Proyek_Akhir_Rakamin_Home_Credit_Indonesia.ipynb

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  "metadata": {
    "colab": {
      "name": "Proyek Akhir Rakamin Home Credit Indonesia.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Ug--3kf0ChnIindlJvyTJ40oHx7t1AyT",
      "authorship_tag": "ABX9TyNvJjZ2+EKUDYPffGIA8JuY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/farizalmustaqim/Rakamin-Project/blob/main/Proyek_Akhir_Rakamin_Home_Credit_Indonesia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Proyek Akhir Rakamin Home Credit Indonesia"
      ],
      "metadata": {
        "id": "sp3ynB_J5cMf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instruksi"
      ],
      "metadata": {
        "id": "a1X1x7FZ5pgf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tahapan Pengerjaan\n",
        "1. Download Dataset yang dibutuhkan.\n",
        "\n",
        "2. Pelajari konteks masalah dari sumber eksternal untuk meningkatkan Subject Matter Knowledge.\n",
        "\n",
        "3. Pahami deskripsi kolom yang tersedia.\n",
        "\n",
        "4. Tentukan goal, objective, dan metrics dari masalah yang ada.\n",
        "\n",
        "5. Lakukan penggalian informasi terkait kondisi data awal.\n",
        "\n",
        "6. Lakukan proses Data Cleaning dan Data Processing.\n",
        "\n",
        "7. Lakukan proses penggalian insight mengacu kepada objective yang sudah ditetapkan.\n",
        "\n",
        "8. Lakukan pemodelan dengan berbagai macam metode (termasuk Logistic Regression) dan hyperparameternya.\n",
        "\n",
        "9. Evaluasi hasil pemodelan.\n",
        "\n",
        "10. Ukur seberapa besar dampak pemodelan dan rekomendasi bisnis terhadap metrics yang telah ditetapkan sebelumnya.\n",
        "\n",
        "11. Push file .ipynb mu ke dalam github.\n",
        "\n",
        "12. Buat file presentasi untuk menjelaskan pekerjaan yang telah dilakukan dan cantumkan link repo github di dalam ppt nya"
      ],
      "metadata": {
        "id": "k38OP8tk6azf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction"
      ],
      "metadata": {
        "id": "EW9h9dgP8uo0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Library/Packages"
      ],
      "metadata": {
        "id": "GRdIdgSf8yjB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ATX7Sddk40QR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from yellowbrick.target import ClassBalance\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn import model_selection\n",
        "import joblib\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Dataset"
      ],
      "metadata": {
        "id": "GgoivsSC83GK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl = pd.read_csv('/content/drive/MyDrive/home-credit-default-risk/Clean_Data.csv')\n",
        "appl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "29Tv0Ms167fy",
        "outputId": "876d697a-b243-42d9-8d1f-db570f785381"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0           103856       0         Cash loans           F            Y   \n",
              "1           120392       0         Cash loans           M            N   \n",
              "2           135040       0         Cash loans           F            N   \n",
              "3           140156       0         Cash loans           M            Y   \n",
              "4           141914       0         Cash loans           M            Y   \n",
              "...            ...     ...                ...         ...          ...   \n",
              "307506      456243       0         Cash loans           F            N   \n",
              "307507      456246       0         Cash loans           F            N   \n",
              "307508      456247       0         Cash loans           F            N   \n",
              "307509      456248       0         Cash loans           F            N   \n",
              "307510      456254       1         Cash loans           F            N   \n",
              "\n",
              "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
              "0                    N             1           2250000     7819200   \n",
              "1                    Y             0           4050000     9856035   \n",
              "2                    Y             0           2070000     4725000   \n",
              "3                    Y             1           1800000     4500000   \n",
              "4                    N             1           1800000     9703800   \n",
              "...                ...           ...               ...         ...   \n",
              "307506               Y             0            810000     2250000   \n",
              "307507               Y             1            945000     2250000   \n",
              "307508               Y             0           1125000     3455100   \n",
              "307509               Y             0           1530000     3319200   \n",
              "307510               Y             0           1710000     3701070   \n",
              "\n",
              "        AMT_ANNUITY  ...  HOUSETYPE_MODE TOTALAREA_MODE WALLSMATERIAL_MODE  \\\n",
              "0          424170.0  ...             NaN          623.0                NaN   \n",
              "1          658665.0  ...  block of flats          877.0       Stone, brick   \n",
              "2          449910.0  ...  block of flats          688.0       Stone, brick   \n",
              "3          478800.0  ...  block of flats         1609.0              Panel   \n",
              "4          343800.0  ...  block of flats          694.0              Block   \n",
              "...             ...  ...             ...            ...                ...   \n",
              "307506     126945.0  ...             NaN            NaN                NaN   \n",
              "307507     106200.0  ...             NaN            NaN                NaN   \n",
              "307508     177705.0  ...             NaN            NaN                NaN   \n",
              "307509     160965.0  ...             NaN            NaN                NaN   \n",
              "307510     202050.0  ...  block of flats           86.0       Stone, brick   \n",
              "\n",
              "       EMERGENCYSTATE_MODE AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
              "0                       No                        0.0   \n",
              "1                       No                        NaN   \n",
              "2                       No                        0.0   \n",
              "3                       No                        0.0   \n",
              "4                       No                        0.0   \n",
              "...                    ...                        ...   \n",
              "307506                 NaN                        0.0   \n",
              "307507                 NaN                        0.0   \n",
              "307508                 NaN                        0.0   \n",
              "307509                 NaN                        NaN   \n",
              "307510                  No                        0.0   \n",
              "\n",
              "       AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
              "0                            0.0                        0.0   \n",
              "1                            NaN                        NaN   \n",
              "2                            0.0                        0.0   \n",
              "3                            0.0                        0.0   \n",
              "4                            0.0                        0.0   \n",
              "...                          ...                        ...   \n",
              "307506                       0.0                        0.0   \n",
              "307507                       0.0                        0.0   \n",
              "307508                       0.0                        0.0   \n",
              "307509                       NaN                        NaN   \n",
              "307510                       0.0                        0.0   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
              "0                            10.0                        0.0   \n",
              "1                             NaN                        NaN   \n",
              "2                             0.0                        0.0   \n",
              "3                             0.0                        0.0   \n",
              "4                             0.0                        0.0   \n",
              "...                           ...                        ...   \n",
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              "307508                       10.0                        0.0   \n",
              "307509                        NaN                        NaN   \n",
              "307510                        0.0                        0.0   \n",
              "\n",
              "       AMT_REQ_CREDIT_BUREAU_YEAR  \n",
              "0                            20.0  \n",
              "1                             NaN  \n",
              "2                            40.0  \n",
              "3                             0.0  \n",
              "4                            30.0  \n",
              "...                           ...  \n",
              "307506                        0.0  \n",
              "307507                       10.0  \n",
              "307508                       20.0  \n",
              "307509                        NaN  \n",
              "307510                        0.0  \n",
              "\n",
              "[307511 rows x 33 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SK_ID_CURR</th>\n",
              "      <th>TARGET</th>\n",
              "      <th>NAME_CONTRACT_TYPE</th>\n",
              "      <th>CODE_GENDER</th>\n",
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              "      <th>WALLSMATERIAL_MODE</th>\n",
              "      <th>EMERGENCYSTATE_MODE</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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              "      <th>2</th>\n",
              "      <td>135040</td>\n",
              "      <td>0</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
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              "      <td>2070000</td>\n",
              "      <td>4725000</td>\n",
              "      <td>449910.0</td>\n",
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              "      <td>block of flats</td>\n",
              "      <td>688.0</td>\n",
              "      <td>Stone, brick</td>\n",
              "      <td>No</td>\n",
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              "      <td>1609.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>694.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "appl.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSBvnwLB1hvD",
        "outputId": "2ff88e36-43f8-44e7-995a-7c17337f186a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 307511 entries, 0 to 307510\n",
            "Data columns (total 33 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   SK_ID_CURR                   307511 non-null  int64  \n",
            " 1   TARGET                       307511 non-null  int64  \n",
            " 2   NAME_CONTRACT_TYPE           307511 non-null  object \n",
            " 3   CODE_GENDER                  307511 non-null  object \n",
            " 4   FLAG_OWN_CAR                 307511 non-null  object \n",
            " 5   FLAG_OWN_REALTY              307511 non-null  object \n",
