Final_Project_virtual_intership_ID_X.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final Project virtual intership ID/X",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Membaca data\n",
        "\n",
        "Melakukan load data dan melihat informasi dari data dan menampilkan 5 baris pertama dari data"
      ],
      "metadata": {
        "id": "AFndsHemfI9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dython"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4HMv-o8Sjt5T",
        "outputId": "75c8a08c-5f7e-4309-ee64-d0775f5c005a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: dython in /usr/local/lib/python3.7/dist-packages (0.7.1.post4)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from dython) (0.11.2)\n",
            "Requirement already satisfied: matplotlib>=3.4.3 in /usr/local/lib/python3.7/dist-packages (from dython) (3.5.3)\n",
            "Requirement already satisfied: scikit-plot>=0.3.7 in /usr/local/lib/python3.7/dist-packages (from dython) (0.3.7)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.7/dist-packages (from dython) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from dython) (1.7.3)\n",
            "Requirement already satisfied: pandas>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from dython) (1.3.5)\n",
            "Requirement already satisfied: scikit-learn>=0.24.2 in /usr/local/lib/python3.7/dist-packages (from dython) (1.0.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (7.1.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (0.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (21.3)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.4.3->dython) (4.37.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.4.3->dython) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.3.2->dython) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib>=3.4.3->dython) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24.2->dython) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24.2->dython) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import joblib\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set();\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import make_column_selector as selector\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from dython.nominal import associations\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline\n",
        "\n",
        "from imblearn.over_sampling import SMOTE"
      ],
      "metadata": {
        "id": "SnoHjuHfjpY5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "RnSseNmTewBx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "532fb8b5-8e0c-44df-e2ef-6c71768beab9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (20) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 466285 entries, 0 to 466284\n",
            "Data columns (total 75 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   Unnamed: 0                   466285 non-null  int64  \n",
            " 1   id                           466285 non-null  int64  \n",
            " 2   member_id                    466285 non-null  int64  \n",
            " 3   loan_amnt                    466285 non-null  int64  \n",
            " 4   funded_amnt                  466285 non-null  int64  \n",
            " 5   funded_amnt_inv              466285 non-null  float64\n",
            " 6   term                         466285 non-null  object \n",
            " 7   int_rate                     466285 non-null  float64\n",
            " 8   installment                  466285 non-null  float64\n",
            " 9   grade                        466285 non-null  object \n",
            " 10  sub_grade                    466285 non-null  object \n",
            " 11  emp_title                    438697 non-null  object \n",
            " 12  emp_length                   445277 non-null  object \n",
            " 13  home_ownership               466285 non-null  object \n",
            " 14  annual_inc                   466281 non-null  float64\n",
            " 15  verification_status          466285 non-null  object \n",
            " 16  issue_d                      466285 non-null  object \n",
            " 17  loan_status                  466285 non-null  object \n",
            " 18  pymnt_plan                   466285 non-null  object \n",
            " 19  url                          466285 non-null  object \n",
            " 20  desc                         125983 non-null  object \n",
            " 21  purpose                      466285 non-null  object \n",
            " 22  title                        466265 non-null  object \n",
            " 23  zip_code                     466285 non-null  object \n",
            " 24  addr_state                   466285 non-null  object \n",
            " 25  dti                          466285 non-null  float64\n",
            " 26  delinq_2yrs                  466256 non-null  float64\n",
            " 27  earliest_cr_line             466256 non-null  object \n",
            " 28  inq_last_6mths               466256 non-null  float64\n",
            " 29  mths_since_last_delinq       215934 non-null  float64\n",
            " 30  mths_since_last_record       62638 non-null   float64\n",
            " 31  open_acc                     466256 non-null  float64\n",
            " 32  pub_rec                      466256 non-null  float64\n",
            " 33  revol_bal                    466285 non-null  int64  \n",
            " 34  revol_util                   465945 non-null  float64\n",
            " 35  total_acc                    466256 non-null  float64\n",
            " 36  initial_list_status          466285 non-null  object \n",
            " 37  out_prncp                    466285 non-null  float64\n",
            " 38  out_prncp_inv                466285 non-null  float64\n",
            " 39  total_pymnt                  466285 non-null  float64\n",
            " 40  total_pymnt_inv              466285 non-null  float64\n",
            " 41  total_rec_prncp              466285 non-null  float64\n",
            " 42  total_rec_int                466285 non-null  float64\n",
            " 43  total_rec_late_fee           466285 non-null  float64\n",
            " 44  recoveries                   466285 non-null  float64\n",
            " 45  collection_recovery_fee      466285 non-null  float64\n",
            " 46  last_pymnt_d                 465909 non-null  object \n",
            " 47  last_pymnt_amnt              466285 non-null  float64\n",
            " 48  next_pymnt_d                 239071 non-null  object \n",
            " 49  last_credit_pull_d           466243 non-null  object \n",
            " 50  collections_12_mths_ex_med   466140 non-null  float64\n",
            " 51  mths_since_last_major_derog  98974 non-null   float64\n",
            " 52  policy_code                  466285 non-null  int64  \n",
            " 53  application_type             466285 non-null  object \n",
            " 54  annual_inc_joint             0 non-null       float64\n",
            " 55  dti_joint                    0 non-null       float64\n",
            " 56  verification_status_joint    0 non-null       float64\n",
            " 57  acc_now_delinq               466256 non-null  float64\n",
            " 58  tot_coll_amt                 396009 non-null  float64\n",
            " 59  tot_cur_bal                  396009 non-null  float64\n",
            " 60  open_acc_6m                  0 non-null       float64\n",
            " 61  open_il_6m                   0 non-null       float64\n",
            " 62  open_il_12m                  0 non-null       float64\n",
            " 63  open_il_24m                  0 non-null       float64\n",
            " 64  mths_since_rcnt_il           0 non-null       float64\n",
            " 65  total_bal_il                 0 non-null       float64\n",
            " 66  il_util                      0 non-null       float64\n",
            " 67  open_rv_12m                  0 non-null       float64\n",
            " 68  open_rv_24m                  0 non-null       float64\n",
            " 69  max_bal_bc                   0 non-null       float64\n",
            " 70  all_util                     0 non-null       float64\n",
            " 71  total_rev_hi_lim             396009 non-null  float64\n",
            " 72  inq_fi                       0 non-null       float64\n",
            " 73  total_cu_tl                  0 non-null       float64\n",
            " 74  inq_last_12m                 0 non-null       float64\n",
            "dtypes: float64(46), int64(7), object(22)\n",
            "memory usage: 266.8+ MB\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('loan_data_2007_2014.csv')\n",
        "# melihat informasi dari data\n",
        "df.info()                                 "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# menampilkan 5 baris pertama dari data\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "r67DU-EktMfI",
        "outputId": "846c2394-b19b-4dc4-c4f8-cf9023794b03"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0       id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
              "0           0  1077501    1296599       5000         5000           4975.0   \n",
              "1           1  1077430    1314167       2500         2500           2500.0   \n",
              "2           2  1077175    1313524       2400         2400           2400.0   \n",
              "3           3  1076863    1277178      10000        10000          10000.0   \n",
              "4           4  1075358    1311748       3000         3000           3000.0   \n",
