Data-Sphere EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2260701, 151)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The dataset file is read using read_csv function from pandas library\n",
    "df= pd.read_csv('./dataset/accepted_2007_to_2018Q4.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108486249"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count of number of missing values\n",
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title', 'emp_length', 'home_ownership', 'annual_inc', 'verification_status', 'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose', 'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs', 'earliest_cr_line', 'fico_range_low', 'fico_range_high', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'policy_code', 'application_type', 'annual_inc_joint', 'dti_joint', 'verification_status_joint', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_act_il', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m', 'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies', 'tax_liens', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit', 'revol_bal_joint', 'sec_app_fico_range_low', 'sec_app_fico_range_high', 'sec_app_earliest_cr_line', 'sec_app_inq_last_6mths', 'sec_app_mort_acc', 'sec_app_open_acc', 'sec_app_revol_util', 'sec_app_open_act_il', 'sec_app_num_rev_accts', 'sec_app_chargeoff_within_12_mths', 'sec_app_collections_12_mths_ex_med', 'sec_app_mths_since_last_major_derog', 'hardship_flag', 'hardship_type', 'hardship_reason', 'hardship_status', 'deferral_term', 'hardship_amount', 'hardship_start_date', 'hardship_end_date', 'payment_plan_start_date', 'hardship_length', 'hardship_dpd', 'hardship_loan_status', 'orig_projected_additional_accrued_interest', 'hardship_payoff_balance_amount', 'hardship_last_payment_amount', 'disbursement_method', 'debt_settlement_flag', 'debt_settlement_flag_date', 'settlement_status', 'settlement_date', 'settlement_amount', 'settlement_percentage', 'settlement_term']\n"
     ]
    }
   ],
   "source": [
    "#Printing all  the features of the data\n",
    "\n",
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid                                             1076751\n",
       "Current                                                 878317\n",
       "Charged Off                                             268559\n",
       "Late (31-120 days)                                       21467\n",
       "In Grace Period                                           8436\n",
       "Late (16-30 days)                                         4349\n",
       "Does not meet the credit policy. Status:Fully Paid        1988\n",
       "Does not meet the credit policy. Status:Charged Off        761\n",
       "Default                                                     40\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are so many features of this dataset but we are interested in loan status.\n",
    "#After running this, we can see that there are 9 types of loan status.\n",
    "\n",
    "df.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 151)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Here, we are interested in two types of loan status i.e. those who paid the loan and those who were charged'''\n",
    "\n",
    "df = df.loc[(df['loan_status'].isin(['Fully Paid', 'Charged Off']))]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "'''Looking at the shape of the data, we can see that there is almost half the data than original\n",
    "data and the same number of features\n",
    "Now, we peform general data processing steps first'''\n",
    "\n",
    "#1. Remove features associated with 90% missing values.\n",
    "''' \n",
    "We use pandas built in method 'isnull()' to find rows associated with missing values.\n",
    "Then ,we sum them up to count for each feature. Then these features are removed and data is \n",
    "sorted for further analysis.\n",
    "'''\n",
    "missing_df = df.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index()\n",
    "miss_90 = list(missing_df[missing_df.missing_value >= 1211000]['index'])\n",
    "print(len(miss_90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 112)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Above we can see that there are 39 features which have 200000 missing values.\n",
    "Now, we use pandas 'drop' method to remove these features.\n",
    "In this function we set the “inplace” option to 'True', which removes these features\n",
    "from original data frame df without returning anything.\n",
    "'''\n",
    "df.drop(miss_90, axis = 1, inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pymnt_plan', 'out_prncp', 'out_prncp_inv', 'policy_code', 'hardship_flag']\n"
     ]
    }
   ],
   "source": [
    "#2. Remove constant features\n",
    "#Now, we remove features that have a unique single value.We cant analyze anything beacause its variance is zero.\n",
    "\n",
    "def find_constant_features(dataFrame):\n",
    "    const_features = []\n",
    "    for column in list(dataFrame.columns):\n",
    "        if dataFrame[column].unique().size < 2:\n",
    "            const_features.append(column)\n",
    "    return const_features\n",
    "const_features = find_constant_features(df)\n",
    "print(const_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 107)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''In the above code, we create a function “find_constant_features” to identify constant features.\n",
    "The function goes through each feature and sees if it has less than two unique values. \n",
    "If so, the features are added to the constant feature list. We can also find out constant feature \n",
    "looking at the variance or standard deviation. If the feature has zero variance or standard deviation, \n",
    "we are sure that the feature has single unique value. The print statement shows that five features have\n",
    "single unique value. So we remove them using “inplace” option true below.\n",
    "'''\n",
    "df.drop(const_features, axis = 1, inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "#3. Remove duplicate features.\n",
    "'''Duplicate features are those have the same value in multiple features with the same/different name.\n",
    "To find out the duplicate features, we use this function.\n",
    "'''\n",
    "def duplicate_columns(frame):\n",
    "    groups = frame.columns.to_series().groupby(frame.dtypes).groups\n",
    "    dups = []\n",
    "    for t, v in groups.items():\n",
    "        cs = frame[v].columns\n",
    "        vs = frame[v]\n",
    "        lcs = len(cs)\n",
    "        for i in range(lcs):\n",
    "            ia = vs.iloc[:,i].values\n",
    "            for j in range(i+1, lcs):\n",
    "                ja = vs.iloc[:,j].values\n",
    "                if np.array_equal(ia, ja):\n",
    "                    dups.append(cs[i])\n",
    "                    break\n",
    "    return dups\n",
    "duplicate_cols = duplicate_columns(df)\n",
    "print(duplicate_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68407277</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.99</td>\n",
       "      <td>123.03</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>leadman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68355089</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>820.28</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68341763</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.78</td>\n",
       "      <td>432.66</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>truck driver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68476807</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.45</td>\n",
       "      <td>289.91</td>\n",
       "      <td>F</td>\n",
       "      <td>F1</td>\n",
       "      <td>Contract Specialist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68426831</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.44</td>\n",
       "      <td>405.18</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>Veterinary Tecnician</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  loan_amnt  funded_amnt  funded_amnt_inv        term  int_rate  \\\n",
       "0  68407277     3600.0       3600.0           3600.0   36 months     13.99   \n",
       "1  68355089    24700.0      24700.0          24700.0   36 months     11.99   \n",
       "2  68341763    20000.0      20000.0          20000.0   60 months     10.78   \n",
       "4  68476807    10400.0      10400.0          10400.0   60 months     22.45   \n",
       "5  68426831    11950.0      11950.0          11950.0   36 months     13.44   \n",
       "\n",
       "   installment grade sub_grade             emp_title  \n",
       "0       123.03     C        C4               leadman  \n",
       "1       820.28     C        C1              Engineer  \n",
       "2       432.66     B        B4          truck driver  \n",
       "4       289.91     F        F1   Contract Specialist  \n",
       "5       405.18     C        C3  Veterinary Tecnician  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 0: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have, duplicate columns, if we check the dataset, we can see that loan_amnt, funded_amnt and funded_amnt_inv have same values\n",
    "#So, we drop such columns as there is no need of duplicates\n",
    "df.drop(['funded_amnt','funded_amnt_inv'], axis=1, inplace=True)\n",
    "\n",
    "#4. Remove Duplicate rows.\n",
    "'''Here, we remove all the duplicate rows using pandas built-in function 'drop_duplicates()' '''\n",
    "df.drop_duplicates(inplace= True)\n",
    "\n",
    "#Remember to do this at end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 105)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, we have performed some basic processing, the shape of the data is:\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature(col_name, isContinuous):\n",
    "    \"\"\"\n",
    "    Visualize a variable with and without faceting on the loan status.\n",
    "    - col_name is the variable name in the dataframe\n",
    "    - full_name is the full variable name\n",
    "    - continuous is True if the variable is continuous, False otherwise\n",
    "    \"\"\"\n",
    "    f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)\n",
    "    \n",
    "    # Plot without loan status\n",
    "    if isContinuous:\n",
    "        sns.distplot(df.loc[df[col_name].notnull(), col_name], kde=False, ax=ax1)\n",
    "    else:\n",
    "        sns.countplot(df[col_name], order=sorted(df[col_name].unique()), color='#5975A4', saturation=1, ax=ax1)\n",
    "    ax1.set_xlabel(col_name)\n",
    "    ax1.set_ylabel('Count')\n",
    "    ax1.set_title(col_name)\n",
    "    plt.xticks(rotation = 90)\n",
    "# Plot with loan status\n",
    "    if isContinuous:\n",
    "        sns.boxplot(x=col_name, y='loan_status', data=df, ax=ax2)\n",
    "        ax2.set_ylabel('')\n",
    "        ax2.set_title(col_name + ' by Loan Status')\n",
    "    else:\n",
    "        data = df.groupby(col_name)['loan_status'].value_counts(normalize=True).to_frame('proportion').reset_index()        \n",
    "        sns.barplot(x = col_name, y = 'proportion', hue= \"loan_status\", data = data, saturation=1, ax=ax2)\n",
    "        ax2.set_ylabel('Loan fraction')\n",
    "        ax2.set_title('Loan status')\n",
    "        plt.xticks(rotation = 90)\n",
    "    ax2.set_xlabel(col_name)\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68407277</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.99</td>\n",
