Code_Tugas_Besar_1_PM_Lanjut.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdWQaHMWc0nB"
      },
      "source": [
        "# ANGGOTA KELOMPOK :\n",
        "\n",
        "\n",
        "1.   Fijar Yasmina Pritama\t1301200215\n",
        "2.   Razita Amalina\t\t1301200283\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQBEP3x7TOLH"
      },
      "source": [
        "# IMPORT LIBRARY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0wDqeR4STR0n"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dpc0qKWlVf2Q"
      },
      "source": [
        "# IMPORT DATA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPUXIKgOPhPx"
      },
      "source": [
        "Data yang digunakan adalah dataset Breast Cancer Wisconsin \n",
        "(Diagnostic). Dataset ini terdiri dari data train dan data test. Library pandas diperlukan untuk proses membaca dan memproses dataset pada program ini."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQSx7b9cVX_N",
        "outputId": "f876cba8-3613-426e-d184-bfd063b6fab8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oB1Dh0lZuTkvTxZoAtbqJeMSqd8BKdhr\n",
            "To: /content/test.csv\n",
            "100% 23.3k/23.3k [00:00<00:00, 29.0MB/s]\n",
            "/usr/local/lib/python3.9/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1AY8aXyuxJwTcWxH6cvNAv1xaLK64O1Lq\n",
            "To: /content/train.csv\n",
            "100% 102k/102k [00:00<00:00, 92.0MB/s]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "#import data train\n",
        "!gdown --id 1oB1Dh0lZuTkvTxZoAtbqJeMSqd8BKdhr\n",
        "\n",
        "#import data test\n",
        "!gdown --id 1AY8aXyuxJwTcWxH6cvNAv1xaLK64O1Lq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "rOPoiMFgQr6L"
      },
      "outputs": [],
      "source": [
        "# making data frame of data train and data test with pandas\n",
        "df_train = pd.read_csv(\"train.csv\", na_values = '?')\n",
        "df_test = pd.read_csv(\"test.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVfcs4z7RASU"
      },
      "source": [
        "## Informasi data train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "rkIycx39cVH_",
        "outputId": "e54ee07b-5820-4fde-dbc0-02f52226c288"
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      "outputs": [
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
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              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
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              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
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              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "      <td>84300903</td>\n",
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              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.5</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38           122.8     1001.0   \n",
              "1    842517         M        20.57         17.77           132.9     1326.0   \n",
              "2  84300903         M        19.69         21.25           130.0     1203.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33            184.6      2019.0   \n",
              "1  ...         24.99          23.41            158.8      1956.0   \n",
              "2  ...         23.57          25.53            152.5      1709.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "\n",
              "[3 rows x 32 columns]"
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          },
          "execution_count": 4,
          "metadata": {},
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      ],
      "source": [
        "df_train.head(3)"
      ]
    },
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 463 entries, 0 to 462\n",
            "Data columns (total 32 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       463 non-null    int64  \n",
            " 1   diagnosis                463 non-null    object \n",
            " 2   radius_mean              463 non-null    float64\n",
            " 3   texture_mean             463 non-null    float64\n",
            " 4   perimeter_mean           463 non-null    float64\n",
            " 5   area_mean                463 non-null    float64\n",
            " 6   smoothness_mean          463 non-null    float64\n",
            " 7   compactness_mean         463 non-null    float64\n",
            " 8   concavity_mean           456 non-null    float64\n",
            " 9   concave points_mean      456 non-null    float64\n",
            " 10  symmetry_mean            463 non-null    float64\n",
            " 11  fractal_dimension_mean   463 non-null    float64\n",
            " 12  radius_se                463 non-null    float64\n",
            " 13  texture_se               463 non-null    float64\n",
            " 14  perimeter_se             463 non-null    float64\n",
            " 15  area_se                  463 non-null    float64\n",
            " 16  smoothness_se            463 non-null    float64\n",
            " 17  compactness_se           463 non-null    float64\n",
            " 18  concavity_se             456 non-null    float64\n",
            " 19  concave points_se        456 non-null    float64\n",
            " 20  symmetry_se              463 non-null    float64\n",
            " 21  fractal_dimension_se     463 non-null    float64\n",
            " 22  radius_worst             463 non-null    float64\n",
            " 23  texture_worst            463 non-null    float64\n",
            " 24  perimeter_worst          463 non-null    float64\n",
            " 25  area_worst               463 non-null    float64\n",
            " 26  smoothness_worst         463 non-null    float64\n",
            " 27  compactness_worst        463 non-null    float64\n",
            " 28  concavity_worst          456 non-null    float64\n",
            " 29  concave points_worst     456 non-null    float64\n",
            " 30  symmetry_worst           463 non-null    float64\n",
            " 31  fractal_dimension_worst  463 non-null    float64\n",
            "dtypes: float64(30), int64(1), object(1)\n",
            "memory usage: 115.9+ KB\n"
          ]
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      "source": [
        "df_train.info()"
      ]
    },
    {
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              "      <td>463.0</td>\n",
              "      <td>3.072130e+07</td>\n",
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              "      <td>8670.000000</td>\n",
              "      <td>865280.000000</td>\n",
              "      <td>897880.000000</td>\n",
              "      <td>8.910720e+06</td>\n",
              "      <td>9.112962e+08</td>\n",
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              "      <th>radius_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.366673e+02</td>\n",
              "      <td>4.224021e+03</td>\n",
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              "      <td>11.800000</td>\n",
              "      <td>13.480000</td>\n",
              "      <td>1.615000e+01</td>\n",
              "      <td>9.000000e+04</td>\n",
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              "      <th>texture_mean</th>\n",
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              "      <td>1.900000e+02</td>\n",
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              "      <th>perimeter_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>9.278639e+01</td>\n",
              "      <td>2.439204e+01</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>75.485000</td>\n",
              "      <td>86.910000</td>\n",
              "      <td>1.064500e+02</td>\n",
              "      <td>1.885000e+02</td>\n",
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              "      <th>area_mean</th>\n",
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              "      <td>6.662950e+02</td>\n",
              "      <td>3.561670e+02</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>427.950000</td>\n",
              "      <td>559.200000</td>\n",
              "      <td>8.041000e+02</td>\n",
              "      <td>2.501000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>9.634058e-02</td>\n",
              "      <td>1.369239e-02</td>\n",
              "      <td>0.062510</td>\n",
              "      <td>0.086580</td>\n",
              "      <td>0.096100</td>\n",
              "      <td>1.053500e-01</td>\n",
              "      <td>1.447000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.046333e-01</td>\n",
              "      <td>5.384442e-02</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.064130</td>\n",
              "      <td>0.092180</td>\n",
              "      <td>1.305500e-01</td>\n",
              "      <td>3.454000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>456.0</td>\n",
              "      <td>9.261361e-02</td>\n",
              "      <td>8.118428e-02</td>\n",
              "      <td>0.000692</td>\n",
              "      <td>0.029878</td>\n",
              "      <td>0.067780</td>\n",
              "      <td>1.356000e-01</td>\n",
              "      <td>4.268000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>456.0</td>\n",
              "      <td>5.101261e-02</td>\n",
              "      <td>3.918427e-02</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>0.020688</td>\n",
              "      <td>0.035875</td>\n",
              "      <td>7.760250e-02</td>\n",
              "      <td>2.012000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.821162e-01</td>\n",
              "      <td>2.802311e-02</td>\n",
              "      <td>0.116700</td>\n",
              "      <td>0.162100</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>1.965000e-01</td>\n",
              "      <td>3.040000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>463.0</td>\n",
              "      <td>6.256210e-02</td>\n",
              "      <td>6.972680e-03</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.057585</td>\n",