              "\n",
              "         term  int_rate  installment grade  ... total_bal_il il_util  \\\n",
              "0   36 months     10.65       162.87     B  ...          NaN     NaN   \n",
              "1   60 months     15.27        59.83     C  ...          NaN     NaN   \n",
              "2   36 months     15.96        84.33     C  ...          NaN     NaN   \n",
              "3   36 months     13.49       339.31     C  ...          NaN     NaN   \n",
              "4   60 months     12.69        67.79     B  ...          NaN     NaN   \n",
              "\n",
              "  open_rv_12m open_rv_24m  max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
              "0         NaN         NaN         NaN      NaN              NaN    NaN   \n",
              "1         NaN         NaN         NaN      NaN              NaN    NaN   \n",
              "2         NaN         NaN         NaN      NaN              NaN    NaN   \n",
              "3         NaN         NaN         NaN      NaN              NaN    NaN   \n",
              "4         NaN         NaN         NaN      NaN              NaN    NaN   \n",
              "\n",
              "  total_cu_tl inq_last_12m  \n",
              "0         NaN          NaN  \n",
              "1         NaN          NaN  \n",
              "2         NaN          NaN  \n",
              "3         NaN          NaN  \n",
              "4         NaN          NaN  \n",
              "\n",
              "[5 rows x 75 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f78b07c4-ac82-4fb4-9082-fd15836a18ed\">\n",
              "    <div 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>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>member_id</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>funded_amnt</th>\n",
              "      <th>funded_amnt_inv</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>...</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",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1077501</td>\n",
              "      <td>1296599</td>\n",
              "      <td>5000</td>\n",
              "      <td>5000</td>\n",
              "      <td>4975.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.65</td>\n",
              "      <td>162.87</td>\n",
              "      <td>B</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1077430</td>\n",
              "      <td>1314167</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>15.27</td>\n",
              "      <td>59.83</td>\n",
              "      <td>C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1077175</td>\n",
              "      <td>1313524</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400</td>\n",
              "      <td>2400.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>15.96</td>\n",
              "      <td>84.33</td>\n",
              "      <td>C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1076863</td>\n",
              "      <td>1277178</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>13.49</td>\n",
              "      <td>339.31</td>\n",
              "      <td>C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1075358</td>\n",
              "      <td>1311748</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>12.69</td>\n",
              "      <td>67.79</td>\n",
              "      <td>B</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 75 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f78b07c4-ac82-4fb4-9082-fd15836a18ed')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f78b07c4-ac82-4fb4-9082-fd15836a18ed 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-f78b07c4-ac82-4fb4-9082-fd15836a18ed');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisis Data\n",
        "\n",
        "Melihat status peminjaman"
      ],
      "metadata": {
        "id": "ewmbc3VpsiZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plotcount(df, y, title, **sns_kwargs):\n",
        "  data = df['loan_status'].value_counts()\n",
        "\n",
        "  plt.figure(figsize=(16,12))\n",
        "  plt.title(title, fontsize=20)\n",
        "  sns.countplot(data = df, y = df['loan_status'], order=data.index, **sns_kwargs)\n",
        "\n",
        "  percentage = data / data.sum()\n",
        "  percentage = percentage.apply(\"{:.2%}\".format)\n",
        "\n",
        "  print(percentage)\n",
        "\n",
        "plotcount(df, y = 'loan_status', title = \"Status Pinjaman\")\n",
        "total_rows = df.count\n",
        "print(total_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZSntNcT5ssJ6",
        "outputId": "ba3b297c-faf4-43ef-c0d7-33671764c016"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current                                                48.09%\n",
            "Fully Paid                                             39.62%\n",
            "Charged Off                                             9.11%\n",
            "Late (31-120 days)                                      1.48%\n",
            "In Grace Period                                         0.67%\n",
            "Does not meet the credit policy. Status:Fully Paid      0.43%\n",
            "Late (16-30 days)                                       0.26%\n",
            "Default                                                 0.18%\n",
            "Does not meet the credit policy. Status:Charged Off     0.16%\n",
            "Name: loan_status, dtype: object\n",
            "<bound method DataFrame.count of         Unnamed: 0       id  member_id  loan_amnt  funded_amnt  \\\n",
            "0                0  1077501    1296599       5000         5000   \n",
            "1                1  1077430    1314167       2500         2500   \n",
            "2                2  1077175    1313524       2400         2400   \n",
            "3                3  1076863    1277178      10000        10000   \n",
            "4                4  1075358    1311748       3000         3000   \n",
            "...            ...      ...        ...        ...          ...   \n",
            "466280      466280  8598660    1440975      18400        18400   \n",
            "466281      466281  9684700   11536848      22000        22000   \n",
            "466282      466282  9584776   11436914      20700        20700   \n",
            "466283      466283  9604874   11457002       2000         2000   \n",
            "466284      466284  9199665   11061576      10000        10000   \n",
            "\n",
            "        funded_amnt_inv        term  int_rate  installment grade  ...  \\\n",
            "0                4975.0   36 months     10.65       162.87     B  ...   \n",
            "1                2500.0   60 months     15.27        59.83     C  ...   \n",
            "2                2400.0   36 months     15.96        84.33     C  ...   \n",
            "3               10000.0   36 months     13.49       339.31     C  ...   \n",
            "4                3000.0   60 months     12.69        67.79     B  ...   \n",
            "...                 ...         ...       ...          ...   ...  ...   \n",
            "466280          18400.0   60 months     14.47       432.64     C  ...   \n",
            "466281          22000.0   60 months     19.97       582.50     D  ...   \n",
            "466282          20700.0   60 months     16.99       514.34     D  ...   \n",
            "466283           2000.0   36 months      7.90        62.59     A  ...   \n",
            "466284           9975.0   36 months     19.20       367.58     D  ...   \n",
            "\n",
            "       total_bal_il il_util open_rv_12m open_rv_24m  max_bal_bc all_util  \\\n",
            "0               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "1               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "2               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "3               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "4               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "...             ...     ...         ...         ...         ...      ...   \n",
            "466280          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466281          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466282          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466283          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466284          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "\n",
            "       total_rev_hi_lim inq_fi total_cu_tl inq_last_12m  \n",
            "0                   NaN    NaN         NaN          NaN  \n",
            "1                   NaN    NaN         NaN          NaN  \n",
            "2                   NaN    NaN         NaN          NaN  \n",
            "3                   NaN    NaN         NaN          NaN  \n",
            "4                   NaN    NaN         NaN          NaN  \n",
            "...                 ...    ...         ...          ...  \n",
            "466280          29900.0    NaN         NaN          NaN  \n",
            "466281          39400.0    NaN         NaN          NaN  \n",
            "466282          13100.0    NaN         NaN          NaN  \n",
            "466283          53100.0    NaN         NaN          NaN  \n",
            "466284          16000.0    NaN         NaN          NaN  \n",
            "\n",
            "[466285 rows x 75 columns]>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena current dan in grace period merupakan status yang netral (Tidak termasuk gagal bayar atau berhasil bayar), maka data tersebut kita hapus saja."
