Tugas_akhir_PDS.ipynb

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  "metadata": {
    "colab": {
      "name": "Tugas akhir PDS.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "xacUiyN_e-Ls",
        "Tu58LA7fQ2lh"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nanangyudi/2020pds_b/blob/main/Tugas_akhir_PDS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7beTr5U697i_"
      },
      "source": [
        "# Prediksi Pelanggan Kartu Kredit\n",
        "Nama : \n",
        "\n",
        "Tito Rhenaldi Priono (175150400111052)\n",
        "\n",
        "Priska Aprillia Putri (175150401111006)\n",
        "\n",
        "Ammar yazid taqiyuddin (175150407111044)\n",
        "\n",
        "Muhamad Lukman Nurhakim (175150407111041)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQvnwhz3KBBU"
      },
      "source": [
        "## Deskripsi Tujuan Bisnis :\n",
        "<br>\n",
        "Layanan kartu kredit saat ini semakin ditinggalkan, dan seorang manajer dari perusahaan merasa terganggu dengan semakin banyaknya pelanggan yang meninggalkan layanan kartu kredit mereka. Oleh karena itu mereka membutuhkan model data sains yang dapat digunakan untuk memprediksi siapa saja pelanggan yang berpotensi berhenti menggunakan layanan kartu kredit. Dengan adanya model tersebut, diharapkan pihak bank dapat secara proaktif memberikan pelayanan yang lebih baik kepada pelanggan, yang tujuannya untuk dapat mengubah keputusannya untuk tidak meninggalkan penggunaan kartu kredit.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_5fBg5YrhIt"
      },
      "source": [
        "## Analisa Pendekatan yang akan dilakukan :\r\n",
        "<br>\r\n",
        "Akan mengumpulkan data seperti nomor pelanggan ,aktivitas pelanggan, nomor pelanggan, jenis kelamin, jumlah tanggungan, kualitas pendidikan, status,pendapatan tahunan, card kategori seperti (blue, silver, gold), dan jangka waktu berhubungan dengan bank kemudian melakukan analisis data untuk mencari tau alasan di balik permasalahan ini dan akan memanfaatkan data untuk memprediksi pelanggan yang cenderung berhenti."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALHXXGlubXeY"
      },
      "source": [
        "## Pengumpulan data :\n",
        "\n",
        "<br>\n",
        "\n",
        "Untuk melakukan penghitungan persentase yang berhenti menggunakan layanan kartu kredit diperlukan kumpulan data yang berhubungan dengan pelanggan untuk melakukan prediksi persentase. Seorang data analis yang diminta untuk melakukan pemodelan data kemudian melakukan pengumpulan data dan kemudian membuat sebuah dataset dengan 21 kolom data.\n",
        "\n",
        "<br>\n",
        "\n",
        "Pemahaman terhadap data yang dikumpulkan :\n",
        "\n",
        "<br>\n",
        "\n",
        "Berikut link Dataset: https://drive.google.com/drive/folders/1b5ProyU3sSfINCa_o52-D_O0vjOaFn4a?usp=sharing\n",
        "\n",
        "<br>\n",
        "\n",
        "Data yang dikumpulkan merupakan data profil dan data terkait layanan kartu kredit. Berikut adalah data yang dikumpulkan :\n",
        "<br>\n",
        "\n",
        "Data profil pelanggan :\n",
        "1. ClientNum : id pelanggan yang bersifat unik\n",
        "2. Attrition_Flag : status pelanggan (pelanggan/ sudah keluar)\n",
        "3. Customer_Age : data tentang umur pelanggan\n",
        "4. Gender : data tentang jenis kelamin pelanggan\n",
        "5. Dependent_count : tingkatan ketergantungan terhadap sumber pendapatan utama.\n",
        "6. Education_level : jenjang edukasi yang telah ditempuh\n",
        "7. Marital_status : data tentang status menikah\n",
        "8. Income_category : data tentang jumlah penghasilan pelanggan\n",
        "\n",
        "Data layanan kartu kredit :\n",
        "1. Card_category : tipe kategori kartu yang dimiliki pelanggan\n",
        "2. Months_on_book : durasi pelanggan menggunakan jasa kartu kredit\n",
        "3. Total_relationship_count : jumlah produk/jasa yang digunakan pelanggan\n",
        "4. Months_Inactive_12_mon : berapa lama masa tidak aktif dalam durasi 12 bulan\n",
        "5. Contacts_Count_12_mon : berapa banyak kontak dalam durasi 12 bulan\n",
        "6. Credit_Limit : limit kredit dalam kartu kredit\n",
        "7. Total_Revolving_Bal : jumlah pinjaman pada kartu kredit\n",
        "8. Avg_Open_To_Buy : Rata - rata pembelian linoe of credit (LOC)\n",
        "9. Total_Amt_Chng_Q4_Q1 : perubahan jumlah transaksi setiap musim (3 bulan)\n",
        "10. Total_Trans_Amt : jumlah total uang yang ditransaksi dalam waktu 12 bulan\n",
        "11. Total_Trans_Ct : jumlah total pelanggan melakukan transaksi dalam waktu 12 bulan\n",
        "12. Total_Ct_Chng_Q4_Q1 : perubahan aktivitas transaksi setiap musim (3 bulan)\n",
        "13. Avg_Utilization_Ratio : Rasio Penggunaan Kartu Rata-rata\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRRUl3eRelf9"
      },
      "source": [
        "## Import Dataset\r\n",
        "Pada langkah ini, kami menggunakan utility dari colab untuk mengimport file .csv, hal ini dilakukan untuk memudahkan mengakses file tanpa harus memasukkan path yang spesifik. Sehingga siapapun dapat menggunakan notebook ini.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xi_5VV9n5ajR",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "fb8a99e0-33bc-45fd-ca96-3217df6d5c97"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7e0ef7ce-99c5-4d7a-aaf8-b6ddc3380383\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7e0ef7ce-99c5-4d7a-aaf8-b6ddc3380383\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving BankChurners.csv to BankChurners.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxqLrhWTeu3T"
      },
      "source": [
        "## Import Utilities dan Library\r\n",
        "Mengimport beberapa utilities dan library yang akan digunakan untuk menghitung prediksi, membuat grafik, dan membuat dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rG4ZM_4TD-rE"
      },
      "source": [
        "import io\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.metrics import r2_score, mean_absolute_error,mean_squared_error\n",
        "import statsmodels.api as sm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xacUiyN_e-Ls"
      },
      "source": [
        "## Dataframe\n",
        "Pada kode dibawah dataset yang sudah berhasil di import akan dimasukkan kedalam dataframe dan ditunjukkan isi dataset tersebut. Disini kami membuat 2 dataframe, df untuk eksplorasi data dan model untuk pemodelan data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvWX9xHOEO52",
        "outputId": "2f7364ff-5f9f-40de-da4b-1b8b4544e787"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['BankChurners.csv']))\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CLIENTNUM</th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Card_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1</th>\n",
              "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>768805383</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12691.0</td>\n",
              "      <td>777</td>\n",
              "      <td>11914.0</td>\n",
              "      <td>1.335</td>\n",
              "      <td>1144</td>\n",
              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
              "      <td>0.061</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.999910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.999940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>713982108</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>51</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.999980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>769911858</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>40</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
              "      <td>High School</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3313.0</td>\n",
              "      <td>2517</td>\n",
              "      <td>796.0</td>\n",
              "      <td>1.405</td>\n",
              "      <td>1171</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>0.999870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>709106358</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Uneducated</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>2.175</td>\n",
              "      <td>816</td>\n",
              "      <td>28</td>\n",
              "      <td>2.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.999980</td>\n",
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              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10122</th>\n",
              "      <td>772366833</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>50</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4003.0</td>\n",
              "      <td>1851</td>\n",
              "      <td>2152.0</td>\n",
              "      <td>0.703</td>\n",
              "      <td>15476</td>\n",
              "      <td>117</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.462</td>\n",
              "      <td>0.000191</td>\n",
              "      <td>0.999810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10123</th>\n",
              "      <td>710638233</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>41</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4277.0</td>\n",
              "      <td>2186</td>\n",
              "      <td>2091.0</td>\n",
              "      <td>0.804</td>\n",
              "      <td>8764</td>\n",
              "      <td>69</td>\n",
              "      <td>0.683</td>\n",
              "      <td>0.511</td>\n",
              "      <td>0.995270</td>\n",
              "      <td>0.004729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10124</th>\n",
              "      <td>716506083</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>44</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>0.819</td>\n",
              "      <td>10291</td>\n",
              "      <td>60</td>\n",
              "      <td>0.818</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.997880</td>\n",
              "      <td>0.002118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10125</th>\n",
              "      <td>717406983</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5281.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5281.0</td>\n",
              "      <td>0.535</td>\n",
              "      <td>8395</td>\n",
              "      <td>62</td>\n",
              "      <td>0.722</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.996710</td>\n",
              "      <td>0.003294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10126</th>\n",
              "      <td>714337233</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>43</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Silver</td>\n",
              "      <td>25</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>10388.0</td>\n",
              "      <td>1961</td>\n",
              "      <td>8427.0</td>\n",
              "      <td>0.703</td>\n",
              "      <td>10294</td>\n",
              "      <td>61</td>\n",
              "      <td>0.649</td>\n",
              "      <td>0.189</td>\n",
              "      <td>0.996620</td>\n",
              "      <td>0.003377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10127 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       CLIENTNUM  ... Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2\n",
              "0      768805383  ...                                           0.999910                                                                                \n",
              "1      818770008  ...                                           0.999940                                                                                \n",
              "2      713982108  ...                                           0.999980                                                                                \n",
              "3      769911858  ...                                           0.999870                                                                                \n",
              "4      709106358  ...                                           0.999980                                                                                \n",
              "...          ...  ...                                                ...                                                                                \n",
              "10122  772366833  ...                                           0.999810                                                                                \n",
              "10123  710638233  ...                                           0.004729                                                                                \n",