       "      <td>123.03</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>leadman</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68355089</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>820.28</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68341763</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.78</td>\n",
       "      <td>432.66</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>truck driver</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68476807</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.45</td>\n",
       "      <td>289.91</td>\n",
       "      <td>F</td>\n",
       "      <td>F1</td>\n",
       "      <td>Contract Specialist</td>\n",
       "      <td>3 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68426831</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.44</td>\n",
       "      <td>405.18</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>Veterinary Tecnician</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  loan_amnt        term  int_rate  installment grade sub_grade  \\\n",
       "0  68407277     3600.0   36 months     13.99       123.03     C        C4   \n",
       "1  68355089    24700.0   36 months     11.99       820.28     C        C1   \n",
       "2  68341763    20000.0   60 months     10.78       432.66     B        B4   \n",
       "4  68476807    10400.0   60 months     22.45       289.91     F        F1   \n",
       "5  68426831    11950.0   36 months     13.44       405.18     C        C3   \n",
       "\n",
       "              emp_title emp_length home_ownership  \n",
       "0               leadman  10+ years       MORTGAGE  \n",
       "1              Engineer  10+ years       MORTGAGE  \n",
       "2          truck driver  10+ years       MORTGAGE  \n",
       "4   Contract Specialist    3 years       MORTGAGE  \n",
       "5  Veterinary Tecnician    4 years           RENT  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "The plot_feature function accepts two parameters; the first one is the feature name and the second one is the\n",
    "boolean variable: whether the feature is continuous or categorical. If the feature is continuous then function\n",
    "plot two subplots: (1) the distribution of the feature and (2) the two boxplots based on loan_status class.\n",
    "On the other hand, if the feature is categorical, then the first subplot is count plot and the second subplot is\n",
    "based on the group_by loan_status classes.\n",
    "\"\"\"\n",
    "\n",
    "df.iloc[0:5, 0: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1556"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Column loan_amnt:\n",
    "len(df.loan_amnt.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('loan_amnt', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    1020743\n",
       " 60 months     324567\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All loans are not unique. Some loans appear many times and some do not. So, the company has certain range to lend.\n",
    "\n",
    "#For Column term\n",
    "\n",
    "df.term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('term', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "654"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Column Interest rate i.e. int_rate\n",
    "\n",
    "len(df.int_rate.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('int_rate', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    392741\n",
       "C    381686\n",
       "A    235090\n",
       "D    200953\n",
       "E     93650\n",
       "F     32058\n",
       "G      9132\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From above graph we can see that those who were charged off, had to pay high interest rate\n",
    "\n",
    "#For Column Grade and sub-grade\n",
    "\n",
    "df.grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#From above code, we know that sub-grade has 35 unique categories\n",
    "plot_feature('grade', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub_grade.value_counts()\n",
    "len(df.sub_grade.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('sub_grade', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "378353"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From above comparisons, we can see that sub-grade and grade both have same distribution and shape.\n",
    "# But sub_grade has more categories or more data\n",
    "\n",
    "#For Column: Employment title\n",
    "\n",
    "len(df.emp_title.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From above value, we can conclude that emp_title has too many unique values, which may not be good for prediction\n",
    "#   So, we remove this feature along with other extra non-relavent features.\n",
    "\n",
    "features_to_be_removed=[]\n",
    "features_to_be_removed.extend(['emp_title', 'id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>url</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C4</td>\n",
       "      <td>leadman</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>190xx</td>\n",
       "      <td>PA</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>small_business</td>\n",
       "      <td>Business</td>\n",
       "      <td>577xx</td>\n",
       "      <td>SD</td>\n",
       "      <td>16.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Dec-1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B4</td>\n",
       "      <td>truck driver</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>NaN</td>\n",
       "      <td>605xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>10.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F1</td>\n",
       "      <td>Contract Specialist</td>\n",
       "      <td>3 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>104433.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>Major purchase</td>\n",
       "      <td>174xx</td>\n",
       "      <td>PA</td>\n",
       "      <td>25.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Jun-1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C3</td>\n",
       "      <td>Veterinary Tecnician</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-2015</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>300xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-1987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sub_grade             emp_title emp_length home_ownership  annual_inc  \\\n",
       "0        C4               leadman  10+ years       MORTGAGE     55000.0   \n",
       "1        C1              Engineer  10+ years       MORTGAGE     65000.0   \n",
       "2        B4          truck driver  10+ years       MORTGAGE     63000.0   \n",
       "4        F1   Contract Specialist    3 years       MORTGAGE    104433.0   \n",
       "5        C3  Veterinary Tecnician    4 years           RENT     34000.0   \n",
       "\n",
       "  verification_status   issue_d loan_status  \\\n",
       "0        Not Verified  Dec-2015  Fully Paid   \n",
       "1        Not Verified  Dec-2015  Fully Paid   \n",
       "2        Not Verified  Dec-2015  Fully Paid   \n",
       "4     Source Verified  Dec-2015  Fully Paid   \n",
       "5     Source Verified  Dec-2015  Fully Paid   \n",
       "\n",
       "                                                 url             purpose  \\\n",
       "0  https://lendingclub.com/browse/loanDetail.acti...  debt_consolidation   \n",
       "1  https://lendingclub.com/browse/loanDetail.acti...      small_business   \n",
       "2  https://lendingclub.com/browse/loanDetail.acti...    home_improvement   \n",
       "4  https://lendingclub.com/browse/loanDetail.acti...      major_purchase   \n",
       "5  https://lendingclub.com/browse/loanDetail.acti...  debt_consolidation   \n",
       "\n",
       "                title zip_code addr_state    dti  delinq_2yrs earliest_cr_line  \n",
       "0  Debt consolidation    190xx         PA   5.91          0.0         Aug-2003  \n",
       "1            Business    577xx         SD  16.06          1.0         Dec-1999  \n",
       "2                 NaN    605xx         IL  10.78          0.0         Aug-2000  \n",
       "4      Major purchase    174xx         PA  25.37          1.0         Jun-1998  \n",
       "5  Debt consolidation    300xx         GA  10.20          0.0         Oct-1987  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, we check next 10 Columns or features using iloc function\n",
    "\n",
    "df.iloc[0:5, 6: 22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10+ years    442199\n",
       "2 years      121743\n",
       "< 1 year     108061\n",
       "3 years      107597\n",
       "1 year        88494\n",
       "5 years       84154\n",
       "4 years       80556\n",
       "6 years       62733\n",
       "8 years       60701\n",
       "7 years       59624\n",
       "9 years       50937\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Column emp_length\n",
    "df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The column emp_length has NaN values, so to remove it we fill it with previous column values.\n",
    "\n",
    "df.emp_length.fillna(method='ffill',inplace=True)\n",
    "df['emp_length'].replace(to_replace='[^0-9]+', value='', inplace=True, regex=True)\n",
    "df['emp_length'] = df['emp_length'].astype(int)\n",
    "plot_feature('emp_length', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    665579\n",
       "RENT        534421\n",
       "OWN         144832\n",
       "ANY            286\n",
       "OTHER          144\n",
       "NONE            48\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Home Ownership\n",
    "df.home_ownership.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('home_ownership', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source Verified    521273\n",
       "Verified           418336\n",
       "Not Verified       405701\n",
       "Name: verification_status, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature verification status\n",
    "\n",
    "df.verification_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.verification_status = df.verification_status.map(lambda x: 1 if x == 'Not Verified' else 0)\n",
    "plot_feature('verification_status', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mar-2016    48937\n",
       "Oct-2015    42986\n",
       "Jul-2015    41053\n",
       "Dec-2015    39002\n",
       "Oct-2014    36072\n",
       "            ...  \n",
       "Aug-2007       33\n",
       "Sep-2008       32\n",
       "Jul-2007       30\n",
       "Sep-2007       18\n",
       "Jun-2007        1\n",
       "Name: issue_d, Length: 139, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In above graph, we can see from right plot that those applicants whose source was verified are more likely to be charged off.\n",
    "\n",
    "\n",
    "#Feature issue_d\n",
    "\n",
    "\n",
    "df.issue_d.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We want to keep our data independent of year.So,we remove years and consider only month\n",
    "\n",
    "df['issue_month'] = pd.Series(df.issue_d).str.replace(r'-\\d+', '')\n",
    "plot_feature('issue_month', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    780321\n",
       "credit_card           295279\n",
       "home_improvement       87504\n",
       "other                  77875\n",
       "major_purchase         29425\n",
       "medical                15554\n",
       "small_business         15416\n",
       "car                    14585\n",
       "moving                  9480\n",
       "vacation                9065\n",
       "house                   7253\n",
       "wedding                 2294\n",
       "renewable_energy         933\n",