              "      <td>0.061300</td>\n",
              "      <td>6.581500e-02</td>\n",
              "      <td>9.744000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>4.150909e-01</td>\n",
              "      <td>2.875619e-01</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.235850</td>\n",
              "      <td>0.330500</td>\n",
              "      <td>5.038000e-01</td>\n",
              "      <td>2.873000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.206298e+00</td>\n",
              "      <td>5.319699e-01</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.829300</td>\n",
              "      <td>1.081000</td>\n",
              "      <td>1.474500e+00</td>\n",
              "      <td>4.885000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.929118e+00</td>\n",
              "      <td>2.101230e+00</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>1.660500</td>\n",
              "      <td>2.331000</td>\n",
              "      <td>3.455500e+00</td>\n",
              "      <td>2.198000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>4.338480e+01</td>\n",
              "      <td>5.686763e+01</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>18.180000</td>\n",
              "      <td>25.170000</td>\n",
              "      <td>4.887000e+01</td>\n",
              "      <td>7.000000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>6.992737e-03</td>\n",
              "      <td>2.971236e-03</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.005232</td>\n",
              "      <td>0.006294</td>\n",
              "      <td>8.077500e-03</td>\n",
              "      <td>3.113000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.571400e-02</td>\n",
              "      <td>1.857698e-02</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.012745</td>\n",
              "      <td>0.020250</td>\n",
              "      <td>3.306500e-02</td>\n",
              "      <td>1.354000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>456.0</td>\n",
              "      <td>3.285725e-02</td>\n",
              "      <td>3.187491e-02</td>\n",
              "      <td>0.000692</td>\n",
              "      <td>0.015547</td>\n",
              "      <td>0.026245</td>\n",
              "      <td>4.282000e-02</td>\n",
              "      <td>3.960000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>456.0</td>\n",
              "      <td>1.208861e-02</td>\n",
              "      <td>6.111503e-03</td>\n",
              "      <td>0.001852</td>\n",
              "      <td>0.007994</td>\n",
              "      <td>0.011100</td>\n",
              "      <td>1.483250e-02</td>\n",
              "      <td>5.279000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.086840e-02</td>\n",
              "      <td>8.671196e-03</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.015170</td>\n",
              "      <td>0.018840</td>\n",
              "      <td>2.376500e-02</td>\n",
              "      <td>7.895000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>463.0</td>\n",
              "      <td>3.788774e-03</td>\n",
              "      <td>2.753408e-03</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.002201</td>\n",
              "      <td>0.003118</td>\n",
              "      <td>4.484000e-03</td>\n",
              "      <td>2.984000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.647235e+01</td>\n",
              "      <td>4.900876e+00</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>13.060000</td>\n",
              "      <td>15.030000</td>\n",
              "      <td>1.951500e+01</td>\n",
              "      <td>3.604000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.557950e+01</td>\n",
              "      <td>6.145821e+00</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>20.940000</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>2.961500e+01</td>\n",
              "      <td>4.954000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.085844e+02</td>\n",
              "      <td>3.400204e+01</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>84.480000</td>\n",
              "      <td>98.270000</td>\n",
              "      <td>1.289000e+02</td>\n",
              "      <td>2.512000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>9.037775e+02</td>\n",
              "      <td>5.838509e+02</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>519.300000</td>\n",
              "      <td>698.700000</td>\n",
              "      <td>1.170000e+03</td>\n",
              "      <td>4.254000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>1.324545e-01</td>\n",
              "      <td>2.294062e-02</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.131400</td>\n",
              "      <td>1.463500e-01</td>\n",
              "      <td>2.226000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.567772e-01</td>\n",
              "      <td>1.619007e-01</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.144950</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>3.437000e-01</td>\n",
              "      <td>1.058000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>456.0</td>\n",
              "      <td>2.816225e-01</td>\n",
              "      <td>2.107553e-01</td>\n",
              "      <td>0.001845</td>\n",
              "      <td>0.116625</td>\n",
              "      <td>0.237650</td>\n",
              "      <td>3.927000e-01</td>\n",
              "      <td>1.252000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>456.0</td>\n",
              "      <td>1.186839e-01</td>\n",
              "      <td>6.585660e-02</td>\n",
              "      <td>0.008772</td>\n",
              "      <td>0.064747</td>\n",
              "      <td>0.102800</td>\n",
              "      <td>1.713000e-01</td>\n",
              "      <td>2.910000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>2.933549e-01</td>\n",
              "      <td>6.426609e-02</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.252600</td>\n",
              "      <td>0.284100</td>\n",
              "      <td>3.204000e-01</td>\n",
              "      <td>6.638000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>463.0</td>\n",
              "      <td>8.381909e-02</td>\n",
              "      <td>1.835371e-02</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.071250</td>\n",
              "      <td>0.079930</td>\n",
              "      <td>9.209500e-02</td>\n",
              "      <td>2.075000e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                         count          mean           std          min  \\\n",
              "id                       463.0  3.072130e+07  1.248655e+08  8670.000000   \n",
              "radius_mean              463.0  2.366673e+02  4.224021e+03     8.196000   \n",
              "texture_mean             463.0  1.951575e+01  8.978351e+00     9.710000   \n",
              "perimeter_mean           463.0  9.278639e+01  2.439204e+01    43.790000   \n",
              "area_mean                463.0  6.662950e+02  3.561670e+02   143.500000   \n",
              "smoothness_mean          463.0  9.634058e-02  1.369239e-02     0.062510   \n",
              "compactness_mean         463.0  1.046333e-01  5.384442e-02     0.019380   \n",
              "concavity_mean           456.0  9.261361e-02  8.118428e-02     0.000692   \n",
              "concave points_mean      456.0  5.101261e-02  3.918427e-02     0.001852   \n",
              "symmetry_mean            463.0  1.821162e-01  2.802311e-02     0.116700   \n",
              "fractal_dimension_mean   463.0  6.256210e-02  6.972680e-03     0.049960   \n",
              "radius_se                463.0  4.150909e-01  2.875619e-01     0.111500   \n",
              "texture_se               463.0  1.206298e+00  5.319699e-01     0.360200   \n",
              "perimeter_se             463.0  2.929118e+00  2.101230e+00     0.757000   \n",
              "area_se                  463.0  4.338480e+01  5.686763e+01     6.802000   \n",
              "smoothness_se            463.0  6.992737e-03  2.971236e-03     0.001713   \n",
              "compactness_se           463.0  2.571400e-02  1.857698e-02     0.002252   \n",
              "concavity_se             456.0  3.285725e-02  3.187491e-02     0.000692   \n",
              "concave points_se        456.0  1.208861e-02  6.111503e-03     0.001852   \n",
              "symmetry_se              463.0  2.086840e-02  8.671196e-03     0.007882   \n",
              "fractal_dimension_se     463.0  3.788774e-03  2.753408e-03     0.000895   \n",
              "radius_worst             463.0  1.647235e+01  4.900876e+00     7.930000   \n",
              "texture_worst            463.0  2.557950e+01  6.145821e+00    12.020000   \n",
              "perimeter_worst          463.0  1.085844e+02  3.400204e+01    50.410000   \n",
              "area_worst               463.0  9.037775e+02  5.838509e+02   185.200000   \n",
              "smoothness_worst         463.0  1.324545e-01  2.294062e-02     0.071170   \n",
              "compactness_worst        463.0  2.567772e-01  1.619007e-01     0.027290   \n",
              "concavity_worst          456.0  2.816225e-01  2.107553e-01     0.001845   \n",
              "concave points_worst     456.0  1.186839e-01  6.585660e-02     0.008772   \n",
              "symmetry_worst           463.0  2.933549e-01  6.426609e-02     0.156500   \n",
              "fractal_dimension_worst  463.0  8.381909e-02  1.835371e-02     0.055040   \n",
              "\n",
              "                                   25%            50%           75%  \\\n",
              "id                       865280.000000  897880.000000  8.910720e+06   \n",
              "radius_mean                  11.800000      13.480000  1.615000e+01   \n",
              "texture_mean                 16.070000      18.770000  2.168000e+01   \n",
              "perimeter_mean               75.485000      86.910000  1.064500e+02   \n",
              "area_mean                   427.950000     559.200000  8.041000e+02   \n",