              "10124  716506083  ...                                           0.002118                                                                                \n",
              "10125  717406983  ...                                           0.003294                                                                                \n",
              "10126  714337233  ...                                           0.003377                                                                                \n",
              "\n",
              "[10127 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMZ12Dhmttj5",
        "outputId": "99477458-5c9a-4cba-9871-c5e91db8e1d7"
      },
      "source": [
        "model = pd.read_csv(io.BytesIO(uploaded['BankChurners.csv']))\n",
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CLIENTNUM</th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
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              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
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              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>768805383</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>45</td>\n",
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              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
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              "      <td>0.000093</td>\n",
              "      <td>0.999910</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>818770008</td>\n",
              "      <td>Existing Customer</td>\n",
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              "      <td>F</td>\n",
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              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0.105</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.999940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>713982108</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>51</td>\n",
              "      <td>M</td>\n",
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              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
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              "      <td>36</td>\n",
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              "      <th>3</th>\n",
              "      <td>769911858</td>\n",
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              "      <td>High School</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Less than $40K</td>\n",
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              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.000134</td>\n",
              "      <td>0.999870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>709106358</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Uneducated</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>2.175</td>\n",
              "      <td>816</td>\n",
              "      <td>28</td>\n",
              "      <td>2.500</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.999980</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10122</th>\n",
              "      <td>772366833</td>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>50</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4003.0</td>\n",
              "      <td>1851</td>\n",
              "      <td>2152.0</td>\n",
              "      <td>0.703</td>\n",
              "      <td>15476</td>\n",
              "      <td>117</td>\n",
              "      <td>0.857</td>\n",
              "      <td>0.462</td>\n",
              "      <td>0.000191</td>\n",
              "      <td>0.999810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10123</th>\n",
              "      <td>710638233</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>41</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4277.0</td>\n",
              "      <td>2186</td>\n",
              "      <td>2091.0</td>\n",
              "      <td>0.804</td>\n",
              "      <td>8764</td>\n",
              "      <td>69</td>\n",
              "      <td>0.683</td>\n",
              "      <td>0.511</td>\n",
              "      <td>0.995270</td>\n",
              "      <td>0.004729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10124</th>\n",
              "      <td>716506083</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>44</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>0.819</td>\n",
              "      <td>10291</td>\n",
              "      <td>60</td>\n",
              "      <td>0.818</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.997880</td>\n",
              "      <td>0.002118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10125</th>\n",
              "      <td>717406983</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>$40K - $60K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5281.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5281.0</td>\n",
              "      <td>0.535</td>\n",
              "      <td>8395</td>\n",
              "      <td>62</td>\n",
              "      <td>0.722</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.996710</td>\n",
              "      <td>0.003294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10126</th>\n",
              "      <td>714337233</td>\n",
              "      <td>Attrited Customer</td>\n",
              "      <td>43</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Silver</td>\n",
              "      <td>25</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>10388.0</td>\n",
              "      <td>1961</td>\n",
              "      <td>8427.0</td>\n",
              "      <td>0.703</td>\n",
              "      <td>10294</td>\n",
              "      <td>61</td>\n",
              "      <td>0.649</td>\n",
              "      <td>0.189</td>\n",
              "      <td>0.996620</td>\n",
              "      <td>0.003377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10127 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       CLIENTNUM  ... Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2\n",
              "0      768805383  ...                                           0.999910                                                                                \n",
              "1      818770008  ...                                           0.999940                                                                                \n",
              "2      713982108  ...                                           0.999980                                                                                \n",
              "3      769911858  ...                                           0.999870                                                                                \n",
              "4      709106358  ...                                           0.999980                                                                                \n",
              "...          ...  ...                                                ...                                                                                \n",
              "10122  772366833  ...                                           0.999810                                                                                \n",
              "10123  710638233  ...                                           0.004729                                                                                \n",
              "10124  716506083  ...                                           0.002118                                                                                \n",
              "10125  717406983  ...                                           0.003294                                                                                \n",
              "10126  714337233  ...                                           0.003377                                                                                \n",
              "\n",
              "[10127 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tu58LA7fQ2lh"
      },
      "source": [
        "## Persiapan Data\n",
        "Pada tahap ini, kami melakukan proses persiapan data dengan melakukan data cleaning. Untuk menjaga konsistensi kedua dataframe kami lakukan cleaning dengan metode yang sama."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3SiJ9TEvuCX"
      },
      "source": [
        "### Persiapan Dataframe \"df\"\n",
        "\n",
        "Pada dataframe \"df\" kami menghapus kolom :\n",
        "1. Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1\n",
        "2. Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2\n",
        "3. CLIENTNUM\n",
        "\n",
        "ketiga kolom tersebut kami hapus karena tidak dipakai pada proses selanjutnya.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gOYGSGNRDmw"
      },
      "source": [
        "df.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
        "                'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2',\n",
        "                'CLIENTNUM'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hREKrt1zwRnz",
        "outputId": "2b9ca03f-b3d8-499d-8aa1-fea7ac132a38"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Card_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12691.0</td>\n",
              "      <td>777</td>\n",
              "      <td>11914.0</td>\n",
              "      <td>1.335</td>\n",
              "      <td>1144</td>\n",
              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
              "      <td>0.061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>51</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Attrition_Flag  Customer_Age  ... Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio\n",
              "0  Existing Customer            45  ...               1.625                  0.061\n",
              "1  Existing Customer            49  ...               3.714                  0.105\n",
              "2  Existing Customer            51  ...               2.333                  0.000\n",
              "\n",
              "[3 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9fVs8nGNbI4"
      },
      "source": [
        "Setelah mengahpus kolom yang tidak dipakai, selanjutnya adalah memeriksa jika terdapat value yang kosong pada setiap field. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqM8hmElQ_Eo",
        "outputId": "13cfdced-6f66-43b2-d3c2-bdca9a5ac90b"
      },
      "source": [
        "df.isnull().sum()/df.shape[0]*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attrition_Flag              0.0\n",
              "Customer_Age                0.0\n",
              "Gender                      0.0\n",
              "Dependent_count             0.0\n",
              "Education_Level             0.0\n",
              "Marital_Status              0.0\n",
              "Income_Category             0.0\n",
              "Card_Category               0.0\n",
              "Months_on_book              0.0\n",
              "Total_Relationship_Count    0.0\n",
              "Months_Inactive_12_mon      0.0\n",
              "Contacts_Count_12_mon       0.0\n",
              "Credit_Limit                0.0\n",
              "Total_Revolving_Bal         0.0\n",
              "Avg_Open_To_Buy             0.0\n",
              "Total_Amt_Chng_Q4_Q1        0.0\n",
              "Total_Trans_Amt             0.0\n",
              "Total_Trans_Ct              0.0\n",
              "Total_Ct_Chng_Q4_Q1         0.0\n",
              "Avg_Utilization_Ratio       0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czm9UupZN1jQ"
      },
      "source": [
        "Tidak ada value yang kosong pada dataframe tersebut. Selanjutnya kami juga memeriksa tipe data untuk memudahkan dalam proses kedepannya."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lNP0gWzMW56",
        "outputId": "b4241f00-0837-4b3d-c0e3-17d5fc0a6191"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attrition_Flag               object\n",
              "Customer_Age                  int64\n",
              "Gender                       object\n",
              "Dependent_count               int64\n",
              "Education_Level              object\n",
              "Marital_Status               object\n",
              "Income_Category              object\n",
              "Card_Category                object\n",
              "Months_on_book                int64\n",
              "Total_Relationship_Count      int64\n",
              "Months_Inactive_12_mon        int64\n",
              "Contacts_Count_12_mon         int64\n",
              "Credit_Limit                float64\n",
