lending_club_loan_data_processing.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khagenA/Data-Analysis-R/blob/master/lending_club_loan_data_processing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qb9ahqOA9TlS"
      },
      "source": [
        "# Lending Club Data: Loan Risk Prediction Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlgtYaeZ9TlU"
      },
      "source": [
        "* LendingClub is an American peer-to-peer lending company, headquartered in San Francisco, California.\n",
        "* LendingClub enables borrowers to create unsecured (no collateral required) personal loans between $\\$1,000$ and $\\$40,000$.\n",
        "* The standard loan period is three years.\n",
        "* Investors can search and browse the loan listings on the LendingClub website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose.\n",
        "* Investors make money from interest. LendingClub makes money by charging borrowers an origination fee and investors a service fee."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbsnaOF09TlU"
      },
      "source": [
        "**Data source: https://www.kaggle.com/wordsforthewise/lending-club on Kaggle.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJ57M5xN9TlU"
      },
      "source": [
        "### Objective:\n",
        "* to enable investors to make an well informed data driven decision: whether to invest or not"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbEc9f2k9z_p",
        "outputId": "6bcc5a5a-55e9-4f3d-ed8e-be1c0d2265b3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/Lending-club-data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMoJkxA2-GIG",
        "outputId": "3796a64f-4283-4dfb-ad50-f7a74b3acbb9"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Lending-club-data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "R6AFMf5W9f20"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o79p-TuO9TlV"
      },
      "source": [
        "### Import required Python library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "2TtyCNY09TlV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from datetime import datetime\n",
        "\n",
        "# Pandas options\n",
        "pd.set_option('display.max_colwidth', 1000, 'display.max_columns', None,'display.max_rows', 155)\n",
        "\n",
        "# Ploting options\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (10,6)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6eriDTa9TlW"
      },
      "source": [
        "### Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-KQK8cV9TlW",
        "outputId": "0fa6d6c0-ee87-4759-9578-2213e8c700ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2.78 s, sys: 721 ms, total: 3.5 s\n",
            "Wall time: 3.79 s\n"
          ]
        }
      ],
      "source": [
        "%time df_loan = pd.read_csv('accepted_2007_to_2018Q4.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek8c33po9TlX",
        "outputId": "59e10626-363d-4948-efdc-2d6be78d2c81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 121746 entries, 0 to 121745\n",
            "Columns: 151 entries, id to settlement_term\n",
            "dtypes: float64(114), int64(1), object(36)\n",
            "memory usage: 140.3+ MB\n"
          ]
        }
      ],
      "source": [
        "df_loan.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCujE-Ee9TlX"
      },
      "source": [
        "**There are 151 features in the data set.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xzm5s-O99TlX"
      },
      "source": [
        "## Features descriptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHxNR4tT9TlX"
      },
      "source": [
        "*Features escriptions taken from https://www.kaggle.com/wendykan/lending-club-loan-data.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "SJ12Z4k29TlX"
      },
      "outputs": [],
      "source": [
        "dictionary = pd.read_excel('LCDataDictionary.xlsx')\n",
        "dictionary.columns=['feature','description']\n",
        "dictionary = dictionary.set_index('feature')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "Gz27g6vN9TlY"
      },
      "outputs": [],
      "source": [
        "# Define functions for visualization\n",
        "def view_cat_plot(df, col_name, rotate=False):\n",
        "    df_ = df[col_name].value_counts(dropna=False, normalize=True) * 100\n",
        "    sns.barplot(x=df_.index, y=df_.values)\n",
        "    plt.xlabel(col_name)\n",
        "    plt.ylabel('Percentage')\n",
        "    if rotate:\n",
        "        plt.xticks(rotation=90)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "HZhAb2cw9TlY"
      },
      "outputs": [],
      "source": [
        "def view_cat_target_plot(self_df, col_name, hue_='loan_status',rotate_index=0,fig_size = (15,5),sort_index = False,sub_plots=(1,2)):\n",
        "    '''\n",
        "        REQUIRED\n",
        "        self_df: data frame\n",
        "        col_name: feature name\n",
        "\n",
        "        OPTIONAL\n",
        "        hue_: hue catatory\n",
        "        rotate_index: index label rotation (default is 0)\n",
        "        fig_size: figure size (width,height)\n",
        "        sort_index: Ordered index lebels to match both plots (default is sorted in reverse order by values)\n",
        "\n",
        "    '''\n",
        "    df_1 = self_df[col_name].value_counts(normalize=True).mul(100)\n",
        "    df_2 = self_df.groupby(col_name)[hue_].value_counts(normalize=True).mul(100).unstack().sort_values(0)\n",