              "smoothness_mean               0.086580       0.096100  1.053500e-01   \n",
              "compactness_mean              0.064130       0.092180  1.305500e-01   \n",
              "concavity_mean                0.029878       0.067780  1.356000e-01   \n",
              "concave points_mean           0.020688       0.035875  7.760250e-02   \n",
              "symmetry_mean                 0.162100       0.180000  1.965000e-01   \n",
              "fractal_dimension_mean        0.057585       0.061300  6.581500e-02   \n",
              "radius_se                     0.235850       0.330500  5.038000e-01   \n",
              "texture_se                    0.829300       1.081000  1.474500e+00   \n",
              "perimeter_se                  1.660500       2.331000  3.455500e+00   \n",
              "area_se                      18.180000      25.170000  4.887000e+01   \n",
              "smoothness_se                 0.005232       0.006294  8.077500e-03   \n",
              "compactness_se                0.012745       0.020250  3.306500e-02   \n",
              "concavity_se                  0.015547       0.026245  4.282000e-02   \n",
              "concave points_se             0.007994       0.011100  1.483250e-02   \n",
              "symmetry_se                   0.015170       0.018840  2.376500e-02   \n",
              "fractal_dimension_se          0.002201       0.003118  4.484000e-03   \n",
              "radius_worst                 13.060000      15.030000  1.951500e+01   \n",
              "texture_worst                20.940000      25.260000  2.961500e+01   \n",
              "perimeter_worst              84.480000      98.270000  1.289000e+02   \n",
              "area_worst                  519.300000     698.700000  1.170000e+03   \n",
              "smoothness_worst              0.116600       0.131400  1.463500e-01   \n",
              "compactness_worst             0.144950       0.211900  3.437000e-01   \n",
              "concavity_worst               0.116625       0.237650  3.927000e-01   \n",
              "concave points_worst          0.064747       0.102800  1.713000e-01   \n",
              "symmetry_worst                0.252600       0.284100  3.204000e-01   \n",
              "fractal_dimension_worst       0.071250       0.079930  9.209500e-02   \n",
              "\n",
              "                                  max  \n",
              "id                       9.112962e+08  \n",
              "radius_mean              9.000000e+04  \n",
              "texture_mean             1.900000e+02  \n",
              "perimeter_mean           1.885000e+02  \n",
              "area_mean                2.501000e+03  \n",
              "smoothness_mean          1.447000e-01  \n",
              "compactness_mean         3.454000e-01  \n",
              "concavity_mean           4.268000e-01  \n",
              "concave points_mean      2.012000e-01  \n",
              "symmetry_mean            3.040000e-01  \n",
              "fractal_dimension_mean   9.744000e-02  \n",
              "radius_se                2.873000e+00  \n",
              "texture_se               4.885000e+00  \n",
              "perimeter_se             2.198000e+01  \n",
              "area_se                  7.000000e+02  \n",
              "smoothness_se            3.113000e-02  \n",
              "compactness_se           1.354000e-01  \n",
              "concavity_se             3.960000e-01  \n",
              "concave points_se        5.279000e-02  \n",
              "symmetry_se              7.895000e-02  \n",
              "fractal_dimension_se     2.984000e-02  \n",
              "radius_worst             3.604000e+01  \n",
              "texture_worst            4.954000e+01  \n",
              "perimeter_worst          2.512000e+02  \n",
              "area_worst               4.254000e+03  \n",
              "smoothness_worst         2.226000e-01  \n",
              "compactness_worst        1.058000e+00  \n",
              "concavity_worst          1.252000e+00  \n",
              "concave points_worst     2.910000e-01  \n",
              "symmetry_worst           6.638000e-01  \n",
              "fractal_dimension_worst  2.075000e-01  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.describe().transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NM2Q9L87RTrY"
      },
      "source": [
        "## Informasi Data Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "I35Px62URY1D",
        "outputId": "f2096859-3b19-491e-d395-59c95dea073b"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>911320501</td>\n",
              "      <td>11.60</td>\n",
              "      <td>18.36</td>\n",
              "      <td>73.88</td>\n",
              "      <td>412.7</td>\n",
              "      <td>0.08508</td>\n",
              "      <td>0.05855</td>\n",
              "      <td>0.03367</td>\n",
              "      <td>0.01777</td>\n",
              "      <td>0.1516</td>\n",
              "      <td>...</td>\n",
              "      <td>12.77</td>\n",
              "      <td>24.02</td>\n",
              "      <td>82.68</td>\n",
              "      <td>495.1</td>\n",
              "      <td>0.1342</td>\n",
              "      <td>0.1808</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.08288</td>\n",
              "      <td>0.3210</td>\n",
              "      <td>0.07863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>911320502</td>\n",
              "      <td>13.17</td>\n",
              "      <td>18.22</td>\n",
              "      <td>84.28</td>\n",
              "      <td>537.3</td>\n",
              "      <td>0.07466</td>\n",
              "      <td>0.05994</td>\n",
              "      <td>0.04859</td>\n",
              "      <td>0.02870</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>...</td>\n",
              "      <td>14.90</td>\n",
              "      <td>23.89</td>\n",
              "      <td>95.10</td>\n",
              "      <td>687.6</td>\n",
              "      <td>0.1282</td>\n",
              "      <td>0.1965</td>\n",
              "      <td>0.1876</td>\n",
              "      <td>0.10450</td>\n",
              "      <td>0.2235</td>\n",
              "      <td>0.06925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9113239</td>\n",
              "      <td>13.24</td>\n",
              "      <td>20.13</td>\n",
              "      <td>86.87</td>\n",
              "      <td>542.9</td>\n",
              "      <td>0.08284</td>\n",
              "      <td>0.12230</td>\n",
              "      <td>0.10100</td>\n",
              "      <td>0.02833</td>\n",
              "      <td>0.1601</td>\n",
              "      <td>...</td>\n",
              "      <td>15.44</td>\n",
              "      <td>25.50</td>\n",
              "      <td>115.00</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.1201</td>\n",
              "      <td>0.5646</td>\n",
              "      <td>0.6556</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.2845</td>\n",
              "      <td>0.12490</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 31 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-d3b2827e-b137-485d-94dd-bd47b9b15944');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "          id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0  911320501        11.60         18.36           73.88      412.7   \n",
              "1  911320502        13.17         18.22           84.28      537.3   \n",
              "2    9113239        13.24         20.13           86.87      542.9   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.08508           0.05855         0.03367              0.01777   \n",
              "1          0.07466           0.05994         0.04859              0.02870   \n",
              "2          0.08284           0.12230         0.10100              0.02833   \n",
              "\n",
              "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0         0.1516  ...         12.77          24.02            82.68   \n",
              "1         0.1454  ...         14.90          23.89            95.10   \n",
              "2         0.1601  ...         15.44          25.50           115.00   \n",
              "\n",
              "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0       495.1            0.1342             0.1808           0.1860   \n",
              "1       687.6            0.1282             0.1965           0.1876   \n",
              "2       733.5            0.1201             0.5646           0.6556   \n",
              "\n",
              "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0               0.08288          0.3210                  0.07863  \n",
              "1               0.10450          0.2235                  0.06925  \n",
              "2               0.13570          0.2845                  0.12490  \n",
              "\n",
              "[3 rows x 31 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "Kqx5HMKtRbL9",
        "outputId": "0c403a49-6895-4f91-ce1b-8c8438cc535a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 106 entries, 0 to 105\n",
            "Data columns (total 31 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       106 non-null    int64  \n",
            " 1   radius_mean              106 non-null    float64\n",
            " 2   texture_mean             106 non-null    float64\n",
            " 3   perimeter_mean           106 non-null    float64\n",
            " 4   area_mean                106 non-null    float64\n",
            " 5   smoothness_mean          106 non-null    float64\n",
            " 6   compactness_mean         106 non-null    float64\n",
            " 7   concavity_mean           106 non-null    float64\n",
            " 8   concave points_mean      106 non-null    float64\n",
            " 9   symmetry_mean            106 non-null    float64\n",
            " 10  fractal_dimension_mean   106 non-null    float64\n",
            " 11  radius_se                106 non-null    float64\n",
            " 12  texture_se               106 non-null    float64\n",