              "Total_Revolving_Bal           int64\n",
              "Avg_Open_To_Buy             float64\n",
              "Total_Amt_Chng_Q4_Q1        float64\n",
              "Total_Trans_Amt               int64\n",
              "Total_Trans_Ct                int64\n",
              "Total_Ct_Chng_Q4_Q1         float64\n",
              "Avg_Utilization_Ratio       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elgyz74_OXLI"
      },
      "source": [
        "Setelah itu, kami juga memeriksa jika sebuah field memiliki outlier atau nilai yang berbeda jauh dengan nilai yang lain."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkhEy9SlR-D_",
        "outputId": "971a6e1f-f2c2-4910-d996-0fd3e6e1e224"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "\n",
        "df.boxplot(column=['Customer_Age','Dependent_count','Months_on_book','Total_Relationship_Count',\n",
        "                   'Months_Inactive_12_mon','Contacts_Count_12_mon','Credit_Limit', 'Total_Revolving_Bal',\n",
        "                   'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1','Total_Trans_Amt','Total_Trans_Ct','Total_Ct_Chng_Q4_Q1'])\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQzBriV2Ojkg"
      },
      "source": [
        "Tidak terdapat outlier pada dataframe tersebut, sehingga dapat dilakukan eksplorasi data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mysk9KyWvmV-"
      },
      "source": [
        "### Persiapan DataFrame \"model\"\n",
        "Pada dataframe \"model\" kami menghapus kolom yang sama seperti pada datafram \"df\", yaitu:\n",
        " 1. Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1\n",
        "\n",
        "    2. Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2\n",
        "\n",
        "   3. CLIENTNUM\n",
        "\n",
        "ketiga kolom tersebut kami hapus karena tidak dipakai pada proses selanjutnya."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoK2yXwYuEnd",
        "outputId": "89a64636-def7-4afb-f345-91bb7651108c"
      },
      "source": [
        "model.drop(columns=['Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
        "                'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2',\n",
        "                'CLIENTNUM'],axis=1,inplace=True)\n",
        "model.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Card_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>High School</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12691.0</td>\n",
              "      <td>777</td>\n",
              "      <td>11914.0</td>\n",
              "      <td>1.335</td>\n",
              "      <td>1144</td>\n",
              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
              "      <td>0.061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>5</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Single</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>51</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Married</td>\n",
              "      <td>$80K - $120K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>40</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
              "      <td>High School</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Less than $40K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3313.0</td>\n",
              "      <td>2517</td>\n",
              "      <td>796.0</td>\n",
              "      <td>1.405</td>\n",
              "      <td>1171</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Existing Customer</td>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>Uneducated</td>\n",
              "      <td>Married</td>\n",
              "      <td>$60K - $80K</td>\n",
              "      <td>Blue</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4716.0</td>\n",
              "      <td>2.175</td>\n",
              "      <td>816</td>\n",
              "      <td>28</td>\n",
              "      <td>2.500</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Attrition_Flag  Customer_Age  ... Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio\n",
              "0  Existing Customer            45  ...               1.625                  0.061\n",
              "1  Existing Customer            49  ...               3.714                  0.105\n",
              "2  Existing Customer            51  ...               2.333                  0.000\n",
              "3  Existing Customer            40  ...               2.333                  0.760\n",
              "4  Existing Customer            40  ...               2.500                  0.000\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy_FoTnJQhA1"
      },
      "source": [
        "Selanjutnya adalah mengubah value teks atau huruf menjadi angka atau numerik. Namun sebelum itu, kami ingin memeriksa value yang unik pada semua field untuk dijadikan angka."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaGtqRwAoton",
        "outputId": "1e336ef0-b48c-486b-836e-7797d209e23b"
      },
      "source": [
        "print(model['Attrition_Flag'].value_counts())\n",
        "print(model['Gender'].value_counts())\n",
        "print(model['Education_Level'].value_counts())\n",
        "print(model['Marital_Status'].value_counts())\n",
        "print(model['Income_Category'].value_counts())\n",
        "print(model['Card_Category'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Existing Customer    8500\n",
            "Attrited Customer    1627\n",
            "Name: Attrition_Flag, dtype: int64\n",
            "F    5358\n",
            "M    4769\n",
            "Name: Gender, dtype: int64\n",
            "Graduate         3128\n",
            "High School      2013\n",
            "Unknown          1519\n",
            "Uneducated       1487\n",
            "College          1013\n",
            "Post-Graduate     516\n",
            "Doctorate         451\n",
            "Name: Education_Level, dtype: int64\n",
            "Married     4687\n",
            "Single      3943\n",
            "Unknown      749\n",
            "Divorced     748\n",
            "Name: Marital_Status, dtype: int64\n",
            "Less than $40K    3561\n",
            "$40K - $60K       1790\n",
            "$80K - $120K      1535\n",
            "$60K - $80K       1402\n",
            "Unknown           1112\n",
            "$120K +            727\n",
            "Name: Income_Category, dtype: int64\n",
            "Blue        9436\n",
            "Silver       555\n",
            "Gold         116\n",
            "Platinum      20\n",
            "Name: Card_Category, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8tf6rgbo1wG",
        "outputId": "0ed53a3c-44c2-442e-c2ab-2fcd26216469"
      },
      "source": [
        "model['Attrition_Flag'].replace({'Existing Customer' : 0, 'Attrited Customer' : 1},inplace = True)\n",
        "model['Gender'].replace({'F': 0, 'M': 1}, inplace = True)\n",
        "model['Education_Level'].replace({'Unknown' : 0, 'Uneducated' : 1, 'High School' : 2, 'College' : 3, \n",
        "                               'Graduate' : 4, 'Post-Graduate' : 5, 'Doctorate' : 6}, inplace = True)\n",
        "model['Marital_Status'].replace({'Unknown' : 0, 'Single' : 1, 'Divorced' : 2, 'Married' : 3}, inplace = True)\n",
        "model['Income_Category'].replace({'Unknown' : 0, 'Less than $40K' : 1, '$40K - $60K' : 2, '$60K - $80K' : 3,\n",
        "                              '$80K - $120K' : 4, '$120K +' : 5}, inplace = True)\n",
        "model['Card_Category'].replace({'Blue' : 0, 'Silver' : 1, 'Gold' : 2, 'Platinum' : 3}, inplace = True)\n",
        "model.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attrition_Flag                int64\n",
              "Customer_Age                  int64\n",
              "Gender                        int64\n",
              "Dependent_count               int64\n",
              "Education_Level               int64\n",
              "Marital_Status                int64\n",
              "Income_Category               int64\n",
              "Card_Category                 int64\n",
              "Months_on_book                int64\n",
              "Total_Relationship_Count      int64\n",
              "Months_Inactive_12_mon        int64\n",
              "Contacts_Count_12_mon         int64\n",
              "Credit_Limit                float64\n",
              "Total_Revolving_Bal           int64\n",
              "Avg_Open_To_Buy             float64\n",
              "Total_Amt_Chng_Q4_Q1        float64\n",
              "Total_Trans_Amt               int64\n",
              "Total_Trans_Ct                int64\n",
              "Total_Ct_Chng_Q4_Q1         float64\n",
              "Avg_Utilization_Ratio       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUGMaDTgQ6Op"
      },
      "source": [
        "Berikut adalah hasil setelah melakukan transformasi data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMOXFAIMv2Q1",
        "outputId": "2bda7836-84e6-4929-d170-3a735beac6a7"
      },
      "source": [
        "model.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Card_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12691.0</td>\n",
              "      <td>777</td>\n",
              "      <td>11914.0</td>\n",
              "      <td>1.335</td>\n",
              "      <td>1144</td>\n",
              "      <td>42</td>\n",
              "      <td>1.625</td>\n",
              "      <td>0.061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8256.0</td>\n",
              "      <td>864</td>\n",
              "      <td>7392.0</td>\n",
              "      <td>1.541</td>\n",
              "      <td>1291</td>\n",
              "      <td>33</td>\n",
              "      <td>3.714</td>\n",
              "      <td>0.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>2.594</td>\n",
              "      <td>1887</td>\n",
              "      <td>20</td>\n",
              "      <td>2.333</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Attrition_Flag  Customer_Age  ...  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio\n",
              "0               0            45  ...                1.625                  0.061\n",
              "1               0            49  ...                3.714                  0.105\n",
              "2               0            51  ...                2.333                  0.000\n",
              "\n",
              "[3 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8uxzchZSliw"
      },
      "source": [
        "## Eksplorasi Data\n",
        "Pada tahap ini, kami menampilkan data dalam bentuk grafik untuk dapat menganalisa dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "borJnGZgVutW",
        "outputId": "853101b2-5eac-46c6-d711-241e98bc3b42"
      },
      "source": [
        "pie = pd.value_counts(df[\"Gender\"]).tolist()\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.pie(x = pie, labels = [\"M\", \"F\"], autopct='%.2f%%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7f2806928390>,\n",
              "  <matplotlib.patches.Wedge at 0x7f2806928b38>],\n",
              " [Text(-0.10035590564411713, 1.0954125671190509, 'M'),\n",
              "  Text(0.10035590564411676, -1.095412567119051, 'F')],\n",
              " [Text(-0.054739584896791156, 0.5974977638831186, '52.91%'),\n",