       "educational              326\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Purpose\n",
    "df.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('purpose', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here, we can see who took loan for what purpose and which category of people paid full or were charged\n",
    "\n",
    "\n",
    "#Feature addr_state\n",
    "\n",
    "len(df.addr_state.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('addr_state',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If we look at the plot, we see that certain states applicants paid back more than others.\n",
    "\n",
    "#Finally, removing remaining features\n",
    "features_to_be_removed.extend(['url','title','zip_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-2003</td>\n",
       "      <td>675.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Dec-1999</td>\n",
       "      <td>715.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21470.0</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-2000</td>\n",
       "      <td>695.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7869.0</td>\n",
       "      <td>56.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Jun-1998</td>\n",
       "      <td>695.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21929.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-1987</td>\n",
       "      <td>690.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8822.0</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   delinq_2yrs earliest_cr_line  fico_range_low  fico_range_high  \\\n",
       "0          0.0         Aug-2003           675.0            679.0   \n",
       "1          1.0         Dec-1999           715.0            719.0   \n",
       "2          0.0         Aug-2000           695.0            699.0   \n",
       "4          1.0         Jun-1998           695.0            699.0   \n",
       "5          0.0         Oct-1987           690.0            694.0   \n",
       "\n",
       "   inq_last_6mths  mths_since_last_delinq  mths_since_last_record  open_acc  \\\n",
       "0             1.0                    30.0                     NaN       7.0   \n",
       "1             4.0                     6.0                     NaN      22.0   \n",
       "2             0.0                     NaN                     NaN       6.0   \n",
       "4             3.0                    12.0                     NaN      12.0   \n",
       "5             0.0                     NaN                     NaN       5.0   \n",
       "\n",
       "   pub_rec  revol_bal  revol_util  \n",
       "0      0.0     2765.0        29.7  \n",
       "1      0.0    21470.0        19.2  \n",
       "2      0.0     7869.0        56.2  \n",
       "4      0.0    21929.0        64.5  \n",
       "5      0.0     8822.0        68.4  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 20: 31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['issue_d','earliest_cr_line', 'mths_since_last_delinq', 'mths_since_last_record', 'inq_last_6mths', \n",
    "                               'mths_since_last_delinq', 'mths_since_last_record'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>w</td>\n",
       "      <td>4421.723917</td>\n",
       "      <td>4421.72</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>821.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jan-2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>w</td>\n",
       "      <td>25679.660000</td>\n",
       "      <td>25679.66</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>979.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>w</td>\n",
       "      <td>22705.924294</td>\n",
       "      <td>22705.92</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2705.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>w</td>\n",
       "      <td>11740.500000</td>\n",
       "      <td>11740.50</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>1340.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jul-2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>w</td>\n",
       "      <td>13708.948530</td>\n",
       "      <td>13708.95</td>\n",
       "      <td>11950.0</td>\n",
       "      <td>1758.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>May-2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   revol_util  total_acc initial_list_status   total_pymnt  total_pymnt_inv  \\\n",
       "0        29.7       13.0                   w   4421.723917          4421.72   \n",
       "1        19.2       38.0                   w  25679.660000         25679.66   \n",
       "2        56.2       18.0                   w  22705.924294         22705.92   \n",
       "4        64.5       35.0                   w  11740.500000         11740.50   \n",
       "5        68.4        6.0                   w  13708.948530         13708.95   \n",
       "\n",
       "   total_rec_prncp  total_rec_int  total_rec_late_fee  recoveries  \\\n",
       "0           3600.0         821.72                 0.0         0.0   \n",
       "1          24700.0         979.66                 0.0         0.0   \n",
       "2          20000.0        2705.92                 0.0         0.0   \n",
       "4          10400.0        1340.50                 0.0         0.0   \n",
       "5          11950.0        1758.95                 0.0         0.0   \n",
       "\n",
       "   collection_recovery_fee last_pymnt_d  \n",
       "0                      0.0     Jan-2019  \n",
       "1                      0.0     Jun-2016  \n",
       "2                      0.0     Jun-2017  \n",
       "4                      0.0     Jul-2016  \n",
       "5                      0.0     May-2017  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 30: 41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    784010\n",
       "f    561300\n",
       "Name: initial_list_status, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.initial_list_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.initial_list_status = df.initial_list_status.map(lambda x: 1 if x== 'w' else 0)\n",
    "#plot_feature('initial_list_status',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', \n",
    "                               'total_rec_int', 'total_rec_late_fee'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>last_fico_range_high</th>\n",
       "      <th>last_fico_range_low</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122.67</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>564.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>144904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>926.35</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>699.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15813.30</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>704.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>189699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10128.96</td>\n",
       "      <td>Mar-2018</td>\n",
       "      <td>704.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>331730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7653.56</td>\n",
       "      <td>May-2017</td>\n",
       "      <td>759.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12798.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   last_pymnt_amnt last_credit_pull_d  last_fico_range_high  \\\n",
       "0           122.67           Mar-2019                 564.0   \n",
       "1           926.35           Mar-2019                 699.0   \n",
       "2         15813.30           Mar-2019                 704.0   \n",
       "4         10128.96           Mar-2018                 704.0   \n",
       "5          7653.56           May-2017                 759.0   \n",
       "\n",
       "   last_fico_range_low  collections_12_mths_ex_med  \\\n",
       "0                560.0                         0.0   \n",
       "1                695.0                         0.0   \n",
       "2                700.0                         0.0   \n",
       "4                700.0                         0.0   \n",
       "5                755.0                         0.0   \n",
       "\n",
       "   mths_since_last_major_derog application_type  acc_now_delinq  tot_coll_amt  \\\n",
       "0                         30.0       Individual             0.0         722.0   \n",
       "1                          NaN       Individual             0.0           0.0   \n",
       "2                          NaN        Joint App             0.0           0.0   \n",
       "4                          NaN       Individual             0.0           0.0   \n",
       "5                          NaN       Individual             0.0           0.0   \n",
       "\n",
       "   tot_cur_bal  \n",
       "0     144904.0  \n",
       "1     204396.0  \n",
       "2     189699.0  \n",
       "4     331730.0  \n",
       "5      12798.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 41: 51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['recoveries', 'collection_recovery_fee', 'last_pymnt_d', \n",
    "                               'last_pymnt_amnt', 'last_credit_pull_d', 'last_fico_range_high',\n",
    "                               'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18005.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10827.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73839.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4522.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   open_acc_6m  open_act_il  open_il_12m  open_il_24m  mths_since_rcnt_il  \\\n",
       "0          2.0          2.0          0.0          1.0                21.0   \n",
       "1          1.0          1.0          0.0          1.0                19.0   \n",
       "2          0.0          1.0          0.0          4.0                19.0   \n",
       "4          1.0          3.0          0.0          3.0                14.0   \n",
       "5          0.0          1.0          0.0          0.0               338.0   \n",
       "\n",
       "   total_bal_il  il_util  open_rv_12m  open_rv_24m  max_bal_bc  \n",
       "0        4981.0     36.0          3.0          3.0       722.0  \n",
       "1       18005.0     73.0          2.0          3.0      6472.0  \n",
       "2       10827.0     73.0          0.0          2.0      2081.0  \n",
       "4       73839.0     84.0          4.0          7.0      9702.0  \n",
       "5        3976.0     99.0          0.0          0.0      4522.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 51: 61]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend([ 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', \n",
    "                              'total_rev_hi_lim', 'avg_cur_bal', 'bc_open_to_buy', \n",
    "                               'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.0</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20701.0</td>\n",
       "      <td>1506.0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>111800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9733.0</td>\n",
       "      <td>57830.0</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31617.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.0</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27644.0</td>\n",
       "      <td>4567.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>76.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>844.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   all_util  total_rev_hi_lim  inq_fi  total_cu_tl  inq_last_12m  \\\n",
       "0      34.0            9300.0     3.0          1.0           4.0   \n",