            " 13  perimeter_se             106 non-null    float64\n",
            " 14  area_se                  106 non-null    float64\n",
            " 15  smoothness_se            106 non-null    float64\n",
            " 16  compactness_se           106 non-null    float64\n",
            " 17  concavity_se             106 non-null    float64\n",
            " 18  concave points_se        106 non-null    float64\n",
            " 19  symmetry_se              106 non-null    float64\n",
            " 20  fractal_dimension_se     106 non-null    float64\n",
            " 21  radius_worst             106 non-null    float64\n",
            " 22  texture_worst            106 non-null    float64\n",
            " 23  perimeter_worst          106 non-null    float64\n",
            " 24  area_worst               106 non-null    float64\n",
            " 25  smoothness_worst         106 non-null    float64\n",
            " 26  compactness_worst        106 non-null    float64\n",
            " 27  concavity_worst          106 non-null    float64\n",
            " 28  concave points_worst     106 non-null    float64\n",
            " 29  symmetry_worst           106 non-null    float64\n",
            " 30  fractal_dimension_worst  106 non-null    float64\n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 25.8 KB\n"
          ]
        }
      ],
      "source": [
        "df_test.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "6FLqUP2NRd56",
        "outputId": "f16a5656-074b-4c2d-a84a-e1f5b9624a6c"
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              "      <td>106.0</td>\n",
              "      <td>2.884536e+07</td>\n",
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              "      <td>91227.000000</td>\n",
              "      <td>914071.750000</td>\n",
              "      <td>919546.000000</td>\n",
              "      <td>925544.250000</td>\n",
              "      <td>9.113205e+08</td>\n",
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              "      <th>radius_mean</th>\n",
              "      <td>106.0</td>\n",
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              "      <td>47.920000</td>\n",
              "      <td>73.907500</td>\n",
              "      <td>84.170000</td>\n",
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              "      <td>1.655000e+02</td>\n",
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              "      <th>area_mean</th>\n",
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              "      <td>170.400000</td>\n",
              "      <td>403.050000</td>\n",
              "      <td>525.600000</td>\n",
              "      <td>680.850000</td>\n",
              "      <td>1.841000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>9.644632e-02</td>\n",
              "      <td>1.565484e-02</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.085088</td>\n",
              "      <td>0.095030</td>\n",
              "      <td>0.104875</td>\n",
              "      <td>1.634000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.030640e-01</td>\n",
              "      <td>4.826571e-02</td>\n",
              "      <td>0.033980</td>\n",
              "      <td>0.067015</td>\n",
              "      <td>0.096870</td>\n",
              "      <td>0.126825</td>\n",
              "      <td>2.770000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>7.825476e-02</td>\n",
              "      <td>7.153486e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030793</td>\n",
              "      <td>0.052275</td>\n",
              "      <td>0.102675</td>\n",
              "      <td>3.514000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>4.314380e-02</td>\n",
              "      <td>3.576638e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020917</td>\n",
              "      <td>0.030680</td>\n",
              "      <td>0.053027</td>\n",
              "      <td>1.520000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.769934e-01</td>\n",
              "      <td>2.426178e-02</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.160400</td>\n",
              "      <td>0.172450</td>\n",
              "      <td>0.191575</td>\n",
              "      <td>2.397000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>106.0</td>\n",
              "      <td>6.382632e-02</td>\n",
              "      <td>7.377300e-03</td>\n",
              "      <td>0.052430</td>\n",
              "      <td>0.058943</td>\n",
              "      <td>0.062715</td>\n",
              "      <td>0.067328</td>\n",
              "      <td>9.575000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>3.618472e-01</td>\n",
              "      <td>2.232313e-01</td>\n",
              "      <td>0.114400</td>\n",
              "      <td>0.219675</td>\n",
              "      <td>0.298500</td>\n",
              "      <td>0.409475</td>\n",
              "      <td>1.291000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.262959e+00</td>\n",
              "      <td>6.311912e-01</td>\n",
              "      <td>0.387100</td>\n",
              "      <td>0.880975</td>\n",
              "      <td>1.146500</td>\n",
              "      <td>1.471500</td>\n",
              "      <td>3.896000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.590623e+00</td>\n",
              "      <td>1.610192e+00</td>\n",
              "      <td>0.771400</td>\n",
              "      <td>1.511250</td>\n",
              "      <td>2.118000</td>\n",
              "      <td>3.059250</td>\n",
              "      <td>9.635000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>3.353589e+01</td>\n",
              "      <td>3.183046e+01</td>\n",
              "      <td>7.254000</td>\n",
              "      <td>17.040000</td>\n",
              "      <td>21.410000</td>\n",
              "      <td>33.090000</td>\n",
              "      <td>1.802000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>7.251698e-03</td>\n",
              "      <td>3.141356e-03</td>\n",
              "      <td>0.002667</td>\n",
              "      <td>0.004954</td>\n",
              "      <td>0.006709</td>\n",
              "      <td>0.008693</td>\n",
              "      <td>2.177000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.444790e-02</td>\n",
              "      <td>1.466542e-02</td>\n",
              "      <td>0.004660</td>\n",
              "      <td>0.013773</td>\n",
              "      <td>0.020755</td>\n",
              "      <td>0.030920</td>\n",
              "      <td>7.471000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.985491e-02</td>\n",
              "      <td>2.122259e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014860</td>\n",
              "      <td>0.025225</td>\n",
              "      <td>0.040140</td>\n",
              "      <td>1.114000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.131695e-02</td>\n",
              "      <td>5.849935e-03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007452</td>\n",
              "      <td>0.010820</td>\n",
              "      <td>0.014717</td>\n",
              "      <td>2.853000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.911792e-02</td>\n",
              "      <td>6.024694e-03</td>\n",
              "      <td>0.010540</td>\n",
              "      <td>0.014990</td>\n",
              "      <td>0.018295</td>\n",
              "      <td>0.021887</td>\n",
              "      <td>3.997000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>106.0</td>\n",
              "      <td>3.821679e-03</td>\n",
              "      <td>2.125420e-03</td>\n",
              "      <td>0.001217</td>\n",
              "      <td>0.002333</td>\n",
              "      <td>0.003268</td>\n",
              "      <td>0.004734</td>\n",
              "      <td>1.148000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.538178e+01</td>\n",
              "      <td>4.439474e+00</td>\n",
              "      <td>8.678000</td>\n",
              "      <td>12.455000</td>\n",
              "      <td>14.475000</td>\n",
              "      <td>16.697500</td>\n",
              "      <td>3.079000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.610406e+01</td>\n",
              "      <td>6.159044e+00</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>21.777500</td>\n",
              "      <td>25.490000</td>\n",
              "      <td>30.192500</td>\n",
              "      <td>4.279000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.014817e+02</td>\n",
              "      <td>3.129869e+01</td>\n",
              "      <td>54.490000</td>\n",
              "      <td>81.447500</td>\n",
              "      <td>95.020000</td>\n",
              "      <td>112.075000</td>\n",
              "      <td>2.115000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>7.792717e+02</td>\n",
              "      <td>4.908942e+02</td>\n",
              "      <td>223.600000</td>\n",
              "      <td>473.900000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>851.825000</td>\n",
              "      <td>2.782000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.319934e-01</td>\n",
              "      <td>2.245709e-02</td>\n",
              "      <td>0.089960</td>\n",
              "      <td>0.116825</td>\n",
              "      <td>0.129500</td>\n",
              "      <td>0.142725</td>\n",
              "      <td>2.006000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.432920e-01</td>\n",
              "      <td>1.357576e-01</td>\n",
              "      <td>0.052130</td>\n",
              "      <td>0.154750</td>\n",
              "      <td>0.214700</td>\n",
              "      <td>0.309275</td>\n",
              "      <td>8.681000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.495790e-01</td>\n",
              "      <td>1.926804e-01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.126125</td>\n",
              "      <td>0.191200</td>\n",
              "      <td>0.345775</td>\n",