              "  Text(0.05473958489679095, -0.5974977638831187, '47.09%')])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HULH1zpwNQM/0WRxwwgVsffBOADYs/g6b7rmJ2c99C0mSDPu5M0//Xeb+yQeY80fvoTJ1BpMOftKI/sx2u53tZbnwZaz98ec46NJXMuP0y1j3i2+Ozxcljb9jgA9Fh9CeWVpyrFpvvAZ4SXQOFdvgti0Mbt302x9vufeXTD50PpuX/4J1N36Fw37vnVQmTd3j5+/YuBaAgXUPs+nOnzL9yZeM6M/duOR7TDvmHHqmzaS9fSskCSRJ9mMpv15erTf+KDqEhuctzzlVrTdOJ7u9ec9XE2kEtq99iNVf/f+ynwwOMv3Jl9B34e/zwEdeTXvHdirTZgLZZtxDLnsDA+sf5dGrP8jhL7kCgIf+838zuHk9VHo46GmXM616BgCb7vwJj133EXZsblGZMoPJhx3N4b//D9kfs30LD3/5Cg5/6T+Q9PSy5f4lPHbth3feBj3MBl4pR9YBZzT7a/dGB9HjWVpyaOg8lpuBk6KzSFJJ3QBc3OyvDUQH0U4uD+XTu7GwSFKk84F3RYfQ4zlpyZlqvfEU4PvA8LsiJUmdMkh2Wu710UGUsbTkSLXemAH8imwHuyQp3v3Aac3+2troIHJ5KG/+BQuLJOXJkcBHo0Mo46QlJ6r1xu8C10TnkCQN68XN/tpXokOUnaUlB6r1Rh+wBPA+UEnKp4eAk10miuXyUD58EAuLJOXZHLIlfAVy0hKsWm88D/h6dA5J0oj8TrO/9r3oEGVlaQlUrTcOBm4DDo/OIkkakXuABc3+2uboIGXk8lCsf8TCIklFcixwRXSIsnLSEqRab5wH/BSLoyQVzQ5gYbO/9ovoIGVjaQlQrTcqwE3A2dFZJEljcgtwrs8m6iy/y4/xGiwsklRkZwBviQ5RNk5aOqxabxwG3AHMis4iSdovW4CTmv21+6KDlIWTls77JywsktQNppK9p6tDnLR0ULXeWARcj09wlqRucnGzv/aj6BBlYGnpkGq90QvcDCyIziJJGle/INuU6wV1grk81DlvxMIiSd3obOBPokOUgZOWDqjWG4cCdwMHRmeRJE2IVcAJzf7ahugg3cxJS2e8HQuLJHWzucDbokN0OyctE6xabxwNLAMmR2eRJE2oLcDJzf5aMzpIt3LSMvH+HguLJJXBVOCfo0N0MyctE6hab5xOdseQ5VCSyuOSZn/t+ugQ3ciL6cT6R/x3LEll0x8doFt5QZ0g1XrjEuBZ0TkkSR13QbXeeGZ0iG5kaZk474kOIEkKc0V0gG5kaZkA1XrjRcDC6BySpDDnVeuNZ0eH6DaWlnFWrTd6gP8TnUOSFC6NDtBtLC3j7+XASdEhJEnhzq3WG8+NDtFNLC3jqFpvJMBbo3NIknIjjQ7QTSwt4+sFwMnRISRJuXFWtd54fnSIbmFpGV/16ACSpNxJhybx2k+WlnFSrTeeBpwXnUOSlDtnAC+MDtENLC3j52+jA0iScuvvogN0A0vLOKjWG+cAT4/OIUnKrbOq9cZTo0MUnaVlfDhlkSTty1uiAxSdT3neT9V640TgNiyAkqS9awOnNPtrt0cHKSovtPvvrfjvUZK0bwnw19EhisxJy36o1htzgfuASdFZJEmFsBU4stlfWx0dpIicEOyfV2NhkSSN3BTgz6NDFJWTljEaejBiEzgiOIokqVgeBOY3+2sD0UGKxknL2D0HC4skafTmAS+ODlFElpaxe210AElSYb0pOkARuTw0BtV641jgLrKd4JIkjcXZzf7azdEhisRJy9j8ORYWSdL+eVV0gKJx0jJK1XpjCrASmB2dRZJUaGuAuc3+2tboIEXhpGX0XoyFRZK0/w7Cpz+PiqVl9NyAK0kaL6+MDlAkLg+NQrXeWADcGp1DktQ1BoFqs792f3SQInDSMjp/HB1AktRVKnhtGTEnLSNUrTcSYAUeKCdJGl/3AMc3+2tekPfBScvIPQULiyRp/B1Ldo3RPlhaRu4PowNIkrqWG3JHwOWhEajWG5OAh4CDo7NIkrrSRrIzW9ZHB8kzJy0jcxkWFknSxJkOPD86RN5ZWkbGpSFJ0kR7UXSAvHN5aB+q9cYBwMNkLViSpImyGZjd7K9tig6SV05a9u35WFgkSRNvGvCs6BB5ZmnZtz+IDiBJKg2XiPbC5aG9qNYbfWRLQ5Ojs0iSSmEdcGizv7YtOkgeOWnZu8uwsEiSOudA4OnRIfLK0rJ3tegAkqTScYloD1we2oNqvVEhO1Du0OgskqRSeQSY0+yv7YgOkjdOWvbsPCwskqTOm43PIhqWpWXPXBqSJEVxiWgYlpY9s7RIkqI8MzpAHrmnZRjVemMe8EB0DklSqVWb/bX7okPkiZOW4T07OoAkqfS89fkJLC3Dc2lIkhTN0vIELg89QbXemAw8CsyIziJJKrWHyW599kI9xEnL7hZhYZEkxTsMOC06RJ5YWnZ3aXQASZKGPCM6QJ5YWnZ3SXQASZKGuK9lF+5p2UW13pgKrAWmRGeRJAnYBBzkU58zTloebyEWFklSfhwAXBgdIi8sLY/n0pAkKW9+JzpAXlhaHm9RdABJkp7ggugAeeGeliHVeqNCtp9lZnQWSZJ20SLb11L6C7aTlp0WYGGRJOVPH3BidIg8sLTs5EYnSVJenRcdIA8sLTtZWiRJeWVpwdKyq/OjA0iStAeWFtyIC0C13phJttEpic4iSdIwtgEzy37InJOWzAIsLJKk/JoMnB4dIpqlJeNTNCVJeVf6JSJLS6b07VWSlHuWlugAOWFpkSTl3bnRAaKVfiNutd5IgHXAjOgskiTtxQAwvcybcZ20wLFYWCRJ+dcLHBcdIpKlxaUhSVJxnBQdIJKlxTuHJEnFYWkpOSctkqSiODk6QCRLS3awnCRJReCkpayq9UYvMD86hyRJI3RidIBIpS4twJFAT3QISZJGaGa13nhSdIgoZS8tR0cHkCRplEq7RGRpkSSpWEq7GdfSIklSsThpKaljogNIkjRKpb2BpOylxUmLJKlo5kUHiGJpkSSpWEp791Bpn/JcrTcOADZG55AkaZTawORmf20gOkinlXnSUo0OIEnSGCTA3OgQESwtkiQVTymXiMpcWuZEB5AkaYwsLSVzSHQASZLGyNJSMrOjA0iSNEalvO3Z0iJJUvE4aSkZS4skqagsLSXjnhZJUlGV8hvvMpeWUv4fLknqCgdGB4hgaZEkqXhmRgeIUMrSUq03KsBB0TkkSRojS0uJHER5v3ZJUvFNqtYbU6NDdFpZL9xuwpUkFV3ppi1lLS2l+z9aktR1SrcZt6ylZXJ0AEmS9lPpvgEva2mZEh1AkqT9ZGkpCSctkqSic3moJJy0SJKKzklLSThpkSQV3fToAJ1maZEkqZh6ogN0WllLi8tDkqSiK901vHRf8BAnLZKkonPSUhJOWiRJRVe6a3jpvuAhTlokSUVXumt4b3SAIEl0AKlb9LBj4KRkRXNRZcnDF1aWbju5ct/MQ1h3VE/SPjQ6m9TlEmhFZ+iospaWbdEBpCI6kA2tcyt33LuosqR1buWOytHJqkMPYOvRScJxwHHR+aSSGYwO0GllLS1bowNI+dZuH5OsWnFhZemqiypLtp5WWT7tcNbM600GjwDOiE4nCbC0lIalRRoyja2bzqjcfe+iyuLHFlaWtY9PVs46kE1HJwnzgfnR+STtkaWlJCwtKqV5PPLQwsrt9y/qWbLxzOSuKU9KHpkzmYH5ScIp0dkkjZqlpSQsLepqvQxsPyVp3ntRZenqCypLB06urJh5MOvnV5L2HGBOdD5J46J01zJLi1Rws1i/ZmFl2X2LKotb51Tu7JmfPHTYNLZVk4QTgBOi80maMBuiA3SapUUqiITBweOTB1ZcUFm66qLK0q0LKvdOP5S1R/Qmg3OBg6LzSeq4jdEBOq2spcVbnpVr09m84cxsc+yahZVlybHJAwfNZPPRSUIVqAbHk5QPTlpKwkmLcuOIZPWDF1SWPrCosmTj6ck9U+Ylj8ybxI6jkoQF0dkk5ZqlpSQsLeq4SQxsW5Asv3dRZcnq8yu37Tipcn/fLNbPryTMA+ZF55NUOJaWktgcHUDd7RBajyysLFuxqLJ4/dmVO3uPSh4+bGq2OfZE4MTofJK6gqWlJB6LDqDukDA4eGKysnlRZcmvL6ws3XpKpTnjUNYe2ZO0DwdmR+eT1NUsLSXxKNmhPKV7QqbGbgab1p1TubO5qLJk7XmV25NjklWHTGfL0UnCMcAx0fkklU7pSkvSbrejM4So1huPAIdE51A+zU8eWnlB5bYHFlWWbDktuWfK3OSxeb3sODJJfEK4pFzYTNo6IDpEp5V10gJgaRFT2Lbl9OSeey/qWfLI+ZXbB09IVs6axYZqknAEcER0Pknag7XRASKUvbS4IbJEDuOx1UMnx244u3LX5COS1YdPYXs1STg5OpskjdJD0QEilL20qAv1sGPgpGRFc1FlycMXVpZuO7ly38xDWHdUT9I+FDg0Op8kjQNLS8lYWrrATDa2zqssay6qLFl7buWOytHJqkMPYOvRScJxwHHR+SRpglhaSmZ1dACNRrt9TLJqxYWVpasurCzdclpl+QFzeGxebzJ4BHB6dDpJ6jBLS8k4acmpqWzdfEbl7uUXV5Y8trBye/v4ZOWsA9l0dJIwH5gfnU+ScsDSUjKWlhyYy6MPnV+5beWiniUbzkzunvykZPXcyQzMTxJOic4mSTlmaSkZl4c6qJeB7ackzXsvqixdfUFl6cDJlRUzD2b9/ErSngPMic4nSQVjaSmZFdEButUs1q85r7KseXFl8bpzKnf2zE9+fei0bHPsCcAJ0fkkqQtYWkrm3ugARZcwOHh88sCKCypLV11UWbr11Mq9BxzG2iN7k8G5wEHR+SSpi5WytJT2GH+Aar3xa+Cw6BxFcABbNp5VuWv5osriNQsry5JjkwcOmsnmo5OE6dHZJKlk1pO2DowOEaHMkxaA5VhadnNEsvrB8yu3rVxUWbz5jOSeyfOSR+dOYuCoJGFBdDZJEvdEB4hS9tJyL3B+dIgokxjYtiBZfu+iypLV51du23FS5f6+WayfX0mYB8yLzidJGtbd0QGilL20LI8O0CmH0HpkYWXZikWVxevPrtzZe1Ty8GFT2VZNEk7EZzBJUpFYWkqq60pLwuDgicnK5kWVJb++sLJ06ymV5oxDWXtkT9I+HJgdnU+StN8sLSVV6DuIZrBp3TmVO4eeu7MsOTZ58JDpbDk6STgGOCY6nyRpQlhaSqowk5b5yUMrL6jc9sCiypItpyX3TJmbPDavlx1HJgmnRWeTJHWUG3FL6n5gOzApOshvTGHbltOS5fcu6ln86PmV23eckKycNYsN1SThCOCI6HySpFCbgQeiQ0Qp9TktANV64y7guIg/+zDWrF5Yuf2+RZXFG86q3DXpyGT1nClsryYJPRF5JEm5t5S0dWp0iChln7QALGOCS0sPOwZOSlY0L6os/fWFlSUDT67cN+MQ1h3Vk7QPBQ6dyD9bktRVSrufBSwtAIuB54zXP2wmG1vnVZYNbY69o3J08tDsA7LNsccRNNGRJHWNZdEBIllastIyBu32Mcmq+y+sLH3wwsrSLadVlh8wh8fm9SaDRwCnj2tCSZIyt0QHiGRpGUFpmcrWzWdU7l6+qLLksfMrt7ePT1bOOpBNRycJRwFHdSCjJElgaSm9O9jlDqK5PPrQ+ZXbVi7qWbLhzOTuyU9KVs+dzMD8JOGU2JiSpJLbBNwZHSJS6e8eAvjWO57xufMqy046mPVHVZL2IdF5JEkaxg2krQuiQ0Ry0gI8u+em7cCZ0TkkSdqLUi8NAVSiA+TEL6MDSJK0D5aW6AA5YWmRJOWdpSU6QE78EnBzjyQpr3YAt0aHiGZpAUhb6yj5KYOSpFy7i7S1OTpENEvLTj+JDiBJ0h7cFB0gDywtO/04OoAkSXvwo+gAeWBp2cn/ICRJefXD6AB54OFyu0r7VgOzo2NIkrSLR0hbh0aHyAMnLY/nvhZJUt64fWGIpeXxXCKSJOWNS0NDLC2PZ2mRJOWN16YhlpbH+wWwJTqEJElDNgE3R4fIC0vLrtLWNuBn0TEkSRpyI2lre3SIvLC07O4H0QEkSRrifpZdWFp2d3V0AEmShlwXHSBPLC27uwFYGx1CkoM5kDIAABLxSURBVFR6a4GfRofIE0vLE6WtHcB3omNIkkrvuqFrkoZYWob37egAkqTS81r0BJaW4bmvRZIUqY3Xot1YWoaTth4Ebo2OIUkqrV+RtlZFh8gbS8ue2XAlSVFcGhqGpWXPLC2SpCiWlmFYWvbsR8D66BCSpNJp4a3Ow7K07El2bLLTFklSp11D2hqIDpFHlpa9+1J0AElS6Xw5OkBeWVr2rkH2hE1JkjphI9m1R8OwtOxN2tqE//FIkjqnMXTt0TAsLfv2xegAkqTS8JqzF5aWffsW2bhOkqSJtIHsmqM9sLTsi0tEkqTOuIq0tTk6RJ5ZWkbGu4gkSRPNpaF9sLSMTAOXiCRJE2c9noK7T5aWkcjGdd+MjiFJ6lrfJG1tiQ6Rd5aWkftUdABJUtf6THSAIrC0jNx1wIroEJKkrrOC7BqjfbC0jFTaGsRpiyRp/H1q6BqjfbC0jM4ngXZ0CElS1xgEPhEdoigsLaORtprAd6NjSJK6xndJW/dFhygKS8vofTw6gCSpa3hNGQVLy+j9N7AmOoQkqfAeBb4WHaJILC2jlba2Av8ZHUOSVHifHbqmaIQsLWPjOE+StL+8loySpWUs0tYtwE+jY0iSCuunpK3F0SGKxtIydu+LDiBJKiyvIWNgaRm7r+IJuZKk0buP7BqiUbK0jFXa2gFcGR1DklQ4Vw5dQzRKlpb98zFgQ3QISVJhbAD+IzpEUVla9kfaauHziCRJI/eJoWuHxsDSsv8+QPbsCEmS9maQ7JqhMbK07K+0dTdwVXQMSVLufYO0tTw6RJFZWsbH+6MDSJJyz9uc91PSbrejM3SHtO8m4NzoGJKkXPoZaeu86BBF56Rl/Px9dABJUm55jRgHTlrGU9r3c+Ds6BiSpFz5BWnrnOgQ3cBJy/iySUuSnuiK6ADdwknLeEv7bgbOjI4hScqFm0lbTuDHiZOW8ee0RZL0G14TxpGlZfx9HbglOoQkKdwvSVtfjw7RTSwt4y1ttbFZS5K8Fow7S8vE+Bpwa3QISVKYW8gm7xpHlpaJkE1b3h4dQ5IU5l1D1wKNI+8emkhp3/8Al0bHkCR11A9IW5dGh+hGTlom1lsAW6EklUcb+OvoEN3K0jKR0tYvgM9Fx5Akdcznht77NQEsLRPvbcCW6BCSpAm3hew9XxPE0jLR0tYK4APRMSRJE+79Q+/5miCWls54N/BIdAhJ0oRZDfxjdIhuZ2nphLS1DkijY0iSJkw69F6vCWRp6ZyPAHdEh5AkjbtlwEejQ5SBpaVT0tYA8MboGJKkcffmofd4TTBLSyelreuAz0fHkCSNmy+Stq6JDlEWlpbO+0ugFR1CkrTfWsCbo0OUiaWl09LWQ8DfRceQJO23t5O2VkWHKBNLS4wPAz+LDiFJGrOfAR+KDlE2PjAxStp3FnAT0BMdRZI0KjuA80hbN0cHKRsnLVGy/9ht6ZJUPP9uYYlhaYn1duDB6BCSpBF7gOy9WwEsLZGy0xM9u0WSiuMvSFvro0OUlaUlWtr6KvC56BiSpH36PGnrK9EhyszSkg9vwGUiScqzVcDro0OUnaUlD9LWGuBV0TEkSXv0KtLWY9Ehys7Skhdp62p84JYk5dFHSVvfjg4hS0ve/DVwb3QISdJvLSd7b1YOWFryJG1tAP4EGAxOIknK3ov/ZOi9WTlgacmbtHU98IHoGNJo7Bhsc+ZHNvCcz20C4OJPbuSM/7uBM/7vBub963pe8PlNw37ep2/ZxvFXbuD4Kzfw6Vu2/fbXv7BkO6d9eAOnfGgDb71uy29//cobt3Hqhzbw7P/cxLYd2WneP1oxwF9evWW3f7Y0Dt5H2vphdAjtZGnJp7cBt0WHkEbqAzdu4+TZO99OfvjK6dzymhnc8poZXHBkDy86uXe3z3lsc5srfrCVGy+fzk2XT+eKH2xlzeY2j24a5G+u28J3X3EAS183g4c2tvnu8gEA/nPxdm597XQuPLKHa+4eoN1u8w/Xb+Udl0zp2Neq0rgNH26bO5aWPEpbW4CXAsN/eyrlyMp1gzTuGuDysybv9nvrtrb53r0DvOCkSbv93jV3D/CMY3o5eFrCQdMSnnFML1ffPcDyNW2OP6TCodOzt6enH93DV27fDkCbNtt3wKbtbSb1JHz21u0867jsnyGNo83Ay0hbW6OD6PEsLXmVtpbimQAqgDdfvYV/evpUKsP0hq8t287vHN3LgVN2/80H1g9yZN/Ot6AjDqzwwPpBjju4wh2PDNJcO8jAYJuv3THA/euypaA3nDuZ8z++kRWtNhcd2cMnb9nO68/dvSxJ++kNpK3F0SG0O0tLnqWtTwGfjo4h7clVd27nsOkJZ88b/mHl/7VkO39w6u5Tlr05aFrCh2tT+f0vb+LiT26i2lehZ6jz/K/TJ/PLP5/BZ180jffdsI03LZzMt+8e4MVf3MRfXr2FQZ9ar/33GdLWJ6JDaHiWlvx7HbA0OoQ0nB+v2ME37hig+v71vOzLm/nevQO8/KubAXhk0yA3PTBI7YTd97MAPGlmhftbO2+UW7lukCfNzN6SnnviJG68fAY/fdV0Tpxd4YRDHv9W9eD6QW56YAcvOGkS//rTbXzhxdOYNTXhu8t3TNBXqpJYCrw2OoT2zNKSd2lrE/ASYGN0FOmJ/vHpU1n5VzNpvnkmn3/xNJ52dC+ffdE0AL582wDPOaGXqb3D7ze57Lherl0+wJrNbdZsbnPt8gEuOy4rOA9vzMrMms1tPvSzbVx+1uOnNe/43lb+/qnZ5tvN29skCVSSbK+LNEYbgZcMvecqpywtRZC2biebuEiF8flhloZ+/uAOLv9GNok5eFrCO54yhXM/toFzP7aBdz5lym831P7F1Vt48r9v4KJPbKS+aAonHLJz+emXq7Jpyllzs1/7wwWTWPDhjfz4/gGeedzwUx1pBF479F6rHEvargEXR9r3CeCV0TEkqct8nLR1eXQI7ZuTlmJ5PXBrdAhJ6iK3Am+MDqGRcdJSNGlfFfgZMDs4iSQV3aPAQtLWPdFBNDJOWoombTWB3wO2ByeRpCLbDrzYwlIslpYiyp5P5MFzkjR2byBtfT86hEbH0lJUaetjwJXRMSSpgK4kbX00OoRGz9JSbH8JXBcdQpIK5Fqy904VkBtxiy7tmwXcCJwQHUWScu4O4HzS1troIBobJy1Fl/3lex7gX0JJ2rM1wHMtLMVmaekGaesO4KV4R5EkDWcAeClp667oINo/lpZukbauA/4EcL1Pkh7vctLWd6JDaP9ZWrpJ2voc8FfRMSQpR95G2vp0dAiNDzfidqO0rx94a3QMSQp2JWnrTdEhNH6ctHSjtFUHPhUdQ5ICfQl4c3QIjS9LS/d6NXBVdAhJCnAt8HLS1mB0EI0vl4e6Wdo3DfgucEF0FEnqkJ8CzyBtbYwOovFnael2ad/BwPXAKdFRJGmCLQGeQtpaEx1EE8PloW6Xth4Dng4si44iSRPoTuB3LSzdzdJSBmnrIeCpWFwkdac7gaeStlZFB9HEsrSUxc7ickd0FEkaR78pLA9GB9HEs7SUyc7icmd0FEkaB3cCl1pYysPSUjbZ+NTiIqnoflNYXBIqEUtLGWXflTwV8OFhkoroDiwspWRpKaudxeXu6CiSNAp34Kbb0rK0lFnaegC4GPhVdBRJGoGlWFhKzdJSdtnm3EuAH0ZHkaS9+DFwsYWl3CwtgrTVAi4DvhkdRZKGcRXZ0fweHFdylhZl0tZm4EX4dGhJ+fIp4IVD71EqOZ89pMdL+xLgn4C3REeRVHr/RNp6a3QI5YelRcNL+/6GrLxIUqe1gbeQtt4bHUT5YmnRnqV9rwQ+CvRGR5FUGtuBPyVtfTY6iPLH0qK9S/t+B/gScFB0FEldby3w+6Sta6ODKJ8sLdq3tO94sjuLToyOIqlr3QU8l7TlQ121R949pH1LW3cB5wPXRUeR1JWuAxZaWLQvlhaNTNpaCzwb+PfoKJK6ypXAszyDRSPh8pBGL+17LfBB3KAraey2A28gbX00OoiKw9KisXGDrqSxexT4PdLWD6KDqFgsLRq7tO844KvAgugokgpjMfB80ta90UFUPO5p0dilrbvJNuh+JjqKpEL4ONmGWwuLxsRJi8ZH2vdnZPtcpkRHkZQ7G4HXkrb+X3QQFZulReMn7TsL+CJwbHQUSbmxFHgJaev26CAqPpeHNH7S1s3AWWQbdCXp08B5FhaNFyctmhhp32uA9wFTo6NI6rjNwOtJW5+MDqLuYmnRxEn7Tgc+C5waHUVSx9xG9vygJdFB1H1cHtLESVu/As4B/gUYDE4jaWINAv8KnG1h0URx0qLOSPsuBj4FHBOcRNL4Ww68krR1fXQQdTcnLeqMtPVD4HTgY9FRJI2rjwCnW1jUCU5a1Hlp37OB/wDmRkeRNGYPAq8ibV0dHUTl4aRFnZe2vkW2OfeL0VEkjcl/AqdaWNRpTloUK+17EdlJuk+KjiJpnx4A3kTa+mp0EJWTkxbFyt78TgY+AOwITiNpeDvI/o6ebGFRJCctyo/sMQAfIbtNWlI+/Az4c9LWL6ODSE5alB/ZYwAWAm8E1gWnkcquBbweON/Corxw0qJ8SvvmAe8HXhIdRSqh/wL+irT1UHQQaVeWFuVb2vdM4L1k+14kTaxlZBttr4sOIg3H5SHlW3ZL5WnA64DVwWmkbvUw2d+xBRYW5ZmTFhVH2ncg8LfAm/Hp0dJ42Ey2DNtP2nIfmXLP0qLiSfuOAt4N/CGQBKeRiqhN9gT2vyNt3R8dRhopS4uKK+07l+ypshdHR5EK5PvAXw/drScViqVFxZf2PQ+4AjgjOoqUY7cA7yRtfTM6iDRWlhZ1h7QvAZ4HvAs4MziNlCe/BP4e+Dppyzd8FZqlRd0n7Xs+lhfpZuAK0tY3ooNI48XSou6VLRu9CzgrOorUQT8nKytXRQeRxpulRd0v7Xsu8E58ppG628/IykojOog0USwtKo+072Lgr8j2vniworrBIPAN4H2kreujw0gTzdKi8kn7jgXeBPwpMCM4jTQWG4BPAB8kbd0THUbqFEuLyivt6wNeTfZU6aOC00gjcR9wJfAfpK1WdBip0ywtUtrXC7yI7PEAFwSnkYbzE+B9wH+TtnZEh5GiWFqkXaV9C4BXAS8HDglOo3J7jOyo/Y+Ttm6NDiPlgaVFGk7aNwV4AVmBeTo+40id0Qa+A3wc+Bppa2twHilXLC3SvqR9VeCVQ68jY8OoS60APgl8krR1X3QYKa8sLdJIpX0V4HfJlo6eB8yMDaSC2wB8E/g0cB1pazA4j5R7lhZpLNK+qcAzgZcCz8VbpzUyG4CrgC8C3yZtbQnOIxWKpUXaX2nfNOBZZAXmOcD02EDKmY1Ag6yofIu0tTk4j1RYlhZpPKV9BwA14PeAy4BZsYEUZB1wNfAlsqKyKTiP1BUsLdJESft6yM59edbQ6wy8C6mb/Qr49tDrJ6StgeA8UtextEidkvbNYWeBeQZOYYpuLXAd2UTlatLWg8F5pK5naZEiZKfwnk92BszFwELcC5N3m4GbgB8C1wA3OE2ROsvSIuVBVmLOIiswi4Zes0Mz6RHgx8CPhl6/IG1tj40klZulRcqrtO8kdpaYc4ATgZ7QTN1rELgLuJFskvIj0tay2EiSnsjSIhVFdmv1ArINvWcOfVyAy0qjtQlYDNyyy2sxaWtjaCpJ+2RpkYosO6X3BLICczrZNOY44FjggMBkebAFWA7cDdzGzoJyl6fPSsVkaZG6UdqXAPPICsxvXsezs9B0ywm+64B7yIrJEz8+QNryDU7qIpYWqYzSvpnAnKHX3F1+vOuvHQYcSLb81MnzZbYAjwEPA6t3+bhql9eDwCrS1poO5pIUzNIiae+yqc10sunMzKGPu/54Ojs3CCd7+dgmKyQbyI62H/5j2toxgV+NpAKztEiSpEKoRAeQJEkaCUuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqBEuLJEkqhN7oAJI0WkmS7AAW7/JLL2i3282gOJI6JGm329EZJGlUkiTZ0G63Z0TnkNRZLg9JkqRCcNIiqXCesDx0b7vdfmFkHkmdYWmRVDguD0nl5PKQJEkqBEuLJEkqBEuLJEkqBPe0SJKkQnDSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCsHSIkmSCuH/B98b45qr6JNVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hJrapxVGi5z"
      },
      "source": [
        "Pada pie chart diatas, pemilik kartu kredit hampir terbagi rata antara wanita dan pria."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orHdR0D4SkPX",
        "outputId": "60114ace-2b64-42d5-d110-2c98a84f4ce6"
      },
      "source": [
        "df['Customer_Age'].plot(kind='hist')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f28069332e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hX7jFbQlGuK8"
      },
      "source": [
        "Pada histogram diatas, kebanyakan pemilik kartu kredit berada pada umur 35 sampai dengan 55 tahun."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "riNv2kUiS2i_",
        "outputId": "25b8f50d-ec63-4702-9d07-93a660a33c36"
      },
      "source": [