            " 6   CNT_CHILDREN                 307511 non-null  int64  \n",
            " 7   AMT_INCOME_TOTAL             307511 non-null  int64  \n",
            " 8   AMT_CREDIT                   307511 non-null  int64  \n",
            " 9   AMT_ANNUITY                  307499 non-null  float64\n",
            " 10  AMT_GOODS_PRICE              307233 non-null  float64\n",
            " 11  NAME_TYPE_SUITE              306219 non-null  object \n",
            " 12  NAME_INCOME_TYPE             307511 non-null  object \n",
            " 13  NAME_EDUCATION_TYPE          307511 non-null  object \n",
            " 14  NAME_FAMILY_STATUS           307511 non-null  object \n",
            " 15  NAME_HOUSING_TYPE            307511 non-null  object \n",
            " 16  OCCUPATION_TYPE              211120 non-null  object \n",
            " 17  REGION_RATING_CLIENT         307511 non-null  int64  \n",
            " 18  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  \n",
            " 19  WEEKDAY_APPR_PROCESS_START   307511 non-null  object \n",
            " 20  HOUR_APPR_PROCESS_START      307511 non-null  int64  \n",
            " 21  ORGANIZATION_TYPE            307511 non-null  object \n",
            " 22  FONDKAPREMONT_MODE           97216 non-null   object \n",
            " 23  HOUSETYPE_MODE               153214 non-null  object \n",
            " 24  TOTALAREA_MODE               159080 non-null  float64\n",
            " 25  WALLSMATERIAL_MODE           151170 non-null  object \n",
            " 26  EMERGENCYSTATE_MODE          161756 non-null  object \n",
            " 27  AMT_REQ_CREDIT_BUREAU_HOUR   265992 non-null  float64\n",
            " 28  AMT_REQ_CREDIT_BUREAU_DAY    265992 non-null  float64\n",
            " 29  AMT_REQ_CREDIT_BUREAU_WEEK   265992 non-null  float64\n",
            " 30  AMT_REQ_CREDIT_BUREAU_MON    265992 non-null  float64\n",
            " 31  AMT_REQ_CREDIT_BUREAU_QRT    265992 non-null  float64\n",
            " 32  AMT_REQ_CREDIT_BUREAU_YEAR   265992 non-null  float64\n",
            "dtypes: float64(9), int64(8), object(16)\n",
            "memory usage: 77.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "mUkoxA499lzU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transformasi Data"
      ],
      "metadata": {
        "id": "Q5NZKQwK9JIo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memindahkan label 'TARGET' index kolom terakhir"
      ],
      "metadata": {
        "id": "KWGZ9aXd7UrQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_cols = [col for col in appl.columns if col != 'TARGET'] + ['TARGET']\n",
        "appl = appl[new_cols]\n",
        "appl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "t-Knukoy3dfr",
        "outputId": "5fa58cd5-81fb-4788-fd56-7dc957195006"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0           103856         Cash loans           F            Y   \n",
              "1           120392         Cash loans           M            N   \n",
              "2           135040         Cash loans           F            N   \n",
              "3           140156         Cash loans           M            Y   \n",
              "4           141914         Cash loans           M            Y   \n",
              "...            ...                ...         ...          ...   \n",
              "307506      456243         Cash loans           F            N   \n",
              "307507      456246         Cash loans           F            N   \n",
              "307508      456247         Cash loans           F            N   \n",
              "307509      456248         Cash loans           F            N   \n",
              "307510      456254         Cash loans           F            N   \n",
              "\n",
              "       FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
              "0                    N             1           2250000     7819200   \n",
              "1                    Y             0           4050000     9856035   \n",
              "2                    Y             0           2070000     4725000   \n",
              "3                    Y             1           1800000     4500000   \n",
              "4                    N             1           1800000     9703800   \n",
              "...                ...           ...               ...         ...   \n",
              "307506               Y             0            810000     2250000   \n",
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              "307508               Y             0           1125000     3455100   \n",
              "307509               Y             0           1530000     3319200   \n",
              "307510               Y             0           1710000     3701070   \n",
              "\n",
              "        AMT_ANNUITY  AMT_GOODS_PRICE  ... TOTALAREA_MODE WALLSMATERIAL_MODE  \\\n",
              "0          424170.0        6750000.0  ...          623.0                NaN   \n",
              "1          658665.0        9315000.0  ...          877.0       Stone, brick   \n",
              "2          449910.0        4545000.0  ...          688.0       Stone, brick   \n",
              "3          478800.0        4500000.0  ...         1609.0              Panel   \n",
              "4          343800.0        8100000.0  ...          694.0              Block   \n",
              "...             ...              ...  ...            ...                ...   \n",
              "307506     126945.0        2250000.0  ...            NaN                NaN   \n",
              "307507     106200.0        2250000.0  ...            NaN                NaN   \n",
              "307508     177705.0        2475000.0  ...            NaN                NaN   \n",
              "307509     160965.0        2250000.0  ...            NaN                NaN   \n",
              "307510     202050.0        3195000.0  ...           86.0       Stone, brick   \n",
              "\n",
              "       EMERGENCYSTATE_MODE AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
              "0                       No                        0.0   \n",
              "1                       No                        NaN   \n",
              "2                       No                        0.0   \n",
              "3                       No                        0.0   \n",
              "4                       No                        0.0   \n",
              "...                    ...                        ...   \n",
              "307506                 NaN                        0.0   \n",
              "307507                 NaN                        0.0   \n",
              "307508                 NaN                        0.0   \n",
              "307509                 NaN                        NaN   \n",
              "307510                  No                        0.0   \n",
              "\n",
              "       AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
              "0                            0.0                        0.0   \n",
              "1                            NaN                        NaN   \n",
              "2                            0.0                        0.0   \n",
              "3                            0.0                        0.0   \n",
              "4                            0.0                        0.0   \n",
              "...                          ...                        ...   \n",
              "307506                       0.0                        0.0   \n",
              "307507                       0.0                        0.0   \n",
              "307508                       0.0                        0.0   \n",
              "307509                       NaN                        NaN   \n",
              "307510                       0.0                        0.0   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
              "0                            10.0                        0.0   \n",
              "1                             NaN                        NaN   \n",
              "2                             0.0                        0.0   \n",
              "3                             0.0                        0.0   \n",
              "4                             0.0                        0.0   \n",
              "...                           ...                        ...   \n",
              "307506                       10.0                       10.0   \n",
              "307507                        0.0                        0.0   \n",
              "307508                       10.0                        0.0   \n",
              "307509                        NaN                        NaN   \n",
              "307510                        0.0                        0.0   \n",
              "\n",
              "       AMT_REQ_CREDIT_BUREAU_YEAR  TARGET  \n",
              "0                            20.0       0  \n",
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              "4                            30.0       0  \n",
              "...                           ...     ...  \n",
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              "307507                       10.0       0  \n",
              "307508                       20.0       0  \n",
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              "307510                        0.0       1  \n",
              "\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>307507</th>\n",
              "      <td>456246</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
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              "      <td>10.0</td>\n",
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              "    <tr>\n",
              "      <th>307508</th>\n",
              "      <td>456247</td>\n",
              "      <td>Cash loans</td>\n",
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              "      <td>Y</td>\n",
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              "      <th>307509</th>\n",
              "      <td>456248</td>\n",
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              "      <td>Y</td>\n",
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              "      <td>1530000</td>\n",
              "      <td>3319200</td>\n",
              "      <td>160965.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>307510</th>\n",
              "      <td>456254</td>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
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              "      <td>3701070</td>\n",
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              "      <td>3195000.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleansing"
      ],
      "metadata": {
        "id": "zN63Jv0g_Pzk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan pengecekan terhadapa missing value pada dataset"
      ],
      "metadata": {
        "id": "seG_Pu9IAUoV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBU-I1EyAZr5",
        "outputId": "3dbc74c6-cd5a-455a-ec91-707532ae9f83"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SK_ID_CURR                          0\n",
              "NAME_CONTRACT_TYPE                  0\n",
              "CODE_GENDER                         0\n",
              "FLAG_OWN_CAR                        0\n",
              "FLAG_OWN_REALTY                     0\n",
              "CNT_CHILDREN                        0\n",
              "AMT_INCOME_TOTAL                    0\n",
              "AMT_CREDIT                          0\n",
              "AMT_ANNUITY                        12\n",