       "1      29.0          111800.0     0.0          0.0           6.0   \n",
       "2      65.0           14000.0     2.0          5.0           1.0   \n",
       "4      78.0           34000.0     2.0          1.0           3.0   \n",
       "5      76.0           12900.0     0.0          0.0           0.0   \n",
       "\n",
       "   acc_open_past_24mths  avg_cur_bal  bc_open_to_buy  bc_util  \\\n",
       "0                   4.0      20701.0          1506.0     37.2   \n",
       "1                   4.0       9733.0         57830.0     27.1   \n",
       "2                   6.0      31617.0          2737.0     55.9   \n",
       "4                  10.0      27644.0          4567.0     77.5   \n",
       "5                   0.0       2560.0           844.0     91.0   \n",
       "\n",
       "   chargeoff_within_12_mths  \n",
       "0                       0.0  \n",
       "1                       0.0  \n",
       "2                       0.0  \n",
       "4                       0.0  \n",
       "5                       0.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 61: 71]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', \n",
    "                               'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', \n",
    "                               'mths_since_recent_bc', 'mths_since_recent_bc_dlq', \n",
    "                               'mths_since_recent_inq', 'mths_since_recent_revol_delinq', \n",
    "                               'num_accts_ever_120_pd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_bc_dlq</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>mths_since_recent_revol_delinq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   delinq_amnt  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  \\\n",
       "0          0.0               148.0                 128.0   \n",
       "1          0.0               113.0                 192.0   \n",
       "2          0.0               125.0                 184.0   \n",
       "4          0.0               128.0                 210.0   \n",
       "5          0.0               338.0                  54.0   \n",
       "\n",
       "   mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  \\\n",
       "0                    3.0             3.0       1.0                   4.0   \n",
       "1                    2.0             2.0       4.0                   2.0   \n",
       "2                   14.0            14.0       5.0                 101.0   \n",
       "4                    4.0             4.0       6.0                   4.0   \n",
       "5                   32.0            32.0       0.0                  36.0   \n",
       "\n",
       "   mths_since_recent_bc_dlq  mths_since_recent_inq  \\\n",
       "0                      69.0                    4.0   \n",
       "1                       NaN                    0.0   \n",
       "2                       NaN                   10.0   \n",
       "4                      12.0                    1.0   \n",
       "5                       NaN                    NaN   \n",
       "\n",
       "   mths_since_recent_revol_delinq  \n",
       "0                            69.0  \n",
       "1                             6.0  \n",
       "2                             NaN  \n",
       "4                            12.0  \n",
       "5                             NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 71: 81]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', \n",
    "                               'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', \n",
    "                               'num_rev_accts', 'num_rev_tl_bal_gt_0', \n",
    "                               'num_sats', 'num_tl_120dpd_2m'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>17.0</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_accts_ever_120_pd  num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  \\\n",
       "0                    2.0             2.0              4.0          2.0   \n",
       "1                    0.0             5.0              5.0         13.0   \n",
       "2                    0.0             2.0              3.0          2.0   \n",
       "4                    0.0             4.0              6.0          5.0   \n",
       "5                    0.0             2.0              3.0          2.0   \n",
       "\n",
       "   num_bc_tl  num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "0        5.0        3.0            4.0            9.0                  4.0   \n",
       "1       17.0        6.0           20.0           27.0                  5.0   \n",
       "2        4.0        6.0            4.0            7.0                  3.0   \n",
       "4        9.0       10.0            7.0           19.0                  6.0   \n",
       "5        2.0        2.0            4.0            4.0                  3.0   \n",
       "\n",
       "   num_sats  \n",
       "0       7.0  \n",
       "1      22.0  \n",
       "2       6.0  \n",
       "4      12.0  \n",
       "5       5.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 81: 91]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', \n",
    "                               'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tot_hi_cred_lim', \n",
    "                               'total_bal_ex_mort', 'total_bc_limit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178050.0</td>\n",
       "      <td>7746.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>13734.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>97.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>314017.0</td>\n",
       "      <td>39475.0</td>\n",
       "      <td>79300.0</td>\n",
       "      <td>24667.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218418.0</td>\n",
       "      <td>18696.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>14877.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>439570.0</td>\n",
       "      <td>95768.0</td>\n",
       "      <td>20300.0</td>\n",
       "      <td>88097.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>12798.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "0       7.0               0.0           0.0                 0.0   \n",
       "1      22.0               0.0           0.0                 0.0   \n",
       "2       6.0               0.0           0.0                 0.0   \n",
       "4      12.0               0.0           0.0                 0.0   \n",
       "5       5.0               0.0           0.0                 0.0   \n",
       "\n",
       "   num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0                 3.0            76.9               0.0                   0.0   \n",
       "1                 2.0            97.4               7.7                   0.0   \n",
       "2                 0.0           100.0              50.0                   0.0   \n",
       "4                 4.0            96.6              60.0                   0.0   \n",
       "5                 0.0           100.0             100.0                   0.0   \n",
       "\n",
       "   tax_liens  tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0        0.0         178050.0             7746.0          2400.0   \n",
       "1        0.0         314017.0            39475.0         79300.0   \n",
       "2        0.0         218418.0            18696.0          6200.0   \n",
       "4        0.0         439570.0            95768.0         20300.0   \n",
       "5        0.0          16900.0            12798.0          9400.0   \n",
       "\n",
       "   total_il_high_credit_limit disbursement_method debt_settlement_flag  \\\n",
       "0                     13734.0                Cash                    N   \n",
       "1                     24667.0                Cash                    N   \n",
       "2                     14877.0                Cash                    N   \n",
       "4                     88097.0                Cash                    N   \n",
       "5                      4000.0                Cash                    N   \n",
       "\n",
       "  issue_month  \n",
       "0         Dec  \n",
       "1         Dec  \n",
       "2         Dec  \n",
       "4         Dec  \n",
       "5         Dec  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 90:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    1312034\n",
       "Y      33276\n",
       "Name: debt_settlement_flag, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.debt_settlement_flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.debt_settlement_flag = df.debt_settlement_flag.map(lambda x: 0 if x == 'N' else 1)\n",
    "plot_feature('debt_settlement_flag',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['debt_settlement_flag', 'total_il_high_credit_limit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, after evaluating and checking all columns, we check the removed features\n",
    "\n",
    "len(set(features_to_be_removed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['emp_title', 'id', 'url', 'title', 'zip_code', 'issue_d', 'earliest_cr_line', 'mths_since_last_delinq', 'mths_since_last_record', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'debt_settlement_flag', 'total_il_high_credit_limit']\n"
     ]
    }
   ],
   "source": [
    "#So, we have 61 features or columns which need to be removed\n",
    "print(features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 44)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally we drop the above features\n",
    "df_selected = df.drop(list(set(features_to_be_removed)), axis = 1)\n",
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.99</td>\n",
       "      <td>123.03</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24700.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>820.28</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.78</td>\n",
       "      <td>432.66</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.45</td>\n",
       "      <td>289.91</td>\n",
       "      <td>F</td>\n",
       "      <td>F1</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>104433.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.44</td>\n",
       "      <td>405.18</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>4</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  emp_length  \\\n",
       "0     3600.0   36 months     13.99       123.03     C        C4          10   \n",
       "1    24700.0   36 months     11.99       820.28     C        C1          10   \n",
       "2    20000.0   60 months     10.78       432.66     B        B4          10   \n",
       "4    10400.0   60 months     22.45       289.91     F        F1           3   \n",
       "5    11950.0   36 months     13.44       405.18     C        C3           4   \n",
       "\n",
       "  home_ownership  annual_inc  verification_status  \n",
       "0       MORTGAGE     55000.0                    1  \n",
       "1       MORTGAGE     65000.0                    1  \n",
       "2       MORTGAGE     63000.0                    1  \n",
       "4       MORTGAGE    104433.0                    0  \n",
       "5           RENT     34000.0                    0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5, 0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>PA</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>675.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>small_business</td>\n",
       "      <td>SD</td>\n",
       "      <td>16.06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>IL</td>\n",