        "df.groupby('Marital_Status').agg({'Total_Trans_Amt':'sum'}).plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f28068b1518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PA++T1A/4ODAvItZHxJ+BebT9gWFmZhVWqT76/sDLJcuNaVtr7WZmtpt0mS9jJdVLapDUsHbt2rzLMTMrjEoF/RpgYMnygLSttfadRMSMiKiLiLra2toKlWVmZpUK+jnA59LRNx8GNkbEq8D9wEmSDky/hD0pbTMzs91krywbSZoFjAP6SmokGUnTHSAibgLmAqcAq4C3gM+n69ZL+g6wKD3UZRHR1pe6ZmZWYZmCPiKmtLM+gK+0sm4mMLP80szMrBK6zJexZma2azjozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBeegNzMrOAe9mVnBZQp6SRMlPSdplaQLW1h/laQl6eN3kjaUrNtesm5OJYs3M7P2tXsrQUk1wPXAiUAjsEjSnIhY0bRNREwv2f4fgVElh3g7IkZWrmQzMytHljP6McCqiHgxIrYCs4FJbWw/BZhVieLMzKzzsgR9f+DlkuXGtG0nkg4BhgAPlTT3lNQg6XFJn+pwpWZm1iHtdt2UaTJwV0RsL2k7JCLWSDoUeEjSsoh4ofmOkuqBeoBBgwZVuCwzsz1XljP6NcDAkuUBaVtLJtOs2yYi1qR/vgg8wnv770u3mxERdRFRV1tbm6EsMzPLIkvQLwKGShoiqQdJmO80ekbSMOBA4DclbQdK2jt93hcYC6xovq+Zme067XbdRMQ2SRcA9wM1wMyIWC7pMqAhIppCfzIwOyKiZPfhwA8lvUPyoXJ56WgdMzPb9TL10UfEXGBus7aLmy1f2sJ+jwEjOlGfmZl1kq+MNTMrOAe9mVnBOejNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgHPRmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcFlCnpJEyU9J2mVpAtbWH+OpLWSlqSPL5asmybp+fQxrZLFm5lZ+9q9Z6ykGuB64ESgEVgkaU4LN/n+SURc0Gzf3sAlQB0QwOJ03z9XpHozM2tXljP6McCqiHgxIrYCs4FJGY//cWBeRKxPw30eMLFjpZqZWUdkCfr+wMsly41pW3OnS1oq6S5JA8vc18zMdpFKfRn7c2BwRBxNctb+43IPIKleUoOkhrVr11aoLDMzyxL0a4CBJcsD0rYdImJdRGxJF38EfDDrviXHmBERdRFRV1tbm6V2MzPLIEvQLwKGShoiqQcwGZhTuoGkfiWLpwHPps/vB06SdKCkA4GT0jYzM9tN2h11ExHbJF1AEtA1wMyIWC7pMqAhIuYAX5V0GrANWA+ck+67XtJ3SD4sAC6LiPW74OcwM7NWtBv0ABExF5jbrO3ikuffAr7Vyr4zgZmdqNHMzDrBV8aamRWcg97MrOAc9GZmBeegNzMrOAe9mVnBOejNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgMk2BYGZWLQZf+Mu8S2jX6stP3a2v5zN6M7OC22PO6P0pb2Z7Kp/Rm5kVnIPezKzgHPRmZgXnoDczK7hMQS9poqTnJK2SdGEL6/9J0gpJSyXNl3RIybrtkpakjznN9zUzs12r3VE3kmqA64ETgUZgkaQ5EbGiZLOngLqIeEvSecC/A59J170dESMrXLeZmWWU5Yx+DLAqIl6MiK3AbGBS6QYR8XBEvJUuPg4MqGyZZmbWUVmCvj/wcslyY9rWmnOB+0qWe0pqkPS4pE91oEYzM+uEil4wJelsoA44oaT5kIhYI+lQ4CFJyyLihRb2rQfqAQYNGlTJsszM9mhZzujXAANLlgekbe8h6e+Ai4DTImJLU3tErEn/fBF4BBjV0otExIyIqIuIutra2sw/gJmZtS1L0C8ChkoaIqkHMBl4z+gZSaOAH5KE/Gsl7QdK2jt93hcYC5R+iWtmZrtYu103EbFN0gXA/UANMDMilku6DGiIiDnA94H9gTslAfw+Ik4DhgM/lPQOyYfK5c1G65iZ2S6WqY8+IuYCc5u1XVzy/O9a2e8xYERnCjQzs87xlbFmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBeegNzMruExBL2mipOckrZJ0YQvr95b0k3T9byUNLln3rbT9OUkfr1zpZmaWRbtBL6kGuB44GTgCmCLpiGabnQv8OSL+BrgK+F667xHAZOBIYCJwQ3o8MzPbTbKc0Y8BVkXEixGxFZgNTGq2zSTgx+nzu4AJkpS2z46ILRHxErAqPZ6Zme0mWYK+P/ByyXJj2tbiNhGxDdgI9Mm4r5mZ7UJ75V1AE0n1QH26+Iak5/KsJ4O+wJ8qeUB9r5JHqzp+PyvL72dlVfT93EXv5SGtrcgS9GuAgSXLA9K2lrZplLQXcACwLuO+AETEDGBGhnq6BEkNEVGXdx1F4fezsvx+Vla1v59Zum4WAUMlDZHUg+TL1TnNtpkDTEufnwE8FBGRtk9OR+UMAYYCT1SmdDMzy6LdM/qI2CbpAuB+oAaYGRHLJV0GNETEHOA/gNskrQLWk3wYkG53B7AC2AZ8JSK276KfxczMWqDkxNvKJak+7W6yCvD7WVl+Pyur2t9PB72ZWcF5CgQzs4Jz0JuZFZyD3sys4LrMBVNdmaR/amt9RFy5u2opAkmfbmt9RNy9u2opEkmHAEMj4kFJ+wB7RcSmvOuqRpL2Bk4HBlOSkxFxWV41dYaDPpte6Z8fAD7Eu9cRfBJfF9ARn0z/fD/wt8BD6fJ44DHAQV8mSV8iubK8N3AYycWJNwET8qyrit1LMpXLYmBLzrV0mkfdlEHSAuDUprMkSb2AX0bE8flWVp0kPQBMi4hX0+V+wC0R4emsyyRpCcmEgb+NiFFp27KIGJFvZdVJ0jMRcVTedVSK++jLcxCwtWR5a9pmHTOwKeRTfwQG5VVMlduSzi4LQDoVic/iOu4xSYX5kHTXTXluBZ6QdE+6/CnenZ7Zyjdf0v3ArHT5M8CDOdZTzf5b0j8D+0g6ETgf+HnONVWzjwLnSHqJpOtGQETE0fmW1THuuimTpNHAcenigoh4Ks96qp2kvweaur4WRMQ9bW1vLZPUjeQGQCeRhNL9wI/C/8E7JP1ieycR8T+7u5ZKcNCXSdJHSUY23CypFtg/vamKdUCzkSL7AjUeKWJ5k/QdYAHwWES8mXc9neWgL4OkS4A64AMRcbikg4E7I2JszqVVpdKRIhFxmKShwE0R4ZEiGUlaRht98dXa1ZA3SZ8n+c39I8AmYCHJb5z35lpYBznoy5CObBgFPFkysmGp/zN1jEeKdF5rXQxNqrWroauQ9NfAPwDfAA6MiF7t7NIl+cvY8myNiJAUAJL2y7ugKrclIrYmtxf2SJGOcJDvGpJ+BBxBMhJsIcl9Np7MtahO8PDK8twh6YfA+9JuhweB/5dzTdWs+UiRO/FIkQ6RtEnS680eL0u6R9KheddXhfqQ3H9jA8k9Nv6U3g+7KrnrpkxpIO0Y2RAR83IuqWp5pEjlpF8eNgK3k7yXk0mukH0SOC8ixuVXXfWSNBz4ODCdZKDAgJxL6hAHfRnS2yG+GhGb0+V9gIMiYnWuhdkeT9LTEXFMs7YlETGypXXWNkmfIPky9njgfcDjwMKImJlrYR3kPvry3EkyN0uT7Wnbh/IppzpJuiMi/qG1ESP+crtD3pL0D8Bd6fIZwOb0uc/myjeRpG/+BxHxSt7FdJbP6MvQdIbUrM1nS2WS1C8iXi3aRSl5Svvhf0AyHDBIzkCnA2uAD0bEr3MsrypJOoh3T+KeiIjX8qynMxz0ZZA0D7g2vSE6kiYBX/W47/JJqgEejIjxeddi1pykM4ErgEdIvvM4DvhmRNzV1n5dlYO+DJIOA/4LODhtagSmRsQL+VVVvSTNBz4dERvzrqXapVdpf4md50//Ql41VTNJTwMnNp3Fp+/vg9X627v76DNKz0DPi4gPS9ofICLeyLmsavcGsCz9TWnHZeYR8dX8Sqpa95L0KT9I8t2RdU63Zl0166ji4egO+owiYns6z40DvnLuxjcZqZR9I+L/5F1EgfyqhZlV5+ZYT6e466YMkm4E+pOMtCk9A3VYlcl99JUl6f+STMBVtWHU1Ug6HWiax2phNc+s6qAvg6SbW2gO94N2jPvoK0fSJmA/krnT/8K786f/Va6FWZfgoLfcSLqXZJI499Fbl5LewP57JPc1FlX+wemgL4OkAcC1lPw6B3wtIhrzq6p6SZrWUntE+K5dGUkaFhEr0xvi7CQiqnYirjxJWgV8MiKezbuWSnDQlyEdHXI7cFvadDZwVkScmF9VtieTNCMi6iU9XNK84z91RHwsh7KqnqRHi3SfCQd9GVq5MnanNssmvdHIv5FMB9uzqT0iPNtiRpLGAL+PiD+ky9OA04HVwKURsT7H8qqWpB8Afw38jOR7D6B6B15U7bjQnKyTdLakmvRxNsn4WuuYm4EbgW3AeJKbr/9nrhVVn5uArQCSjif54PwxsBGYkWNd1e6vgLdIZlb9ZPr4RK4VdYLP6MuQzs1yLe/OJ/IYyRQIv8+1sColaXFEfLD0rlJNbXnXVi1K51qSdD2wNiIuTZf922YHSerd/LchSUOq9f7QvmCqPG9FxGl5F1EgW9I56Z+XdAHJBFz751xTtamRtFd6U4wJJPfgbeL/3x33c0knR8TrsGNe+juBo/Itq2PcdVOeRyU9IOlcSe/Lu5gC+BqwL/BV4IPAVKDFkTjWqlkkd+q6F3ibZCQYkv6GpPvGOuZfScJ+f0kfJJn++eyca+owd92UKf3yazLwKWAFMDsi3K9suZH0YaAf8EBEvJm2HQ7s7+GVHSfpU8D/BnoBp0fE73IuqcMc9B0kqS9wJcnwypq866kmkua0td7dY5YXSdfy3hu1TABeIBnFVLUX87kPrwyS/gr4e969H+c9wJhci6pOHwFeJul2+C3JVYdmXUFDs+XFuVRRYT6jL4Okl0jG1d4REb/Ju55qlU5odiIwBTga+CUwKyKW51qYWUE56MsgSeE3rKIk7U0S+N8H/iUirsu5JDMkjQUuBQ4h6flomuumKi/mc9BnIOnqiPi6pJ/T8s2s3adcpjTgTyUJ+cHAHGBmRKzJsy4zAEkrSe65u5iSG7lERFVeIOk++mya5ra5ItcqCkLSrSTjkeeSnMU/k3NJZs1tjIj78i6iUnxGX6b03pFExNq8a6lWkt7h3WmJS/8BVvVUsFYcki4HakjugFY6101VDld10Gck6VLgApKLzEQyP8u1EXFZnnWZWeWVzAbaFJBNJyFVORuogz4DSf8EnAzUN811IelQkgm5fhURV+VZn5lVRvp/Hd4d8hvAWuDX1TrPDXgKhKymAlNK/6Ij4kWSS6I/l1tVZlZpvdLH/umjF1AH3Cdpcp6FdYbP6DOQ9ExEtDiZUVvrzKwYJPUmuZl9i3fy6up8Rp/N1g6uM7MCSKcsrtoruD28MptjJL3eQrsouTOSmRWTpPHAn/Ouo6Mc9Bl40jKzPYOkZex8UWRv4BWq+Ps499GbmaXSu8iVCmBd0/TP1cpBb2ZWcP4y1sys4Bz0ZmYF56A3Mys4B711eZJC0n+WLO8laa2kX5R5nIMl3ZU+HynplAz7jGvrdSQdJOkXkp6WtELS3LR9sKTPZjh+pu3MOsNBb9XgTeAoSfukyycCZc1bL2mviHglIs5Im0YC7QZ9BpcB8yLimIg4ArgwbR8MZAnwrNuZdZiD3qrFXJIblUBys5JZTSskjZH0G0lPSXpM0gfS9nMkzZH0EDA/PXt+RlIPkoD+jKQlkj7T2jEy6Ac0Ni1ExNL06eXAcenxp6evvVDSk+njb1vZ7hxJO+6ylf62ME5SjaRb0vqXSZpe/ltoeypfMGXVYjZwcdqNcjQwEzguXbcSOC4itkn6O+BfgdPTdaOBoyNivaTBABGxVdLFQF1EXAA7bvze2jHacj3wE0kXAA8CN0fEKyRn9t+IiE+kx98XODEiNksaSvJBVdfCdue08jojgf5N8ypJel+G2jdTc/QAAAGRSURBVMwAB71ViYhYmgb1FJKz+1IHAD9OAzSA7iXr5qXzlLSnrWO0Vdf96ZTVE0mmsn5KUkuT3HUHrpM0kuTWdIdnOX6JF4FDJV1LcjP1B8rc3/Zg7rqxajKH5HaOs5q1fwd4OD3b/STvnX8o6xWNbR2jTRGxPiJuj4ipwCLg+BY2mw78ETiG5Ey+RyuH28Z7/1/2TF/jz+m+jwBfBn6UtT4zB71Vk5kk95hd1qz9AN79cvacjMfaRDLXeGeOgaSPpd0ySOoFHAb8vpXjvxoR75Dc36Bp/qTm260GRkrqJmkgMCY9dl+gW0T8FPg2SZeUWSYOeqsaEdEYEde0sOrfgX+T9BTZuyMfBo5o+jK2g8cA+CDQIGkp8BvgRxGxCFgKbE+HXU4HbgCmSXoaGMa7v2k03+5R4CVgBXAN0HSP0v7AI5KWAP8JfKuMGm0P57luzMwKzmf0ZmYF51E3ZhlI+jzwtWbNj0bEV/Kox6wc7roxMys4d92YmRWcg97MrOAc9GZmBeegNzMrOAe9mVnB/X+xsPphlcK+6wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJefufnoHHVz"
      },
      "source": [
        "Pada grafik diatas, mayoritas pemiliki kartu kredit adalah orang yang sudah menikah dan orang yang masih lajang. Mereka pun yang paling berkontribusi dalam total transaksi kartu kredit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12vMNeGaS4P3",
        "outputId": "e12faed0-5841-40ee-86ee-7005514482d2"
      },
      "source": [
        "df.groupby('Education_Level').agg({'Total_Trans_Amt':'sum'}).plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f280682dd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z2aqqKp8bETcBN7XZ94OyrwM4If1jZmYZ8ExRM7OCcEI3MysIJ3Qzs4JwQjczKwgndDOzgnBCNzMrCCd0M7OCcEI3MysIJ3Qzs4JwQjczKwgndDOzgnBCNzMrCCd0M7OCcEI3MysIJ3Qzs4JwQjczKwgndDOzgnBCNzMrCCd0M7OCcEI3MyuIqhaJbgSDJ93Yo+efe9rePXp+M7OeVtUVuqRxkp6WNEfSpE7a7ScpJLXULkQzM6tGxYQuqTdwAbAXsA0wQdI27bTrCxwHPFjrIM3MrLJqrtB3AuZExHMR8Q5wJTC+nXY/Ak4HltQwPjMzq1I1CX0g8ELZ9rx033KSRgObRkSnHd2SJkpqldQ6f/78LgdrZmYdW+1RLpJ6AWcB36rUNiIuiYiWiGhpbm5e3bc2M7My1ST0F4FNy7YHpftK+gLbAXdImgvsAkz1g1Ezs/qqJqE/DAyTNETSmsCBwNTSwYh4LSIGRMTgiBgMPADsExGtPRKxmZm1q2JCj4ilwDHALcCTwNURMUvSqZL26ekAzcysOlVNLIqIm4Cb2uz7QQdtx65+WGZm1lWe+m9mVhBO6GZmBeGEbmZWEE7oZmYF4YRuZlYQTuhmZgXhhG5mVhBO6GZmBeGEbmZWEE7oZmYF4YRuZlYQTuhmZgXhhG5mVhBO6GZmBeGEbmZWEE7oZmYF4YRuZlYQTuhmZgXhhG5mVhBVrSlqZtkZPOnGHj3/3NP27tHzW/34Ct3MrCCqSuiSxkl6WtIcSZPaOX6CpNmSZkqaJmnz2odqZmadqZjQJfUGLgD2ArYBJkjapk2zGUBLRGwP/A74Sa0DNTOzzlVzhb4TMCcinouId4ArgfHlDSLi9oh4M918ABhU2zDNzKySahL6QOCFsu156b6OHAHc3N4BSRMltUpqnT9/fvVRmplZRTV9KCrpYKAFOKO94xFxSUS0RERLc3NzLd/azOx9r5phiy8Cm5ZtD0r3rUTSx4GTgI9FxNu1Ca84PPTMzHpaNVfoDwPDJA2RtCZwIDC1vIGkUcD/AvtExMu1D9PMzCqpmNAjYilwDHAL8CRwdUTMknSqpH3SZmcA6wHXSHpU0tQOTmdmZj2kqpmiEXETcFObfT8o+/rjNY7LzMy6yDNFzcwKwgndzKwgnNDNzArCCd3MrCCc0M3MCsIJ3cysIJzQzcwKwgndzKwgnNDNzArCCd3MrCCc0M3MCsIJ3cysIJzQzcwKwgndzKwgnNDNzArCCd3MrCCc0M3MCsIJ3cysIJzQzcwKwgndzKwgnNDNzAqiqoQuaZykpyXNkTSpneNrSboqPf6gpMG1DtTMzDpXMaFL6g1cAOwFbANMkLRNm2ZHAK9GxIeAs4HTax2omZl1rpor9J2AORHxXES8A1wJjG/TZjzwq/Tr3wF7SlLtwjQzs0oUEZ03kPYHxkXEV9LtQ4CdI+KYsjZPpG3mpdt/Tdu80uZcE4GJ6eZWwNO1+kbaMQB4pWKrxuX4s5Xn+PMcOzj+SjaPiOb2DqzRg2+6ioi4BLikHu8lqTUiWurxXj3B8Wcrz/HnOXZw/Kujmi6XF4FNy7YHpfvabSNpDaAfsKAWAZqZWXWqSegPA8MkDZG0JnAgMLVNm6nAYenX+wN/iUp9OWZmVlMVu1wiYqmkY4BbgN7ApRExS9KpQGtETAV+CUyWNAdYSJL0s1aXrp0e5Pizlef48xw7OP5uq/hQ1MzM8sEzRc3MCsIJ3cysIJzQzcwKoq7j0M0ajaR9OzseEdfVK5ZakLRORLyZdRyWjUIldEnrAN8CNouIIyUNA7aKiBsyDq0qkjYC/h+wSUTsldbM+UhE/DLj0KoiaXNgWETcJmltYI2IWJx1XBV8ppNjAeQioUvaFfgFsB6wmaQdgK9GxNHZRtY5SX8k+XduV0TsU8dwuk3SWsB+wGDK8mpEnFrPOAqV0IHLgOnAR9LtF4FrgFwkdOByku/hpHT7GeAqkmGhDU3SkSRlHTYEhpJMQLsY2DPLuCqJiC9lHUONnA18inSOSEQ8Jumj2YZUlTPTv/cF/g34dbo9AfhnJhF1z/XAayT55+2sgihaQh8aEV+QNAEgIt7MWZGwARFxtaTvwfI5AMuyDqpKXycp5PYgQEQ8K+mD2YZUPUn9gJOBUhK8Ezg1Il7LLqquiYgX2vy4N/zPTkTcCSDpp22my/9RUmtGYXXHoIgYl3UQRXso+k56qx8AkoaS4adlN/xLUn9WxL8Lyad+HrydVuMElpeAyNMkh0uBxcDn0z+vk9wt5cULabdLSOoj6UTgyayD6oJ1JW1R2pA0BFg3w3i66j5JI7IOomhX6CcDfwI2lfQbYAxweKYRdc0JJLfMQyXdCzQDB2QbUtXulPSfwNqSPgEcDfwx45i6YmhE7Fe2/UNJj2YWTdcdBZwLDCTpavwzyf9BXhwP3CHpOUDA5sBXsw2pS/4dOFzS8yQXkQIiIravZxCFmymaXuHuQvIP+kDbEr6NLH2wsoyktLBIygv3ioiGv8uQ1ItkoZNPksR+S0T8PNuoqifpfuDbEXFPuj0GODMiPtL5KxuDpDERcW+lfY0s/fkfnm4+lYef+5J0QMAqIuJvdY2jSAld0uh2dr8G/C0iltY7nq6S9EhEjK60rxFJOi4izq20r1FJGkmySEs/kg+khcBhETEz08CqlOefHVg+Qu0EklrfeRyh9iPgLuC+iPhXVnEUrcvlQmA0MJPkl3I7YBbQT9LXIuLPWQbXEUn/RnKrvLakUSSxA6wPrJNZYF1zGMktf7nD29nXkCLiUWAHSeun269nHFJVJH0E2BVolnRC2aH1SYrp5UXeR6g9RzIy5zxJi4G7gbsi4vp6BlG0hP4ScEREzAJIx3GfCnyHZDxxQyZ0kuFmh5MM9TurbP9i4D+zCKha6YiiLwJDJJWXVe5LcpWbC21HuUjKyyiXNUnGnq9B8m9e8jpJKeu8yPUItYi4DLgsvTj7PHAiyTDevp2+sMaKltC3LCVzgIiYLWl4RDzXyD8bEfEr4FeS9ouIa7OOp4vuA/5OsuzWT8v2Lya5U8qLS4EnSH4ZAQ4huWrsdCZp1tJhf3dKurze/bU1lusRapJ+AWxDMnb+bpIP00fqHUfREvosSReRLGQN8AVgdvqw5d3swqpORFwraW9gW6CpbH9dZ5t1RZpE/saKW+W8yvsolzclncGqPzt7ZBdSl5zCqiPU8jTpqz9JF9cikjvTV7J4ble0ceiHA3OAb6Z/nkv3vQvsnllUVZJ0McmH0LEk/egHkAzfaniSdpH0sKQ3JL0jaZmkXPRDp96S9O+ljXSUy1sZxtNVvwGeAoYAPwTmkqw2lgvp8619SX5fpwAtEXF7pkF1QUR8LiJ2Bn4CbADcLmleveMo1CgXgPS2bbOIeDrrWLpK0syI2L7s7/WAmyNit6xjqySd1XcgyYOsFuBQki6w72UaWJXS2idXsPIol8Mj4rFMA6uSpOkR8eHSz0667+GI2DHr2KohaVpE7FlpX6OS9B/AbiTPYDYAHgDujohL6xlHobpcJO0DnEHyoGhIOhTt1LwU+AGWpH+/KWkTkoW2N84wni6JiDmSekfEMpIHRDOAXCT0NHHnbpRLmVKX4t/TbruXSOrqNDRJTSQjuQZI+gArj/AamFlgXTeOpO/83Ih4KasgCpXQSUYp7ATcAclQtHQKcV78UdIGJB9Kj5A8IMrL5Jw3lSwi/qikn5A8KM1Nl17banmlh+iN/Pyijf9OR+p8CzifJCEen21IVfkqSffoJiTDFksJ/XXgZ1kF1VURcYySaqk7pvNhHoqIl+sdR6G6XCQ9EBG7SJoREaPSfctvQRtZOtNyl4i4L91eC2jKwbA5YPlMuZeBPiSJpB9wYUTMyTSwKkn6Eyuq5S0vahURP+3wRVYzko6NiPOzjqO7JB1AUjnyDpIPpd1IZh7/rq5xFCyh/xKYBkwiudr6BtAnIo7KNLAqlX8QWX1JeiIitss6ju6SdBntFEOLiC9nEE63SNqOZOhf+SidK7KLqHqSHgM+Uboql9QM3BYRO9QzjqJ1uRxLUkv8bZIn5bcAP8o0oq6ZJmk/4LrI2SdtWpSovYSyRTvNG9F9kkZExONZB9JN5TMqm4DPkfSj54Kkk4GxJAn9JmAv4B6SB9V50KtNF8sCMuhyLNQVet6lU4bXJbnlf4sVFdvWzzSwKqRF0UqaSIZcbhgRP8gopKpIepzkg2gNYBjJUNfMquXVStqFd09E7Jp1LNVI/x92AGZExA5pf/SvI+ITGYdWlXQOwPYkF5KQDD+eGRHfrWscRUjoKsgyVkVTGkqXdRyd6ahKXkleZ19K2gq4MSI+lHUs1ZD0UETsJGk6yZyRxcCTETG8wksbRnp3PSbdvDsifl/vGIrS5XJm5Sb5kA69LK2ac0eOqs2VV/XrRTIWveF/vkoJW8liIrMiXQM1Hb64Ncks2IaX3t0F6Z0F8A+grleHq6k1HeH1c5IH028A92cbUtekZTsyLd1RiCv0opB0GrAjyaw/SKq3teZhco6k8ll9S0lmKp6Zlwle6Zj50aVnF2mXRWteys8WiaTBwPp5KV0MIGlf4HTggyQfqpl0lxYioZf1g65yiBz1g0qaCYyMiPfS7d4kfYq5iD/PJD0aESPb7Gv4Ia9qfw2A5SKi7gWiukPS54C/lIbpplfrYyPiD9lGVh1Jc4DPRESmy/41/C1xlf4j6wBqaANWlJ3tl2Ug1WhTg3sVEXFWZ8cbyHOSvgFclG4fTfKAtNGVxsk3kXRzPUZyIbM90Ep+iqadXN7nHBGL0pEvuUjowD+zTuZQkIRe/uCqNFsr3cxkttZq+B9gRtp9IZK+9EbvbinVe96K5N+9VBP9M8BDmUTUPUcB5wHfT7dvI6ln3dAiYncASdeRdBk9nm5vR1LBMC/aG+KXp/zUKukqkg+g5WV/I+K6egZRiC6XEkmfJ5k2fwcZztZaHZI2ZuUPpH9kGU+1JN0F7F32ULEvySiLj3b+SqsFSbMiYttK+xqVpEtJSs9ekO76Osmw18MzC6oL0oldbUW9J3YVLaE3xGyt7spzxTlJTwPbR7qwb1q6YGZEbJVtZJ2TdCTJaKJnlRRw+SXJLOO/kVRbzEsf9BTgX8Cv010HAetFxITsoqqepHWB/wI+nu66FfjvyHB9zq6QtGFELGyzb0hEPF/POPJ0S1ONhpit1VUFqTh3BfCQpFI/6GdJFl1udMcBl6dfTyCZ3LIFMIpkPdSGL12c+hLwNZLvB5IFiy/quHljSRP3pKzjWA1/lLRXqUqnpK1JSknXtZxE0a7QG2K2VldJOo4VFedeZOWKcz+PiFxUnZP0YaC0SMRdETEjy3iqUT66RdJvgQcj4tx0+xEPW6yP9LlRe6UjcrHiUlqy+DvA3iTPk64ADopk8fH6xVGEhC7pQ8BGEXFvOh60lFQWAb+JiL9mF1318l5xDkDSB1m5uNL/ZRhORZIeIfklfJWkm2WPWLHI+JMRsXWW8VVL0jCSh+pti1vlopZOejFQ0kTS7bU0Ir6TUUhdJumzJEm9L7BfRDxT7xiK0uVyDulokPSp8nUAkkakxz6TXWhd8p6kDSJiEUDa/TIhIi7MOK6K0hmuPyW5y3gZ2IxkSbRGfyj3A5Lhfb2BqWXJ/GPkY9hiyWUk6wGcTTJ1/kvkoLuxJCKmt9l1r6SGHyUl6XxWvrPoB/wVOEYSEfGNusZTkCv0DpfakvR4RIyod0zd0cHkllyU1E0fSO9B8hB6lKTdgYMj4oiMQ6tI0hpA34h4tWzfuiS/H29kF1n1tGIJuuU/73mopVMiqXx1pV7Ah4HzcvBQ/bDOjkdEXZ8jFeUKfYNOjq1dtyhWX29JKpt+3ptkOb08eDciFkjqJalXRNwu6Zysg6pGJKuzv9pmXy5GV5R5Oy1X8KykY0iexayXcUxdMZ0VtWiWAs8DDX8xUO+EXUlREnqrpCMjYqXl2iR9heQHJS/+BFwl6X/T7a+m+/JgkZJFre8CfiPpZZJhdFYfx5GMlPoGyRoAuwOdXj02kojI01KRq5A0hmQi1+YkebVUdqSuzzCK0uWyEfB74B1WJPAWkqvbz+Vock4vkiReGnd+K/CLSBZdbmhpF8VbJLfLB5H0Jf4mIgGtsr8AAAlYSURBVBZkGtj7QHond3pEnJh1LF2VDmLoUL1nWnaXpKdIll5su4RhXX/+C5HQS9J+29K4z1kR8Zcs4+kOJQstb0Vy+/l0RLxb4SWZSxPKbaVp6HklaSArrrAAiIi7souoekrX0806jq4qm2H5QWBXoPQ7uztwX0Tkok6TpAcjYufM4yhSQs87SWNJJuPMJbll2xQ4LA9JRdI0YN/IyaLWbUk6nWTewmxWXGFF5GRxFEkXkUxCu4ayrq4cXeH+meRn/e/p9sbA5RHxqWwjq05a+ro3yQi78loudZ1pXJQ+9KL4KfDJSGuIS9qSZJJUHkYqvAE8LulWVk4odR22tRo+C2xVKl2QQ00kM6PLJ+IE6RDeHNi0lMxT/yQZ+poXpavz0u9qaaGRuk6MckJvLH2ibEGIiHhGUp8sA+qC5eP/WTEuVx20bUTPAX0ou7rKk4j4UtYxrKZpkm5h5Vnet2UYT1XKykeXVhYLYD7Jeq51reMCTuiNplXSL1i5wFJrhvFUJGk8MCgiLki3HwKaSX6wG7rkAqw0MeRN4NG066j8lrmh7zAkbQsMjYip6fbZrKij/7O8FBeLiGPSRS5K1TkviQzW5OyGvu3s2xw4SdIpEXFlPYNxH3oDSSsUfp0VpQvuBi5s5G4ASfcCB0bEC+n2oyS3mesBlzV6pchGmxjSVUoWSP+fiLgv3Z5NUrVwHZLp55/NMr6uULJg97CIuE3SOkDvUjnmvEknSt1W71pAvkJvIBHxtqTJwOSImJ91PFVas5TMU/ekZUQXpkMZG1qjJ+wqbFxK5qnXI1msGElfzSimLkvLGE8ENgSGkjzgvZgVQ3hzJSIWpuWY68oJvQGk//EnA8eQ1t+QtAw4PyJOzTK2KnygfCMijinbbK5zLN2m9telfY2ky+u/G3g8/Uq3/G2GLn6wzrGsjq8DOwEPAqT16fMU/0rSIdSvVmxYY07ojeF4YAywY+lBiqQtgIskHR8RZ2caXece7GCW7lfJ1xJ0N5MMV/xtun0gSbfFP0jqpTdqgbeXJO0cEQ+W75S0C/BSRjF1x9sR8U7pojatr9Pw/cEdXAhsSPJvf2jd43EfevYkzSBZaemVNvubgT83cnGu9CqqtI5i6QHch4G1gM9GxD+ziq0r2qt9XtrXyAXeJO0EXEXyoVP+738Y8IWIyMWHqqSfkJS7PhQ4lmSR7tkRcVKmgVWQ9vuXC2BBVrWAfIXeGPq0TeYAETG/0YctRrJC1K6S9mBFqdwbczhLt7eknUoJUNKOJBNFICkW1ZAi4iFJO5N01x2e7p4F7JKXD9PUJJJiXI+TlL+4CfhFphFVIcoWqG8EvkJvAJ2tjONVc+ojTeCXkozOEclqUV8hSY57R8TVGYZnVhUn9AaQPgBt7xZNQFNENPRVepFI6geQ1xIGAJJuyEsNlJJGqVaYd+5yaQAR0btyK+sJkg6OiF+Xzfgr7QcgIs7KJLDVk5eFxcv9knaqFVrXOKHb+11prHx7M/7yquEX527HaxFxc9ZB5J27XMwKQNJxEXFupX2NqlGqFeadE7q9r0k6r7PjjV7LpaSDYZe5WI8WQNLt6Zflhd0iIuparTDv3OVi73flSxT+kGTGbm5ImgB8ERgiaWrZofWBhdlEVb1Gq1aYd75CN0vl6Yq2JJ3YMgT4H5Kx3CWLgZnpAtgNS1J7H6AbAp8C6l6tMO+c0M1SeR7zX1rTNSLeSxdGGQ7cnIclDNuTVbXCvOuVdQBmVhN3AU3puqh/Bg4hKQeQS2nFzjwtkNIQ3Idu72uSFrPiQdw6kl4vHSJ5KLd+NpF1mSLiTUlHkNTQ/0lamz6XsqpWmHdO6Pa+FhFFGX8uSR8hWeXqiHRfw09Ya7RqhXnnhG5WDN8Evgf8PiJmpeWXb6/wmkbQtkRBptUK884PRc0KRNJ6ABHxRtaxWP35oahZAUgakdbVnwXMljQ9XUDa3kec0M2K4X+BEyJi84jYDPgW8PMKr7GCcUI3K4Z1I2J5n3lE3MGKwmP2PuGHombF8Jyk/wImp9sHA89lGI9lwFfoZsXwZaCZpFrhtcCAdJ+9j3iUi1mOSWoCjgI+RLIe56V5ne5vq88J3SzHJF0FvAvcDewFzI2Ib2YblWXFCd0sxyQ9HhEj0q/XAB5yQav3L/ehm+Xb8u6VRi+Vaz3PV+hmOSZpGVCaJi9gbeBN8ldczGrACd3MrCDc5WJmVhBO6GZmBeGEbmZWEE7olglJyyQ9WvZnUjttxkq6ob3Xr8b7jpW0a9n2UZJqtpBCT8Tc5vx3SGrpqfNbvrmWi2XlrYgYmcH7jgXeAO4DiIiLM4jBrEf4Ct0aiqRxkp6S9Aiwb9n+UySdWLb9hKTB6deHSpop6TFJk9N9n5H0oKQZkm6TtFHa/ijg+PSuYLfy80oaKemB9Fy/l/SBdP8dkk6X9JCkZyTt1o3v65OS7pf0iKRrJK2Xfq/XlLVZfnXfXvsu/2Pa+44TumVl7TZdLl9I65L8HPgM8GHg3yqdJF3E4fvAHhGxA3BceugeYJeIGAVcCXwnIuYCFwNnR8TIiLi7zemuAL4bEduT1EU5uezYGhGxE8lSbyfTBZIGpDF+PJ3F2QqcANwG7CypVOb2C8CVnbQ365S7XCwrq3S5SBoJPB8Rz6bbvwYmVjjPHsA1EfEKQEQsTPcPAq6StDGwJvB8ZyeR1A/YICLuTHf9CrimrMl16d/TgcEVYmprF2Ab4F5JpPHcHxFLJf0J+Iyk3wF7A98BPtZe+y6+p70POaFbXixl5TvKpgrtzwfOioipksYCp6zm+7+d/r2Mrv/eCLg1Iia0c+xK4BhgIdAaEYuVZPGO2pt1yF0u1kieAgZLGppulye0ucBoAEmjgSHp/r8AB0jqnx7bMN3fD3gx/fqwsvMsBvq2feOIeA14tax//BDgzrbtuukBYIykD6Uxritpy/TYnSTf15Ekyb1Se7MOOaFbVtr2oZ8WEUtIulhuTB+KvlzW/lpgQ0mzSK5onwGIiFnAj4E7JT0GnJW2PwW4RtJ04JWy8/wR+FzpoWibmA4DzpA0ExgJnNrN721PSfNKf0hqlR8OTEnPfT8wPI1/GXADSenbG9J98ztqb9YZ13IxMysIX6GbmRWEH4qadYOkTwGnt9n9fER8Lot4zMBdLmZmheEuFzOzgnBCNzMrCCd0M7OCcEI3MysIJ3Qzs4L4/6pEz4OcfIYfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GeVfa7kHbaT"
      },
      "source": [
        "pada grafik diatas, dapat dilihat bahwa ada inkonsistensi antar tingkat edukasi dengan jumlah transaksi yang dilakukan. maka tingkat edukasi seseorang tidak memengaruhi total transaksi kartu kredit. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfSrf0vpnFK_",
        "outputId": "079041ae-70b5-4e4b-94b0-f606519f60ca"
      },
      "source": [
        "df.groupby('Income_Category').agg({'Total_Trans_Amt':'sum'}).plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f28068add68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EWoC5kmbl64i2HjMCOA0YGxF/lfTOzirYzMzaVkuXyz7Akoh4PCJeB2YCk6qO+Tzwg4j4K0BEPF9smWZm1pFaAn0Q8FTF85Z8W6WRwEhJv5d0T95Fsx5JUyU1S2petmzZplVsZmZtKuqiaC9gBDAOmAL8WNLbqw+KiGkR0RQRTQMGDCjorc3MDGoL9KeBIRXPB+fbKrUAsyLi7xHxBPAIWcCbmVmd1BLoc4ERkoZL6gMcBsyqOuYXZK1zJPUn64J5vMA6zcysAx0GekSsAU4EbgMWAVdHxAJJZ0uamB92G7Bc0kLgDuDrEbG8s4o2M7P11TQOPSJuBm6u2nZGxeMATsm/zMysBL5T1MwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBJR01wuZmapS2G9W7fQzcwS4UA3M0uEA93MLBEOdDOzRDjQzcwSUVOgS5og6U+Slkg6tZ3jDpYUkpqKK9HMzGrRYaBL6gn8ADgA2BWYImnXNo7rB3wFuLfoIs3MrGO1tND3AZZExOMR8TowE5jUxnHfAr4DvFpgfWZmVqNaAn0Q8FTF85Z82zqS9gKGRER9R+abmdk6m31RVFIP4HzgqzUcO1VSs6TmZcuWbe5bm5lZhVoC/WlgSMXzwfm2Vv2A3YE7JS0F3g/MauvCaERMi4imiGgaMGDApldtZmbrqSXQ5wIjJA2X1Ac4DJjVujMiVkZE/4gYFhHDgHuAiRHR3CkVm5lZmzoM9IhYA5wI3AYsAq6OiAWSzpY0sbMLNDOz2tQ022JE3AzcXLXtjA0cO27zyzIzs43lO0XNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLRcGuKprDun5lZZ3AL3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS0RNgS5pgqQ/SVoi6dQ29p8iaaGk+ZJmS9qx+FLNzKw9HQa6pJ7AD4ADgF2BKZJ2rTrsAaApIkYD1wLfLbpQMzNrXy0t9H2AJRHxeES8DswEJlUeEBF3RMQr+dN7gMHFlmlmZh2pJdAHAU9VPG/Jt23IccAtm1OUmZltvEIXuJD0WaAJ+MgG9k8FpgIMHTq0yLc2M+v2ammhPw0MqXg+ON/2FpI+BpwOTIyI19p6oYiYFhFNEdE0YMCATanXzMw2oJZAnwuMkDRcUh/gMGBW5QGSxgD/TRbmzxdfppmZdaTDQI+INcCJwG3AIuDqiFgg6WxJE/PDvgdsA1wj6UFJszbwcmZm1klq6kOPiJuBm6u2nVHx+GMF12VmZhvJd4qamSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJcKCbmSXCgW5mloiaAl3SBEl/krRE0qlt7N9C0lX5/nslDSu6UDMza1+HgS6pJ/AD4ABgV2CKpF2rDjsO+GtEvBe4APhO0YWamVn7ammh7wMsiYjHI+J1YCYwqeqYScD/5I+vBcZLUnFlmplZRxQR7R8gHQJMiIjj8+dHAvtGxIkVxzycH9OSP38sP+aFqteaCkzNn/4D8KeiTqQG/YEXOjyqcfn8GlfK5wY+v6LtGBED2trRq45FEBHTgGn1fM9WkpojoqmM964Hn1/jSvncwOdXT7V0uTwNDKl4Pjjf1uYxknoB2wLLiyjQzMxqU0ugzwVGSBouqQ9wGDCr6phZwNH540OA30RHfTlmZlaoDrtcImKNpBOB24CewOURsUDS2UBzRMwCfgJMl7QEeJEs9LuaUrp66sjn17hSPjfw+dVNhxdFzcysMfhOUTOzRDjQzcwS4UA3s8JIGtLOvn+uZy3dUbcIdElHd3yUmRXg9rbmcpJ0LHBR3avpZrpFoANfKbuAIqXeCkr5/FI+t9wpwK8kjWjdIOk04GTgI6VVVbB8QsLDJf2bpDNav8quq7sEempSbwWlfH4pnxsRcTPwBeAWSbtLuhD4JLBf69QgibiRbA6rNcDLFV+lquut//Uk6Q4gAAHvlfSb/HFExEdLLW7ztbaCDoyIR2FdK+hw0mgFpXx+KZ8bABExW9LngDuBu4GPRsSr5VZVuMERMaHsIqolG+jAMfmfAv4X+Fx5pRQrIm6W9BpZK+hTwPFks2LuFxF/Lbe6zZfy+aV8bgCSVvNmQ2oLYDzwfD77akTE28qsr0B3SxoVEQ+VXUilbnFjkaQ/RsReZddRNEkfBm4gawV9JrVWUMrnl/K5dQeSFgLvBZ4AXuPNT/+jS62rmwT69RFxUNl1FKWNVtDfgbW8+Y+qoVtBKZ9fyufWKm+N7wMMyjc9DdyX0vxOknZsa3tE/LnetVTqFoFuZvUh6ePApcCjvDkr62Cy1uwXI+JXZdVWJEnfAu4C7o6I0i+GtnKgm9WZpJ3JRkhUtmBvjIjF5VVVDEmLgAMiYmnV9uHAzRGxSymFFSy/6Pth4APAamAOcFdE3FhqXQ70xrSBUJgVEYvKq6o4qZ6fpG8AU8iWcmwdxjeYbIbSmRFxblm1FUHSo8AuEbGmansfYGG+7nAyJL0b+AzwNeAdEdGv1Hq6W6BL6pOvjdqwukEoJHt+kh4BdouIv1dt7wMsiIgRbX9nY8iHYH6G7Gf3VL55KHAocHVEfLus2ook6TJgV+A5stb574A/Vv8iq3tdKQa6pDMi4uw2tm9L9tF2XP2rKk43CIVkz0/SYuCfqi+e5RfZfhUR/1BOZcWRtAttf7paWF5VxZJ0AzAQWAj8lqy75fFyq0p3HPqHJJ0TEae3bpD0LrJFOq4vr6zCvEH2j6n6ivoO+b5Gl/L5nQTMzrsmKluw7wVO3OB3NZC8W2xd15ikvVIKc4CI+DSs++X1T8AdknpGxOAy60q1hd4XuBZ4JCJOyeeVuAU4LyJ+VG51m0/SBOASspEE64VCRNxaVm1F6Abn14P1h/XNjYi15VVVDElt3e8xi+z2f0XEH+tcUqfI5935MLAf8HbgHmBORFxeal0pBjqApN7AVWSD/j8InBQRN5RbVXHaCIUWsiUBGz4UIO3QS5mkN8jC7bWKze/Pt6Uw7QYAki4h6zufExHPlF1PqyQDXdIp+cPewL+SDylq3R8R55dRV1Ek7QssiohVkrYCTgXGkPXn/VdErCy1wM1UsRj5MxHxa0lHkA0PWwj8uLpvvdHkI3gGAfdGxEsV2yck8OnjYODLwLkRcUu+7YmIGF5uZcXLu3H3zp/eFxHPl1kPpBvoZ7a3PyL+o161dAZJC4A98gW8pwGvkHUxjc+3N/RdsZJ+RnZ9ZytgBbAN2bWP8WT/Zht2fntJXwb+hayPeU/gK61jl1OZokLSNsC3yEYmfRW4MyLeU25VxZI0GTiPbAIykXW/fD0iri21rhQDPXWSFrXeoFEdApIejIg9y6tu80maHxGjJfUi62oZGBFr81vK55U9X8bmkPQQ8IGIeCmfRvdaYHpEXCTpgYgYU2qBBZI0BjifbMTSO8uup0iS5gH/2NoqlzQA+HVE7FFmXUnOhy6pl6T/I+kWSfPzr1sknZD3rTe6h/M71QDmSWoCkDSSbG6QRtcj73bpR9ZK3zbfvgVZN1oj69HazZLfTTkOOEDS+WQtvWRExAPAR8kuZqemR1UXy3K6QJ6mOmxxOtlH9f/grTemHA38P7KbHBrZ8cBFkr4JvAD8QdJTZCNCji+1smL8BFgM9AROB66R9DjZxbWZZRZWgOck7RkRDwLkLfV/Bi4HRpVbWjEkvRN4OZ/jZAvgS5L6ARdFxLPlVleYWyXdBszInx8K3FxiPUCiXS6SHomIkRu7r9FIehswnOwXc0tEPFdySYWRNBAgIp6R9HbgY8CTEXFfuZVtHkmDgTUR8Zc29o2NiN+XUFahlC0mc0xEPCnpu8AAsl/QEyJi/3KrK05+AXhs/nROVxhFl2oL/cX8osV1EfEGrBsGNxlo+EUEWkXEKmBe2XV0hsqhYBGxgqyvueFF+8uwjQQaOtCVLci+EzAuv+ZxKPBd4CVgR0lHAQ9GxPwSyyxERFwHXFd2HZVK7/PpJIcBh5B9vH0kv5X8L8BB+T6zrqihR1/l7iRbW3M+WXfnc8Av8+0v5H+WOmd4ESQdJOlRSSslrZK0WtKqsutKsoWeX2w6FEDS9vm25WXW1JkkfTIifll2HZ0lpfOTtKGWqYB31bOWzhARf5Z0Mdk0G28An8+7XoYCyyPiyXIrLMx3gU92tdk/k+xDh3X9ywMi4rGq7aNT+LhXqXWYX9l1dJaUzk/Sc2Rzf1R3/YlssYSB9a+qePlY9Dci4pX8+dZA77z7rOFJ+n1EjO34yPpKsoUu6TPAhWSL0/Ymu0AzN999BdDwN29USWq4WxtSOr+bgG1aR7lUknRn/cvpHJV3wObPu8yqPgVplnQV8AsqpjmIiFIn/0sy0IF/A94XEc9K2geYLum0/Cp0SuHQKs2PWW9K5vwi4rh29h1ez1pss7yN7A7tj1dsC0qezTXVQO/ZOt41Iu6TtD9wk6QhJBQOZlaar0bEi5UblC2zV6pUR7mslrRT65M83MeRTbq/W1lFmVkyfplfpwPWzYte+oX7VAP9C1R1rUTEamACcGwpFXWuZG4o2oBkz0/SJ8uuoTN01WF9BfovslDfRtL7yO6T+GzJNaU7ysWsEaQ0gqeSpCV0wWF9RZL0KbLpufsBB0fEIyWXlGwf+gZJeigikpgzw5KQ4kV6gOdSDPN8jH1lK3hb4DHgRElExJfLqSyTZKBL2tB84ALeXc9aOoOytQuTXbkn9fOrkupH5C45rK8AzVXP7y+lig1IMtDJlp77GW3/Z+lb51o6w2XA5zo8qnGlfn7dQZcc1re5IuJ/yq6hPUn2oUu6Hzg6Ih5uY99TETGkhLIKI+lKYGlEnFG1vR9weURMLqeyYqR+fpVS7UNPnaSxwFnAjmQNY5GtmVrqykypttBPAjZ0Rf3T9SykkxwL3CLp+Ii4DEDSHsDVZJ9OGl3q51cpyRE8kvoCx5ENE173qTgiUhll9hPgZLIuly7TPZhkoEfEnHb2VfeBNZx8LdGDgDslPUO24PDpwPER8etyq9t8qZ9fpYj4x7Jr6CTTyeZA/yfgbOAIsnVUU7GydRHsriTJLhd466opkrYETiEbXtTwq6ZIupRsetK/At8HHgKOSGWBi9TPrztoXR+1Yn3Y3mSLQLy/7NqKIOlcshW1ruetF33/WFpRJNpCz80EjiGbm/k/eHPVlJ8Djb5qyoNky5WNJltjcyTw43wB4ociotGXaUv2/LrRCJ7WtW1XSNqdbD2ClBaK3jf/8335nyK76PvRcsrJJBnoqa+aEhHTKp/ny5q1BuCBNPi6m4mfX3cZwTNN0juAfwdmAdsAZ7T/LV2fpFPyhzflfwawDPhdRDxRTlVvSrLLRdKOwC3A4cD2wLfJVjAScE3+eGVErCytyM0kaWeyuWkG5ZueBmalcjNHqufXnUbwpEjSmW1s3o7sWsFZZX96TDLQASR9gWxYUeuqKTflq6b8d0QcUGpxm0nSN4ApZC3V1jUqB5MtrzczIs4tq7YipHx+knqRNTauamsET3XQNypJWwAHA8Oo6AmIiLPLqqkzSdoO+HVElLrWQrKBDumumpKvkbpbRPy9ansfYEFEjCinsmJ0g/PrR7a25r+T6AgeSbcCK6ka1hcR/7e0ojpZ64XgMmtIsg+9VcKrprwBDGT9xXZ3yPc1umTPr2IEz3eBn5KN4Nk3wRE8gyNiQtlF1Eu+5kL1soJ1l3SgJ+wkYLakR4Gn8m1DgfcCJ5ZWVXFSPr9kR/BUuVvSqIh4qOxCipT/nKq7NbYDngGOqn9Fb5V0l0vKJPUA9uGtFw3npjIkro3zawGaUzm/VlUjeHaPiCNLLmmzVAReL2AE8DjZOO3WW+MbepqDfMBFpQCWd5VP/w70REi6MiJKbyEUQdK+wKKIWCVpK+BUYAywEPivRh6dBOmO4IE2A+8tIqK6G80K5EBvQJJmtbH5o8BvACJiYn0rKpakBcAe+RQA08hm7bsWGJ9v39D0yF1eyiN4Kv/aAFkAAAaVSURBVEmaXv1po61tViz3oTemwWSt1cvIPvIJ2BtIZQRBj4hYkz9uqhgK9jtJD5ZVVEGOo+0RPOcDC4AkAp2qtXsl9eTNuyqtk6S6pmjqmsiGg51OdoPUncDfIuK3EfHbUisrxsOSWu+mnCepCUDSSN68pbxRtY7gqdbwI3gAJJ0maTUwOl9LdFX+/HngxpLLS567XBpYfkHtArIpWCdGxNCSSyqEpG2Bi4APAy8Ae5GNdnkK+HJEzCuxvM0iaQJwCdDmCJ6IuLWs2ook6dsRcVrZdXQ3DvQESDoQGBsR/1Z2LUWS9DZgOFnXYEsqY7VTH6Fk5XGgm9VRfrfrYcAzEfFrSUcAHyC7JvLj6r51s43hQDerI0k/I/vEsRWwgmwWwuvJRvAoIo4usTxrcA50szqqWPChF1lXy8CIWJtP8zyv0W+8aSVpJ7JustckjSO7cerKRp9HqavzKBez+uqRd7v0I2ulb5tv34JsKoBUXAeslfReYBowhGxxGetEHoduVl8/IVs5qyfZsNNrJD0OvJ/GXrij2hv5jWGfBi6OiIslPVB2Ualzl4tZnUkaCBARz0h6O/Ax4MmIuK/cyooj6V7gQrJfWp+MiCckPRwRu5dcWtIc6GZWOEm7AicAf4iIGZKGA5+JiO+UXFrSHOhm1qnytUWHNOoavo3EF0XNrHCS7pT0tnxptj+Szfl+ftl1pc6BbmadYduIWAUcRDZccV+yawXWiRzoZtYZeknaAfgMcFPZxXQXDnQz6wxnA7cBj0XEXEnvIZuQzDqRL4qamSXCLXQzK5ykkZJmS3o4fz5a0jfLrit1DnQz6ww/Bk4jX5AkH7J4WKkVdQMOdDPrDFu1cefrmjaPtMI40M2sM7yQz7gYAJIOAZ4tt6T0+aKomRUuH9UyDfgg8FfgCeCIiPhzqYUlzoFuZp1G0tZAj4hYLemkiLiw7JpS5kA3s7qQ9GQqC5l3Ve5DN7N6UdkFpM6Bbmb14u6ATuYVi8ysMJJW03ZwC9iyzuV0O+5DNzNLhLtczMwS4UA3M0uEA93MLBEOdDOzRDjQrW4kvVR2DbWQdJSkhyU9JOkBSV/r4PhP5avcm5XKgW5WQdIBwEnAxyNiFPB+YGUH3/YpoFMDXZKHGFuHHOhWd5LG5avCXytpsaSfSVK+b29Jd0uaJ+k+Sf0k9ZX004oW8/75scdI+oWk2yUtlXSipFPyY+7JV5xH0k6SbpV0v6Q5knZup7zTgK9FxDMAEfFaRPw4f53PS5qb13adpK0kfRCYCHxP0oP5e7X5fvn2e/Lz+M/WTyzKfK/iU8GhFX9PcyTNAhZKOlvSSRV/j+dI+krBPx5rZBHhL3/V5Qt4Kf9zHFmrdzBZo+IPwIeAPsDjwN75cW8ju/ntq8Dl+badgSeBvsAxwBKgHzAgf80T8uMuAE7KH88GRuSP9wV+006NL5KtWN/Wvu0rHv8n8KX88RXAIRX72nw/ssWSp+SPT6j4+zgYuB3oCbwrP78d8r+nl4Hh+XHDgD/mj3sAj1XW5C9/+WOcleW+iGgBkPQgWVitBJ6NiLkAEbEq3/8h4OJ822JJfwZG5q9zR0SsBlZLWgn8Mt/+EDBa0jZkU7hek38IANhiE2veXdJ/Am8HtiFbBPktOni/D5B1zwD8HDgvf/whYEZErAWek/RbYG9gFdnf0xP5uS+VtFzSGLLgfyAilm/iuViCHOhWltcqHq9l0/8tVr7OGxXP38hfswewIiL2rPH1FgDvA37Txr4rgE9FxDxJx5C1oKtt7Pt15OWq55eRfTJ5N3B5Qe9hiXAfunUlfwJ2kLQ3QN5/3guYAxyRbxsJDM2P7VDeyn9C0uT8+yVpj3a+5dtk/eHvzo/vI+n4fF8/4FlJvVvrya3O93X0fveQda/AW9fXnAMcKqmnpAHAfkD18m2tbgAmkLXg1/uEYN2bA926jIh4HTgUuFjSPLJ+5b7ApUAPSQ8BVwHHRMRrG36l9RwBHJe/5gJgUjs13AxcAvxa0gLgj2R9+QD/DtwL/B5YXPFtM4Gv5xdjd2rn/U4CTpE0H3gvb46euQGYD8wj+2TwrxHxlw3U9zpwB3B13kVjto4n5zKrE0lbAX+LiJB0GNkF0g3+ctnAa/Qg+yUzOSIe7Yw6rXG5D92sft4HXJIP0VwBHLsx35zfvHQTcIPD3NriFrp1S5JOByZXbb4mIs4pox6zIjjQzcwS4YuiZmaJcKCbmSXCgW5mlggHuplZIhzoZmaJ+P+Vi+jDRYK07AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v1B6QU_GINDC"
      },
      "source": [
        "Pada grafik diatas, terdapat juga inkosistensi antar kategori pemasukan. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLKkcsDMTXov",
        "outputId": "913cdb14-4b2d-48c6-ce49-02ed05d9fe10"
      },
      "source": [
        "plt.scatter(x=df['Total_Trans_Ct'],y=df['Total_Trans_Amt'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f280673b860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEaPAAJ-MAuE"
      },
      "source": [
        "Grafik diatas menunjukkan jumlah seseorang melakukan transaksi dengan jumlah uang yang ditransaksikan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-syGc4GnYLn"
      },
      "source": [
        "## Pemodelan Data\n",
        "Pada tahap berikut, kami akan memodelkan data menggunakan metode Random Forest Classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "id": "e5V_yFrhnW9g",
        "outputId": "4ca26085-3f1a-4572-80ba-24331fd707d0"
      },
      "source": [