              "AMT_GOODS_PRICE                   278\n",
              "NAME_TYPE_SUITE                  1292\n",
              "NAME_INCOME_TYPE                    0\n",
              "NAME_EDUCATION_TYPE                 0\n",
              "NAME_FAMILY_STATUS                  0\n",
              "NAME_HOUSING_TYPE                   0\n",
              "OCCUPATION_TYPE                 96391\n",
              "REGION_RATING_CLIENT                0\n",
              "REGION_RATING_CLIENT_W_CITY         0\n",
              "WEEKDAY_APPR_PROCESS_START          0\n",
              "HOUR_APPR_PROCESS_START             0\n",
              "ORGANIZATION_TYPE                   0\n",
              "FONDKAPREMONT_MODE             210295\n",
              "HOUSETYPE_MODE                 154297\n",
              "TOTALAREA_MODE                 148431\n",
              "WALLSMATERIAL_MODE             156341\n",
              "EMERGENCYSTATE_MODE            145755\n",
              "AMT_REQ_CREDIT_BUREAU_HOUR      41519\n",
              "AMT_REQ_CREDIT_BUREAU_DAY       41519\n",
              "AMT_REQ_CREDIT_BUREAU_WEEK      41519\n",
              "AMT_REQ_CREDIT_BUREAU_MON       41519\n",
              "AMT_REQ_CREDIT_BUREAU_QRT       41519\n",
              "AMT_REQ_CREDIT_BUREAU_YEAR      41519\n",
              "TARGET                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bisa dilihat karena terdapat kolom yang terlalu banyak missing valuenya maka kita drop kolom tersebut"
      ],
      "metadata": {
        "id": "JMrw2m4_-z11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl.drop('SK_ID_CURR', axis=1, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKZsgWwfrBCj",
        "outputId": "8f04702c-95e2-46f5-bf19-b82042079f1a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  errors=errors,\n"
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    {
      "cell_type": "code",
      "source": [
        "appl.drop(appl.loc[:, 'FLAG_OWN_CAR':'FLAG_OWN_REALTY'].columns,axis = 1, inplace= True)\n",
        "appl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "rMuY3cbakC2c",
        "outputId": "712b903e-86bd-4683-e438-33d0b7a8f32f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4913: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  errors=errors,\n"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       NAME_CONTRACT_TYPE CODE_GENDER  CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
              "0              Cash loans           F             1           2250000   \n",
              "1              Cash loans           M             0           4050000   \n",
              "2              Cash loans           F             0           2070000   \n",
              "3              Cash loans           M             1           1800000   \n",
              "4              Cash loans           M             1           1800000   \n",
              "...                   ...         ...           ...               ...   \n",
              "307506         Cash loans           F             0            810000   \n",
              "307507         Cash loans           F             1            945000   \n",
              "307508         Cash loans           F             0           1125000   \n",
              "307509         Cash loans           F             0           1530000   \n",
              "307510         Cash loans           F             0           1710000   \n",
              "\n",
              "        AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_TYPE_SUITE  \\\n",
              "0          7819200     424170.0        6750000.0   Unaccompanied   \n",
              "1          9856035     658665.0        9315000.0   Unaccompanied   \n",
              "2          4725000     449910.0        4545000.0   Unaccompanied   \n",
              "3          4500000     478800.0        4500000.0   Unaccompanied   \n",
              "4          9703800     343800.0        8100000.0   Unaccompanied   \n",
              "...            ...          ...              ...             ...   \n",
              "307506     2250000     126945.0        2250000.0   Unaccompanied   \n",
              "307507     2250000     106200.0        2250000.0   Unaccompanied   \n",
              "307508     3455100     177705.0        2475000.0   Unaccompanied   \n",
              "307509     3319200     160965.0        2250000.0   Unaccompanied   \n",
              "307510     3701070     202050.0        3195000.0   Unaccompanied   \n",
              "\n",
              "            NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  ...  \\\n",
              "0                    Working  Secondary / secondary special  ...   \n",
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              "2              State servant               Higher education  ...   \n",
              "3       Commercial associate               Higher education  ...   \n",
              "4                    Working               Higher education  ...   \n",
              "...                      ...                            ...  ...   \n",
              "307506  Commercial associate  Secondary / secondary special  ...   \n",
              "307507               Working  Secondary / secondary special  ...   \n",
              "307508  Commercial associate               Higher education  ...   \n",
              "307509               Working  Secondary / secondary special  ...   \n",
              "307510  Commercial associate  Secondary / secondary special  ...   \n",
              "\n",
              "       TOTALAREA_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  \\\n",
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              "3              1609.0              Panel                  No   \n",
              "4               694.0              Block                  No   \n",
              "...               ...                ...                 ...   \n",
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              "307507            NaN                NaN                 NaN   \n",
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              "307509            NaN                NaN                 NaN   \n",
              "307510           86.0       Stone, brick                  No   \n",
              "\n",
              "        AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
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              "\n",
              "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
              "0                             0.0                       10.0   \n",
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              "       AMT_REQ_CREDIT_BUREAU_QRT AMT_REQ_CREDIT_BUREAU_YEAR TARGET  \n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>2070000</td>\n",
              "      <td>4725000</td>\n",
              "      <td>449910.0</td>\n",
              "      <td>4545000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>State servant</td>\n",
              "      <td>Higher education</td>\n",
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              "      <td>688.0</td>\n",
              "      <td>Stone, brick</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>1800000</td>\n",
              "      <td>4500000</td>\n",
              "      <td>478800.0</td>\n",
              "      <td>4500000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>...</td>\n",
              "      <td>1609.0</td>\n",
              "      <td>Panel</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>1800000</td>\n",
              "      <td>9703800</td>\n",
              "      <td>343800.0</td>\n",
              "      <td>8100000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>...</td>\n",
              "      <td>694.0</td>\n",
              "      <td>Block</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</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>307506</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>810000</td>\n",
              "      <td>2250000</td>\n",
              "      <td>126945.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307507</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>945000</td>\n",
              "      <td>2250000</td>\n",
              "      <td>106200.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
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              "      <td>Secondary / secondary special</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307508</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1125000</td>\n",
              "      <td>3455100</td>\n",
              "      <td>177705.0</td>\n",
              "      <td>2475000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
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              "      <td>Higher education</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>307509</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1530000</td>\n",
              "      <td>3319200</td>\n",
              "      <td>160965.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307510</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1710000</td>\n",
              "      <td>3701070</td>\n",
              "      <td>202050.0</td>\n",
              "      <td>3195000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>...</td>\n",
              "      <td>86.0</td>\n",
              "      <td>Stone, brick</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>307511 rows × 30 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "appl = appl.drop(appl.loc[:, 'FONDKAPREMONT_MODE':'AMT_REQ_CREDIT_BUREAU_QRT'].columns,axis = 1)\n",
        "appl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "Okpf3lgr-zRv",
        "outputId": "9f0a8057-41ec-414e-c227-f9096cc99a9d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       NAME_CONTRACT_TYPE CODE_GENDER  CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
              "0              Cash loans           F             1           2250000   \n",
              "1              Cash loans           M             0           4050000   \n",
              "2              Cash loans           F             0           2070000   \n",
              "3              Cash loans           M             1           1800000   \n",
              "4              Cash loans           M             1           1800000   \n",
              "...                   ...         ...           ...               ...   \n",
              "307506         Cash loans           F             0            810000   \n",
              "307507         Cash loans           F             1            945000   \n",
              "307508         Cash loans           F             0           1125000   \n",
              "307509         Cash loans           F             0           1530000   \n",
              "307510         Cash loans           F             0           1710000   \n",
              "\n",
              "        AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE NAME_TYPE_SUITE  \\\n",
              "0          7819200     424170.0        6750000.0   Unaccompanied   \n",
              "1          9856035     658665.0        9315000.0   Unaccompanied   \n",
              "2          4725000     449910.0        4545000.0   Unaccompanied   \n",
              "3          4500000     478800.0        4500000.0   Unaccompanied   \n",
              "4          9703800     343800.0        8100000.0   Unaccompanied   \n",
              "...            ...          ...              ...             ...   \n",