       "      <td>10.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>PA</td>\n",
       "      <td>25.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>GA</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8822.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  loan_status             purpose addr_state    dti  delinq_2yrs  \\\n",
       "0  Fully Paid  debt_consolidation         PA   5.91          0.0   \n",
       "1  Fully Paid      small_business         SD  16.06          1.0   \n",
       "2  Fully Paid    home_improvement         IL  10.78          0.0   \n",
       "4  Fully Paid      major_purchase         PA  25.37          1.0   \n",
       "5  Fully Paid  debt_consolidation         GA  10.20          0.0   \n",
       "\n",
       "   fico_range_low  fico_range_high  open_acc  pub_rec  revol_bal  \n",
       "0           675.0            679.0       7.0      0.0     2765.0  \n",
       "1           715.0            719.0      22.0      0.0    21470.0  \n",
       "2           695.0            699.0       6.0      0.0     7869.0  \n",
       "4           695.0            699.0      12.0      0.0    21929.0  \n",
       "5           690.0            694.0       5.0      0.0     8822.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5, 10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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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>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4981.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3976.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   revol_util  total_acc  initial_list_status application_type  open_acc_6m  \\\n",
       "0        29.7       13.0                    1       Individual          2.0   \n",
       "1        19.2       38.0                    1       Individual          1.0   \n",
       "2        56.2       18.0                    1        Joint App          0.0   \n",
       "4        64.5       35.0                    1       Individual          1.0   \n",
       "5        68.4        6.0                    1       Individual          0.0   \n",
       "\n",
       "   open_act_il  open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  \n",
       "0          2.0          0.0          1.0                21.0        4981.0  \n",
       "1          1.0          0.0          1.0                19.0       18005.0  \n",
       "2          1.0          0.0          4.0                19.0       10827.0  \n",
       "4          3.0          0.0          3.0                14.0       73839.0  \n",
       "5          1.0          0.0          0.0               338.0        3976.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5, 20:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>722.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6472.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2081.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9702.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4522.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   il_util  open_rv_12m  open_rv_24m  max_bal_bc  all_util  inq_fi  \\\n",
       "0     36.0          3.0          3.0       722.0      34.0     3.0   \n",
       "1     73.0          2.0          3.0      6472.0      29.0     0.0   \n",
       "2     73.0          0.0          2.0      2081.0      65.0     2.0   \n",
       "4     84.0          4.0          7.0      9702.0      78.0     2.0   \n",
       "5     99.0          0.0          0.0      4522.0      76.0     0.0   \n",
       "\n",
       "   total_cu_tl  inq_last_12m  acc_open_past_24mths  mort_acc  \\\n",
       "0          1.0           4.0                   4.0       1.0   \n",
       "1          0.0           6.0                   4.0       4.0   \n",
       "2          5.0           1.0                   6.0       5.0   \n",
       "4          1.0           3.0                  10.0       6.0   \n",
       "5          0.0           0.0                   0.0       0.0   \n",
       "\n",
       "   pub_rec_bankruptcies  tax_liens disbursement_method issue_month  \n",
       "0                   0.0        0.0                Cash         Dec  \n",
       "1                   0.0        0.0                Cash         Dec  \n",
       "2                   0.0        0.0                Cash         Dec  \n",
       "4                   0.0        0.0                Cash         Dec  \n",
       "5                   0.0        0.0                Cash         Dec  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5, 30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed2=(['dti','delinq_2yrs','open_acc_6m','open_act_il','open_il_12m','open_il_24m','mths_since_rcnt_il',\n",
    "                               'il_util','open_rv_12m','open_rv_24m','inq_fi','total_cu_tl',\n",
    "                               'inq_last_12m','acc_open_past_24mths','mort_acc','pub_rec_bankruptcies','tax_liens'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 27)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally we drop the above features\n",
    "df_selected = df_selected.drop(list(set(features_to_be_removed2)), axis = 1)\n",
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 24)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_be_removed3=(['total_bal_il','max_bal_bc','all_util'])\n",
    "df_selected = df_selected.drop(list(set(features_to_be_removed3)), axis = 1)\n",
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>missing_value</th>\n",
       "      <th>feature_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>emp_length</td>\n",
       "      <td>0</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>initial_list_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>verification_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pub_rec</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>open_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>installment</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>application_type</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>addr_state</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>disbursement_method</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>purpose</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>home_ownership</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>term</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>loan_status</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>issue_month</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>857</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  index  missing_value feature_type\n",
       "15           emp_length              0        int32\n",
       "2   initial_list_status              0        int64\n",
       "12  verification_status              0        int64\n",
       "0             loan_amnt              0      float64\n",
       "3             total_acc              0      float64\n",
       "4             revol_bal              0      float64\n",
       "5               pub_rec              0      float64\n",
       "6              open_acc              0      float64\n",
       "7       fico_range_high              0      float64\n",
       "8        fico_range_low              0      float64\n",
       "13           annual_inc              0      float64\n",
       "18          installment              0      float64\n",
       "19             int_rate              0      float64\n",
       "1      application_type              0       object\n",
       "9            addr_state              0       object\n",
       "10  disbursement_method              0       object\n",
       "11              purpose              0       object\n",
       "14       home_ownership              0       object\n",
       "16            sub_grade              0       object\n",
       "17                grade              0       object\n",
       "20                 term              0       object\n",
       "21          loan_status              0       object\n",
       "22          issue_month              0       object\n",
       "23           revol_util            857      float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets check if there are missing values in the above features\n",
    "df_dtypes = pd.merge(df_selected.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index(),\n",
    "         df_selected.dtypes.to_frame('feature_type').reset_index(),\n",
    "         on = 'index',\n",
    "         how = 'inner')\n",
    "\n",
    "df_dtypes.sort_values(['missing_value', 'feature_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From above code, we see that 'desc' and 'revol_util' have missing values.\n",
    "#Filling the 'revol_util' column's missing values with the mean\n",
    "df_selected['revol_util']=df_selected['revol_util'].fillna(df_selected['revol_util'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>index</th>\n",
       "      <th>missing_value</th>\n",
       "      <th>feature_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>emp_length</td>\n",
       "      <td>0</td>\n",
       "      <td>int32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>initial_list_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>verification_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pub_rec</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>open_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>installment</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>application_type</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>addr_state</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>purpose</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>loan_status</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>home_ownership</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>term</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>disbursement_method</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>issue_month</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  index  missing_value feature_type\n",
       "16           emp_length              0        int32\n",
       "2   initial_list_status              0        int64\n",
       "13  verification_status              0        int64\n",
       "0             loan_amnt              0      float64\n",
       "3             total_acc              0      float64\n",
       "4            revol_util              0      float64\n",
       "5             revol_bal              0      float64\n",
       "6               pub_rec              0      float64\n",
       "7              open_acc              0      float64\n",
       "8       fico_range_high              0      float64\n",
       "9        fico_range_low              0      float64\n",
       "14           annual_inc              0      float64\n",
       "19          installment              0      float64\n",
       "20             int_rate              0      float64\n",
       "1      application_type              0       object\n",
       "10           addr_state              0       object\n",
       "11              purpose              0       object\n",
       "12          loan_status              0       object\n",
       "15       home_ownership              0       object\n",
       "17            sub_grade              0       object\n",
       "18                grade              0       object\n",
       "21                 term              0       object\n",
       "22  disbursement_method              0       object\n",
