show()\n",
    "data.loan_amnt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 50% of folks ask for around ~9000 as loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    33826.000000\n",
       "mean      9527.230321\n",
       "std       5853.902101\n",
       "min          0.000000\n",
       "25%       5000.000000\n",
       "50%       8350.000000\n",
       "75%      13000.000000\n",
       "max      26814.098160\n",
       "Name: funded_amnt_inv, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data, x=\"funded_amnt_inv\", kde = True)\n",
    "data.funded_amnt_inv.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plots its evident that loan amount asked for and loan amount funded by investors at around multiples of 5000(5000, 10000, 12500, 15000, 20000, 25000) and 50% of folks are disbursed loan amount at around 8200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    33826.000000\n",
       "mean        11.836514\n",
       "std          3.611809\n",
       "min          5.420000\n",
       "25%          8.900000\n",
       "50%         11.710000\n",
       "75%         14.270000\n",
       "max         24.400000\n",
       "Name: int_rate, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"int_rate\", data=data)\n",
    "sns.displot(data, x=\"int_rate\", kde = True)\n",
    "\n",
    "data.int_rate.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A lot of folks are issued interest at ~8% and betwen (10 - 14.5)% \n",
    "* Very less people are issued interest between (8-10)%\n",
    "* 50% of folks are issued loan at interest rate ~11.5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"funded_amnt_inv\", x=\"loan_status\", palette=\"muted\", hue=\"verification_status\", data=data)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above plot , we can not see much influence of verification_status on Defaults for a given amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='loan_status', ylabel='funded_amnt_inv'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=\"funded_amnt_inv\", x=\"loan_status\", palette=\"muted\", hue=\"grade\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grade seems to have no to less impact whether the loan would be defaulted for a given amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "MORTGAGE    13.40\n",
       "OTHER       18.89\n",
       "OWN         14.33\n",
       "RENT        14.94\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"home_ownership\"])['loan_status'].count()/data.groupby([\"home_ownership\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_ownership\n",
       "MORTGAGE    11146.602111\n",
       "OTHER       10637.500000\n",
       "OWN          9453.203343\n",
       "RENT         9422.616726\n",
       "Name: loan_amnt, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby([\"home_ownership\"])['loan_amnt'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **An individual with home ownership status as \"OTHER\" and they generally take a higher loan amount and there is a very high tendency of their loan being \"Charged off\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='loan_amnt_category', ylabel='annual_inc'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'loan_amnt_category',\n",
    "            y = 'annual_inc',\n",
    "            data = data,\n",
    "            palette = \"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Loan amount value is inversely proportional to amount of folks asking for that particular loan amount**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annual_inc_category\n",
       "0-40000        17.64\n",
       "40000-55146    14.88\n",
       "55146-75821    13.65\n",
       ">75821         10.70\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"annual_inc_category\"])['loan_status'].count()/data.groupby([\"annual_inc_category\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **As the Annual income increases the tendency of some to default decreases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rates_bucket\n",
       "HIGH         14.98\n",
       "LOW           5.25\n",
       "MEDIUM       10.22\n",
       "VERY HIGH    23.92\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"int_rates_bucket\"])['loan_status'].count()/data.groupby([\"int_rates_bucket\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **The higher the Interest rates higher are the chances of an individual to default on the loan which suggests there is a higher likelihood of loan getting defaulted when loan is disbursed with higher interest rates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "issue_y\n",
       "2007    28.57\n",
       "2008    15.38\n",
       "2009    12.62\n",
       "2010    12.66\n",
       "2011    15.54\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"issue_y\"])['loan_status'].count()/data.groupby([\"issue_y\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2007, followed by 2011 and 2008 experienced most number of people with charged off loans, the amount of fully paid loan also increased, This is probably as after effect of recession happened**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "668xx      2\n",
       "768xx      2\n",
       "374xx      2\n",
       "798xx      2\n",
       "814xx      2\n",
       "468xx      2\n",
       "412xx      2\n",
       "413xx      2\n",
       "239xx      2\n",
       "881xx      2\n",
       "567xx      2\n",
       "969xx      2\n",
       "623xx      2\n",
       "638xx      2\n",
       "035xx      2\n",
       "058xx      2\n",
       "267xx      2\n",
       "391xx      2\n",
       "022xx      2\n",
       "203xx      2\n",
       "576xx      2\n",
       "390xx      2\n",
       "386xx      1\n",
       "789xx      1\n",
       "381xx      1\n",
       "592xx      1\n",
       "753xx      1\n",
       "378xx      1\n",
       "965xx      1\n",