              "307506     2250000     126945.0        2250000.0   Unaccompanied   \n",
              "307507     2250000     106200.0        2250000.0   Unaccompanied   \n",
              "307508     3455100     177705.0        2475000.0   Unaccompanied   \n",
              "307509     3319200     160965.0        2250000.0   Unaccompanied   \n",
              "307510     3701070     202050.0        3195000.0   Unaccompanied   \n",
              "\n",
              "            NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0                    Working  Secondary / secondary special   \n",
              "1       Commercial associate               Higher education   \n",
              "2              State servant               Higher education   \n",
              "3       Commercial associate               Higher education   \n",
              "4                    Working               Higher education   \n",
              "...                      ...                            ...   \n",
              "307506  Commercial associate  Secondary / secondary special   \n",
              "307507               Working  Secondary / secondary special   \n",
              "307508  Commercial associate               Higher education   \n",
              "307509               Working  Secondary / secondary special   \n",
              "307510  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "          NAME_FAMILY_STATUS  NAME_HOUSING_TYPE        OCCUPATION_TYPE  \\\n",
              "0                    Married  House / apartment               Laborers   \n",
              "1       Single / not married  House / apartment                    NaN   \n",
              "2                    Married  House / apartment            Accountants   \n",
              "3                    Married  House / apartment               Managers   \n",
              "4                    Married  House / apartment  High skill tech staff   \n",
              "...                      ...                ...                    ...   \n",
              "307506               Married  House / apartment         Medicine staff   \n",
              "307507               Married  House / apartment               Laborers   \n",
              "307508  Single / not married  House / apartment             Core staff   \n",
              "307509             Separated  House / apartment            Sales staff   \n",
              "307510               Married  House / apartment               Laborers   \n",
              "\n",
              "        REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  \\\n",
              "0                          1                            1   \n",
              "1                          1                            1   \n",
              "2                          2                            2   \n",
              "3                          2                            2   \n",
              "4                          2                            2   \n",
              "...                      ...                          ...   \n",
              "307506                     2                            2   \n",
              "307507                     2                            2   \n",
              "307508                     2                            2   \n",
              "307509                     2                            2   \n",
              "307510                     2                            2   \n",
              "\n",
              "       WEEKDAY_APPR_PROCESS_START  HOUR_APPR_PROCESS_START  \\\n",
              "0                       WEDNESDAY                        8   \n",
              "1                        SATURDAY                       17   \n",
              "2                        THURSDAY                       15   \n",
              "3                       WEDNESDAY                        9   \n",
              "4                         TUESDAY                       14   \n",
              "...                           ...                      ...   \n",
              "307506                    TUESDAY                       15   \n",
              "307507                     MONDAY                       16   \n",
              "307508                   SATURDAY                       11   \n",
              "307509                  WEDNESDAY                       11   \n",
              "307510                  WEDNESDAY                        9   \n",
              "\n",
              "             ORGANIZATION_TYPE  AMT_REQ_CREDIT_BUREAU_YEAR  TARGET  \n",
              "0       Business Entity Type 2                        20.0       0  \n",
              "1       Business Entity Type 3                         NaN       0  \n",
              "2                       Postal                        40.0       0  \n",
              "3       Business Entity Type 3                         0.0       0  \n",
              "4                Self-employed                        30.0       0  \n",
              "...                        ...                         ...     ...  \n",
              "307506                Medicine                         0.0       0  \n",
              "307507                  School                        10.0       0  \n",
              "307508                    Bank                        20.0       0  \n",
              "307509           Self-employed                         NaN       0  \n",
              "307510  Business Entity Type 1                         0.0       1  \n",
              "\n",
              "[307511 rows x 20 columns]"
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              "      <th>1</th>\n",
              "      <td>Cash loans</td>\n",
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              "      <td>4050000</td>\n",
              "      <td>9856035</td>\n",
              "      <td>658665.0</td>\n",
              "      <td>9315000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>SATURDAY</td>\n",
              "      <td>17</td>\n",
              "      <td>Business Entity Type 3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>2070000</td>\n",
              "      <td>4725000</td>\n",
              "      <td>449910.0</td>\n",
              "      <td>4545000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>State servant</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Accountants</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>THURSDAY</td>\n",
              "      <td>15</td>\n",
              "      <td>Postal</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>1800000</td>\n",
              "      <td>4500000</td>\n",
              "      <td>478800.0</td>\n",
              "      <td>4500000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Managers</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>9</td>\n",
              "      <td>Business Entity Type 3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>1800000</td>\n",
              "      <td>9703800</td>\n",
              "      <td>343800.0</td>\n",
              "      <td>8100000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>High skill tech staff</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>14</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>30.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307506</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>810000</td>\n",
              "      <td>2250000</td>\n",
              "      <td>126945.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Medicine staff</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>15</td>\n",
              "      <td>Medicine</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307507</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>945000</td>\n",
              "      <td>2250000</td>\n",
              "      <td>106200.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>MONDAY</td>\n",
              "      <td>16</td>\n",
              "      <td>School</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307508</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1125000</td>\n",
              "      <td>3455100</td>\n",
              "      <td>177705.0</td>\n",
              "      <td>2475000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Core staff</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>SATURDAY</td>\n",
              "      <td>11</td>\n",
              "      <td>Bank</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307509</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1530000</td>\n",
              "      <td>3319200</td>\n",
              "      <td>160965.0</td>\n",
              "      <td>2250000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Separated</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>11</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307510</th>\n",
              "      <td>Cash loans</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>1710000</td>\n",
              "      <td>3701070</td>\n",
              "      <td>202050.0</td>\n",
              "      <td>3195000.0</td>\n",
              "      <td>Unaccompanied</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>WEDNESDAY</td>\n",
              "      <td>9</td>\n",
              "      <td>Business Entity Type 1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>307511 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16609dca-c810-4dd3-a9e4-998b06bb15da')\"\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",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-16609dca-c810-4dd3-a9e4-998b06bb15da 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-16609dca-c810-4dd3-a9e4-998b06bb15da');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah di bersihkan dari kolom yang terlalu banyak mengandung Dilakukan handling missing value dengan nilai 0"
      ],
      "metadata": {
        "id": "ujAe1Aj5BQHy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl = appl.fillna(0)"
      ],
      "metadata": {
        "id": "F918743-qhwh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Unique Value"
      ],
      "metadata": {
        "id": "HaZv5pN1oVTJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def unik_value(df):\n",
        "    col = []\n",
        "    d_type = []\n",
        "    uniques = []\n",
        "    n_uniques = []\n",
        "    \n",
        "    for i in df.columns:\n",
        "        col.append(i)\n",
        "        d_type.append(df[i].dtypes)\n",
        "        uniques.append(df[i].unique()[:5])\n",
        "        n_uniques.append(df[i].nunique())\n",
        "    \n",
        "    return pd.DataFrame({'Nama Kolom': col, 'Tipe': d_type, 'Unik sample': uniques, 'Jumlah Unik Value': n_uniques})"
      ],
      "metadata": {
        "id": "T4myD6--oZF7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unik_value(appl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "BQM8AY7_obt6",
        "outputId": "56701816-ed15-44e2-ce4f-1de68dfe14d0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Nama Kolom     Tipe  \\\n",
              "0            NAME_CONTRACT_TYPE   object   \n",
              "1                   CODE_GENDER   object   \n",
              "2                  CNT_CHILDREN    int64   \n",
              "3              AMT_INCOME_TOTAL    int64   \n",
              "4                    AMT_CREDIT    int64   \n",
              "5                   AMT_ANNUITY  float64   \n",
              "6               AMT_GOODS_PRICE  float64   \n",
              "7               NAME_TYPE_SUITE   object   \n",
              "8              NAME_INCOME_TYPE   object   \n",
              "9           NAME_EDUCATION_TYPE   object   \n",