       "23          issue_month              0       object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets check if there are missing values in the above features\n",
    "df_dtypes = pd.merge(df_selected.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index(),\n",
    "         df_selected.dtypes.to_frame('feature_type').reset_index(),\n",
    "         on = 'index',\n",
    "         how = 'inner')\n",
    "\n",
    "df_dtypes.sort_values(['missing_value', 'feature_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 24)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>...</th>\n",
       "      <th>fico_range_high</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>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.99</td>\n",
       "      <td>123.03</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>679.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24700.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>820.28</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>719.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21470.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>10.78</td>\n",
       "      <td>432.66</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>10</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>699.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7869.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10400.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>22.45</td>\n",
       "      <td>289.91</td>\n",
       "      <td>F</td>\n",
       "      <td>F1</td>\n",
       "      <td>3</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>104433.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>699.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21929.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.44</td>\n",
       "      <td>405.18</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>4</td>\n",
       "      <td>RENT</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>694.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8822.0</td>\n",
       "      <td>68.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Cash</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt        term  int_rate  installment grade sub_grade  emp_length  \\\n",
       "0     3600.0   36 months     13.99       123.03     C        C4          10   \n",
       "1    24700.0   36 months     11.99       820.28     C        C1          10   \n",
       "2    20000.0   60 months     10.78       432.66     B        B4          10   \n",
       "4    10400.0   60 months     22.45       289.91     F        F1           3   \n",
       "5    11950.0   36 months     13.44       405.18     C        C3           4   \n",
       "\n",
       "  home_ownership  annual_inc  verification_status  ... fico_range_high  \\\n",
       "0       MORTGAGE     55000.0                    1  ...           679.0   \n",
       "1       MORTGAGE     65000.0                    1  ...           719.0   \n",
       "2       MORTGAGE     63000.0                    1  ...           699.0   \n",
       "4       MORTGAGE    104433.0                    0  ...           699.0   \n",
       "5           RENT     34000.0                    0  ...           694.0   \n",
       "\n",
       "  open_acc pub_rec  revol_bal  revol_util  total_acc  initial_list_status  \\\n",
       "0      7.0     0.0     2765.0        29.7       13.0                    1   \n",
       "1     22.0     0.0    21470.0        19.2       38.0                    1   \n",
       "2      6.0     0.0     7869.0        56.2       18.0                    1   \n",
       "4     12.0     0.0    21929.0        64.5       35.0                    1   \n",
       "5      5.0     0.0     8822.0        68.4        6.0                    1   \n",
       "\n",
       "   application_type  disbursement_method  issue_month  \n",
       "0        Individual                 Cash          Dec  \n",
       "1        Individual                 Cash          Dec  \n",
       "2         Joint App                 Cash          Dec  \n",
       "4        Individual                 Cash          Dec  \n",
       "5        Individual                 Cash          Dec  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing all extra strings from certain columns\n",
    "df_selected.term = df_selected.term.str.replace('months', '').astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding\n",
    "#Now, we encode few of the categorical features'''\n",
    "from pandas.api.types import CategoricalDtype\n",
    "df_selected.issue_month = df_selected.issue_month.astype(CategoricalDtype(categories=np.unique(df_selected.issue_month))).cat.codes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.purpose = df_selected.purpose.astype(CategoricalDtype(categories=np.unique(df_selected.purpose))).cat.codes\n",
    "df_selected.home_ownership = df_selected.home_ownership.astype(CategoricalDtype(categories = np.unique(df_selected.home_ownership))).cat.codes\n",
    "df_selected.grade = df_selected.grade.astype(CategoricalDtype(categories = np.unique(df_selected.grade))).cat.codes\n",
    "df_selected.sub_grade = df_selected.sub_grade.astype(CategoricalDtype(categories = np.unique(df_selected.sub_grade))).cat.codes\n",
    "df_selected.addr_state = df_selected.addr_state.astype(CategoricalDtype(categories = np.unique(df_selected.addr_state))).cat.codes\n",
    "df_selected.loan_status = df_selected.loan_status.astype(CategoricalDtype(categories=np.unique(df_selected.loan_status))).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.application_type = df_selected.application_type.astype(CategoricalDtype(categories=np.unique(df_selected.application_type))).cat.codes\n",
    "df_selected.disbursement_method = df_selected.disbursement_method.astype(CategoricalDtype(categories=np.unique(df_selected.disbursement_method))).cat.codes\n",
    "df_selected.term = df_selected.term.astype(CategoricalDtype(categories = np.unique(df_selected.term))).cat.codes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>...</th>\n",
       "      <th>fico_range_high</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>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.99</td>\n",
       "      <td>123.03</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>679.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2765.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.99</td>\n",
       "      <td>820.28</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>719.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21470.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.78</td>\n",
       "      <td>432.66</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>699.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7869.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10400.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.45</td>\n",
       "      <td>289.91</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>104433.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>699.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21929.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.44</td>\n",
       "      <td>405.18</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>694.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8822.0</td>\n",
       "      <td>68.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  term  int_rate  installment  grade  sub_grade  emp_length  \\\n",
       "0     3600.0     0     13.99       123.03      2         13          10   \n",
       "1    24700.0     0     11.99       820.28      2         10          10   \n",
       "2    20000.0     1     10.78       432.66      1          8          10   \n",
       "4    10400.0     1     22.45       289.91      5         25           3   \n",
       "5    11950.0     0     13.44       405.18      2         12           4   \n",
       "\n",
       "   home_ownership  annual_inc  verification_status  ...  fico_range_high  \\\n",
       "0               1     55000.0                    1  ...            679.0   \n",
       "1               1     65000.0                    1  ...            719.0   \n",
       "2               1     63000.0                    1  ...            699.0   \n",
       "4               1    104433.0                    0  ...            699.0   \n",
       "5               5     34000.0                    0  ...            694.0   \n",
       "\n",
       "   open_acc  pub_rec  revol_bal  revol_util  total_acc  initial_list_status  \\\n",
       "0       7.0      0.0     2765.0        29.7       13.0                    1   \n",
       "1      22.0      0.0    21470.0        19.2       38.0                    1   \n",
       "2       6.0      0.0     7869.0        56.2       18.0                    1   \n",
       "4      12.0      0.0    21929.0        64.5       35.0                    1   \n",
       "5       5.0      0.0     8822.0        68.4        6.0                    1   \n",
       "\n",
       "   application_type  disbursement_method  issue_month  \n",
       "0                 0                    0            2  \n",
       "1                 0                    0            2  \n",
       "2                 1                    0            2  \n",
       "4                 0                    0            2  \n",
       "5                 0                    0            2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.iloc[0:5,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1345310, 24)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>...</th>\n",
       "      <th>fico_range_high</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>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "      <td>1.345310e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.441997e+04</td>\n",
       "      <td>2.412581e-01</td>\n",
       "      <td>1.323962e+01</td>\n",
       "      <td>4.380755e+02</td>\n",
       "      <td>1.745809e+00</td>\n",
       "      <td>1.069302e+01</td>\n",
       "      <td>6.052432e+00</td>\n",
       "      <td>2.911998e+00</td>\n",
       "      <td>7.624764e+04</td>\n",
       "      <td>3.015669e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>7.001852e+02</td>\n",
       "      <td>1.159352e+01</td>\n",
       "      <td>2.152760e-01</td>\n",
       "      <td>1.624811e+04</td>\n",
       "      <td>5.181002e+01</td>\n",
       "      <td>2.498084e+01</td>\n",
       "      <td>5.827727e-01</td>\n",
       "      <td>1.917774e-02</td>\n",
       "      <td>5.128929e-03</td>\n",
       "      <td>5.531292e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.717051e+03</td>\n",
       "      <td>4.278467e-01</td>\n",
       "      <td>4.768716e+00</td>\n",
       "      <td>2.615126e+02</td>\n",
       "      <td>1.295071e+00</td>\n",
       "      <td>6.449912e+00</td>\n",
       "      <td>3.562249e+00</td>\n",
       "      <td>1.915676e+00</td>\n",
       "      <td>6.992510e+04</td>\n",
       "      <td>4.589384e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.185316e+01</td>\n",
       "      <td>5.473789e+00</td>\n",
       "      <td>6.018647e-01</td>\n",
       "      <td>2.232817e+04</td>\n",
       "      <td>2.451326e+01</td>\n",
       "      <td>1.199847e+01</td>\n",
       "      <td>4.931013e-01</td>\n",
       "      <td>1.371494e-01</td>\n",
       "      <td>7.143268e-02</td>\n",
       "      <td>3.412514e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.310000e+00</td>\n",
       "      <td>4.930000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.290000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.750000e+00</td>\n",