        "model.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <th>Customer_Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Dependent_count</th>\n",
              "      <th>Education_Level</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Income_Category</th>\n",
              "      <th>Card_Category</th>\n",
              "      <th>Months_on_book</th>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <th>Credit_Limit</th>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Attrition_Flag</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.018203</td>\n",
              "      <td>-0.037272</td>\n",
              "      <td>0.018991</td>\n",
              "      <td>0.008796</td>\n",
              "      <td>-0.024117</td>\n",
              "      <td>-0.013577</td>\n",
              "      <td>0.002354</td>\n",
              "      <td>0.013687</td>\n",
              "      <td>-0.150005</td>\n",
              "      <td>0.152449</td>\n",
              "      <td>0.204491</td>\n",
              "      <td>-0.023873</td>\n",
              "      <td>-0.263053</td>\n",
              "      <td>-0.000285</td>\n",
              "      <td>-0.131063</td>\n",
              "      <td>-0.168598</td>\n",
              "      <td>-0.371403</td>\n",
              "      <td>-0.290054</td>\n",
              "      <td>-0.178410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Customer_Age</th>\n",
              "      <td>0.018203</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017312</td>\n",
              "      <td>-0.122254</td>\n",
              "      <td>-0.002369</td>\n",
              "      <td>0.040298</td>\n",
              "      <td>0.023508</td>\n",
              "      <td>-0.018235</td>\n",
              "      <td>0.788912</td>\n",
              "      <td>-0.010931</td>\n",
              "      <td>0.054361</td>\n",
              "      <td>-0.018452</td>\n",
              "      <td>0.002476</td>\n",
              "      <td>0.014780</td>\n",
              "      <td>0.001151</td>\n",
              "      <td>-0.062042</td>\n",
              "      <td>-0.046446</td>\n",
              "      <td>-0.067097</td>\n",
              "      <td>-0.012143</td>\n",
              "      <td>0.007114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>-0.037272</td>\n",
              "      <td>-0.017312</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.004563</td>\n",
              "      <td>-0.005087</td>\n",
              "      <td>0.006491</td>\n",
              "      <td>0.786608</td>\n",
              "      <td>0.080093</td>\n",
              "      <td>-0.006728</td>\n",
              "      <td>0.003157</td>\n",
              "      <td>-0.011163</td>\n",
              "      <td>0.039987</td>\n",
              "      <td>0.420806</td>\n",
              "      <td>0.029658</td>\n",
              "      <td>0.418059</td>\n",
              "      <td>0.026712</td>\n",
              "      <td>0.024890</td>\n",
              "      <td>-0.067454</td>\n",
              "      <td>-0.005800</td>\n",
              "      <td>-0.257851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dependent_count</th>\n",
              "      <td>0.018991</td>\n",
              "      <td>-0.122254</td>\n",
              "      <td>0.004563</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000472</td>\n",
              "      <td>0.004839</td>\n",
              "      <td>0.066278</td>\n",
              "      <td>0.030469</td>\n",
              "      <td>-0.103062</td>\n",
              "      <td>-0.039076</td>\n",
              "      <td>-0.010768</td>\n",
              "      <td>-0.040505</td>\n",
              "      <td>0.068065</td>\n",
              "      <td>-0.002688</td>\n",
              "      <td>0.068291</td>\n",
              "      <td>-0.035439</td>\n",
              "      <td>0.025046</td>\n",
              "      <td>0.049912</td>\n",
              "      <td>0.011087</td>\n",
              "      <td>-0.037135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Education_Level</th>\n",
              "      <td>0.008796</td>\n",
              "      <td>-0.002369</td>\n",
              "      <td>-0.005087</td>\n",
              "      <td>0.000472</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015478</td>\n",
              "      <td>-0.011677</td>\n",
              "      <td>0.014989</td>\n",
              "      <td>0.006613</td>\n",
              "      <td>0.000766</td>\n",
              "      <td>0.005761</td>\n",
              "      <td>-0.006280</td>\n",
              "      <td>-0.002354</td>\n",
              "      <td>-0.006800</td>\n",
              "      <td>-0.001743</td>\n",
              "      <td>-0.010040</td>\n",
              "      <td>-0.007460</td>\n",
              "      <td>-0.004307</td>\n",
              "      <td>-0.016692</td>\n",
              "      <td>-0.001849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marital_Status</th>\n",
              "      <td>-0.024117</td>\n",
              "      <td>0.040298</td>\n",
              "      <td>0.006491</td>\n",
              "      <td>0.004839</td>\n",
              "      <td>0.015478</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.017770</td>\n",
              "      <td>-0.052296</td>\n",
              "      <td>0.030598</td>\n",
              "      <td>0.020427</td>\n",
              "      <td>-0.004915</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>-0.053580</td>\n",
              "      <td>0.036352</td>\n",
              "      <td>-0.056828</td>\n",
              "      <td>0.050805</td>\n",
              "      <td>-0.062698</td>\n",
              "      <td>-0.116268</td>\n",
              "      <td>0.008999</td>\n",
              "      <td>0.046623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income_Category</th>\n",
              "      <td>-0.013577</td>\n",
              "      <td>0.023508</td>\n",
              "      <td>0.786608</td>\n",
              "      <td>0.066278</td>\n",
              "      <td>-0.011677</td>\n",
              "      <td>0.017770</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.077326</td>\n",
              "      <td>0.022122</td>\n",
              "      <td>-0.003202</td>\n",
              "      <td>-0.016310</td>\n",
              "      <td>0.023113</td>\n",
              "      <td>0.475972</td>\n",
              "      <td>0.034718</td>\n",
              "      <td>0.472760</td>\n",
              "      <td>0.011352</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>-0.054569</td>\n",
              "      <td>-0.012657</td>\n",
              "      <td>-0.246476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Card_Category</th>\n",
              "      <td>0.002354</td>\n",
              "      <td>-0.018235</td>\n",
              "      <td>0.080093</td>\n",
              "      <td>0.030469</td>\n",
              "      <td>0.014989</td>\n",
              "      <td>-0.052296</td>\n",
              "      <td>0.077326</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012535</td>\n",
              "      <td>-0.094077</td>\n",
              "      <td>-0.014629</td>\n",
              "      <td>-0.000442</td>\n",
              "      <td>0.492446</td>\n",
              "      <td>0.026304</td>\n",
              "      <td>0.489985</td>\n",
              "      <td>0.007385</td>\n",
              "      <td>0.196003</td>\n",
              "      <td>0.134275</td>\n",
              "      <td>-0.007261</td>\n",
              "      <td>-0.198711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Months_on_book</th>\n",
              "      <td>0.013687</td>\n",
              "      <td>0.788912</td>\n",
              "      <td>-0.006728</td>\n",
              "      <td>-0.103062</td>\n",
              "      <td>0.006613</td>\n",
              "      <td>0.030598</td>\n",
              "      <td>0.022122</td>\n",
              "      <td>-0.012535</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.009203</td>\n",
              "      <td>0.074164</td>\n",
              "      <td>-0.010774</td>\n",
              "      <td>0.007507</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.006732</td>\n",
              "      <td>-0.048959</td>\n",
              "      <td>-0.038591</td>\n",
              "      <td>-0.049819</td>\n",
              "      <td>-0.014072</td>\n",
              "      <td>-0.007541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Relationship_Count</th>\n",
              "      <td>-0.150005</td>\n",
              "      <td>-0.010931</td>\n",
              "      <td>0.003157</td>\n",
              "      <td>-0.039076</td>\n",
              "      <td>0.000766</td>\n",
              "      <td>0.020427</td>\n",
              "      <td>-0.003202</td>\n",
              "      <td>-0.094077</td>\n",
              "      <td>-0.009203</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003675</td>\n",
              "      <td>0.055203</td>\n",
              "      <td>-0.071386</td>\n",
              "      <td>0.013726</td>\n",
              "      <td>-0.072601</td>\n",
              "      <td>0.050119</td>\n",
              "      <td>-0.347229</td>\n",
              "      <td>-0.241891</td>\n",
              "      <td>0.040831</td>\n",
              "      <td>0.067663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Months_Inactive_12_mon</th>\n",
              "      <td>0.152449</td>\n",
              "      <td>0.054361</td>\n",
              "      <td>-0.011163</td>\n",
              "      <td>-0.010768</td>\n",
              "      <td>0.005761</td>\n",
              "      <td>-0.004915</td>\n",
              "      <td>-0.016310</td>\n",
              "      <td>-0.014629</td>\n",
              "      <td>0.074164</td>\n",
              "      <td>-0.003675</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.029493</td>\n",
              "      <td>-0.020394</td>\n",
              "      <td>-0.042210</td>\n",
              "      <td>-0.016605</td>\n",
              "      <td>-0.032247</td>\n",
              "      <td>-0.036982</td>\n",
              "      <td>-0.042787</td>\n",
              "      <td>-0.038989</td>\n",
              "      <td>-0.007503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contacts_Count_12_mon</th>\n",
              "      <td>0.204491</td>\n",
              "      <td>-0.018452</td>\n",
              "      <td>0.039987</td>\n",
              "      <td>-0.040505</td>\n",
              "      <td>-0.006280</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>0.023113</td>\n",
              "      <td>-0.000442</td>\n",
              "      <td>-0.010774</td>\n",
              "      <td>0.055203</td>\n",
              "      <td>0.029493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020817</td>\n",
              "      <td>-0.053913</td>\n",
              "      <td>0.025646</td>\n",
              "      <td>-0.024445</td>\n",
              "      <td>-0.112774</td>\n",
              "      <td>-0.152213</td>\n",
              "      <td>-0.094997</td>\n",
              "      <td>-0.055471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Credit_Limit</th>\n",
              "      <td>-0.023873</td>\n",
              "      <td>0.002476</td>\n",
              "      <td>0.420806</td>\n",
              "      <td>0.068065</td>\n",
              "      <td>-0.002354</td>\n",
              "      <td>-0.053580</td>\n",
              "      <td>0.475972</td>\n",
              "      <td>0.492446</td>\n",
              "      <td>0.007507</td>\n",
              "      <td>-0.071386</td>\n",
              "      <td>-0.020394</td>\n",
              "      <td>0.020817</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042493</td>\n",
              "      <td>0.995981</td>\n",
              "      <td>0.012813</td>\n",
              "      <td>0.171730</td>\n",
              "      <td>0.075927</td>\n",
              "      <td>-0.002020</td>\n",
              "      <td>-0.482965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Revolving_Bal</th>\n",
              "      <td>-0.263053</td>\n",
              "      <td>0.014780</td>\n",
              "      <td>0.029658</td>\n",
              "      <td>-0.002688</td>\n",
              "      <td>-0.006800</td>\n",
              "      <td>0.036352</td>\n",
              "      <td>0.034718</td>\n",
              "      <td>0.026304</td>\n",
              "      <td>0.008623</td>\n",
              "      <td>0.013726</td>\n",
              "      <td>-0.042210</td>\n",
              "      <td>-0.053913</td>\n",
              "      <td>0.042493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.047167</td>\n",