              "10           NAME_FAMILY_STATUS   object   \n",
              "11            NAME_HOUSING_TYPE   object   \n",
              "12              OCCUPATION_TYPE   object   \n",
              "13         REGION_RATING_CLIENT    int64   \n",
              "14  REGION_RATING_CLIENT_W_CITY    int64   \n",
              "15   WEEKDAY_APPR_PROCESS_START   object   \n",
              "16      HOUR_APPR_PROCESS_START    int64   \n",
              "17            ORGANIZATION_TYPE   object   \n",
              "18   AMT_REQ_CREDIT_BUREAU_YEAR  float64   \n",
              "19                       TARGET    int64   \n",
              "\n",
              "                                          Unik sample  Jumlah Unik Value  \n",
              "0                       [Cash loans, Revolving loans]                  2  \n",
              "1                                         [F, M, XNA]                  3  \n",
              "2                                     [1, 0, 2, 3, 4]                 15  \n",
              "3       [2250000, 4050000, 2070000, 1800000, 1125000]               2548  \n",
              "4       [7819200, 9856035, 4725000, 4500000, 9703800]               5603  \n",
              "5   [424170.0, 658665.0, 449910.0, 478800.0, 34380...              13673  \n",
              "6   [6750000.0, 9315000.0, 4545000.0, 4500000.0, 8...               1003  \n",
              "7   [Unaccompanied, Family, Spouse, partner, Other...                  8  \n",
              "8   [Working, Commercial associate, State servant,...                  8  \n",
              "9   [Secondary / secondary special, Higher educati...                  5  \n",
              "10  [Married, Single / not married, Civil marriage...                  6  \n",
              "11  [House / apartment, With parents, Municipal ap...                  6  \n",
              "12  [Laborers, 0, Accountants, Managers, High skil...                 19  \n",
              "13                                          [1, 2, 3]                  3  \n",
              "14                                          [1, 2, 3]                  3  \n",
              "15   [WEDNESDAY, SATURDAY, THURSDAY, TUESDAY, FRIDAY]                  7  \n",
              "16                                 [8, 17, 15, 9, 14]                 24  \n",
              "17  [Business Entity Type 2, Business Entity Type ...                 58  \n",
              "18                      [20.0, 0.0, 40.0, 30.0, 10.0]                 25  \n",
              "19                                             [0, 1]                  2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1a9a876d-0eea-4341-9744-73a9d0c7d3d9\">\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>Nama Kolom</th>\n",
              "      <th>Tipe</th>\n",
              "      <th>Unik sample</th>\n",
              "      <th>Jumlah Unik Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NAME_CONTRACT_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Cash loans, Revolving loans]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CODE_GENDER</td>\n",
              "      <td>object</td>\n",
              "      <td>[F, M, XNA]</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CNT_CHILDREN</td>\n",
              "      <td>int64</td>\n",
              "      <td>[1, 0, 2, 3, 4]</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AMT_INCOME_TOTAL</td>\n",
              "      <td>int64</td>\n",
              "      <td>[2250000, 4050000, 2070000, 1800000, 1125000]</td>\n",
              "      <td>2548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AMT_CREDIT</td>\n",
              "      <td>int64</td>\n",
              "      <td>[7819200, 9856035, 4725000, 4500000, 9703800]</td>\n",
              "      <td>5603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AMT_ANNUITY</td>\n",
              "      <td>float64</td>\n",
              "      <td>[424170.0, 658665.0, 449910.0, 478800.0, 34380...</td>\n",
              "      <td>13673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AMT_GOODS_PRICE</td>\n",
              "      <td>float64</td>\n",
              "      <td>[6750000.0, 9315000.0, 4545000.0, 4500000.0, 8...</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NAME_TYPE_SUITE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Unaccompanied, Family, Spouse, partner, Other...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NAME_INCOME_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Working, Commercial associate, State servant,...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NAME_EDUCATION_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Secondary / secondary special, Higher educati...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NAME_FAMILY_STATUS</td>\n",
              "      <td>object</td>\n",
              "      <td>[Married, Single / not married, Civil marriage...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NAME_HOUSING_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[House / apartment, With parents, Municipal ap...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>OCCUPATION_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Laborers, 0, Accountants, Managers, High skil...</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>REGION_RATING_CLIENT</td>\n",
              "      <td>int64</td>\n",
              "      <td>[1, 2, 3]</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>REGION_RATING_CLIENT_W_CITY</td>\n",
              "      <td>int64</td>\n",
              "      <td>[1, 2, 3]</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>WEEKDAY_APPR_PROCESS_START</td>\n",
              "      <td>object</td>\n",
              "      <td>[WEDNESDAY, SATURDAY, THURSDAY, TUESDAY, FRIDAY]</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>HOUR_APPR_PROCESS_START</td>\n",
              "      <td>int64</td>\n",
              "      <td>[8, 17, 15, 9, 14]</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ORGANIZATION_TYPE</td>\n",
              "      <td>object</td>\n",
              "      <td>[Business Entity Type 2, Business Entity Type ...</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>AMT_REQ_CREDIT_BUREAU_YEAR</td>\n",
              "      <td>float64</td>\n",
              "      <td>[20.0, 0.0, 40.0, 30.0, 10.0]</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>TARGET</td>\n",
              "      <td>int64</td>\n",
              "      <td>[0, 1]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a9a876d-0eea-4341-9744-73a9d0c7d3d9')\"\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-1a9a876d-0eea-4341-9744-73a9d0c7d3d9 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-1a9a876d-0eea-4341-9744-73a9d0c7d3d9');\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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "8tx_VQYX92td"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistika Deskriptif"
      ],
      "metadata": {
        "id": "GhzNRi3o6N8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "C6-bqTdj6Qdg",
        "outputId": "06d703c7-cf2d-4700-f874-f2e7b4d378eb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CNT_CHILDREN  AMT_INCOME_TOTAL    AMT_CREDIT   AMT_ANNUITY  \\\n",
              "count  307511.000000      3.075110e+05  3.075110e+05  3.075110e+05   \n",
              "mean        0.417052      1.693836e+06  5.990260e+06  2.710752e+05   \n",
              "std         0.722121      2.613687e+06  4.024908e+06  1.449444e+05   \n",
              "min         0.000000      2.565000e+05  4.500000e+05  0.000000e+00   \n",
              "25%         0.000000      1.125000e+06  2.700000e+06  1.652400e+05   \n",
              "50%         0.000000      1.477665e+06  5.135310e+06  2.490300e+05   \n",
              "75%         1.000000      2.025000e+06  8.086500e+06  3.459600e+05   \n",
              "max        19.000000      1.170000e+09  4.050000e+07  2.580255e+06   \n",
              "\n",
              "       AMT_GOODS_PRICE  REGION_RATING_CLIENT  REGION_RATING_CLIENT_W_CITY  \\\n",
              "count     3.075110e+05         307511.000000                307511.000000   \n",
              "mean      5.379095e+06              2.052463                     2.031521   \n",
              "std       3.696338e+06              0.509034                     0.502737   \n",
              "min       0.000000e+00              1.000000                     1.000000   \n",
              "25%       2.385000e+06              2.000000                     2.000000   \n",
              "50%       4.500000e+06              2.000000                     2.000000   \n",
              "75%       6.795000e+06              2.000000                     2.000000   \n",
              "max       4.050000e+07              3.000000                     3.000000   \n",
              "\n",
              "       HOUR_APPR_PROCESS_START  AMT_REQ_CREDIT_BUREAU_YEAR         TARGET  \n",
              "count            307511.000000               307511.000000  307511.000000  \n",
              "mean                 12.063419                   16.434469       0.080729  \n",
              "std                   3.265832                   18.558207       0.272419  \n",
              "min                   0.000000                    0.000000       0.000000  \n",
              "25%                  10.000000                    0.000000       0.000000  \n",
              "50%                  12.000000                   10.000000       0.000000  \n",
              "75%                  14.000000                   30.000000       0.000000  \n",
              "max                  23.000000                  250.000000       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-883323b6-a28a-425a-b3b2-5dc8727f4140\">\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>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>AMT_GOODS_PRICE</th>\n",
              "      <th>REGION_RATING_CLIENT</th>\n",
              "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>307511.000000</td>\n",
              "      <td>3.075110e+05</td>\n",
              "      <td>3.075110e+05</td>\n",
              "      <td>3.075110e+05</td>\n",
              "      <td>3.075110e+05</td>\n",
              "      <td>307511.000000</td>\n",
              "      <td>307511.000000</td>\n",
              "      <td>307511.000000</td>\n",
              "      <td>307511.000000</td>\n",
              "      <td>307511.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.417052</td>\n",
              "      <td>1.693836e+06</td>\n",
              "      <td>5.990260e+06</td>\n",
              "      <td>2.710752e+05</td>\n",
              "      <td>5.379095e+06</td>\n",
              "      <td>2.052463</td>\n",
              "      <td>2.031521</td>\n",
              "      <td>12.063419</td>\n",
              "      <td>16.434469</td>\n",
              "      <td>0.080729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.722121</td>\n",
              "      <td>2.613687e+06</td>\n",
              "      <td>4.024908e+06</td>\n",
              "      <td>1.449444e+05</td>\n",
              "      <td>3.696338e+06</td>\n",
              "      <td>0.509034</td>\n",
              "      <td>0.502737</td>\n",
              "      <td>3.265832</td>\n",
              "      <td>18.558207</td>\n",
              "      <td>0.272419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.565000e+05</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.125000e+06</td>\n",
              "      <td>2.700000e+06</td>\n",
              "      <td>1.652400e+05</td>\n",
              "      <td>2.385000e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.477665e+06</td>\n",