      ],
      "metadata": {
        "id": "5Gtxlg7mti0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(df[(df['loan_status'] == \"Current\")].index, inplace=True)\n",
        "df.drop(df[(df['loan_status'] == \"In Grace Period\")].index, inplace = True)\n",
        "plotcount(df, y = 'loan_status', title = \"Status Pinjaman\")\n",
        "total_rows = df.count\n",
        "print(total_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kithSeQnAZJv",
        "outputId": "6696a0c1-63c4-4759-8273-6bc48c1c1363"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fully Paid                                             77.32%\n",
            "Charged Off                                            17.78%\n",
            "Late (31-120 days)                                      2.89%\n",
            "Does not meet the credit policy. Status:Fully Paid      0.83%\n",
            "Late (16-30 days)                                       0.51%\n",
            "Default                                                 0.35%\n",
            "Does not meet the credit policy. Status:Charged Off     0.32%\n",
            "Name: loan_status, dtype: object\n",
            "<bound method DataFrame.count of         Unnamed: 0       id  member_id  loan_amnt  funded_amnt  \\\n",
            "0                0  1077501    1296599       5000         5000   \n",
            "1                1  1077430    1314167       2500         2500   \n",
            "2                2  1077175    1313524       2400         2400   \n",
            "3                3  1076863    1277178      10000        10000   \n",
            "5                5  1075269    1311441       5000         5000   \n",
            "...            ...      ...        ...        ...          ...   \n",
            "466276      466276  9695736   11547808       8525         8525   \n",
            "466277      466277  9007579   10799568      18000        18000   \n",
            "466278      466278  9745590   11597671      10000        10000   \n",
            "466281      466281  9684700   11536848      22000        22000   \n",
            "466283      466283  9604874   11457002       2000         2000   \n",
            "\n",
            "        funded_amnt_inv        term  int_rate  installment grade  ...  \\\n",
            "0                4975.0   36 months     10.65       162.87     B  ...   \n",
            "1                2500.0   60 months     15.27        59.83     C  ...   \n",
            "2                2400.0   36 months     15.96        84.33     C  ...   \n",
            "3               10000.0   36 months     13.49       339.31     C  ...   \n",
            "5                5000.0   36 months      7.90       156.46     A  ...   \n",
            "...                 ...         ...       ...          ...   ...  ...   \n",
            "466276           8525.0   60 months     18.25       217.65     D  ...   \n",
            "466277          17975.0   36 months      7.90       563.23     A  ...   \n",
            "466278          10000.0   36 months     18.25       362.78     D  ...   \n",
            "466281          22000.0   60 months     19.97       582.50     D  ...   \n",
            "466283           2000.0   36 months      7.90        62.59     A  ...   \n",
            "\n",
            "       total_bal_il il_util open_rv_12m open_rv_24m  max_bal_bc all_util  \\\n",
            "0               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "1               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "2               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "3               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "5               NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "...             ...     ...         ...         ...         ...      ...   \n",
            "466276          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466277          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466278          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466281          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "466283          NaN     NaN         NaN         NaN         NaN      NaN   \n",
            "\n",
            "       total_rev_hi_lim inq_fi total_cu_tl inq_last_12m  \n",
            "0                   NaN    NaN         NaN          NaN  \n",
            "1                   NaN    NaN         NaN          NaN  \n",
            "2                   NaN    NaN         NaN          NaN  \n",
            "3                   NaN    NaN         NaN          NaN  \n",
            "5                   NaN    NaN         NaN          NaN  \n",
            "...                 ...    ...         ...          ...  \n",
            "466276          49800.0    NaN         NaN          NaN  \n",
            "466277          50300.0    NaN         NaN          NaN  \n",
            "466278          11100.0    NaN         NaN          NaN  \n",
            "466281          39400.0    NaN         NaN          NaN  \n",
            "466283          53100.0    NaN         NaN          NaN  \n",
            "\n",
            "[238913 rows x 75 columns]>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Credit risk yang seharusnya diterima pada waktu yang akan data adalah status peminjamannya \"Fully Paid\" atau berhasil dilunaskan, sementara yang ditolak adalah pinjaman dengan status charged off, default dan does not meet the credit policy."
      ],
      "metadata": {
        "id": "rTCXnXbit78U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "approved =[\n",
        "    \"Fully Paid\"\n",
        "]\n",
        "dissaproved = [\n",
        "    \"Charged Off\",\n",
        "    \"Does not meet the credit policy. Status:Fully Paid\",\n",
        "    \"Default\",\n",
        "    \"Does not meet the credit policy. Status:Charged Off\"\n",
        "]"
      ],
      "metadata": {
        "id": "Pvzb0drSByVI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita lakukan standarisasi, dimana jika pinjaman diterima karena statusnya adalah fully paid akan bernilai 1, selain itu bernilai 0"
      ],
      "metadata": {
        "id": "1iG9bloQuahT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def label_loan_status(value):\n",
        "    if value in approved:\n",
        "        return 1\n",
        "    return 0\n",
        "\n",
        "label_loan_status(\"Fully Paid\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OstHkjw1EXW0",
        "outputId": "e934e58d-a071-4881-86cd-6081aa940f39"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = df[df[\"loan_status\"].isin(approved + dissaproved)].copy()\n",
        "new_df[\"loan_status\"] = new_df[\"loan_status\"].apply(label_loan_status)\n",
        "plotcount(new_df, y=\"loan_status\", title=\"Status Pinjaman\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "8fVfdT9VExOs",
        "outputId": "91302fa8-68b8-4032-ea1a-f268615a08a7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    80.04%\n",
            "0    19.96%\n",
            "Name: loan_status, dtype: object\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari grafik diatas, dapat dilihat bahwa distribusi data tidak seimbang, sehingga perlu dilakukan data sampling sebelum melakukan pelatihan model machine learning"
      ],
      "metadata": {
        "id": "eOvS66xxvFOY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya kita akan menghapus kolom yang menurut penulis memeliki pengaruh kecil atau tidak berpengaruh dalam penentu keputusan apakah status pinjaman tersebut berhasil dibayar lunas atau tidak."