       "      <td>2.484800e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.578000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.740000e+02</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.943000e+03</td>\n",
       "      <td>3.350000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.200000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.274000e+01</td>\n",
       "      <td>3.754300e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.940000e+02</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.113400e+04</td>\n",
       "      <td>5.210000e+01</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.599000e+01</td>\n",
       "      <td>5.807300e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>9.000000e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.140000e+02</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.975575e+04</td>\n",
       "      <td>7.070000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.099000e+01</td>\n",
       "      <td>1.719830e+03</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.099920e+07</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>8.500000e+02</td>\n",
       "      <td>9.000000e+01</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>2.904836e+06</td>\n",
       "      <td>8.923000e+02</td>\n",
       "      <td>1.760000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.100000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt          term      int_rate   installment         grade  \\\n",
       "count  1.345310e+06  1.345310e+06  1.345310e+06  1.345310e+06  1.345310e+06   \n",
       "mean   1.441997e+04  2.412581e-01  1.323962e+01  4.380755e+02  1.745809e+00   \n",
       "std    8.717051e+03  4.278467e-01  4.768716e+00  2.615126e+02  1.295071e+00   \n",
       "min    5.000000e+02  0.000000e+00  5.310000e+00  4.930000e+00  0.000000e+00   \n",
       "25%    8.000000e+03  0.000000e+00  9.750000e+00  2.484800e+02  1.000000e+00   \n",
       "50%    1.200000e+04  0.000000e+00  1.274000e+01  3.754300e+02  2.000000e+00   \n",
       "75%    2.000000e+04  0.000000e+00  1.599000e+01  5.807300e+02  2.000000e+00   \n",
       "max    4.000000e+04  1.000000e+00  3.099000e+01  1.719830e+03  6.000000e+00   \n",
       "\n",
       "          sub_grade    emp_length  home_ownership    annual_inc  \\\n",
       "count  1.345310e+06  1.345310e+06    1.345310e+06  1.345310e+06   \n",
       "mean   1.069302e+01  6.052432e+00    2.911998e+00  7.624764e+04   \n",
       "std    6.449912e+00  3.562249e+00    1.915676e+00  6.992510e+04   \n",
       "min    0.000000e+00  1.000000e+00    0.000000e+00  0.000000e+00   \n",
       "25%    6.000000e+00  2.000000e+00    1.000000e+00  4.578000e+04   \n",
       "50%    1.000000e+01  6.000000e+00    4.000000e+00  6.500000e+04   \n",
       "75%    1.400000e+01  1.000000e+01    5.000000e+00  9.000000e+04   \n",
       "max    3.400000e+01  1.000000e+01    5.000000e+00  1.099920e+07   \n",
       "\n",
       "       verification_status  ...  fico_range_high      open_acc       pub_rec  \\\n",
       "count         1.345310e+06  ...     1.345310e+06  1.345310e+06  1.345310e+06   \n",
       "mean          3.015669e-01  ...     7.001852e+02  1.159352e+01  2.152760e-01   \n",
       "std           4.589384e-01  ...     3.185316e+01  5.473789e+00  6.018647e-01   \n",
       "min           0.000000e+00  ...     6.290000e+02  0.000000e+00  0.000000e+00   \n",
       "25%           0.000000e+00  ...     6.740000e+02  8.000000e+00  0.000000e+00   \n",
       "50%           0.000000e+00  ...     6.940000e+02  1.100000e+01  0.000000e+00   \n",
       "75%           1.000000e+00  ...     7.140000e+02  1.400000e+01  0.000000e+00   \n",
       "max           1.000000e+00  ...     8.500000e+02  9.000000e+01  8.600000e+01   \n",
       "\n",
       "          revol_bal    revol_util     total_acc  initial_list_status  \\\n",
       "count  1.345310e+06  1.345310e+06  1.345310e+06         1.345310e+06   \n",
       "mean   1.624811e+04  5.181002e+01  2.498084e+01         5.827727e-01   \n",
       "std    2.232817e+04  2.451326e+01  1.199847e+01         4.931013e-01   \n",
       "min    0.000000e+00  0.000000e+00  2.000000e+00         0.000000e+00   \n",
       "25%    5.943000e+03  3.350000e+01  1.600000e+01         0.000000e+00   \n",
       "50%    1.113400e+04  5.210000e+01  2.300000e+01         1.000000e+00   \n",
       "75%    1.975575e+04  7.070000e+01  3.200000e+01         1.000000e+00   \n",
       "max    2.904836e+06  8.923000e+02  1.760000e+02         1.000000e+00   \n",
       "\n",
       "       application_type  disbursement_method   issue_month  \n",
       "count      1.345310e+06         1.345310e+06  1.345310e+06  \n",
       "mean       1.917774e-02         5.128929e-03  5.531292e+00  \n",
       "std        1.371494e-01         7.143268e-02  3.412514e+00  \n",
       "min        0.000000e+00         0.000000e+00  0.000000e+00  \n",
       "25%        0.000000e+00         0.000000e+00  3.000000e+00  \n",
       "50%        0.000000e+00         0.000000e+00  5.000000e+00  \n",
       "75%        0.000000e+00         0.000000e+00  9.000000e+00  \n",
       "max        1.000000e+00         1.000000e+00  1.100000e+01  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('float_format', '{:f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>...</th>\n",
       "      <th>fico_range_high</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>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14419.972014</td>\n",
       "      <td>0.241258</td>\n",
       "      <td>13.239619</td>\n",
       "      <td>438.075533</td>\n",
       "      <td>1.745809</td>\n",
       "      <td>10.693016</td>\n",
       "      <td>6.052432</td>\n",
       "      <td>2.911998</td>\n",
       "      <td>76247.636414</td>\n",
       "      <td>0.301567</td>\n",
       "      <td>...</td>\n",
       "      <td>700.185177</td>\n",
       "      <td>11.593520</td>\n",
       "      <td>0.215276</td>\n",
       "      <td>16248.114860</td>\n",
       "      <td>51.810022</td>\n",
       "      <td>24.980839</td>\n",
       "      <td>0.582773</td>\n",
       "      <td>0.019178</td>\n",
       "      <td>0.005129</td>\n",
       "      <td>5.531292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8717.050787</td>\n",
       "      <td>0.427847</td>\n",
       "      <td>4.768716</td>\n",
       "      <td>261.512604</td>\n",
       "      <td>1.295071</td>\n",
       "      <td>6.449912</td>\n",
       "      <td>3.562249</td>\n",
       "      <td>1.915676</td>\n",
       "      <td>69925.098427</td>\n",
       "      <td>0.458938</td>\n",
       "      <td>...</td>\n",
       "      <td>31.853161</td>\n",
       "      <td>5.473789</td>\n",
       "      <td>0.601865</td>\n",
       "      <td>22328.168731</td>\n",
       "      <td>24.513261</td>\n",
       "      <td>11.998474</td>\n",
       "      <td>0.493101</td>\n",
       "      <td>0.137149</td>\n",
       "      <td>0.071433</td>\n",
       "      <td>3.412514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>248.480000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45780.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>674.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5943.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.740000</td>\n",
       "      <td>375.430000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11134.000000</td>\n",
       "      <td>52.100000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.990000</td>\n",
       "      <td>580.730000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>90000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19755.750000</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>1719.830000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10999200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>2904836.000000</td>\n",
       "      <td>892.300000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           loan_amnt           term       int_rate    installment  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000 1345310.000000   \n",
       "mean    14419.972014       0.241258      13.239619     438.075533   \n",
       "std      8717.050787       0.427847       4.768716     261.512604   \n",
       "min       500.000000       0.000000       5.310000       4.930000   \n",
       "25%      8000.000000       0.000000       9.750000     248.480000   \n",
       "50%     12000.000000       0.000000      12.740000     375.430000   \n",
       "75%     20000.000000       0.000000      15.990000     580.730000   \n",
       "max     40000.000000       1.000000      30.990000    1719.830000   \n",
       "\n",
       "               grade      sub_grade     emp_length  home_ownership  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000  1345310.000000   \n",
       "mean        1.745809      10.693016       6.052432        2.911998   \n",
       "std         1.295071       6.449912       3.562249        1.915676   \n",
       "min         0.000000       0.000000       1.000000        0.000000   \n",
       "25%         1.000000       6.000000       2.000000        1.000000   \n",
       "50%         2.000000      10.000000       6.000000        4.000000   \n",
       "75%         2.000000      14.000000      10.000000        5.000000   \n",
       "max         6.000000      34.000000      10.000000        5.000000   \n",
       "\n",
       "           annual_inc  verification_status  ...  fico_range_high  \\\n",
       "count  1345310.000000       1345310.000000  ...   1345310.000000   \n",
       "mean     76247.636414             0.301567  ...       700.185177   \n",
       "std      69925.098427             0.458938  ...        31.853161   \n",
       "min          0.000000             0.000000  ...       629.000000   \n",
       "25%      45780.000000             0.000000  ...       674.000000   \n",
       "50%      65000.000000             0.000000  ...       694.000000   \n",
       "75%      90000.000000             1.000000  ...       714.000000   \n",
       "max   10999200.000000             1.000000  ...       850.000000   \n",
       "\n",
       "            open_acc        pub_rec      revol_bal     revol_util  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000 1345310.000000   \n",
       "mean       11.593520       0.215276   16248.114860      51.810022   \n",
       "std         5.473789       0.601865   22328.168731      24.513261   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         8.000000       0.000000    5943.000000      33.500000   \n",
       "50%        11.000000       0.000000   11134.000000      52.100000   \n",
       "75%        14.000000       0.000000   19755.750000      70.700000   \n",
       "max        90.000000      86.000000 2904836.000000     892.300000   \n",
       "\n",
       "           total_acc  initial_list_status  application_type  \\\n",
       "count 1345310.000000       1345310.000000    1345310.000000   \n",
       "mean       24.980839             0.582773          0.019178   \n",
       "std        11.998474             0.493101          0.137149   \n",
       "min         2.000000             0.000000          0.000000   \n",
       "25%        16.000000             0.000000          0.000000   \n",
       "50%        23.000000             1.000000          0.000000   \n",
       "75%        32.000000             1.000000          0.000000   \n",
       "max       176.000000             1.000000          1.000000   \n",
       "\n",
       "       disbursement_method    issue_month  \n",
       "count       1345310.000000 1345310.000000  \n",