              "      <td>5.135310e+06</td>\n",
              "      <td>2.490300e+05</td>\n",
              "      <td>4.500000e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.025000e+06</td>\n",
              "      <td>8.086500e+06</td>\n",
              "      <td>3.459600e+05</td>\n",
              "      <td>6.795000e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.170000e+09</td>\n",
              "      <td>4.050000e+07</td>\n",
              "      <td>2.580255e+06</td>\n",
              "      <td>4.050000e+07</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-883323b6-a28a-425a-b3b2-5dc8727f4140')\"\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-883323b6-a28a-425a-b3b2-5dc8727f4140 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-883323b6-a28a-425a-b3b2-5dc8727f4140');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Korelasi Dataset"
      ],
      "metadata": {
        "id": "IDhoDkmc6dGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "appl.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "uU19Mn_E6fvW",
        "outputId": "023b227b-16c4-4b16-98c2-8839ce138824"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  \\\n",
              "CNT_CHILDREN                     1.000000          0.011053    0.002145   \n",
              "AMT_INCOME_TOTAL                 0.011053          1.000000    0.145565   \n",
              "AMT_CREDIT                       0.002145          0.145565    1.000000   \n",
              "AMT_ANNUITY                      0.021414          0.175704    0.770080   \n",
              "AMT_GOODS_PRICE                 -0.002008          0.148133    0.986687   \n",
              "REGION_RATING_CLIENT             0.025423         -0.076088   -0.101776   \n",
              "REGION_RATING_CLIENT_W_CITY      0.024781         -0.081664   -0.110915   \n",
              "HOUR_APPR_PROCESS_START         -0.007292          0.033421    0.052738   \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR      -0.029618          0.017301   -0.026404   \n",
              "TARGET                           0.019187         -0.004277   -0.030369   \n",
              "\n",
              "                             AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
              "CNT_CHILDREN                    0.021414        -0.002008   \n",
              "AMT_INCOME_TOTAL                0.175704         0.148133   \n",
              "AMT_CREDIT                      0.770080         0.986687   \n",
              "AMT_ANNUITY                     1.000000         0.775063   \n",
              "AMT_GOODS_PRICE                 0.775063         1.000000   \n",
              "REGION_RATING_CLIENT           -0.128457        -0.103720   \n",
              "REGION_RATING_CLIENT_W_CITY    -0.141615        -0.112168   \n",
              "HOUR_APPR_PROCESS_START         0.052224         0.062089   \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR     -0.005801        -0.028479   \n",
              "TARGET                         -0.012794        -0.039565   \n",
              "\n",
              "                             REGION_RATING_CLIENT  \\\n",
              "CNT_CHILDREN                             0.025423   \n",
              "AMT_INCOME_TOTAL                        -0.076088   \n",
              "AMT_CREDIT                              -0.101776   \n",
              "AMT_ANNUITY                             -0.128457   \n",
              "AMT_GOODS_PRICE                         -0.103720   \n",
              "REGION_RATING_CLIENT                     1.000000   \n",
              "REGION_RATING_CLIENT_W_CITY              0.950842   \n",
              "HOUR_APPR_PROCESS_START                 -0.285699   \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR               0.011498   \n",
              "TARGET                                   0.058899   \n",
              "\n",
              "                             REGION_RATING_CLIENT_W_CITY  \\\n",
              "CNT_CHILDREN                                    0.024781   \n",
              "AMT_INCOME_TOTAL                               -0.081664   \n",
              "AMT_CREDIT                                     -0.110915   \n",
              "AMT_ANNUITY                                    -0.141615   \n",
              "AMT_GOODS_PRICE                                -0.112168   \n",
              "REGION_RATING_CLIENT                            0.950842   \n",
              "REGION_RATING_CLIENT_W_CITY                     1.000000   \n",
              "HOUR_APPR_PROCESS_START                        -0.264815   \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR                      0.011161   \n",
              "TARGET                                          0.060893   \n",
              "\n",
              "                             HOUR_APPR_PROCESS_START  \\\n",
              "CNT_CHILDREN                               -0.007292   \n",
              "AMT_INCOME_TOTAL                            0.033421   \n",
              "AMT_CREDIT                                  0.052738   \n",
              "AMT_ANNUITY                                 0.052224   \n",
              "AMT_GOODS_PRICE                             0.062089   \n",
              "REGION_RATING_CLIENT                       -0.285699   \n",
              "REGION_RATING_CLIENT_W_CITY                -0.264815   \n",
              "HOUR_APPR_PROCESS_START                     1.000000   \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR                 -0.020892   \n",
              "TARGET                                     -0.024166   \n",
              "\n",
              "                             AMT_REQ_CREDIT_BUREAU_YEAR    TARGET  \n",
              "CNT_CHILDREN                                  -0.029618  0.019187  \n",
              "AMT_INCOME_TOTAL                               0.017301 -0.004277  \n",
              "AMT_CREDIT                                    -0.026404 -0.030369  \n",
              "AMT_ANNUITY                                   -0.005801 -0.012794  \n",
              "AMT_GOODS_PRICE                               -0.028479 -0.039565  \n",
              "REGION_RATING_CLIENT                           0.011498  0.058899  \n",
              "REGION_RATING_CLIENT_W_CITY                    0.011161  0.060893  \n",
              "HOUR_APPR_PROCESS_START                       -0.020892 -0.024166  \n",
              "AMT_REQ_CREDIT_BUREAU_YEAR                     1.000000  0.005522  \n",
              "TARGET                                         0.005522  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f986603f-85bb-4bd8-8e80-fa20deadf90a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>AMT_CREDIT</th>\n",
              "      <th>AMT_ANNUITY</th>\n",
              "      <th>AMT_GOODS_PRICE</th>\n",
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              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <th>TARGET</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>CNT_CHILDREN</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011053</td>\n",
              "      <td>0.002145</td>\n",
              "      <td>0.021414</td>\n",
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              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <td>0.011053</td>\n",
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              "      <td>0.017301</td>\n",
              "      <td>-0.004277</td>\n",
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              "      <th>AMT_CREDIT</th>\n",
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              "      <td>0.145565</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.770080</td>\n",
              "      <td>0.986687</td>\n",
              "      <td>-0.101776</td>\n",
              "      <td>-0.110915</td>\n",
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              "      <td>-0.026404</td>\n",
              "      <td>-0.030369</td>\n",
              "    </tr>\n",
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              "      <th>AMT_ANNUITY</th>\n",
              "      <td>0.021414</td>\n",
              "      <td>0.175704</td>\n",
              "      <td>0.770080</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.775063</td>\n",
              "      <td>-0.128457</td>\n",
              "      <td>-0.141615</td>\n",
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              "      <td>-0.012794</td>\n",
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              "      <td>-0.002008</td>\n",
              "      <td>0.148133</td>\n",
              "      <td>0.986687</td>\n",
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              "      <td>-0.103720</td>\n",
              "      <td>-0.112168</td>\n",
              "      <td>0.062089</td>\n",
              "      <td>-0.028479</td>\n",
              "      <td>-0.039565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>REGION_RATING_CLIENT</th>\n",
              "      <td>0.025423</td>\n",
              "      <td>-0.076088</td>\n",
              "      <td>-0.101776</td>\n",
              "      <td>-0.128457</td>\n",
              "      <td>-0.103720</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.285699</td>\n",
              "      <td>0.011498</td>\n",
              "      <td>0.058899</td>\n",
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              "    <tr>\n",
              "      <th>REGION_RATING_CLIENT_W_CITY</th>\n",
              "      <td>0.024781</td>\n",
              "      <td>-0.081664</td>\n",
              "      <td>-0.110915</td>\n",
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              "      <th>HOUR_APPR_PROCESS_START</th>\n",
              "      <td>-0.007292</td>\n",
              "      <td>0.033421</td>\n",
              "      <td>0.052738</td>\n",
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              "      <td>-0.285699</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.020892</td>\n",
              "      <td>-0.024166</td>\n",
              "    </tr>\n",
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              "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
              "      <td>-0.029618</td>\n",
              "      <td>0.017301</td>\n",
              "      <td>-0.026404</td>\n",
              "      <td>-0.005801</td>\n",
              "      <td>-0.028479</td>\n",
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              "      <th>TARGET</th>\n",
              "      <td>0.019187</td>\n",
              "      <td>-0.004277</td>\n",
              "      <td>-0.030369</td>\n",
              "      <td>-0.012794</td>\n",
              "      <td>-0.039565</td>\n",
              "      <td>0.058899</td>\n",
              "      <td>0.060893</td>\n",
              "      <td>-0.024166</td>\n",
              "      <td>0.005522</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering"
      ],
      "metadata": {
        "id": "zpREaIvP-Kgs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding"
      ],
      "metadata": {
        "id": "mnuAapOHF7Tp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "objList = appl.select_dtypes(include = \"object\").columns\n",
        "print (objList)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGrunw8cF-sG",
        "outputId": "bfff07ba-413a-4c51-fd5c-75840e845813"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'NAME_TYPE_SUITE',\n",
            "       'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS',\n",
            "       'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START',\n",