        "    if sort_index: #match index lebel order in both plots\n",
        "            df_1.sort_index(inplace=True)\n",
        "            df_2.sort_index(inplace=True)\n",
        "\n",
        "    fig, axes = plt.subplots(figsize = fig_size,nrows=sub_plots[0], ncols=sub_plots[1])\n",
        "    ax1=sns.barplot(x=df_1.index,y=df_1.values,ax=axes[0])\n",
        "    ax1.set(xlabel=col_name, ylabel='Percentage')\n",
        "    ax1.set_xticklabels(ax1.get_xticklabels(), rotation=rotate_index)\n",
        "    ax2=df_2.plot(kind='bar', stacked='True',ax=axes[1],rot=rotate_index)\n",
        "    ax2.set(xlabel=col_name, ylabel='Percentage')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c65n472q9TlY"
      },
      "source": [
        "## Target variable: Loan status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "TepEVOM19TlY",
        "outputId": "fae0ab98-b146-408a-c25b-eac482d4e859"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_plot(df_loan,'loan_status',rotate=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iCdZbDi9TlY"
      },
      "source": [
        "### Select binary classitication: \"Fully Paid\" and \"Charged Off\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "f4IrNmWY9TlY"
      },
      "outputs": [],
      "source": [
        "#df[df['loan_status'] == 'Fully Paid') | (df['loan_status'] == 'Charged Off')}\n",
        "df=df_loan.loc[df_loan['loan_status'].isin(['Fully Paid','Charged Off'])]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "AEdJx7_e9TlY",
        "outputId": "8bb9a1f4-2982-4db8-fdd0-3bb42d6b5dc9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_plot(df,'loan_status')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzz7SOH19TlZ"
      },
      "source": [
        "### Target variable is imbalance:\n",
        "* May need to use SMOTE from imblearn.over_sampling to balance the target variable\n",
        "* May need to optimize the threshold in binary classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxR4bjQ59TlZ"
      },
      "source": [
        "### Convert target variable to numeric\n",
        "#### Need to predict 1 for Charged Of and 0 for not Charged Off"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "nYTh5E9e9TlZ"
      },
      "outputs": [],
      "source": [
        "df['loan_status'] = df['loan_status'].map({'Fully Paid':0,'Charged Off':1})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov5JnPl09TlZ"
      },
      "source": [
        "# Feature Selection/Engneering: Dimensionality Reduction\n",
        "* **Missing data**\n",
        "    * Drop feature if missiing value is greater than **MV_FEATURE*100** percent\n",
        "    * Drop the sample observations with NaN value (approx 12 percent.) or replace NaN with mean or median   \n",
        "* **Categorical varaibles**\n",
        "    * Ordinal: Convert ordinal to numeric type\n",
        "    * Nominal: Drop features if number of categories exceed **CV_MAX**\n",
        "* **Pairwise correlations**\n",
        "    * Drop highly pairwise correlated feature to avoid multicollinearity (correlation > **PC_MAX**)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16lsOaIy9TlZ"
      },
      "source": [
        "### Adjust the following variables to improve the performance of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "OgiPnGJb9TlZ"
      },
      "outputs": [],
      "source": [
        "# Drop feature if missiing value is MV_FEATURE*100 percent\n",
        "MV_FEATURE = 0.5\n",
        "\n",
        "# Drop categorical feature if the number of categories exceeds\n",
        "CV_MAX = 55\n",
        "\n",
        "# Drop one of the features if a pairwise correlation is greater than PC_MAX\n",
        "PC_MAX=0.95"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONf_tQbG9TlZ"
      },
      "source": [
        "### Dealing with missing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "U1QOr8Ia9TlZ"
      },
      "outputs": [],
      "source": [
        "df_null = pd.DataFrame({'Count': df.isna().sum(),'Percent': df.isna().mean()*100})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "X_R19Yyf9Tla"
      },
      "outputs": [],
      "source": [
        "df_null_filtered = df_null[df_null['Percent'] > 5].sort_values(by='Percent',ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vpBLPrB19Tla",
        "outputId": "38e39118-501e-4fe7-d117-739dd4ccdb4d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Percent', ylabel='None'>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x2000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,20))\n",
        "sns.barplot(x='Percent',y=df_null_filtered.index,data=df_null_filtered)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hgdZ80P9Tla"
      },
      "source": [
        "###### There are no features with missing values in between 20-50%. So, any features with missing values greater than 50% may not be available to the lender most of the time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35tOXj0M9Tla"
      },
      "source": [
        "#### Drop any features with more missing values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "y43HYlbe9Tla"
      },
      "outputs": [],
      "source": [
        "df.dropna(axis=1,thresh=int(MV_FEATURE*len(df_loan)),inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tl_yQqa09Tla",
        "outputId": "61e8414a-81d1-4e2d-d5bf-76e8dc4a43b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(107135, 93)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETz0x_KZ9Tlb",