      ],
      "metadata": {
        "id": "3JH85h1wx6jI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Informasi rinci mengenai kolom dan baris data\n",
        "data_stat = pd.DataFrame()\n",
        "data_stat.index = new_df.columns\n",
        "data_stat[\"unique_value\"] = new_df.nunique()\n",
        "data_stat[\"missing_rate\"] = new_df.isna().mean()\n",
        "data_stat[\"dtype\"] = new_df.dtypes\n",
        "data_stat\n"
      ],
      "metadata": {
        "id": "5V2dGgcyyR76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "fe8be4a5-87f3-48a0-bb26-0ee93a0a8568"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  unique_value  missing_rate    dtype\n",
              "Unnamed: 0              230795      0.000000    int64\n",
              "id                      230795      0.000000    int64\n",
              "member_id               230795      0.000000    int64\n",
              "loan_amnt                 1308      0.000000    int64\n",
              "funded_amnt               1311      0.000000    int64\n",
              "...                        ...           ...      ...\n",
              "all_util                     0      1.000000  float64\n",
              "total_rev_hi_lim          9021      0.288026  float64\n",
              "inq_fi                       0      1.000000  float64\n",
              "total_cu_tl                  0      1.000000  float64\n",
              "inq_last_12m                 0      1.000000  float64\n",
              "\n",
              "[75 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74387e31-563e-4cde-9797-0101c7ff3e3e\">\n",
              "    <div 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>unique_value</th>\n",
              "      <th>missing_rate</th>\n",
              "      <th>dtype</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>230795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>230795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>member_id</th>\n",
              "      <td>230795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>1308</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>funded_amnt</th>\n",
              "      <td>1311</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_util</th>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rev_hi_lim</th>\n",
              "      <td>9021</td>\n",
              "      <td>0.288026</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq_fi</th>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_cu_tl</th>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq_last_12m</th>\n",
              "      <td>0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>75 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74387e31-563e-4cde-9797-0101c7ff3e3e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-74387e31-563e-4cde-9797-0101c7ff3e3e 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-74387e31-563e-4cde-9797-0101c7ff3e3e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Kolom yang tidak terdapat nilai\n",
        "miss_col = data_stat[data_stat[\"missing_rate\"] == 1].index.to_list()\n",
        "print(\"Kolom yang tidak memiliki nilai:\")\n",
        "print(miss_col)\n",
        "print()\n",
        "\n",
        "# Kolom yang terlalu unik\n",
        "vari_col = data_stat[data_stat[\"unique_value\"] == new_df.shape[0]].index.to_list()\n",
        "print(\"Kolom yang terlalu unik:\")\n",
        "print(vari_col)\n",
        "print()\n",
        "\n",
        "# Kolom dengan kategori yang banyak\n",
        "cat_col_stat = data_stat[data_stat[\"dtype\"] == \"object\"]\n",
        "vari_cat_col = cat_col_stat[cat_col_stat[\"unique_value\"] > 1000].index.to_list()\n",
        "print(\"Kolom dengan kategori yang banyak:\")\n",
        "print(vari_cat_col)\n",
        "print()\n",
        "\n",
        "# Kolom yang terdiri dari satu nilai\n",
        "single_valued_col = data_stat[data_stat[\"unique_value\"] == 1].index.to_list()\n",
        "print(\"Kolom yang terlalu unik:\")\n",
        "print(single_valued_col)\n",
        "print()\n",
        "\n",
        "removed_features = miss_col + vari_col + vari_cat_col + single_valued_col"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-mypJDXPq7F",
        "outputId": "c03d32d8-599d-45da-f72b-102f0009f380"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kolom yang tidak memiliki nilai:\n",
            "['annual_inc_joint', 'dti_joint', 'verification_status_joint', 'open_acc_6m', 'open_il_6m', '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', 'inq_fi', 'total_cu_tl', 'inq_last_12m']\n",
            "\n",
            "Kolom yang terlalu unik:\n",
            "['Unnamed: 0', 'id', 'member_id', 'url']\n",
            "\n",
            "Kolom dengan kategori yang banyak:\n",
            "['emp_title', 'url', 'desc', 'title']\n",
            "\n",
            "Kolom yang terlalu unik:\n",
            "['policy_code', 'application_type']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "hYX4kImjK2rZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menghitung korelasi kolom loan_status dengan kolom lainnya untuk mengetahui parameter yang mempengaruhi nilai pada loan_status"
      ],
      "metadata": {
        "id": "ysVTk4vHovod"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hilangkan fitur yang tidak terpakai\n",
        "pre_df = new_df.loc[:, ~new_df.columns.isin(removed_features)].copy()\n",
        "pre_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOwcz9TKo8ix",
        "outputId": "7b5083dc-1b47-49b7-d5f8-097b42e3b21e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 230795 entries, 0 to 466283\n",
            "Data columns (total 49 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   loan_amnt                    230795 non-null  int64  \n",
            " 1   funded_amnt                  230795 non-null  int64  \n",
            " 2   funded_amnt_inv              230795 non-null  float64\n",
            " 3   term                         230795 non-null  object \n",
            " 4   int_rate                     230795 non-null  float64\n",
            " 5   installment                  230795 non-null  float64\n",
            " 6   grade                        230795 non-null  object \n",
            " 7   sub_grade                    230795 non-null  object \n",
            " 8   emp_length                   222040 non-null  object \n",
            " 9   home_ownership               230795 non-null  object \n",
            " 10  annual_inc                   230791 non-null  float64\n",
            " 11  verification_status          230795 non-null  object \n",
            " 12  issue_d                      230795 non-null  object \n",
            " 13  loan_status                  230795 non-null  int64  \n",
            " 14  pymnt_plan                   230795 non-null  object \n",
            " 15  purpose                      230795 non-null  object \n",
            " 16  zip_code                     230795 non-null  object \n",
            " 17  addr_state                   230795 non-null  object \n",
            " 18  dti                          230795 non-null  float64\n",
            " 19  delinq_2yrs                  230766 non-null  float64\n",
            " 20  earliest_cr_line             230766 non-null  object \n",
            " 21  inq_last_6mths               230766 non-null  float64\n",
            " 22  mths_since_last_delinq       101111 non-null  float64\n",
            " 23  mths_since_last_record       27646 non-null   float64\n",
            " 24  open_acc                     230766 non-null  float64\n",
            " 25  pub_rec                      230766 non-null  float64\n",
            " 26  revol_bal                    230795 non-null  int64  \n",
            " 27  revol_util                   230569 non-null  float64\n",
            " 28  total_acc                    230766 non-null  float64\n",
            " 29  initial_list_status          230795 non-null  object \n",
            " 30  out_prncp                    230795 non-null  float64\n",
            " 31  out_prncp_inv                230795 non-null  float64\n",
            " 32  total_pymnt                  230795 non-null  float64\n",
            " 33  total_pymnt_inv              230795 non-null  float64\n",
            " 34  total_rec_prncp              230795 non-null  float64\n",