              "      <td>1.170000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>1.046327e-01</td>\n",
              "      <td>5.942066e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.069820</td>\n",
              "      <td>0.097990</td>\n",
              "      <td>0.135700</td>\n",
              "      <td>2.650000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>2.757519e-01</td>\n",
              "      <td>4.773145e-02</td>\n",
              "      <td>0.156600</td>\n",
              "      <td>0.241550</td>\n",
              "      <td>0.268750</td>\n",
              "      <td>0.305975</td>\n",
              "      <td>4.677000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>106.0</td>\n",
              "      <td>8.449934e-02</td>\n",
              "      <td>1.679521e-02</td>\n",
              "      <td>0.058710</td>\n",
              "      <td>0.072590</td>\n",
              "      <td>0.080280</td>\n",
              "      <td>0.091690</td>\n",
              "      <td>1.409000e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                         count          mean           std           min  \\\n",
              "id                       106.0  2.884536e+07  1.262798e+08  91227.000000   \n",
              "radius_mean              106.0  1.359878e+01  3.437080e+00      7.691000   \n",
              "texture_mean             106.0  1.996783e+01  4.680723e+00     11.280000   \n",
              "perimeter_mean           106.0  8.839887e+01  2.367094e+01     47.920000   \n",
              "area_mean                106.0  6.050689e+02  3.296871e+02    170.400000   \n",
              "smoothness_mean          106.0  9.644632e-02  1.565484e-02      0.052630   \n",
              "compactness_mean         106.0  1.030640e-01  4.826571e-02      0.033980   \n",
              "concavity_mean           106.0  7.825476e-02  7.153486e-02      0.000000   \n",
              "concave points_mean      106.0  4.314380e-02  3.576638e-02      0.000000   \n",
              "symmetry_mean            106.0  1.769934e-01  2.426178e-02      0.106000   \n",
              "fractal_dimension_mean   106.0  6.382632e-02  7.377300e-03      0.052430   \n",
              "radius_se                106.0  3.618472e-01  2.232313e-01      0.114400   \n",
              "texture_se               106.0  1.262959e+00  6.311912e-01      0.387100   \n",
              "perimeter_se             106.0  2.590623e+00  1.610192e+00      0.771400   \n",
              "area_se                  106.0  3.353589e+01  3.183046e+01      7.254000   \n",
              "smoothness_se            106.0  7.251698e-03  3.141356e-03      0.002667   \n",
              "compactness_se           106.0  2.444790e-02  1.466542e-02      0.004660   \n",
              "concavity_se             106.0  2.985491e-02  2.122259e-02      0.000000   \n",
              "concave points_se        106.0  1.131695e-02  5.849935e-03      0.000000   \n",
              "symmetry_se              106.0  1.911792e-02  6.024694e-03      0.010540   \n",
              "fractal_dimension_se     106.0  3.821679e-03  2.125420e-03      0.001217   \n",
              "radius_worst             106.0  1.538178e+01  4.439474e+00      8.678000   \n",
              "texture_worst            106.0  2.610406e+01  6.159044e+00     14.200000   \n",
              "perimeter_worst          106.0  1.014817e+02  3.129869e+01     54.490000   \n",
              "area_worst               106.0  7.792717e+02  4.908942e+02    223.600000   \n",
              "smoothness_worst         106.0  1.319934e-01  2.245709e-02      0.089960   \n",
              "compactness_worst        106.0  2.432920e-01  1.357576e-01      0.052130   \n",
              "concavity_worst          106.0  2.495790e-01  1.926804e-01      0.000000   \n",
              "concave points_worst     106.0  1.046327e-01  5.942066e-02      0.000000   \n",
              "symmetry_worst           106.0  2.757519e-01  4.773145e-02      0.156600   \n",
              "fractal_dimension_worst  106.0  8.449934e-02  1.679521e-02      0.058710   \n",
              "\n",
              "                                   25%            50%            75%  \\\n",
              "id                       914071.750000  919546.000000  925544.250000   \n",
              "radius_mean                  11.495000      13.010000      14.855000   \n",
              "texture_mean                 16.590000      19.550000      22.647500   \n",
              "perimeter_mean               73.907500      84.170000      96.322500   \n",
              "area_mean                   403.050000     525.600000     680.850000   \n",
              "smoothness_mean               0.085088       0.095030       0.104875   \n",
              "compactness_mean              0.067015       0.096870       0.126825   \n",
              "concavity_mean                0.030793       0.052275       0.102675   \n",
              "concave points_mean           0.020917       0.030680       0.053027   \n",
              "symmetry_mean                 0.160400       0.172450       0.191575   \n",
              "fractal_dimension_mean        0.058943       0.062715       0.067328   \n",
              "radius_se                     0.219675       0.298500       0.409475   \n",
              "texture_se                    0.880975       1.146500       1.471500   \n",
              "perimeter_se                  1.511250       2.118000       3.059250   \n",
              "area_se                      17.040000      21.410000      33.090000   \n",
              "smoothness_se                 0.004954       0.006709       0.008693   \n",
              "compactness_se                0.013773       0.020755       0.030920   \n",
              "concavity_se                  0.014860       0.025225       0.040140   \n",
              "concave points_se             0.007452       0.010820       0.014717   \n",
              "symmetry_se                   0.014990       0.018295       0.021887   \n",
              "fractal_dimension_se          0.002333       0.003268       0.004734   \n",
              "radius_worst                 12.455000      14.475000      16.697500   \n",
              "texture_worst                21.777500      25.490000      30.192500   \n",
              "perimeter_worst              81.447500      95.020000     112.075000   \n",
              "area_worst                  473.900000     641.000000     851.825000   \n",
              "smoothness_worst              0.116825       0.129500       0.142725   \n",
              "compactness_worst             0.154750       0.214700       0.309275   \n",
              "concavity_worst               0.126125       0.191200       0.345775   \n",
              "concave points_worst          0.069820       0.097990       0.135700   \n",
              "symmetry_worst                0.241550       0.268750       0.305975   \n",
              "fractal_dimension_worst       0.072590       0.080280       0.091690   \n",
              "\n",
              "                                  max  \n",
              "id                       9.113205e+08  \n",
              "radius_mean              2.463000e+01  \n",
              "texture_mean             3.062000e+01  \n",
              "perimeter_mean           1.655000e+02  \n",
              "area_mean                1.841000e+03  \n",
              "smoothness_mean          1.634000e-01  \n",
              "compactness_mean         2.770000e-01  \n",
              "concavity_mean           3.514000e-01  \n",
              "concave points_mean      1.520000e-01  \n",
              "symmetry_mean            2.397000e-01  \n",
              "fractal_dimension_mean   9.575000e-02  \n",
              "radius_se                1.291000e+00  \n",
              "texture_se               3.896000e+00  \n",
              "perimeter_se             9.635000e+00  \n",
              "area_se                  1.802000e+02  \n",
              "smoothness_se            2.177000e-02  \n",
              "compactness_se           7.471000e-02  \n",
              "concavity_se             1.114000e-01  \n",
              "concave points_se        2.853000e-02  \n",
              "symmetry_se              3.997000e-02  \n",
              "fractal_dimension_se     1.148000e-02  \n",
              "radius_worst             3.079000e+01  \n",
              "texture_worst            4.279000e+01  \n",
              "perimeter_worst          2.115000e+02  \n",
              "area_worst               2.782000e+03  \n",
              "smoothness_worst         2.006000e-01  \n",
              "compactness_worst        8.681000e-01  \n",
              "concavity_worst          1.170000e+00  \n",
              "concave points_worst     2.650000e-01  \n",
              "symmetry_worst           4.677000e-01  \n",
              "fractal_dimension_worst  1.409000e-01  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test.describe().transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3fhhpSDdEWC"
      },
      "source": [
        "# DATA PREPARATION"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2QME4zCTjPc"
      },
      "source": [
        "## Mengkonversi Kolom Diagnosis (Data Formatting)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yYnJceEUZDK"
      },
      "source": [
        "Kolom Diagnosis pada data train (df_train) memiliki tipe object. Oleh karena itu, kami akan merubahnya menjadi tipe integer supaya mempermudah proses prediksi\n",
        "\n",
        "Kolom Diagnosis memiliki 2 label untuk mengategorikan jenis kanker payudara: \n",
        "1.  B => benign (kanker jinak)\n",
        "2. M => malignant (kanker ganas)\n",
        "\n",
        "Dengan konversi kolom object menjadi tipe integer maka hasilnya akan menjadi seperti ini :\n",
        "1. B => 0\n",
        "2. M => 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "IHNm_VVaVUZb",
        "outputId": "78311f34-1de8-4dbc-d471-41df47e525fb"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
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              "      <th>0</th>\n",