        "outputId": "b0e2930f-113c-4ff7-aac3-8ef51aeac81e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate',\n",
              "       'installment', 'grade', 'sub_grade', 'emp_title', 'emp_length',\n",
              "       'home_ownership', 'annual_inc', 'verification_status', 'issue_d',\n",
              "       'loan_status', 'pymnt_plan', 'url', 'purpose', 'title', 'zip_code',\n",
              "       'addr_state', 'dti', 'delinq_2yrs', 'earliest_cr_line',\n",
              "       'fico_range_low', 'fico_range_high', 'inq_last_6mths', 'open_acc',\n",
              "       'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
              "       'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt',\n",
              "       'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',\n",
              "       'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
              "       'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d',\n",
              "       'last_fico_range_high', 'last_fico_range_low',\n",
              "       'collections_12_mths_ex_med', 'policy_code', 'application_type',\n",
              "       'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim',\n",
              "       'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util',\n",
              "       'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct',\n",
              "       'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl',\n",
              "       'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_inq',\n",
              "       'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl',\n",
              "       'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl',\n",
              "       'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m',\n",
              "       'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m',\n",
              "       'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies',\n",
              "       'tax_liens', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit',\n",
              "       'total_il_high_credit_limit', 'hardship_flag', 'disbursement_method',\n",
              "       'debt_settlement_flag'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "jMVkEUFw9Tlb"
      },
      "outputs": [],
      "source": [
        "# earliest_cr_line need to change to numeric variables\n",
        "earliest_cr_line = df_loan['earliest_cr_line'].astype('datetime64[ns]')\n",
        "issue_d = df_loan['issue_d'].astype('datetime64[ns]')\n",
        "df['earliest_cr_line'] = (issue_d.dt.year-earliest_cr_line.dt.year)*12 + (issue_d.dt.month-earliest_cr_line.dt.month)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vpW1nyg9Tlb"
      },
      "source": [
        "## Data leakage known as future data\n",
        "### List of variable that are not available while taking the decision to lend the costumer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "jxAIDzTe9Tlb"
      },
      "outputs": [],
      "source": [
        "features_not_available = ['funded_amnt','funded_amnt_inv','installment','issue_d','out_prncp',\n",
        "                         'out_prncp_inv','total_pymnt','total_pymnt_inv','total_rec_prncp','total_rec_int',\n",
        "                         'total_rec_late_fee','recoveries','collection_recovery_fee','last_pymnt_d',\n",
        "                         'last_credit_pull_d','last_fico_range_high','last_fico_range_low','collections_12_mths_ex_med',\n",
        "                        'disbursement_method','debt_settlement_flag']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "pccNihk49Tlb",
        "outputId": "5a82588f-7031-4855-fa2e-5ee06dc34b7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                   description\n",
              "feature                                                                                                                       \n",
              "funded_amnt                                                     The total amount committed to that loan at that point in time.\n",
              "funded_amnt_inv                                   The total amount committed by investors for that loan at that point in time.\n",
              "installment                                                   The monthly payment owed by the borrower if the loan originates.\n",
              "issue_d                                                                                    The month which the loan was funded\n",
              "out_prncp                                                              Remaining outstanding principal for total amount funded\n",
              "out_prncp_inv                                  Remaining outstanding principal for portion of total amount funded by investors\n",
              "total_pymnt                                                                  Payments received to date for total amount funded\n",
              "total_pymnt_inv                                      Payments received to date for portion of total amount funded by investors\n",
              "total_rec_prncp                                                                                     Principal received to date\n",
              "total_rec_int                                                                                        Interest received to date\n",