            " 35  total_rec_int                230795 non-null  float64\n",
            " 36  total_rec_late_fee           230795 non-null  float64\n",
            " 37  recoveries                   230795 non-null  float64\n",
            " 38  collection_recovery_fee      230795 non-null  float64\n",
            " 39  last_pymnt_d                 230419 non-null  object \n",
            " 40  last_pymnt_amnt              230795 non-null  float64\n",
            " 41  next_pymnt_d                 3581 non-null    object \n",
            " 42  last_credit_pull_d           230772 non-null  object \n",
            " 43  collections_12_mths_ex_med   230650 non-null  float64\n",
            " 44  mths_since_last_major_derog  40232 non-null   float64\n",
            " 45  acc_now_delinq               230766 non-null  float64\n",
            " 46  tot_coll_amt                 164320 non-null  float64\n",
            " 47  tot_cur_bal                  164320 non-null  float64\n",
            " 48  total_rev_hi_lim             164320 non-null  float64\n",
            "dtypes: float64(29), int64(4), object(16)\n",
            "memory usage: 96.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mencari fitur numerik yang akan digunakan"
      ],
      "metadata": {
        "id": "LApP5L_Pef5L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlations = (pre_df.select_dtypes(exclude=object)\n",
        "                         .corr()\n",
        "                         .dropna(how=\"all\", axis=0)\n",
        "                         .dropna(how=\"all\", axis=1)\n",
        ")\n",
        "\n",
        "data = correlations[\"loan_status\"].abs().sort_values(ascending=False)\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZv5_HNqdrg3",
        "outputId": "311e6806-6013-4b62-83c6-8f00f3702130"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_status                    1.000000\n",
              "total_rec_prncp                0.460338\n",
              "recoveries                     0.445422\n",
              "last_pymnt_amnt                0.384781\n",
              "total_pymnt_inv                0.358588\n",
              "total_pymnt                    0.355349\n",
              "collection_recovery_fee        0.300094\n",
              "int_rate                       0.241059\n",
              "total_rec_late_fee             0.152231\n",
              "inq_last_6mths                 0.116393\n",
              "dti                            0.107257\n",
              "out_prncp                      0.099816\n",
              "out_prncp_inv                  0.099814\n",
              "revol_util                     0.089180\n",
              "tot_cur_bal                    0.076230\n",
              "mths_since_last_record         0.062190\n",
              "annual_inc                     0.061623\n",
              "total_rev_hi_lim               0.052931\n",
              "loan_amnt                      0.049253\n",
              "funded_amnt                    0.048790\n",
              "funded_amnt_inv                0.039854\n",
              "installment                    0.033419\n",
              "total_rec_int                  0.032997\n",
              "total_acc                      0.030680\n",
              "mths_since_last_delinq         0.029299\n",
              "delinq_2yrs                    0.019631\n",
              "open_acc                       0.013934\n",
              "revol_bal                      0.011371\n",
              "mths_since_last_major_derog    0.010022\n",
              "acc_now_delinq                 0.006406\n",
              "pub_rec                        0.006355\n",
              "collections_12_mths_ex_med     0.005582\n",
              "tot_coll_amt                   0.001637\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita masukkan kolom dengan nilai korelasi lebih dari 0.1 ke dalam dataframe yang akan digunakan untuk pelatihan machine learning"
      ],
      "metadata": {
        "id": "BIwTEPXxgpUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = 0\n",
        "num_app = []\n",
        "for i in data.values:\n",
        "  if i > 0.1 :\n",
        "    num_app.append(data.index[a])\n",
        "  a += 1\n",
        "\n",
        "num_app"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOgh2rX2gm4W",
        "outputId": "faff3239-0185-43cf-c41d-4ef3da61d8f1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['loan_status',\n",
              " 'total_rec_prncp',\n",
              " 'recoveries',\n",
              " 'last_pymnt_amnt',\n",
              " 'total_pymnt_inv',\n",
              " 'total_pymnt',\n",
              " 'collection_recovery_fee',\n",
              " 'int_rate',\n",
              " 'total_rec_late_fee',\n",
              " 'inq_last_6mths',\n",
              " 'dti']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kolom-kolom yang berdata tanggal\n",
        "date_cols = [\"issue_d\", \"earliest_cr_line\", \"last_pymnt_d\", \"last_credit_pull_d\", \"next_pymnt_d\"]\n",
        "\n",
        "for col in date_cols:\n",
        "    print(pre_df[col].value_counts().iloc[:5])\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGki68Gdg5aH",
        "outputId": "4940b479-f3b5-4eb2-9ad2-4976dac44736"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14-Oct    8808\n",
            "14-Jul    8614\n",
            "14-Apr    6744\n",
            "13-Nov    6665\n",
            "13-Oct    6653\n",
            "Name: issue_d, dtype: int64\n",
            "\n",
            "Oct-00    1972\n",
            "Oct-99    1837\n",
            "Nov-00    1796\n",
            "1-Oct     1779\n",
            "Aug-00    1758\n",
            "Name: earliest_cr_line, dtype: int64\n",
            "\n",
            "15-Jul    11703\n",
            "15-Mar    10087\n",
            "15-Jun    10078\n",
            "15-Apr    10051\n",
            "15-Aug     9932\n",
            "Name: last_pymnt_d, dtype: int64\n",
            "\n",
            "16-Jan    93758\n",
            "15-Dec    13753\n",
            "15-Nov     8199\n",
            "15-Sep     7942\n",
            "15-Oct     7805\n",
            "Name: last_credit_pull_d, dtype: int64\n",
            "\n",
            "16-Feb    795\n",
            "11-Mar    107\n",
            "11-Apr    101\n",
            "11-Feb     91\n",
            "11-Jan     79\n",
            "Name: next_pymnt_d, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Korelasi antar tanggal dan status pinjaman\n",
        "used_cols = date_cols + [\"loan_status\"] \n",
        "complete_correlation = associations(\n",
        "    pre_df[used_cols], \n",
        "    filename='date_correlation.png',\n",
        "    figsize=(10,10)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "3P9gzdBhiATB",
        "outputId": "f96b3f7b-345a-46b1-ea43-35ffd098501a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_cols_app = [\"issue_d\", \"last_pymnt_d\", \"last_credit_pull_d\", \"next_pymnt_d\"]"
      ],
      "metadata": {
        "id": "wxYXUypbhoM9"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Kolom-kolom yang berdata kategorik\n",
        "cat_features = pre_df.select_dtypes(include=object).columns\n",
        "cat_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "826kdmnwhk5m",
        "outputId": "0a77d0cf-9059-45ea-b0fd-6d293ec73079"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'emp_length', 'home_ownership',\n",
              "       'verification_status', 'issue_d', 'pymnt_plan', 'purpose', 'zip_code',\n",
              "       'addr_state', 'earliest_cr_line', 'initial_list_status', 'last_pymnt_d',\n",
              "       'next_pymnt_d', 'last_credit_pull_d'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "other_cat_cols = cat_features[~cat_features.isin(date_cols)]\n",
        "other_cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awSJjHTTlrZ9",
        "outputId": "73dab471-ebfa-48c6-8c9d-93daffa5a0f0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'emp_length', 'home_ownership',\n",
              "       'verification_status', 'pymnt_plan', 'purpose', 'zip_code',\n",
              "       'addr_state', 'initial_list_status'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Korelasi antar fitur kategorikal dan status pinjaman\n",