       "mean              0.005129       5.531292  \n",
       "std               0.071433       3.412514  \n",
       "min               0.000000       0.000000  \n",
       "25%               0.000000       3.000000  \n",
       "50%               0.000000       5.000000  \n",
       "75%               0.000000       9.000000  \n",
       "max               1.000000      11.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "standardized_df=(df-df.min())/(df.max()-df.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>...</th>\n",
       "      <th>fico_range_high</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>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
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       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
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       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
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       "      <td>1345310.000000</td>\n",
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       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "      <td>1345310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.352404</td>\n",
       "      <td>0.241258</td>\n",
       "      <td>0.308786</td>\n",
       "      <td>0.252578</td>\n",
       "      <td>0.290968</td>\n",
       "      <td>0.314500</td>\n",
       "      <td>0.561381</td>\n",
       "      <td>0.582400</td>\n",
       "      <td>0.006932</td>\n",
       "      <td>0.301567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322105</td>\n",
       "      <td>0.128817</td>\n",
       "      <td>0.002503</td>\n",
       "      <td>0.005593</td>\n",
       "      <td>0.058063</td>\n",
       "      <td>0.132074</td>\n",
       "      <td>0.582773</td>\n",
       "      <td>0.019178</td>\n",
       "      <td>0.005129</td>\n",
       "      <td>0.502845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.220685</td>\n",
       "      <td>0.427847</td>\n",
       "      <td>0.185698</td>\n",
       "      <td>0.152494</td>\n",
       "      <td>0.215845</td>\n",
       "      <td>0.189703</td>\n",
       "      <td>0.395805</td>\n",
       "      <td>0.383135</td>\n",
       "      <td>0.006357</td>\n",
       "      <td>0.458938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144132</td>\n",
       "      <td>0.060820</td>\n",
       "      <td>0.006998</td>\n",
       "      <td>0.007687</td>\n",
       "      <td>0.027472</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>0.493101</td>\n",
       "      <td>0.137149</td>\n",
       "      <td>0.071433</td>\n",
       "      <td>0.310229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.189873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.172897</td>\n",
       "      <td>0.142020</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.004162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203620</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002046</td>\n",
       "      <td>0.037543</td>\n",
       "      <td>0.080460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.291139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.289330</td>\n",
       "      <td>0.216048</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.005910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.122222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003833</td>\n",
       "      <td>0.058388</td>\n",
       "      <td>0.120690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.493671</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.415888</td>\n",
       "      <td>0.335763</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.155556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006801</td>\n",
       "      <td>0.079233</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           loan_amnt           term       int_rate    installment  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000 1345310.000000   \n",
       "mean        0.352404       0.241258       0.308786       0.252578   \n",
       "std         0.220685       0.427847       0.185698       0.152494   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.189873       0.000000       0.172897       0.142020   \n",
       "50%         0.291139       0.000000       0.289330       0.216048   \n",
       "75%         0.493671       0.000000       0.415888       0.335763   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "               grade      sub_grade     emp_length  home_ownership  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000  1345310.000000   \n",
       "mean        0.290968       0.314500       0.561381        0.582400   \n",
       "std         0.215845       0.189703       0.395805        0.383135   \n",
       "min         0.000000       0.000000       0.000000        0.000000   \n",
       "25%         0.166667       0.176471       0.111111        0.200000   \n",
       "50%         0.333333       0.294118       0.555556        0.800000   \n",
       "75%         0.333333       0.411765       1.000000        1.000000   \n",
       "max         1.000000       1.000000       1.000000        1.000000   \n",
       "\n",
       "          annual_inc  verification_status  ...  fico_range_high  \\\n",
       "count 1345310.000000       1345310.000000  ...   1345310.000000   \n",
       "mean        0.006932             0.301567  ...         0.322105   \n",
       "std         0.006357             0.458938  ...         0.144132   \n",
       "min         0.000000             0.000000  ...         0.000000   \n",
       "25%         0.004162             0.000000  ...         0.203620   \n",
       "50%         0.005910             0.000000  ...         0.294118   \n",
       "75%         0.008182             1.000000  ...         0.384615   \n",
       "max         1.000000             1.000000  ...         1.000000   \n",
       "\n",
       "            open_acc        pub_rec      revol_bal     revol_util  \\\n",
       "count 1345310.000000 1345310.000000 1345310.000000 1345310.000000   \n",
       "mean        0.128817       0.002503       0.005593       0.058063   \n",
       "std         0.060820       0.006998       0.007687       0.027472   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.088889       0.000000       0.002046       0.037543   \n",
       "50%         0.122222       0.000000       0.003833       0.058388   \n",
       "75%         0.155556       0.000000       0.006801       0.079233   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "           total_acc  initial_list_status  application_type  \\\n",
       "count 1345310.000000       1345310.000000    1345310.000000   \n",
       "mean        0.132074             0.582773          0.019178   \n",
       "std         0.068957             0.493101          0.137149   \n",
       "min         0.000000             0.000000          0.000000   \n",
       "25%         0.080460             0.000000          0.000000   \n",
       "50%         0.120690             1.000000          0.000000   \n",
       "75%         0.172414             1.000000          0.000000   \n",
       "max         1.000000             1.000000          1.000000   \n",
       "\n",
       "       disbursement_method    issue_month  \n",
       "count       1345310.000000 1345310.000000  \n",
       "mean              0.005129       0.502845  \n",
       "std               0.071433       0.310229  \n",
       "min               0.000000       0.000000  \n",
       "25%               0.000000       0.272727  \n",
       "50%               0.000000       0.454545  \n",
       "75%               0.000000       0.818182  \n",
       "max               1.000000       1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardized_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.983154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>grade</td>\n",
       "      <td>0.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>installment</td>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.969219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>grade</td>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.956461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>open_acc</td>\n",
       "      <td>0.694536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.481361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.466561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>installment</td>\n",
       "      <td>0.460003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>installment</td>\n",
       "      <td>0.454756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0.434954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0.431013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0.431013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>grade</td>\n",
       "      <td>0.420230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>grade</td>\n",
       "      <td>0.420230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.416419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.416419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>term</td>\n",
       "      <td>0.411837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>term</td>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.410991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>grade</td>\n",
       "      <td>term</td>\n",
       "      <td>0.405170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>term</td>\n",
       "      <td>0.402193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.401893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             level_0         level_1  correlation\n",
       "349  fico_range_high  fico_range_low     1.000000\n",
       "122        sub_grade        int_rate     0.983154\n",
       "124        sub_grade           grade     0.972357\n",
       "72       installment       loan_amnt     0.969219\n",
       "98             grade        int_rate     0.956461\n",
       "471        total_acc        open_acc     0.694536\n",
       "192       annual_inc       loan_amnt     0.481361\n",
       "408        revol_bal       loan_amnt     0.466561\n",
       "195       annual_inc     installment     0.460003\n",
       "411        revol_bal     installment     0.454756\n",
       "449       revol_util       revol_bal     0.434954\n",
       "341  fico_range_high       sub_grade     0.431013\n",
       "317   fico_range_low       sub_grade     0.431013\n",
       "316   fico_range_low           grade     0.420230\n",
       "340  fico_range_high           grade     0.420230\n",
       "314   fico_range_low        int_rate     0.416419\n",
       "338  fico_range_high        int_rate     0.416419\n",
       "121        sub_grade            term     0.411837\n",
       "24              term       loan_amnt     0.410991\n",
       "97             grade            term     0.405170\n",
       "49          int_rate            term     0.402193\n",
       "416        revol_bal      annual_inc     0.401893"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CORRELATION\n",
    "\n",
    "corr = df.corr(method = 'spearman')\n",
    "new_corr = corr.abs()\n",
    "new_corr.loc[:,:] = np.tril(new_corr, k=-1)\n",
    "new_corr = new_corr.stack().to_frame('correlation').reset_index().sort_values(by='correlation', ascending=False)\n",
    "new_corr[new_corr.correlation > 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('./dataset/Cleaned_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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