              "total_rec_late_fee                                                                                  Late fees received to date\n",
              "recoveries                                                                                      post charge off gross recovery\n",
              "collection_recovery_fee                                                                         post charge off collection fee\n",
              "last_pymnt_d                                                                                   Last month payment was received\n",
              "last_credit_pull_d                                                        The most recent month LC pulled credit for this loan\n",
              "last_fico_range_high                                      The upper boundary range the borrower’s last FICO pulled belongs to.\n",
              "last_fico_range_low                                       The lower boundary range the borrower’s last FICO pulled belongs to.\n",
              "collections_12_mths_ex_med                                    Number of collections in 12 months excluding medical collections\n",
              "disbursement_method                The method by which the borrower receives their loan. Possible values are: CASH, DIRECT_PAY\n",
              "debt_settlement_flag        Flags whether or not the borrower, who has charged-off, is working with a debt-settlement company."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9996219e-cc8c-4cc7-86fb-93a22ee938f2\" class=\"colab-df-container\">\n",
              "    <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>description</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>feature</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>funded_amnt</th>\n",
              "      <td>The total amount committed to that loan at that point in time.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <td>The total amount committed by investors for that loan at that point in time.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>issue_d</th>\n",
              "      <td>The month which the loan was funded</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp</th>\n",
              "      <td>Remaining outstanding principal for total amount funded</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>out_prncp_inv</th>\n",
              "      <td>Remaining outstanding principal for portion of total amount funded by investors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt</th>\n",
              "      <td>Payments received to date for total amount funded</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <td>Payments received to date for portion of total amount funded by investors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_prncp</th>\n",
              "      <td>Principal received to date</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_int</th>\n",
              "      <td>Interest received to date</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <td>Late fees received to date</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recoveries</th>\n",
              "      <td>post charge off gross recovery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <td>post charge off collection fee</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_pymnt_d</th>\n",
              "      <td>Last month payment was received</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_credit_pull_d</th>\n",
              "      <td>The most recent month LC pulled credit for this loan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_fico_range_high</th>\n",
              "      <td>The upper boundary range the borrower’s last FICO pulled belongs to.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_fico_range_low</th>\n",
              "      <td>The lower boundary range the borrower’s last FICO pulled belongs to.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>collections_12_mths_ex_med</th>\n",
              "      <td>Number of collections in 12 months excluding medical collections</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>disbursement_method</th>\n",
              "      <td>The method by which the borrower receives their loan. Possible values are: CASH, DIRECT_PAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_settlement_flag</th>\n",
              "      <td>Flags whether or not the borrower, who has charged-off, is working with a debt-settlement company.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9996219e-cc8c-4cc7-86fb-93a22ee938f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9996219e-cc8c-4cc7-86fb-93a22ee938f2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9996219e-cc8c-4cc7-86fb-93a22ee938f2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a2bfbed4-8e13-410c-a10e-9b36c53b0784\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2bfbed4-8e13-410c-a10e-9b36c53b0784')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a2bfbed4-8e13-410c-a10e-9b36c53b0784 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dictionary\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"funded_amnt\",\n          \"collections_12_mths_ex_med\",\n          \"last_fico_range_high\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"The total amount committed to that loan at that point in time.\",\n          \"Number of collections in 12 months excluding medical collections\",\n          \"The upper boundary range the borrower\\u2019s last FICO pulled belongs to.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "dictionary.loc[features_not_available]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "ui_Wkdjc9Tlc"