              "      <td>0.058174</td>\n",
              "      <td>0.064370</td>\n",
              "      <td>0.056060</td>\n",
              "      <td>0.089861</td>\n",
              "      <td>0.624022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg_Open_To_Buy</th>\n",
              "      <td>-0.000285</td>\n",
              "      <td>0.001151</td>\n",
              "      <td>0.418059</td>\n",
              "      <td>0.068291</td>\n",
              "      <td>-0.001743</td>\n",
              "      <td>-0.056828</td>\n",
              "      <td>0.472760</td>\n",
              "      <td>0.489985</td>\n",
              "      <td>0.006732</td>\n",
              "      <td>-0.072601</td>\n",
              "      <td>-0.016605</td>\n",
              "      <td>0.025646</td>\n",
              "      <td>0.995981</td>\n",
              "      <td>-0.047167</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.007595</td>\n",
              "      <td>0.165923</td>\n",
              "      <td>0.070885</td>\n",
              "      <td>-0.010076</td>\n",
              "      <td>-0.538808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
              "      <td>-0.131063</td>\n",
              "      <td>-0.062042</td>\n",
              "      <td>0.026712</td>\n",
              "      <td>-0.035439</td>\n",
              "      <td>-0.010040</td>\n",
              "      <td>0.050805</td>\n",
              "      <td>0.011352</td>\n",
              "      <td>0.007385</td>\n",
              "      <td>-0.048959</td>\n",
              "      <td>0.050119</td>\n",
              "      <td>-0.032247</td>\n",
              "      <td>-0.024445</td>\n",
              "      <td>0.012813</td>\n",
              "      <td>0.058174</td>\n",
              "      <td>0.007595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.039678</td>\n",
              "      <td>0.005469</td>\n",
              "      <td>0.384189</td>\n",
              "      <td>0.035235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Trans_Amt</th>\n",
              "      <td>-0.168598</td>\n",
              "      <td>-0.046446</td>\n",
              "      <td>0.024890</td>\n",
              "      <td>0.025046</td>\n",
              "      <td>-0.007460</td>\n",
              "      <td>-0.062698</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.196003</td>\n",
              "      <td>-0.038591</td>\n",
              "      <td>-0.347229</td>\n",
              "      <td>-0.036982</td>\n",
              "      <td>-0.112774</td>\n",
              "      <td>0.171730</td>\n",
              "      <td>0.064370</td>\n",
              "      <td>0.165923</td>\n",
              "      <td>0.039678</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.807192</td>\n",
              "      <td>0.085581</td>\n",
              "      <td>-0.083034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Trans_Ct</th>\n",
              "      <td>-0.371403</td>\n",
              "      <td>-0.067097</td>\n",
              "      <td>-0.067454</td>\n",
              "      <td>0.049912</td>\n",
              "      <td>-0.004307</td>\n",
              "      <td>-0.116268</td>\n",
              "      <td>-0.054569</td>\n",
              "      <td>0.134275</td>\n",
              "      <td>-0.049819</td>\n",
              "      <td>-0.241891</td>\n",
              "      <td>-0.042787</td>\n",
              "      <td>-0.152213</td>\n",
              "      <td>0.075927</td>\n",
              "      <td>0.056060</td>\n",
              "      <td>0.070885</td>\n",
              "      <td>0.005469</td>\n",
              "      <td>0.807192</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.112324</td>\n",
              "      <td>0.002838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
              "      <td>-0.290054</td>\n",
              "      <td>-0.012143</td>\n",
              "      <td>-0.005800</td>\n",
              "      <td>0.011087</td>\n",
              "      <td>-0.016692</td>\n",
              "      <td>0.008999</td>\n",
              "      <td>-0.012657</td>\n",
              "      <td>-0.007261</td>\n",
              "      <td>-0.014072</td>\n",
              "      <td>0.040831</td>\n",
              "      <td>-0.038989</td>\n",
              "      <td>-0.094997</td>\n",
              "      <td>-0.002020</td>\n",
              "      <td>0.089861</td>\n",
              "      <td>-0.010076</td>\n",
              "      <td>0.384189</td>\n",
              "      <td>0.085581</td>\n",
              "      <td>0.112324</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.074143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg_Utilization_Ratio</th>\n",
              "      <td>-0.178410</td>\n",
              "      <td>0.007114</td>\n",
              "      <td>-0.257851</td>\n",
              "      <td>-0.037135</td>\n",
              "      <td>-0.001849</td>\n",
              "      <td>0.046623</td>\n",
              "      <td>-0.246476</td>\n",
              "      <td>-0.198711</td>\n",
              "      <td>-0.007541</td>\n",
              "      <td>0.067663</td>\n",
              "      <td>-0.007503</td>\n",
              "      <td>-0.055471</td>\n",
              "      <td>-0.482965</td>\n",
              "      <td>0.624022</td>\n",
              "      <td>-0.538808</td>\n",
              "      <td>0.035235</td>\n",
              "      <td>-0.083034</td>\n",
              "      <td>0.002838</td>\n",
              "      <td>0.074143</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Attrition_Flag  ...  Avg_Utilization_Ratio\n",
              "Attrition_Flag                  1.000000  ...              -0.178410\n",
              "Customer_Age                    0.018203  ...               0.007114\n",
              "Gender                         -0.037272  ...              -0.257851\n",
              "Dependent_count                 0.018991  ...              -0.037135\n",
              "Education_Level                 0.008796  ...              -0.001849\n",
              "Marital_Status                 -0.024117  ...               0.046623\n",
              "Income_Category                -0.013577  ...              -0.246476\n",
              "Card_Category                   0.002354  ...              -0.198711\n",
              "Months_on_book                  0.013687  ...              -0.007541\n",
              "Total_Relationship_Count       -0.150005  ...               0.067663\n",
              "Months_Inactive_12_mon          0.152449  ...              -0.007503\n",
              "Contacts_Count_12_mon           0.204491  ...              -0.055471\n",
              "Credit_Limit                   -0.023873  ...              -0.482965\n",
              "Total_Revolving_Bal            -0.263053  ...               0.624022\n",
              "Avg_Open_To_Buy                -0.000285  ...              -0.538808\n",
              "Total_Amt_Chng_Q4_Q1           -0.131063  ...               0.035235\n",
              "Total_Trans_Amt                -0.168598  ...              -0.083034\n",
              "Total_Trans_Ct                 -0.371403  ...               0.002838\n",
              "Total_Ct_Chng_Q4_Q1            -0.290054  ...               0.074143\n",
              "Avg_Utilization_Ratio          -0.178410  ...               1.000000\n",
              "\n",
              "[20 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BdZ7UKOEUuU"
      },
      "source": [
        "Berdasarkan tabel korelasi diatas, akan digunakan beberapa field yang memiliki korelasi tinggi (> 0.15) dengan atribut 'Attrition_Flag'. Field yang akan digunakan adalah 'Total_Relationship_Count' 'Months_Inactive_12_mon', 'Contacts_Count_12_mon', 'Total_Revolving_Bal', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_zy-x7Vt8R5B"
      },
      "source": [
        "X = model[['Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon',\n",
        "        'Total_Revolving_Bal', 'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1',\n",
        "        'Avg_Utilization_Ratio']]\n",
        "y = model['Attrition_Flag']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SwWXvuC83hu"
      },
      "source": [
        "clf=RandomForestClassifier(n_estimators=100, max_depth=13, random_state=3)\n",
        "\n",
        "clf.fit(X,y)\n",
        "\n",
        "clf.fit(X_train,y_train)\n",
        "\n",
        "y_pred=clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VRMrZb749oC"
      },
      "source": [
        "## Evaluasi Model\n",
        "\n",
        "Berdasarkan model yang diatas, kami melakukan evaluasi terkait akurasi dari model tersebut."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oDDNwgB7SBj"
      },
      "source": [
        "def find_model_accuracy(threshold,y_test,predictions):\n",
        "    \n",
        "    #Membuat kelas prediksi mengunggakan prediksi dengan nilai ambang\n",
        "    predicted_classes = np.where(predictions>threshold, 1, 0)\n",
        "    acc_score = accuracy_score(y_test, predicted_classes)\n",
        "    print('***********************************************')\n",
        "    print('   Accuracy Score of Model is '+str(acc_score))\n",
        "    print('***********************************************')\n",
        "    \n",
        "    #Membuat Confusion Matrix dari test dan predicted results\n",
        "    confusion_mat = confusion_matrix(y_test,predicted_classes)\n",
        "    #Membuat dataframe Confusion Matrix\n",
        "    confusion_df  = pd.DataFrame(confusion_mat,index=['Actual Neg', 'Actual Pos'],columns=['Predicted Neg','Predicted Pos'])\n",
        "    print('             Model Results                     ')\n",
        "    print('             *************                     ')\n",
        "    #Menghitung akurasi True Positive dan False Positive \n",
        "    TN = confusion_mat[0][0]\n",
        "    TP = confusion_mat[1][1]\n",
        "    FN = confusion_mat[1][0]\n",
        "    FP = confusion_mat[0][1]\n",
        "    total = TN+TP+FN+FP\n",
        "    acc = (TN + TP)/total\n",
        "    missClassification = (FN+FP)/total\n",
        "    nullErrorRate = (TN+FP)/total\n",
        "    print('Accuray Of the Model '+ str(acc))\n",
        "    print('Misclassification Rate '+str(missClassification))\n",
        "    print('Null Error Rate '+ str(nullErrorRate))\n",
        "    print('***********************************************')\n",
        "    print('             Confusion Matrix                  ')\n",
        "    print('             ----------------                  ')\n",
        "    #Membuat sebuah map dengan sns\n",
        "    sns.heatmap(data=confusion_df,cmap='coolwarm',annot=True)\n",
        "    plt.show() \n",
        "    print('***********************************************')\n",
        "    #Menunjukkan Classification Report\n",
        "    print(classification_report(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "4CGYGjHM9GR0",
        "outputId": "1bad25c2-e09b-4d2d-f298-23f39a3831c8"
      },
      "source": [
        "find_model_accuracy(0.75,y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "***********************************************\n",
            "   Accuracy Score of Model is 0.9598552155314248\n",
            "***********************************************\n",
            "             Model Results                     \n",
            "             *************                     \n",
            "Accuray Of the Model 0.9598552155314248\n",
            "Misclassification Rate 0.04014478446857519\n",
            "Null Error Rate 0.8450148075024679\n",
            "***********************************************\n",
            "             Confusion Matrix                  \n",
            "             ----------------                  \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "***********************************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.98      2568\n",
            "           1       0.90      0.84      0.87       471\n",
            "\n",
            "    accuracy                           0.96      3039\n",
            "   macro avg       0.93      0.91      0.92      3039\n",
            "weighted avg       0.96      0.96      0.96      3039\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmM4twK6Dw5N"
      },
      "source": [
        "Kesimpulan\n",
        "\n",
        "<br>\n",
        "\n",
        "berdasarkan model tersebut dapat dilihat bahwa masih terdapat 77 false - negative yang ingin kita hindari. selain itu, model ini juga memiliki akurasi yang tinggi dengan nilai 0,959 atau 0,96. "
      ]
    }
  ]
}