            "       'ORGANIZATION_TYPE'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Label Encoding untuk mengubah object menjadi tipe data numeric\n",
        "\n",
        "le = LabelEncoder()\n",
        "one = OneHotEncoder()\n",
        "for feat in objList:\n",
        "    appl[feat] = le.fit_transform(appl[feat].astype(str))\n",
        "\n",
        "\n",
        "print (appl.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNEnoaLQF-OU",
        "outputId": "df145b46-eb51-406a-c869-6745bd1f357c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 307511 entries, 0 to 307510\n",
            "Data columns (total 20 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   NAME_CONTRACT_TYPE           307511 non-null  int64  \n",
            " 1   CODE_GENDER                  307511 non-null  int64  \n",
            " 2   CNT_CHILDREN                 307511 non-null  int64  \n",
            " 3   AMT_INCOME_TOTAL             307511 non-null  int64  \n",
            " 4   AMT_CREDIT                   307511 non-null  int64  \n",
            " 5   AMT_ANNUITY                  307511 non-null  float64\n",
            " 6   AMT_GOODS_PRICE              307511 non-null  float64\n",
            " 7   NAME_TYPE_SUITE              307511 non-null  int64  \n",
            " 8   NAME_INCOME_TYPE             307511 non-null  int64  \n",
            " 9   NAME_EDUCATION_TYPE          307511 non-null  int64  \n",
            " 10  NAME_FAMILY_STATUS           307511 non-null  int64  \n",
            " 11  NAME_HOUSING_TYPE            307511 non-null  int64  \n",
            " 12  OCCUPATION_TYPE              307511 non-null  int64  \n",
            " 13  REGION_RATING_CLIENT         307511 non-null  int64  \n",
            " 14  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  \n",
            " 15  WEEKDAY_APPR_PROCESS_START   307511 non-null  int64  \n",
            " 16  HOUR_APPR_PROCESS_START      307511 non-null  int64  \n",
            " 17  ORGANIZATION_TYPE            307511 non-null  int64  \n",
            " 18  AMT_REQ_CREDIT_BUREAU_YEAR   307511 non-null  float64\n",
            " 19  TARGET                       307511 non-null  int64  \n",
            "dtypes: float64(3), int64(17)\n",
            "memory usage: 46.9 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "appl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "TgAij5TdIVur",
        "outputId": "0601ba02-2680-4989-c9a5-f3c73f93a28b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        NAME_CONTRACT_TYPE  CODE_GENDER  CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
              "0                        0            0             1           2250000   \n",
              "1                        0            1             0           4050000   \n",
              "2                        0            0             0           2070000   \n",
              "3                        0            1             1           1800000   \n",
              "4                        0            1             1           1800000   \n",
              "...                    ...          ...           ...               ...   \n",
              "307506                   0            0             0            810000   \n",
              "307507                   0            0             1            945000   \n",
              "307508                   0            0             0           1125000   \n",
              "307509                   0            0             0           1530000   \n",
              "307510                   0            0             0           1710000   \n",
              "\n",
              "        AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  NAME_TYPE_SUITE  \\\n",
              "0          7819200     424170.0        6750000.0                7   \n",
              "1          9856035     658665.0        9315000.0                7   \n",
              "2          4725000     449910.0        4545000.0                7   \n",
              "3          4500000     478800.0        4500000.0                7   \n",
              "4          9703800     343800.0        8100000.0                7   \n",
              "...            ...          ...              ...              ...   \n",
              "307506     2250000     126945.0        2250000.0                7   \n",
              "307507     2250000     106200.0        2250000.0                7   \n",
              "307508     3455100     177705.0        2475000.0                7   \n",
              "307509     3319200     160965.0        2250000.0                7   \n",
              "307510     3701070     202050.0        3195000.0                7   \n",
              "\n",
              "        NAME_INCOME_TYPE  NAME_EDUCATION_TYPE  NAME_FAMILY_STATUS  \\\n",
              "0                      7                    4                   1   \n",
              "1                      1                    1                   3   \n",
              "2                      4                    1                   1   \n",
              "3                      1                    1                   1   \n",
              "4                      7                    1                   1   \n",
              "...                  ...                  ...                 ...   \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72f05cd6-0b61-480f-92e5-1d62ab4f4cef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </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",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72f05cd6-0b61-480f-92e5-1d62ab4f4cef 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-72f05cd6-0b61-480f-92e5-1d62ab4f4cef');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "appl.var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYpl9ZVzjvTU",
        "outputId": "160e74d2-6332-45a4-8f45-12db1d512485"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NAME_CONTRACT_TYPE             8.614765e-02\n",
              "CODE_GENDER                    2.249581e-01\n",
              "CNT_CHILDREN                   5.214593e-01\n",
              "AMT_INCOME_TOTAL               6.831357e+12\n",
              "AMT_CREDIT                     1.619988e+13\n",
              "AMT_ANNUITY                    2.100889e+10\n",
              "AMT_GOODS_PRICE                1.366291e+13\n",
              "NAME_TYPE_SUITE                3.301507e+00\n",
              "NAME_INCOME_TYPE               6.474607e+00\n",
              "NAME_EDUCATION_TYPE            1.686761e+00\n",
              "NAME_FAMILY_STATUS             1.366291e+00\n",
              "NAME_HOUSING_TYPE              9.047211e-01\n",
              "OCCUPATION_TYPE                3.015132e+01\n",
              "REGION_RATING_CLIENT           2.591155e-01\n",
              "REGION_RATING_CLIENT_W_CITY    2.527445e-01\n",
              "WEEKDAY_APPR_PROCESS_START     4.620401e+00\n",
              "HOUR_APPR_PROCESS_START        1.066566e+01\n",
              "ORGANIZATION_TYPE              4.187572e+02\n",
              "AMT_REQ_CREDIT_BUREAU_YEAR     3.444070e+02\n",
              "TARGET                         7.421192e-02\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split Feature dan Label"
      ],
      "metadata": {
        "id": "PDVphRB6H2c6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = appl.iloc[:, 0:18]\n",
        "y = appl.iloc[:, 19]"
      ],
      "metadata": {
        "id": "TFpCgknVH7eo"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Sampling"
      ],
      "metadata": {
        "id": "6A_5IPqQ58Mt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sebelum Random Sampling\n",
        "\n",
        "check_proportion = ClassBalance()\n",
        "check_proportion.fit(y)\n",
        "check_proportion.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "WdcrzUF65-5n",
        "outputId": "d69ab473-4b76-4295-b82d-2754f73629d7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea38a685d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "smote = SMOTE()\n",
        "X_smote, y_smote = smote.fit_resample(X, y)"
      ],
      "metadata": {
        "id": "WBRNFPxvnJP_"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sesudah Random SMOTE Sampling\n",
        "\n",
        "check_proportion = ClassBalance()\n",
        "check_proportion.fit(y_smote)\n",
        "check_proportion.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2og7tJOgttaB",
        "outputId": "a72f3c10-fb0e-4720-a095-319497fda5b3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea3852b610>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split Data Train dan Data Test"
      ],
      "metadata": {
        "id": "AS90XP3voszH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_smote, y_smote, test_size=0.2, random_state= 0)"
      ],
      "metadata": {
        "id": "eHu9CQmGLKag"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Scalling"
      ],
      "metadata": {
        "id": "lExZY4ewVPp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# data standarization with sklearn\n",
        "# ﬁt scaler on training data\n",
        "std = StandardScaler().ﬁt(X_train)\n",
        "\n",
        "# transform training data\n",
        "X_train_std = std.transform(X_train)\n",
        "# transform testing dataabs\n",
        "X_test_std = std.transform(X_test)\n"
      ],
      "metadata": {
        "id": "LiHQNYc-ynf9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning Modelling"
      ],
      "metadata": {
        "id": "FS5dZ-3R-GAy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression Model"
      ],
      "metadata": {
        "id": "LJYfSOpQYRcw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_logistic = LogisticRegression()\n",
        "model_logistic.fit(X_train_std, y_train)\n",
        "\n",
        "lr_prediction = model_logistic.predict(X_test_std)\n",
        "print('Akurasi Logistik Regresi = ', metrics.accuracy_score(y_test,lr_prediction)*100,\"%\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldPpanMrJEOx",
        "outputId": "31a71588-f125-4996-e879-9f7531d8d11d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Logistik Regresi =  64.50320583683396 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simpan model Logistic Regression"
      ],
      "metadata": {
        "id": "TmnbRyItQi0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model = 'model_logistic.sav'\n",
        "joblib.dump(model_logistic, lr_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xiu9ITeqQmiW",
        "outputId": "194ac628-307d-4db3-be82-fb35732d1d04"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_logistic.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model"
      ],
      "metadata": {
        "id": "Q-Rt3AVQHxZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Algoritma Random Forest\n",
        "\n",
        "model_rf = RandomForestClassifier()\n",
        "model_rf.fit(X_train_std, y_train)\n",
        "\n",
        "rf_prediction = model_rf.predict(X_test_std)\n",
        "print('Akurasi Random Forest = ', metrics.accuracy_score(y_test,rf_prediction)*100,\"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXoc9kpBINWt",
        "outputId": "6b8e35ce-a990-42e0-a8b2-365e4f3891ba"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Random Forest =  91.61353084236127 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simpan model Random Forest"
      ],
      "metadata": {
        "id": "WkScV01tSaNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = 'model_random_forest.sav'\n",
        "joblib.dump(model_rf, rf_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKgnFPRoSdj3",