      },
      "outputs": [],
      "source": [
        "df = df.drop(features_not_available,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3CoAQOS9Tlc",
        "outputId": "28f813b6-c05f-4a8e-c2a5-b679aedc6a3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NaN sample obervations: 10.4905%.\n"
          ]
        }
      ],
      "source": [
        "# Fraction of samples with matimum missing value\n",
        "frac_nan = df.isna().sum().max()/len(df)\n",
        "print(f'NaN sample obervations: {(frac_nan*100):g}%.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7C-JoDWl9Tlc"
      },
      "source": [
        "#### We do have a large sample. So, drop any rows with NaN value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "J_vvzQM09Tlc"
      },
      "outputs": [],
      "source": [
        "df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NzIgINq9Tlc",
        "outputId": "e2ba845d-8ceb-4e44-e6e7-2f913a5491d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We do have 81k obserbations remaining for further processing.\n"
          ]
        }
      ],
      "source": [
        "print(f'We do have {str(df.shape[0])[:-3]}k obserbations remaining for further processing.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVmCrTzy9Tlc"
      },
      "source": [
        "# Catagorical variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "hEKsytwI9Tld"
      },
      "outputs": [],
      "source": [
        "df_object = df.select_dtypes(['object'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "QR1Pif5a9Tld",
        "outputId": "276a28d2-ac8b-423c-fe20-a6895cae99dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "url                    81215\n",
              "emp_title              33164\n",
              "zip_code                 875\n",
              "addr_state                49\n",
              "sub_grade                 35\n",
              "title                     14\n",
              "purpose                   12\n",
              "emp_length                11\n",
              "grade                      7\n",
              "home_ownership             4\n",
              "verification_status        3\n",
              "term                       2\n",
              "initial_list_status        2\n",
              "application_type           2\n",
              "pymnt_plan                 1\n",
              "hardship_flag              1\n",
              "dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>url</th>\n",
              "      <td>81215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_title</th>\n",
              "      <td>33164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code</th>\n",
              "      <td>875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>addr_state</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub_grade</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grade</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>term</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_list_status</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pymnt_plan</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hardship_flag</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "df_object.nunique().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqnTVEOM9Tld"
      },
      "source": [
        "### Dorp features with many catagories > CV_MAX  or single catatory\n",
        "##### For example, ID  and url features are not relevant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "QMjk2Jmb9Tld"
      },
      "outputs": [],
      "source": [
        "drop_list_not_relevent = list(df_object.nunique()[(df_object.nunique()>CV_MAX) | (df_object.nunique() == 1)].index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "CbCw6QWR9Tld"
      },
      "outputs": [],
      "source": [
        "df.drop(labels=drop_list_not_relevent,axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "Dm3iLnyh9Tld",
        "outputId": "24523188-9d59-4ef0-ea24-a8ef19146f2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "term                    2\n",
              "grade                   7\n",
              "sub_grade              35\n",
              "emp_length             11\n",
              "home_ownership          4\n",
              "verification_status     3\n",
              "purpose                12\n",
              "title                  14\n",
              "addr_state             49\n",
              "initial_list_status     2\n",
              "application_type        2\n",
              "dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>term</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grade</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sub_grade</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verification_status</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>addr_state</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>initial_list_status</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "df.select_dtypes(['object']).nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzE9-fLO9Tld"
      },
      "source": [
        "## Let's investigate the categorical variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txIz4x8X9Tld"
      },
      "source": [
        "### Term"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "OThwOr2F9Tle",