        "used_cols = other_cat_cols.to_list() + [\"loan_status\"]\n",
        "complete_correlation = associations(\n",
        "    pre_df[used_cols], \n",
        "    filename='cat_correlation.png',\n",
        "    figsize=(10,10)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "oRhf1RdtkqB-",
        "outputId": "407d4159-a6bf-47ef-d384-9aba64064558"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#kolom_kategori yang akan dipakai\n",
        "cat_app = ['term', 'grade', 'sub_grade']"
      ],
      "metadata": {
        "id": "9tkA-jp8nW2y"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = num_app + date_cols_app + cat_app\n",
        "app_df = pre_df[app]"
      ],
      "metadata": {
        "id": "W16S2r-Knjta"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mencari kolom yang terdapat nilai null"
      ],
      "metadata": {
        "id": "1khKI5LCuDp1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "app_df.isna().mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pl-zBBZt57g",
        "outputId": "77be46f7-7d50-4f8b-f9bb-7cf72cbef70e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "next_pymnt_d               0.984484\n",
              "last_pymnt_d               0.001629\n",
              "inq_last_6mths             0.000126\n",
              "last_credit_pull_d         0.000100\n",
              "total_rec_prncp            0.000000\n",
              "grade                      0.000000\n",
              "term                       0.000000\n",
              "issue_d                    0.000000\n",
              "dti                        0.000000\n",
              "loan_status                0.000000\n",
              "total_rec_late_fee         0.000000\n",
              "int_rate                   0.000000\n",
              "collection_recovery_fee    0.000000\n",
              "total_pymnt                0.000000\n",
              "total_pymnt_inv            0.000000\n",
              "last_pymnt_amnt            0.000000\n",
              "recoveries                 0.000000\n",
              "sub_grade                  0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "memasukkan nilai pada kolom yang terdapat data null"
      ],
      "metadata": {
        "id": "KpH4xdUouPh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# kita isi kolom next_pymnt_d, last_pymnt_d dan inq_lat_6mths dengan nilai lunas\n",
        "# karena terdapat kemungkinan hutang sudah dibayar lunas\n",
        "\n",
        "app_df[\"next_pymnt_d\"] = app_df[\"next_pymnt_d\"].fillna(\"lunas\")\n",
        "app_df[\"last_pymnt_d\"] = app_df[\"last_pymnt_d\"].fillna(\"lunas\")\n",
        "app_df[\"last_credit_pull_d\"] = app_df[\"last_credit_pull_d\"].fillna(\"lunas\")\n",
        "\n",
        "# kolom inq_last_6months akan diisi dengan nilai modus\n",
        "mode = app_df[\"inq_last_6mths\"].mode().values[0]\n",
        "app_df[\"inq_last_6mths\"] = app_df[\"inq_last_6mths\"].fillna(mode)\n",
        "\n",
        "# cek apakah masih ada kolom yang terdapat nilai null\n",
        "app_df.isna().mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCPm0-B5uPHF",
        "outputId": "b55556ce-a002-4636-fa16-0c47f3b73929"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_status                0.0\n",
              "total_rec_prncp            0.0\n",
              "grade                      0.0\n",
              "term                       0.0\n",
              "next_pymnt_d               0.0\n",
              "last_credit_pull_d         0.0\n",
              "last_pymnt_d               0.0\n",
              "issue_d                    0.0\n",
              "dti                        0.0\n",
              "inq_last_6mths             0.0\n",
              "total_rec_late_fee         0.0\n",
              "int_rate                   0.0\n",
              "collection_recovery_fee    0.0\n",
              "total_pymnt                0.0\n",
              "total_pymnt_inv            0.0\n",
              "last_pymnt_amnt            0.0\n",
              "recoveries                 0.0\n",
              "sub_grade                  0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Melakukan pemodelan data"
      ],
      "metadata": {
        "id": "IWkl0xVUveMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = app_df\n"
      ],
      "metadata": {
        "id": "QGmkPYP168UJ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = train_df.drop(columns = \"loan_status\")\n",
        "y = app_df[\"loan_status\"]"
      ],
      "metadata": {
        "id": "22vCoFOIvXM_"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_features = x.select_dtypes(exclude=\"object\")\n",
        "cat_features = x.select_dtypes(include=\"object\")"
      ],
      "metadata": {
        "id": "xbMaHtme7n8H"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalisasi fitur numerik\n",
        "num_features = (num_features - num_features.mean()) / num_features.std()\n",
        "num_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "8IV1SjqL7qkz",
        "outputId": "22a16e86-13a0-4655-d8af-148b0b547dc3"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        total_rec_prncp  recoveries  last_pymnt_amnt  total_pymnt_inv  \\\n",
              "0             -0.765957   -0.222400        -0.822146        -0.800159   \n",
              "1             -1.320543   -0.071386        -0.829680        -1.310168   \n",
              "2             -1.083314   -0.222400        -0.752787        -1.099216   \n",
              "3             -0.155655   -0.222400        -0.795193        -0.123979   \n",
              "5             -0.765957   -0.222400        -0.823681        -0.821350   \n",
              "...                 ...         ...              ...              ...   \n",
              "466276        -1.332307    1.273040        -0.815471        -1.202181   \n",
              "466277        -0.817113   -0.222400        -0.765357        -0.822146   \n",
              "466278        -0.155655   -0.222400        -0.049229        -0.106227   \n",
              "466281        -1.152029   -0.222400        -0.844495        -0.922172   \n",
              "466283        -1.132138   -0.222400        -0.629414        -1.191961   \n",
              "\n",
              "        total_pymnt  collection_recovery_fee  int_rate  total_rec_late_fee  \\\n",
              "0         -0.812658                -0.149837 -0.716859           -0.139146   \n",
              "1         -1.326547                -0.140651  0.340654           -0.139146   \n",
              "2         -1.115272                -0.149837  0.498595           -0.139146   \n",
              "3         -0.138548                -0.149837 -0.066786            2.788316   \n",
              "5         -0.836983                -0.149837 -1.346331           -0.139146   \n",
              "...             ...                      ...       ...                 ...   \n",
              "466276    -1.218395                -0.053887  1.022774           -0.139146   \n",
              "466277    -0.836953                -0.149837 -1.346331           -0.139146   \n",
              "466278    -0.120771                -0.149837  1.022774           -0.139146   \n",
              "466281    -0.937959                -0.149837  1.416480           -0.139146   \n",
              "466283    -1.208159                -0.149837 -1.346331           -0.139146   \n",
              "\n",
              "        inq_last_6mths       dti  \n",
              "0             0.078612  1.472931  \n",
              "1             3.480063 -2.000498  \n",
              "2             0.928975 -0.994312  \n",
              "3             0.078612  0.475868  \n",
              "5             1.779338 -0.671081  \n",
              "...                ...       ...  \n",
              "466276       -0.771750 -0.530319  \n",
              "466277       -0.771750 -0.784472  \n",
              "466278       -0.771750 -0.622857  \n",
              "466281        3.480063  0.273848  \n",
              "466283        0.078612 -1.428327  \n",
              "\n",
              "[230795 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a167ab7c-01e8-4e6a-85b8-c17eb0561b97\">\n",
              "    <div 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>total_rec_prncp</th>\n",
              "      <th>recoveries</th>\n",