        "outputId": "cbbf7482-5fe5-4d03-b182-f0822a5119ea"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_random_forest.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree"
      ],
      "metadata": {
        "id": "3S-SRpdKSYhG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Algoritma Decision Tree\n",
        "\n",
        "model_dt = DecisionTreeClassifier()\n",
        "model_dt.fit(X_train_std, y_train)\n",
        "\n",
        "dt_prediction = model_dt.predict(X_test_std)\n",
        "print('Akurasi Decision Tree = ', metrics.accuracy_score(y_test,dt_prediction)*100,\"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdL2QlIVSnw0",
        "outputId": "2a2cd615-d959-4c68-ccb8-d53fac1c9ac0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Decision Tree =  84.2653106345346 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simpan model Decission Tree"
      ],
      "metadata": {
        "id": "hRf6vrlESkbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_model = 'model_decision_tree.sav'\n",
        "joblib.dump(model_dt, dt_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hokUHG_-TALX",
        "outputId": "5774ec50-a502-44b3-fb31-fc9012ca917a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_decision_tree.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation Machine Learning Model"
      ],
      "metadata": {
        "id": "GTRhqFx6-5gv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regresion Evaluation Model"
      ],
      "metadata": {
        "id": "6mxKAWBLj8TJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Mengkomputasi performa model\n",
        "NewprediksiBenar = (lr_prediction == y_test).sum()\n",
        "NewprediksiSalah = (lr_prediction != y_test).sum()\n",
        "    \n",
        "print(\"prediksi benar         :\", NewprediksiBenar, \" data\")\n",
        "print(\"prediksi salah         :\", NewprediksiSalah, \" data\")\n",
        "print(\"Akurasi Algoritma      :\", NewprediksiBenar/(NewprediksiBenar+NewprediksiSalah)*100,\"%\")\n",
        "\n",
        "# Menggunakan Confusion Matrix untuk Model Evaluasi\n",
        "CM = confusion_matrix(y_test,lr_prediction)\n",
        "\n",
        "TN = CM[0][0]\n",
        "FN = CM[1][0]\n",
        "TP = CM[1][1]\n",
        "FP = CM[0][1]\n",
        "precision    = TP/(TP+FP)\n",
        "recall       = TP/(TP+FN)\n",
        "f1 = 2*recall*precision/(recall+precision)\n",
        "\n",
        "print (\"TRUE NEGATIVE (TN)    :\",TN)\n",
        "print (\"FALSE NEGATIVE (FN)   :\",FN)\n",
        "print (\"TRUE POSITIVE (TP)    :\",TP)\n",
        "print (\"FALSE POSITIVE (FP)   :\",FP)\n",
        "print (\"PRECISION             :\",precision*100,\"%\")\n",
        "print (\"RECALL                :\",recall*100,\"%\")\n",
        "print (\"F1                    :\",f1*100,\"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDsnQLLBJnYn",
        "outputId": "b14cb76c-4a0a-495e-8040-1965194f4be6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prediksi benar         : 72937  data\n",
            "prediksi salah         : 40138  data\n",
            "Akurasi Algoritma      : 64.50320583683396 %\n",
            "TRUE NEGATIVE (TN)    : 35078\n",
            "FALSE NEGATIVE (FN)   : 18896\n",
            "TRUE POSITIVE (TP)    : 37859\n",
            "FALSE POSITIVE (FP)   : 21242\n",
            "PRECISION             : 64.05813776416642 %\n",
            "RECALL                : 66.7060170910052 %\n",
            "F1                    : 65.3552686093081 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,lr_prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gz8kB5EbJ39u",
        "outputId": "af962b09-ef2d-47ea-96cf-d6c135874dfb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.62      0.64     56320\n",
            "           1       0.64      0.67      0.65     56755\n",
            "\n",
            "    accuracy                           0.65    113075\n",
            "   macro avg       0.65      0.64      0.64    113075\n",
            "weighted avg       0.65      0.65      0.64    113075\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Evaluation Model"
      ],
      "metadata": {
        "id": "mE5lPwm3JcMb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Mengkomputasi performa model\n",
        "NewprediksiBenar = (rf_prediction == y_test).sum()\n",
        "NewprediksiSalah = (rf_prediction != y_test).sum()\n",
        "    \n",
        "print(\"prediksi benar         :\", NewprediksiBenar, \" data\")\n",
        "print(\"prediksi salah         :\", NewprediksiSalah, \" data\")\n",
        "print(\"Akurasi Algoritma      :\", NewprediksiBenar/(NewprediksiBenar+NewprediksiSalah)*100,\"%\")\n",
        "\n",
        "# Menggunakan Confusion Matrix untuk Model Evaluasi\n",
        "CM = confusion_matrix(y_test,rf_prediction)\n",
        "\n",
        "TN = CM[0][0]\n",
        "FN = CM[1][0]\n",
        "TP = CM[1][1]\n",
        "FP = CM[0][1]\n",
        "precision    = TP/(TP+FP)\n",
        "recall       = TP/(TP+FN)\n",
        "f1 = 2*recall*precision/(recall+precision)\n",
        "\n",
        "print (\"TRUE NEGATIVE (TN)    :\",TN)\n",
        "print (\"FALSE NEGATIVE (FN)   :\",FN)\n",
        "print (\"TRUE POSITIVE (TP)    :\",TP)\n",
        "print (\"FALSE POSITIVE (FP)   :\",FP)\n",
        "print (\"PRECISION             :\",precision*100,\"%\")\n",
        "print (\"RECALL                :\",recall*100,\"%\")\n",
        "print (\"F1                    :\",f1*100,\"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlHrXRj6jcB0",
        "outputId": "61edaaed-45b2-4b49-8423-bcebea7b6769"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prediksi benar         : 103592  data\n",
            "prediksi salah         : 9483  data\n",
            "Akurasi Algoritma      : 91.61353084236127 %\n",
            "TRUE NEGATIVE (TN)    : 52986\n",
            "FALSE NEGATIVE (FN)   : 6149\n",
            "TRUE POSITIVE (TP)    : 50606\n",
            "FALSE POSITIVE (FP)   : 3334\n",
            "PRECISION             : 93.81905821282906 %\n",
            "RECALL                : 89.16571227204652 %\n",
            "F1                    : 91.43321739915984 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,rf_prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBWOXExTkKFC",
        "outputId": "f6c2b463-7adb-4c44-a40c-d80f37223e79"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.94      0.92     56320\n",
            "           1       0.94      0.89      0.91     56755\n",
            "\n",
            "    accuracy                           0.92    113075\n",
            "   macro avg       0.92      0.92      0.92    113075\n",
            "weighted avg       0.92      0.92      0.92    113075\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decission Tree Evaluation Model"
      ],
      "metadata": {
        "id": "FlJUrCiq5ey6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Mengkomputasi performa model\n",
        "NewprediksiBenar = (rf_prediction == y_test).sum()\n",
        "NewprediksiSalah = (rf_prediction != y_test).sum()\n",
        "    \n",
        "print(\"prediksi benar         :\", NewprediksiBenar, \" data\")\n",
        "print(\"prediksi salah         :\", NewprediksiSalah, \" data\")\n",
        "print(\"Akurasi Algoritma      :\", NewprediksiBenar/(NewprediksiBenar+NewprediksiSalah)*100,\"%\")\n",
        "\n",
        "# Menggunakan Confusion Matrix untuk Model Evaluasi\n",
        "CM = confusion_matrix(y_test,rf_prediction)\n",
        "\n",
        "TN = CM[0][0]\n",
        "FN = CM[1][0]\n",
        "TP = CM[1][1]\n",
        "FP = CM[0][1]\n",
        "precision    = TP/(TP+FP)\n",
        "recall       = TP/(TP+FN)\n",
        "f1 = 2*recall*precision/(recall+precision)\n",
        "\n",
        "print (\"TRUE NEGATIVE (TN)    :\",TN)\n",
        "print (\"FALSE NEGATIVE (FN)   :\",FN)\n",
        "print (\"TRUE POSITIVE (TP)    :\",TP)\n",
        "print (\"FALSE POSITIVE (FP)   :\",FP)\n",
        "print (\"PRECISION             :\",precision*100,\"%\")\n",
        "print (\"RECALL                :\",recall*100,\"%\")\n",
        "print (\"F1                    :\",f1*100,\"%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlB4admc5nB7",
        "outputId": "4075289d-b2b2-4ad9-86e7-d9192c91a832"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prediksi benar         : 103592  data\n",
            "prediksi salah         : 9483  data\n",
            "Akurasi Algoritma      : 91.61353084236127 %\n",
            "TRUE NEGATIVE (TN)    : 52986\n",
            "FALSE NEGATIVE (FN)   : 6149\n",
            "TRUE POSITIVE (TP)    : 50606\n",
            "FALSE POSITIVE (FP)   : 3334\n",
            "PRECISION             : 93.81905821282906 %\n",
            "RECALL                : 89.16571227204652 %\n",
            "F1                    : 91.43321739915984 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deployment"
      ],
      "metadata": {
        "id": "hrs3uD1tSPyE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression Test"
      ],
      "metadata": {
        "id": "--2nDfGdTJMj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# muat model yang telah dibuat\n",
        "\n",
        "loaded_model_lr = joblib.load(lr_model)\n",
        "result = loaded_model_lr.score(X_test_std, y_test)\n",
        "print('Akurasi Prediksi=',round(result,2)*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05Ed1wMmSSxB",
        "outputId": "1f901f19-b2eb-4087-aea8-1489f9d2e5eb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Prediksi= 65.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Test"
      ],
      "metadata": {
        "id": "HUVYGpmPTmcZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# muat model yang telah dibuat\n",
        "\n",
        "loaded_model_rf = joblib.load(rf_model)\n",
        "result = loaded_model_rf.score(X_test_std, y_test)\n",
        "print('Akurasi Prediksi=',round(result,2)*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfax55_iXAkG",
        "outputId": "b4e62235-db26-43ed-953c-6d8b8fe0f781"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Prediksi= 92.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Test"
      ],
      "metadata": {
        "id": "9E21Vo8SXFrb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# muat model yang telah dibuat\n",
        "\n",
        "loaded_model_dt = joblib.load(dt_model)\n",
        "result = loaded_model_dt.score(X_test_std, y_test)\n",
        "print('Akurasi Prediksi=',round(result,2)*100,'%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_cgO9i6XIoq",
        "outputId": "705d7c8d-5e7b-4c03-a7d4-ef3148180deb"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi Prediksi= 84.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Business Acumen"
      ],
      "metadata": {
        "id": "dFCGSyfO--KY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dilihat dari pemodelan dan evaluasi diatas, didapatkan insight yang ketika pengujian menggunakan model Machine Learning Logistic Regression sebagai salah satu model klasifikasi untuk menentukan apakah nasabah Home Credit berhak mendapatkan pinjaman atau tidak dengan akurasi yang kurang memuaskan karena hanya mendapatkan akurasi prediksi sekitar 64% akan tetapi untuk Random Forest memiliki akurasi prediksi sampai 91 % dan Decission Tree sampai 84%.\n",
        "\n",
        "Data akurasi tersebut didapat dari evaluasi model dengan Confusion Matrix dan sudah di validasi.\n",
        "\n",
        "Model juga telah di deploy menggunakan data test yang sudah diolah menjadi data input sesuai dengan feature-feature yang ada pada data train."
      ],
      "metadata": {
        "id": "mW-xIItrlVuY"
      }
    }
  ]
}