        "outputId": "38c4a81b-649f-454b-a44d-8dba231b435f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "term\n",
              " 36 months    61115\n",
              " 60 months    20100\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>term</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>36 months</th>\n",
              "      <td>61115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60 months</th>\n",
              "      <td>20100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "df['term'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "CPEcgUvF9Tle"
      },
      "outputs": [],
      "source": [
        "df['term']=df['term'].apply(lambda tm: np.int8(tm.split()[0]) if type(tm) == str else np.nan)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "4GrRtZOy9Tle",
        "outputId": "0211eb65-b57a-42d9-cee2-19ee5ff0171a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'term')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czyd51bh9Tle"
      },
      "source": [
        "* Long term involves more risk than short term."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTopRNj39Tle"
      },
      "source": [
        "### Grade"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "8RqvAl6p9Tle",
        "outputId": "0f39c5c7-8d9a-4f1d-d3df-8f02bc7ce853"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'grade')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBx68O2v9Tle"
      },
      "source": [
        "* Many borrowers do have grade B and few do have grade G.\n",
        "* Grade are graded from A (lowest risk/lowest rate) to G (highest risk/highest rate), with subgrades per rate.\n",
        "* In latest data, Lending Club has removed F and G grade because of hight refaults rate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOyK_Z6S9Tlf"
      },
      "source": [
        "### Employ Length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "Kbr8wgYj9Tlf",
        "outputId": "2d362223-77c0-4855-9d23-0a84fc827eec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "10+ years    28431\n",
              "2 years       7691\n",
              "< 1 year      7472\n",
              "3 years       7065\n",
              "1 year        5777\n",
              "5 years       5196\n",
              "4 years       5077\n",
              "8 years       4471\n",
              "6 years       3431\n",
              "9 years       3370\n",
              "7 years       3234\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10+ years</th>\n",
              "      <td>28431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2 years</th>\n",
              "      <td>7691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>&lt; 1 year</th>\n",
              "      <td>7472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3 years</th>\n",
              "      <td>7065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1 year</th>\n",
              "      <td>5777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5 years</th>\n",
              "      <td>5196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4 years</th>\n",
              "      <td>5077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8 years</th>\n",
              "      <td>4471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6 years</th>\n",
              "      <td>3431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9 years</th>\n",
              "      <td>3370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7 years</th>\n",
              "      <td>3234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "df['emp_length'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShlJrQFg9Tlf"
      },
      "source": [
        "##### Convert ordinal catgory to numeric type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "-z9DGvNM9Tlf"
      },
      "outputs": [],
      "source": [
        "df['emp_length'].replace(to_replace=['10+ years','< 1 year'],value=['10 years','0 year'],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "scrolled": true,
        "id": "sYn-3Gas9Tlf"
      },
      "outputs": [],
      "source": [
        "df['emp_length'] = df['emp_length'].apply(lambda year: np.int8(year.split()[0]) if type(year) == str else np.nan )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "4bu91Lyl9Tlf",
        "outputId": "2dc99c8d-ca42-4761-cb2b-8279485a5dd1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'emp_length')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOUsCOS_9Tlf"
      },
      "source": [
        "### Home Ownership"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "jqYknGn89Tlf",
        "outputId": "d0898f05-8d8c-41ae-ee94-4b79c249b980"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "home_ownership\n",
              "MORTGAGE    40720\n",
              "RENT        31921\n",
              "OWN          8573\n",
              "ANY             1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_ownership</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MORTGAGE</th>\n",
              "      <td>40720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RENT</th>\n",
              "      <td>31921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OWN</th>\n",
              "      <td>8573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANY</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "df['home_ownership'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "_6CxS7Mt9Tlf"
      },
      "outputs": [],
      "source": [