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              "      <td>122.80</td>\n",
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              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
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              "      <td>2019.0</td>\n",
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              "      <td>0.6656</td>\n",
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              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</td>\n",
              "      <td>20.570</td>\n",
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              "      <td>132.90</td>\n",
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              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
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              "      <td>0.1866</td>\n",
              "      <td>0.24160</td>\n",
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              "      <td>0.2750</td>\n",
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              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.690</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.45040</td>\n",
              "      <td>0.24300</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.420</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.68690</td>\n",
              "      <td>0.25750</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
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              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
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              "      <td>0.40000</td>\n",
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              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>0.05073</td>\n",
              "      <td>0.01206</td>\n",
              "      <td>0.01762</td>\n",
              "      <td>...</td>\n",
              "      <td>14.34</td>\n",
              "      <td>31.88</td>\n",
              "      <td>91.06</td>\n",
              "      <td>628.5</td>\n",
              "      <td>0.1218</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>0.04462</td>\n",
              "      <td>0.05921</td>\n",
              "      <td>0.2306</td>\n",
              "      <td>0.06291</td>\n",
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              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>9112712</td>\n",
              "      <td>0</td>\n",
              "      <td>9.755</td>\n",
              "      <td>28.20</td>\n",
              "      <td>61.68</td>\n",
              "      <td>290.9</td>\n",
              "      <td>0.07984</td>\n",
              "      <td>0.04626</td>\n",
              "      <td>0.01541</td>\n",
              "      <td>0.01043</td>\n",
              "      <td>...</td>\n",
              "      <td>10.67</td>\n",
              "      <td>36.92</td>\n",
              "      <td>68.03</td>\n",
              "      <td>349.9</td>\n",
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              "      <td>0.07190</td>\n",
              "      <td>0.04866</td>\n",
              "      <td>0.2321</td>\n",
              "      <td>0.07211</td>\n",
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              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>911296201</td>\n",
              "      <td>1</td>\n",
              "      <td>17.080</td>\n",
              "      <td>27.15</td>\n",
              "      <td>111.20</td>\n",
              "      <td>930.9</td>\n",
              "      <td>0.09898</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.10070</td>\n",
              "      <td>0.06431</td>\n",
              "      <td>...</td>\n",
              "      <td>22.96</td>\n",
              "      <td>34.49</td>\n",
              "      <td>152.10</td>\n",
              "      <td>1648.0</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>0.2444</td>\n",
              "      <td>0.26390</td>\n",
              "      <td>0.15550</td>\n",
              "      <td>0.3010</td>\n",
              "      <td>0.09060</td>\n",
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              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>911296202</td>\n",
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              "      <td>27.420</td>\n",
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              "      <td>2501.0</td>\n",
              "      <td>0.10840</td>\n",
              "      <td>0.19880</td>\n",
              "      <td>0.36350</td>\n",
              "      <td>0.16890</td>\n",
              "      <td>...</td>\n",
              "      <td>36.04</td>\n",
              "      <td>31.37</td>\n",
              "      <td>251.20</td>\n",
              "      <td>4254.0</td>\n",
              "      <td>0.1357</td>\n",
              "      <td>0.4256</td>\n",
              "      <td>0.68330</td>\n",
              "      <td>0.26250</td>\n",
              "      <td>0.2641</td>\n",
              "      <td>0.07427</td>\n",
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              "    <tr>\n",
              "      <th>462</th>\n",
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              "      <td>0</td>\n",
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              "      <td>646.1</td>\n",
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              "      <td>0.05223</td>\n",
              "      <td>0.03476</td>\n",
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              "      <td>...</td>\n",
              "      <td>15.40</td>\n",
              "      <td>31.98</td>\n",
              "      <td>100.40</td>\n",
              "      <td>734.6</td>\n",
              "      <td>0.1017</td>\n",
              "      <td>0.1460</td>\n",
              "      <td>0.14720</td>\n",
              "      <td>0.05563</td>\n",
              "      <td>0.2345</td>\n",
              "      <td>0.06464</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>463 rows × 32 columns</p>\n",
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            "text/plain": [
              "            id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
              "0       842302          1       17.990         10.38          122.80   \n",
              "1       842517          1       20.570         17.77          132.90   \n",
              "2     84300903          1       19.690         21.25          130.00   \n",
              "3     84348301          1       11.420         20.38           77.58   \n",
              "4     84358402          1       20.290         14.34          135.10   \n",
              "..         ...        ...          ...           ...             ...   \n",
              "458    9112594          0       13.000         25.13           82.61   \n",
              "459    9112712          0        9.755         28.20           61.68   \n",
              "460  911296201          1       17.080         27.15          111.20   \n",
              "461  911296202          1       27.420         26.27          186.90   \n",
              "462    9113156          0       14.400         26.99           92.25   \n",
              "\n",
              "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
              "0       1001.0          0.11840           0.27760         0.30010   \n",
              "1       1326.0          0.08474           0.07864         0.08690   \n",
              "2       1203.0          0.10960           0.15990         0.19740   \n",
              "3        386.1          0.14250           0.28390         0.24140   \n",
              "4       1297.0          0.10030           0.13280         0.19800   \n",
              "..         ...              ...               ...             ...   \n",
              "458      520.2          0.08369           0.05073         0.01206   \n",
              "459      290.9          0.07984           0.04626         0.01541   \n",
              "460      930.9          0.09898           0.11100         0.10070   \n",
              "461     2501.0          0.10840           0.19880         0.36350   \n",
              "462      646.1          0.06995           0.05223         0.03476   \n",
              "\n",
              "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0                0.14710  ...         25.38          17.33           184.60   \n",
              "1                0.07017  ...         24.99          23.41           158.80   \n",
              "2                0.12790  ...         23.57          25.53           152.50   \n",
              "3                0.10520  ...         14.91          26.50            98.87   \n",
              "4                0.10430  ...         22.54          16.67           152.20   \n",
              "..                   ...  ...           ...            ...              ...   \n",
              "458              0.01762  ...         14.34          31.88            91.06   \n",
              "459              0.01043  ...         10.67          36.92            68.03   \n",
              "460              0.06431  ...         22.96          34.49           152.10   \n",
              "461              0.16890  ...         36.04          31.37           251.20   \n",
              "462              0.01737  ...         15.40          31.98           100.40   \n",
              "\n",
              "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0        2019.0            0.1622             0.6656          0.71190   \n",
              "1        1956.0            0.1238             0.1866          0.24160   \n",
              "2        1709.0            0.1444             0.4245          0.45040   \n",
              "3         567.7            0.2098             0.8663          0.68690   \n",
              "4        1575.0            0.1374             0.2050          0.40000   \n",
              "..          ...               ...                ...              ...   \n",
              "458       628.5            0.1218             0.1093          0.04462   \n",