              "      <th>last_pymnt_amnt</th>\n",
              "      <th>total_pymnt_inv</th>\n",
              "      <th>total_pymnt</th>\n",
              "      <th>collection_recovery_fee</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>total_rec_late_fee</th>\n",
              "      <th>inq_last_6mths</th>\n",
              "      <th>dti</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.765957</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.822146</td>\n",
              "      <td>-0.800159</td>\n",
              "      <td>-0.812658</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>-0.716859</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>0.078612</td>\n",
              "      <td>1.472931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.320543</td>\n",
              "      <td>-0.071386</td>\n",
              "      <td>-0.829680</td>\n",
              "      <td>-1.310168</td>\n",
              "      <td>-1.326547</td>\n",
              "      <td>-0.140651</td>\n",
              "      <td>0.340654</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>3.480063</td>\n",
              "      <td>-2.000498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.083314</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.752787</td>\n",
              "      <td>-1.099216</td>\n",
              "      <td>-1.115272</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>0.498595</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>0.928975</td>\n",
              "      <td>-0.994312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.155655</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.795193</td>\n",
              "      <td>-0.123979</td>\n",
              "      <td>-0.138548</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>-0.066786</td>\n",
              "      <td>2.788316</td>\n",
              "      <td>0.078612</td>\n",
              "      <td>0.475868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.765957</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.823681</td>\n",
              "      <td>-0.821350</td>\n",
              "      <td>-0.836983</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>-1.346331</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>1.779338</td>\n",
              "      <td>-0.671081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466276</th>\n",
              "      <td>-1.332307</td>\n",
              "      <td>1.273040</td>\n",
              "      <td>-0.815471</td>\n",
              "      <td>-1.202181</td>\n",
              "      <td>-1.218395</td>\n",
              "      <td>-0.053887</td>\n",
              "      <td>1.022774</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>-0.771750</td>\n",
              "      <td>-0.530319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466277</th>\n",
              "      <td>-0.817113</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.765357</td>\n",
              "      <td>-0.822146</td>\n",
              "      <td>-0.836953</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>-1.346331</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>-0.771750</td>\n",
              "      <td>-0.784472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466278</th>\n",
              "      <td>-0.155655</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.049229</td>\n",
              "      <td>-0.106227</td>\n",
              "      <td>-0.120771</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>1.022774</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>-0.771750</td>\n",
              "      <td>-0.622857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466281</th>\n",
              "      <td>-1.152029</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.844495</td>\n",
              "      <td>-0.922172</td>\n",
              "      <td>-0.937959</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>1.416480</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>3.480063</td>\n",
              "      <td>0.273848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466283</th>\n",
              "      <td>-1.132138</td>\n",
              "      <td>-0.222400</td>\n",
              "      <td>-0.629414</td>\n",
              "      <td>-1.191961</td>\n",
              "      <td>-1.208159</td>\n",
              "      <td>-0.149837</td>\n",
              "      <td>-1.346331</td>\n",
              "      <td>-0.139146</td>\n",
              "      <td>0.078612</td>\n",
              "      <td>-1.428327</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230795 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a167ab7c-01e8-4e6a-85b8-c17eb0561b97')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a167ab7c-01e8-4e6a-85b8-c17eb0561b97 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-a167ab7c-01e8-4e6a-85b8-c17eb0561b97');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# OneHotEncode fitur kategorik\n",
        "cat_features = pd.get_dummies(cat_features)\n",
        "cat_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "o5B9ob057y1_",
        "outputId": "15ef1896-6e1a-44a5-85fc-24cad262e8bc"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        issue_d_10-Apr  issue_d_10-Aug  issue_d_10-Dec  issue_d_10-Feb  \\\n",
              "0                    0               0               0               0   \n",
              "1                    0               0               0               0   \n",
              "2                    0               0               0               0   \n",
              "3                    0               0               0               0   \n",
              "5                    0               0               0               0   \n",
              "...                ...             ...             ...             ...   \n",
              "466276               0               0               0               0   \n",
              "466277               0               0               0               0   \n",
              "466278               0               0               0               0   \n",
              "466281               0               0               0               0   \n",
              "466283               0               0               0               0   \n",
              "\n",
              "        issue_d_10-Jan  issue_d_10-Jul  issue_d_10-Jun  issue_d_10-Mar  \\\n",
              "0                    0               0               0               0   \n",
              "1                    0               0               0               0   \n",
              "2                    0               0               0               0   \n",
              "3                    0               0               0               0   \n",
              "5                    0               0               0               0   \n",
              "...                ...             ...             ...             ...   \n",
              "466276               0               0               0               0   \n",
              "466277               0               0               0               0   \n",
              "466278               0               0               0               0   \n",
              "466281               0               0               0               0   \n",
              "466283               0               0               0               0   \n",
              "\n",
              "        issue_d_10-May  issue_d_10-Nov  ...  sub_grade_F1  sub_grade_F2  \\\n",
              "0                    0               0  ...             0             0   \n",
              "1                    0               0  ...             0             0   \n",
              "2                    0               0  ...             0             0   \n",
              "3                    0               0  ...             0             0   \n",
              "5                    0               0  ...             0             0   \n",
              "...                ...             ...  ...           ...           ...   \n",
              "466276               0               0  ...             0             0   \n",
              "466277               0               0  ...             0             0   \n",