        "df['home_ownership'].replace(['ANY','NONE'],'OTHER',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "xLh8ZRjC9Tlg",
        "outputId": "93fcd7ff-c6f9-4e7e-d6a5-be917508bf34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'home_ownership')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7NZJZk89Tlg"
      },
      "source": [
        "### Varification status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "xYWuDf8m9Tlg",
        "outputId": "5d616878-c9f6-48a4-9759-3e4a084838b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'verification_status')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udGJBJ8C9Tlg"
      },
      "source": [
        "## Purpose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "Yun7GsFp9Tlg",
        "outputId": "5d7e1770-d5d5-40ff-cff6-59755615a7ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "purpose\n",
              "debt_consolidation    46379\n",
              "credit_card           19845\n",
              "home_improvement       5349\n",
              "other                  4288\n",
              "major_purchase         1645\n",
              "medical                 956\n",
              "small_business          742\n",
              "car                     736\n",
              "moving                  475\n",
              "vacation                453\n",
              "house                   290\n",
              "renewable_energy         57\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>debt_consolidation</th>\n",
              "      <td>46379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_card</th>\n",
              "      <td>19845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>home_improvement</th>\n",
              "      <td>5349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>other</th>\n",
              "      <td>4288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>major_purchase</th>\n",
              "      <td>1645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medical</th>\n",
              "      <td>956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>small_business</th>\n",
              "      <td>742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>car</th>\n",
              "      <td>736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moving</th>\n",
              "      <td>475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vacation</th>\n",
              "      <td>453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>house</th>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>renewable_energy</th>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ],
      "source": [
        "df['purpose'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "j1futrDv9Tlg",
        "outputId": "3fd0b46f-d6d8-47bf-8b55-e5ef99a04b25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'purpose',rotate_index=90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "Yv9alEJO9Tlg",
        "outputId": "4c3bb8e5-5734-404a-83dd-74baf805cb9c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "view_cat_target_plot(df,'addr_state',rotate_index=90,sub_plots=(2,1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3T1WiwyL9Tlh"
      },
      "source": [
        "## Pairwise correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3JdIjSy9Tlh",
        "outputId": "2ed3ae01-fbbd-470d-968d-2cc5e7895e60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999999349383427"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ],
      "source": [
        "df['fico_range_low'].corr(df['fico_range_high'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "gRQq5FYl9Tlh"
      },
      "outputs": [],
      "source": [
        "df['fico_score'] = (df['fico_range_low']+ df['fico_range_high'])/2.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "l2yiiSmV9Tlh"
      },
      "outputs": [],
      "source": [
        "df.drop(['fico_range_low','fico_range_high'],inplace=True,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Identify non-numeric columns\n",
        "non_numeric_columns = df.select_dtypes(exclude=[np.number]).columns\n",
        "print(\"Non-numeric columns:\", non_numeric_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fVPjs0K_ExD",
        "outputId": "c21f677b-56f0-4359-f367-980107dc9d41"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non-numeric columns: Index(['grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
            "       'purpose', 'title', 'addr_state', 'initial_list_status',\n",
            "       'application_type'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove non-numeric columns\n",
        "df_numeric = df.select_dtypes(include=[np.number])"
      ],
      "metadata": {
        "id": "S-WSa5yq_77M"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "Fgauibiw9Tlh"
      },
      "outputs": [],
      "source": [
        "corr_stack = df_numeric.corr().stack()\n",
        "corr = corr_stack[corr_stack.index.get_level_values(0) != corr_stack.index.get_level_values(1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "bAvgxEjo9Tlh",
        "outputId": "b0963524-0997-48b2-af5d-13d334b2d509"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "open_acc             num_sats               0.999355\n",
              "tot_cur_bal          tot_hi_cred_lim        0.985215\n",
              "num_actv_rev_tl      num_rev_tl_bal_gt_0    0.995319\n",
              "num_rev_tl_bal_gt_0  num_actv_rev_tl        0.995319\n",
              "num_sats             open_acc               0.999355\n",