              "459       349.9            0.1110             0.1109          0.07190   \n",
              "460      1648.0            0.1600             0.2444          0.26390   \n",
              "461      4254.0            0.1357             0.4256          0.68330   \n",
              "462       734.6            0.1017             0.1460          0.14720   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                 0.26540          0.4601                  0.11890  \n",
              "1                 0.18600          0.2750                  0.08902  \n",
              "2                 0.24300          0.3613                  0.08758  \n",
              "3                 0.25750          0.6638                  0.17300  \n",
              "4                 0.16250          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "458               0.05921          0.2306                  0.06291  \n",
              "459               0.04866          0.2321                  0.07211  \n",
              "460               0.15550          0.3010                  0.09060  \n",
              "461               0.26250          0.2641                  0.07427  \n",
              "462               0.05563          0.2345                  0.06464  \n",
              "\n",
              "[463 rows x 32 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "convert_diagnosis = {'M':1,'B':0}\n",
        "df_train['diagnosis'] = df_train['diagnosis'].map(convert_diagnosis)\n",
        "df_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5kWRyZa2-Jn",
        "outputId": "a9a4b0e6-fe0f-4de8-8f79-9f87f2bfcf18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                           int64\n",
              "diagnosis                    int64\n",
              "radius_mean                float64\n",
              "texture_mean               float64\n",
              "perimeter_mean             float64\n",
              "area_mean                  float64\n",
              "smoothness_mean            float64\n",
              "compactness_mean           float64\n",
              "concavity_mean             float64\n",
              "concave points_mean        float64\n",
              "symmetry_mean              float64\n",
              "fractal_dimension_mean     float64\n",
              "radius_se                  float64\n",
              "texture_se                 float64\n",
              "perimeter_se               float64\n",
              "area_se                    float64\n",
              "smoothness_se              float64\n",
              "compactness_se             float64\n",
              "concavity_se               float64\n",
              "concave points_se          float64\n",
              "symmetry_se                float64\n",
              "fractal_dimension_se       float64\n",
              "radius_worst               float64\n",
              "texture_worst              float64\n",
              "perimeter_worst            float64\n",
              "area_worst                 float64\n",
              "smoothness_worst           float64\n",
              "compactness_worst          float64\n",
              "concavity_worst            float64\n",
              "concave points_worst       float64\n",
              "symmetry_worst             float64\n",
              "fractal_dimension_worst    float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMHK1_CWdei9"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sEwnRpJhKGx"
      },
      "source": [
        "##Checking and Removing Missing Value in Data Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1abscSGwgR7H",
        "outputId": "0937c4c9-43c9-4d4e-e0e1-1180d76b416d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                         0\n",
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             7\n",
              "concave points_mean        7\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               7\n",
              "concave points_se          7\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            7\n",
              "concave points_worst       7\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_train.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2rUEw0R_leji",
        "outputId": "d62e0b46-38e1-4055-d731-999a9f41995d"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                     column_name  percent_missing\n",
              "id                                            id         0.000000\n",
              "diagnosis                              diagnosis         0.000000\n",
              "radius_mean                          radius_mean         0.000000\n",
              "texture_mean                        texture_mean         0.000000\n",
              "perimeter_mean                    perimeter_mean         0.000000\n",
              "area_mean                              area_mean         0.000000\n",
              "smoothness_mean                  smoothness_mean         0.000000\n",
              "compactness_mean                compactness_mean         0.000000\n",
              "concavity_mean                    concavity_mean         1.511879\n",
              "concave points_mean          concave points_mean         1.511879\n",
              "symmetry_mean                      symmetry_mean         0.000000\n",
              "fractal_dimension_mean    fractal_dimension_mean         0.000000\n",
              "radius_se                              radius_se         0.000000\n",
              "texture_se                            texture_se         0.000000\n",
              "perimeter_se                        perimeter_se         0.000000\n",
              "area_se                                  area_se         0.000000\n",
              "smoothness_se                      smoothness_se         0.000000\n",
              "compactness_se                    compactness_se         0.000000\n",
              "concavity_se                        concavity_se         1.511879\n",
              "concave points_se              concave points_se         1.511879\n",
              "symmetry_se                          symmetry_se         0.000000\n",
              "fractal_dimension_se        fractal_dimension_se         0.000000\n",
              "radius_worst                        radius_worst         0.000000\n",
              "texture_worst                      texture_worst         0.000000\n",
              "perimeter_worst                  perimeter_worst         0.000000\n",
              "area_worst                            area_worst         0.000000\n",
              "smoothness_worst                smoothness_worst         0.000000\n",
              "compactness_worst              compactness_worst         0.000000\n",
              "concavity_worst                  concavity_worst         1.511879\n",
              "concave points_worst        concave points_worst         1.511879\n",
              "symmetry_worst                    symmetry_worst         0.000000\n",
              "fractal_dimension_worst  fractal_dimension_worst         0.000000"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "percent_missing = df_train.isnull().sum() * 100 / len(df_train)\n",
        "tabel_missing_value = pd.DataFrame({'column_name': df_train.columns,\n",
        "                                 'percent_missing': percent_missing})\n",
        "tabel_missing_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cFCtFH1vWBd",
        "outputId": "8da451c8-4e26-4863-b471-edef65369f20"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                         0\n",
              "diagnosis                  0\n",
              "radius_mean                0\n",
              "texture_mean               0\n",
              "perimeter_mean             0\n",
              "area_mean                  0\n",
              "smoothness_mean            0\n",
              "compactness_mean           0\n",
              "concavity_mean             0\n",
              "concave points_mean        0\n",
              "symmetry_mean              0\n",
              "fractal_dimension_mean     0\n",
              "radius_se                  0\n",
              "texture_se                 0\n",
              "perimeter_se               0\n",
              "area_se                    0\n",
              "smoothness_se              0\n",
              "compactness_se             0\n",
              "concavity_se               0\n",
              "concave points_se          0\n",
              "symmetry_se                0\n",
              "fractal_dimension_se       0\n",
              "radius_worst               0\n",
              "texture_worst              0\n",
              "perimeter_worst            0\n",
              "area_worst                 0\n",
              "smoothness_worst           0\n",
              "compactness_worst          0\n",
              "concavity_worst            0\n",
              "concave points_worst       0\n",
              "symmetry_worst             0\n",
              "fractal_dimension_worst    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#replace missing value with mean\n",
        "\n",
        "df_train.fillna(df_train.mean(numeric_only=True).round(1), inplace=True)\n",
        "df_train.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8E_BwkWbxYY"
      },
      "source": [
        "## Mengecek Pencilan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wKzzI7zAcdyl",
        "outputId": "ced0f2ae-181a-4ca8-f7d2-9e2035ead94c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-00dd13df8d09>:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
            "  plt.figure(figsize=(17,1))\n"
          ]
        },
        {
          "data": {