              "466278               0               0  ...             0             0   \n",
              "466281               0               0  ...             0             0   \n",
              "466283               0               0  ...             0             0   \n",
              "\n",
              "        sub_grade_F3  sub_grade_F4  sub_grade_F5  sub_grade_G1  sub_grade_G2  \\\n",
              "0                  0             0             0             0             0   \n",
              "1                  0             0             0             0             0   \n",
              "2                  0             0             0             0             0   \n",
              "3                  0             0             0             0             0   \n",
              "5                  0             0             0             0             0   \n",
              "...              ...           ...           ...           ...           ...   \n",
              "466276             0             0             0             0             0   \n",
              "466277             0             0             0             0             0   \n",
              "466278             0             0             0             0             0   \n",
              "466281             0             0             0             0             0   \n",
              "466283             0             0             0             0             0   \n",
              "\n",
              "        sub_grade_G3  sub_grade_G4  sub_grade_G5  \n",
              "0                  0             0             0  \n",
              "1                  0             0             0  \n",
              "2                  0             0             0  \n",
              "3                  0             0             0  \n",
              "5                  0             0             0  \n",
              "...              ...           ...           ...  \n",
              "466276             0             0             0  \n",
              "466277             0             0             0  \n",
              "466278             0             0             0  \n",
              "466281             0             0             0  \n",
              "466283             0             0             0  \n",
              "\n",
              "[230795 rows x 438 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f9c95d9-ea21-4da5-a13f-8cb654f46b9c\">\n",
              "    <div 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>issue_d_10-Apr</th>\n",
              "      <th>issue_d_10-Aug</th>\n",
              "      <th>issue_d_10-Dec</th>\n",
              "      <th>issue_d_10-Feb</th>\n",
              "      <th>issue_d_10-Jan</th>\n",
              "      <th>issue_d_10-Jul</th>\n",
              "      <th>issue_d_10-Jun</th>\n",
              "      <th>issue_d_10-Mar</th>\n",
              "      <th>issue_d_10-May</th>\n",
              "      <th>issue_d_10-Nov</th>\n",
              "      <th>...</th>\n",
              "      <th>sub_grade_F1</th>\n",
              "      <th>sub_grade_F2</th>\n",
              "      <th>sub_grade_F3</th>\n",
              "      <th>sub_grade_F4</th>\n",
              "      <th>sub_grade_F5</th>\n",
              "      <th>sub_grade_G1</th>\n",
              "      <th>sub_grade_G2</th>\n",
              "      <th>sub_grade_G3</th>\n",
              "      <th>sub_grade_G4</th>\n",
              "      <th>sub_grade_G5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466276</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466277</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466278</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466281</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466283</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>230795 rows × 438 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f9c95d9-ea21-4da5-a13f-8cb654f46b9c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5f9c95d9-ea21-4da5-a13f-8cb654f46b9c 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-5f9c95d9-ea21-4da5-a13f-8cb654f46b9c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gabungkan Fitur\n",
        "features_full = pd.concat([num_features, cat_features], axis=1)\n",
        "x = features_full"
      ],
      "metadata": {
        "id": "slf74bFS71vj"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=1)"
      ],
      "metadata": {
        "id": "4HUdjMaH-sVl"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan Over sampling menggunakan SMOTE"
      ],
      "metadata": {
        "id": "_1HfMha7DGsk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sm = SMOTE(random_state = 2)\n",
        "x_train_res, y_train_res = sm.fit_resample(x_train, y_train.ravel())"
      ],
      "metadata": {
        "id": "GLb702Hs84vl"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train == 1)))\n",
        "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train == 0)))\n",
        "\n",
        "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res == 1)))\n",
        "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res == 0)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUMR8igcCimH",
        "outputId": "8c9cf6ee-8c7d-460b-e9c5-bc9ef5085a9f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before OverSampling, counts of label '1': 147661\n",
            "Before OverSampling, counts of label '0': 36975 \n",
            "\n",
            "After OverSampling, counts of label '1': 147661\n",
            "After OverSampling, counts of label '0': 147661\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1 = LogisticRegression()\n",
        "lr1.fit(x_train_res, y_train_res.ravel())\n",
        "predictions = lr1.predict(x_test)\n",
        "  \n",
        "# print classification report\n",
        "print(classification_report(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KifwlFmyB26_",
        "outputId": "5f7907c6-c4ed-49d2-c5ac-9234baa83ae9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97      9081\n",
            "           1       0.99      0.99      0.99     37078\n",
            "\n",
            "    accuracy                           0.99     46159\n",
            "   macro avg       0.98      0.98      0.98     46159\n",
            "weighted avg       0.99      0.99      0.99     46159\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(lr1, \"logres.z\")\n",
        "logres = joblib.load(\"logres.z\")"
      ],
      "metadata": {
        "id": "EzmEOapjDzPB"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logres.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHUqV2UuD323",
        "outputId": "59c5fc1e-479f-408f-aae1-5067ce99ba0d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9884421239628242"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_true=y_train, y_pred=logres.predict(x_train))\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Or8a-DTIEC4N",
        "outputId": "06de9d42-5db9-4004-8db7-2d422b7fd4da"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97     36975\n",
            "           1       0.99      0.99      0.99    147661\n",
            "\n",
            "    accuracy                           0.99    184636\n",
            "   macro avg       0.98      0.98      0.98    184636\n",
            "weighted avg       0.99      0.99      0.99    184636\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logres.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGzxJRZ6EJLJ",
        "outputId": "9fdd6785-7f9f-48f7-a8fb-6e4a6539d93c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9868931302671202"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_true=y_test, y_pred=logres.predict(x_test))\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpP6hZ6-EL3L",
        "outputId": "4f5592a0-b51e-4207-f07a-817125bc39d4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.96      0.97      9081\n",
            "           1       0.99      0.99      0.99     37078\n",
            "\n",
            "    accuracy                           0.99     46159\n",
            "   macro avg       0.98      0.98      0.98     46159\n",
            "weighted avg       0.99      0.99      0.99     46159\n",
            "\n"
          ]
        }
      ]
    }
  ]
}