              "tot_hi_cred_lim      tot_cur_bal            0.985215\n",
              "dtype: float64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <th>num_sats</th>\n",
              "      <td>0.999355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <td>0.985215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <td>0.995319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_rev_tl_bal_gt_0</th>\n",
              "      <th>num_actv_rev_tl</th>\n",
              "      <td>0.995319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_sats</th>\n",
              "      <th>open_acc</th>\n",
              "      <td>0.999355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tot_hi_cred_lim</th>\n",
              "      <th>tot_cur_bal</th>\n",
              "      <td>0.985215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ],
      "source": [
        "pair_corr_high = corr[corr > PC_MAX]\n",
        "pair_corr_high"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mll1ANC29Tlh",
        "outputId": "5045b95c-790c-41a4-d53e-adb60eb1d072"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dropped following highly pairwise correlated features:\n",
            " ['num_sats' 'tot_hi_cred_lim' 'num_rev_tl_bal_gt_0']\n"
          ]
        }
      ],
      "source": [
        "# dropping ALL duplicte values\n",
        "drop_pair_list = pair_corr_high.drop_duplicates().index.get_level_values(1).unique()\n",
        "print(f'Dropped following highly pairwise correlated features:\\n {drop_pair_list.values}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "69FYx4Ud9Tli"
      },
      "outputs": [],
      "source": [
        "df.drop(drop_pair_list,axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-Zgv8959Tli"
      },
      "source": [
        "## Exploratory data analysis for a few sample numeric valriables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "CIaUs8Km9Tli",
        "outputId": "9b9a20b3-996d-46c5-928d-380c0bffb15e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='loan_status', ylabel='loan_amnt'>"
            ]
          },
          "metadata": {},
          "execution_count": 125
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x='loan_status',y='loan_amnt',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "mDpEGUAL9Tli"
      },
      "outputs": [],
      "source": [
        "#sns.boxplot(x='loan_status',y='last_fico_range_high',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "9vwlBRal9Tli",
        "outputId": "028909d0-c808-45e3-bcd6-b61e5817d4a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='loan_status', ylabel='int_rate'>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x='loan_status',y='int_rate',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "gn2XVjYs9Tli",
        "outputId": "1b3d00a9-587b-4818-d756-d9046e3c6673"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='loan_status', ylabel='fico_score'>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x='loan_status',y='fico_score',data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVL6Yysg9Tli",
        "outputId": "e299269b-94d6-4500-90f5-b98b4d2b586b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "64 selected fetures names: \n",
            " ['id' 'loan_amnt' 'term' 'int_rate' 'grade' 'sub_grade' 'emp_length'\n",
            " 'home_ownership' 'annual_inc' 'verification_status' 'loan_status'\n",
            " 'purpose' 'title' 'addr_state' 'dti' 'delinq_2yrs' 'earliest_cr_line'\n",
            " 'inq_last_6mths' 'open_acc' 'pub_rec' 'revol_bal' 'revol_util'\n",
            " 'total_acc' 'initial_list_status' 'last_pymnt_amnt' 'policy_code'\n",
            " 'application_type' 'acc_now_delinq' 'tot_coll_amt' 'tot_cur_bal'\n",
            " 'total_rev_hi_lim' 'acc_open_past_24mths' 'avg_cur_bal' 'bc_open_to_buy'\n",
            " 'bc_util' 'chargeoff_within_12_mths' 'delinq_amnt' 'mo_sin_old_il_acct'\n",
            " 'mo_sin_old_rev_tl_op' 'mo_sin_rcnt_rev_tl_op' 'mo_sin_rcnt_tl'\n",
            " 'mort_acc' 'mths_since_recent_bc' 'mths_since_recent_inq'\n",
            " 'num_accts_ever_120_pd' 'num_actv_bc_tl' 'num_actv_rev_tl' 'num_bc_sats'\n",
            " 'num_bc_tl' 'num_il_tl' 'num_op_rev_tl' 'num_rev_accts'\n",
            " 'num_tl_120dpd_2m' 'num_tl_30dpd' 'num_tl_90g_dpd_24m'\n",
            " 'num_tl_op_past_12m' 'pct_tl_nvr_dlq' 'percent_bc_gt_75'\n",
            " 'pub_rec_bankruptcies' 'tax_liens' 'total_bal_ex_mort' 'total_bc_limit'\n",
            " 'total_il_high_credit_limit' 'fico_score']\n"
          ]
        }
      ],
      "source": [
        "print(f'{df.shape[1]} selected fetures names: \\n {df.columns.values}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2sqPtyv9Tli",
        "outputId": "d81a8dcb-8263-40f4-ac32-54027f8b2194"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final data contains 81k observations and 64 features.\n"
          ]
        }
      ],
      "source": [
        "print(f'Final data contains {str(df.shape[0])[:-3]}k observations and {df.shape[1]} features.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9M79gQCJ9Tlj"
      },
      "source": [
        "### write processed data to the file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "1RxhRG6i9Tlj"
      },
      "outputs": [],
      "source": [
        "df.to_csv('loan_data_final_'+str(PC_MAX)+'.csv',index=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "6edN8uvC9Tlj"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}