       "792xx      1\n",
       "373xx      1\n",
       "673xx      1\n",
       "396xx      1\n",
       "414xx      1\n",
       "040xx      1\n",
       "836xx      1\n",
       "681xx      1\n",
       "388xx      1\n",
       "823xx      1\n",
       "379xx      1\n",
       "380xx      1\n",
       "044xx      1\n",
       "385xx      1\n",
       "094xx      1\n",
       "250xx      1\n",
       "463xx      1\n",
       "096xx      1\n",
       "999xx      1\n",
       "575xx      1\n",
       "963xx      1\n",
       "734xx      1\n",
       "595xx      1\n",
       "599xx      1\n",
       "471xx      1\n",
       "375xx      1\n",
       "747xx      1\n",
       "845xx      1\n",
       "397xx      1\n",
       "369xx      1\n",
       "884xx      1\n",
       "169xx      1\n",
       "093xx      1\n",
       "408xx      1\n",
       "669xx      1\n",
       "528xx      1\n",
       "289xx      1\n",
       "663xx      1\n",
       "252xx      1\n",
       "353xx      1\n",
       "677xx      1\n",
       "090xx      1\n",
       "831xx      1\n",
       "736xx      1\n",
       "332xx      1\n",
       "461xx      1\n",
       "479xx      1\n",
       "Name: zip_code, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby([\"zip_code\"]).defaulted.count().plot()\n",
    "data.zip_code.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Folks with zipcode ranging from 010xx-12xx, living around 338xx or 754xx, 622xx, 882xx-90xx have higher tendency of defaulting the loans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grade = round(defaultedLoan['addr_state'].value_counts()/data['addr_state'].value_counts()*100, 2).sort_values(ascending=False)\n",
    "grade.plot(kind='bar', figsize=(10, 5))\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Defaults %')\n",
    "plt.title('Defaults % by State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **We can infer that loans from States 'TN' , 'NV' and 'AK' have higher probability of Defaults.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pub_rec_bankruptcies\n",
       "0.0    13.91\n",
       "1.0    21.68\n",
       "2.0    40.00\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"pub_rec_bankruptcies\"])['loan_status'].count()/data.groupby([\"pub_rec_bankruptcies\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **As the publicly recorded bankcruptcies increases the Charge off/Defaulting percentage of an individual also increases which suggests there is a higher likelihood of loan getting defaulted when loan is disbursed to individual with higher publicly recorded bankruptices although we dont have a lot entries for individual with 2 publicly recorded bankruptcies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dti_bucket  loan_status\n",
       "HIGH        Charged Off     468\n",
       "            Fully Paid     2590\n",
       "LOW         Charged Off    1071\n",
       "            Fully Paid     6871\n",
       "MEDIUM      Charged Off    1010\n",
       "            Fully Paid     5831\n",
       "VERY HIGH   Charged Off    1307\n",
       "            Fully Paid     6789\n",
       "VERY LOW    Charged Off     965\n",
       "            Fully Paid     6924\n",
       "Name: defaulted, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby([\"dti_bucket\", \"loan_status\"]).defaulted.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dti_bucket\n",
       "HIGH         15.30\n",
       "LOW          13.49\n",
       "MEDIUM       14.76\n",
       "VERY HIGH    16.14\n",
       "VERY LOW     12.23\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"dti_bucket\"])['loan_status'].count()/data.groupby([\"dti_bucket\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **As the dti keeps on increasing the Charge off percentage also increases which suggests there is a higher likelihood of loan getting defaulted when loan is disbursed to individual with higher dti**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade  sub_grade\n",
       "A      A1            2.41\n",
       "       A2            4.91\n",
       "       A3            5.26\n",
       "       A4            6.22\n",
       "       A5            8.24\n",
       "B      B1            9.15\n",
       "       B2           10.78\n",
       "       B3           11.91\n",
       "       B4           13.41\n",
       "       B5           13.83\n",
       "C      C1           15.79\n",
       "       C2           16.47\n",
       "       C3           18.50\n",
       "       C4           17.64\n",
       "       C5           18.25\n",
       "D      D1           17.84\n",
       "       D2           20.81\n",
       "       D3           22.24\n",
       "       D4           23.20\n",
       "       D5           25.67\n",
       "E      E1           26.84\n",
       "       E2           25.70\n",
       "       E3           23.61\n",
       "       E4           29.88\n",
       "       E5           29.00\n",
       "F      F1           27.59\n",
       "       F2           29.61\n",
       "       F3           30.08\n",
       "       F4           33.61\n",
       "       F5           49.37\n",
       "G      G1           33.33\n",
       "       G2           40.00\n",
       "       G3           45.71\n",
       "       G4           22.50\n",
       "       G5           45.00\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data[data['loan_status'] == 'Charged Off'].groupby([\"grade\", \"sub_grade\"])['loan_status'].count()/data.groupby([\"grade\", \"sub_grade\"])['loan_status'].count()*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **When grouped by grade, subgrades, It's found that as we move to lower grades the Charge off proportions keeps on increasing which suggests there is higher chance of loans being defaulted when disbursed to individual with lower employment grades**"
   ]
  }
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