            "image/png": 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vjdtvvz3++c9/ntdnKD4BAOhRL774YixatCjpGAAA9JKTJ0/GddddF6tWrerS/MGDB+OWW26Jm266KRobG+Pll1+Ot99+O772ta+d1+f069K3AwBAJ11yySVJRwAAoBdNmTIlpkyZcs7jZ86ciR/84Afx/PPPx/Hjx+Paa6+NZcuWRW1tbURENDQ0RC6Xi8WLF0c2+999m9/73vfilltuiffeey/69+/fqRx2fAIA0KP+/63uTU1NMW3atCgtLY2hQ4fG2rVrkw0HAECvmzVrVrz22mvxwgsvxJ49e+KOO+6IyZMnx4EDByIiYvTo0ZHNZmPNmjWRy+Wiubk5nn322Zg4cWKnS88IOz4BAOhF99xzTxw5ciT++Mc/Rv/+/WP27NnR1NSUdCwAAHrJoUOHYs2aNXHo0KGoqqqKiP/u5ty0aVOsWbMmlixZEkOHDo3f/e53ceedd8YDDzwQuVwurr/++vjtb397Xt+l+AQAoFfs378/Nm7cGDt27IixY8dGRMTq1avj6quvTjgZAAC9Ze/evZHL5eLKK69st37mzJkYNGhQREQcPXo07r///pgxY0ZMnz49WltbY/78+fH1r389Nm/eHJlMplPfpfgEAKBXvPnmm9GvX78YPXp029qwYcNi4MCByYUCAKBXnThxIoqKiqKhoSGKioraHbvooosiImLVqlVRUVERTzzxRNux5557Lqqrq2P79u0xfvz4Tn2X4hMAAAAA6BU1NTWRy+WiqakpvvzlL5/1nHfeeaftoUYf+KAkzefznf4uDzcCAKBXDBs2LN5///1oaGhoW9u3b18cP348uVAAAHS7EydORGNjYzQ2NkZExMGDB6OxsTEOHToUV155ZdTV1cXdd98dL774Yhw8eDB27NgRS5cujd/85jcREXHzzTfHzp07Y+HChXHgwIHYvXt33HvvvXH55ZdHTU1Np3MoPgEA6BVXXXVVTJ48OR544IHYvn17NDQ0xH333RelpaVJRwMAoBvt2rUrampq2krKOXPmRE1NTcyfPz8iItasWRN33313fPe7342rrroqbr311ti5c2dcdtllERFx0003xbp16+Kll16KmpqamDx5chQXF8emTZvO62/HTKFQKHT/Pw8AAP6rtrY2Ro4cGStWrIijR4/GfffdF7///e+jsrIyFi9eHPPmzYv6+vqor69POioAACmi+AQAAAAAUset7gAAAABA6ig+AQAAAIDUUXwCAAAAAKmj+AQAAAAAUkfxCQAAAACkjuITAAAAAEgdxScAAAAAkDqKTwAAAAAgdRSfAAAAAEDqKD4BAAAAgNRRfAIAAAAAqaP4BAAAAABS5/8AgF/rCGY+8nwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#menampilkan box plot\n",
        "for column in df_train:\n",
        "        plt.figure(figsize=(17,1))\n",
        "        sns.boxplot(data=df_train, x=column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6I1VAnPTAbK"
      },
      "source": [
        "## Menghilangkan Outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DPfRB7yuamWW"
      },
      "outputs": [],
      "source": [
        "def InterQuartile(data_frame):\n",
        "  for i in data_frame.columns:\n",
        "    q1 = data_frame[i].quantile(0.25)\n",
        "    q3 = data_frame[i].quantile(0.75)\n",
        "    inter_quartile_range = q3-q1\n",
        "\n",
        "    terkecil = q1 - (inter_quartile_range *1.5) \n",
        "    terbesar = q3 + (inter_quartile_range * 1.5)\n",
        "\n",
        "    data_frame.loc[data_frame[i] > terbesar , i] = terbesar \n",
        "    data_frame.loc[data_frame[i] < terkecil , i] = terkecil\n",
        "\n",
        "InterQuartile(df_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ysCLrqzga0JI",
        "outputId": "0bf7a0dd-e3a4-44da-9968-6fc1145985f9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-17-4d86ec5179bb>:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n",
            "  plt.figure(figsize=(17,1))\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1700x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#menampilkan box plot setelah menghilangkan pencilan dengan metode inter quartile range\n",
        "for column in df_train:\n",
        "        plt.figure(figsize=(17,1))\n",
        "        sns.boxplot(data=df_train, x=column)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qUPCC97idES0"
      },
      "outputs": [],
      "source": [
        "#menyimpan file data train setelah proses prepocessing\n",
        "df_train.to_csv('data_train_prepocessed.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOt0QVsewm8b"
      },
      "source": [
        "# Eksperimen"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oNZAn3Spd801"
      },
      "source": [
        "Pada bagian experimen, proses split data train sebesar 75% dan data test sebesar 25% dengan variabel random state 32. Experimen ini akan memprediksi kolom diagnosis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6sTqT40BgNGn"
      },
      "outputs": [],
      "source": [
        "x = df_train.drop(['diagnosis'], axis = 1)\n",
        "y = df_train['diagnosis'].astype(int)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20SOP33KV-ow",
        "outputId": "cd936f37-4b17-4ff5-d2fd-7d3ab643fd92"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "458    0\n",
              "459    0\n",
              "460    1\n",
              "461    1\n",
              "462    0\n",
              "Name: diagnosis, Length: 463, dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDS7BC-Jggv1"
      },
      "source": [
        "### Proses pembuatan model data menggunakan AUTO ML yaitu TPOT."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtRjunFnhdrj",
        "outputId": "f9bf5122-74f9-4af0-e9e1-9b836787dea9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: TPOT in /usr/local/lib/python3.9/dist-packages (0.11.7)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.22.4)\n",
            "Requirement already satisfied: stopit>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.1.2)\n",
            "Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.7.5)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.10.1)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.5.3)\n",
            "Requirement already satisfied: update-checker>=0.16 in /usr/local/lib/python3.9/dist-packages (from TPOT) (0.18.0)\n",
            "Requirement already satisfied: deap>=1.2 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.3.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.9/dist-packages (from TPOT) (1.2.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.9/dist-packages (from TPOT) (4.65.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.2->TPOT) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.2->TPOT) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-learn>=0.22.0->TPOT) (3.1.0)\n",
            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.9/dist-packages (from update-checker>=0.16->TPOT) (2.27.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.2->TPOT) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.3.0->update-checker>=0.16->TPOT) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests>=2.3.0->update-checker>=0.16->TPOT) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.3.0->update-checker>=0.16->TPOT) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.3.0->update-checker>=0.16->TPOT) (3.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install TPOT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ombv9NMkaEBU"
      },
      "outputs": [],
      "source": [
        "from tpot import TPOTClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztWGgIS3hme8"
      },
      "outputs": [],
      "source": [
        "tpot = TPOTClassifier(generations=10, population_size=20, cv=5, n_jobs=2, random_state=42, verbosity=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "95b938e445494af9bdbe9a91f47b04fd",
            "7ebe099c68964569b1b91cc8d9e1024e",
            "c27ad0ccfb8e4ec98d521c28408657b8",
            "bc5a3623b852448eae184de8f18b8f09",
            "a1b43005f0ff427f8162c2430570fc2f",
            "34c11de166ca476b8ac01204a25cb681",
            "834b267529724586ab6211d07a8b6e99",
            "c18c4a5842a7404f8b9b84c3b74bd07d",
            "ffb422b32abd405aac315ab076933833",
            "694f51044a374ba587e2a30ca0c41211",
            "e179443d49ba4aa9a1b9a99e379330be"
          ]
        },
        "id": "U87sLnfYjZhh",
        "outputId": "adf5fd2e-ec6e-4421-949b-ff4e52c608ba"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95b938e445494af9bdbe9a91f47b04fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Optimization Progress:   0%|          | 0/220 [00:00<?, ?pipeline/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Generation 1 - Current best internal CV score: 0.9756756756756756\n",
            "\n",
            "Generation 2 - Current best internal CV score: 0.9756756756756756\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:stopit:Code block execution exceeded 10 seconds timeout\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/stopit/utils.py\", line 145, in wrapper\n",
            "    result = func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/tpot/decorators.py\", line 57, in time_limited_call\n",
            "    func(*args)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/sklearn/pipeline.py\", line 405, in fit\n",
            "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 620, in inner_f\n",
            "    return func(**kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/sklearn.py\", line 1490, in fit\n",
            "    self._Booster = train(\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 620, in inner_f\n",
            "    return func(**kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/training.py\", line 185, in train\n",
            "    bst.update(dtrain, i, obj)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 1918, in update\n",
            "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
            "stopit.utils.TimeoutException\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Generation 3 - Current best internal CV score: 0.9756756756756756\n",
            "\n",
            "Generation 4 - Current best internal CV score: 0.9756756756756756\n",
            "\n",
            "Generation 5 - Current best internal CV score: 0.9756756756756756\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:stopit:Code block execution exceeded 10 seconds timeout\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/stopit/utils.py\", line 145, in wrapper\n",
            "    result = func(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/tpot/decorators.py\", line 57, in time_limited_call\n",
            "    func(*args)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/sklearn/pipeline.py\", line 405, in fit\n",
            "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 620, in inner_f\n",
            "    return func(**kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/sklearn.py\", line 1490, in fit\n",
            "    self._Booster = train(\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 620, in inner_f\n",
            "    return func(**kwargs)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/training.py\", line 185, in train\n",
            "    bst.update(dtrain, i, obj)\n",
            "  File \"/usr/local/lib/python3.9/dist-packages/xgboost/core.py\", line 1918, in update\n",
            "    _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n",
            "stopit.utils.TimeoutException\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Generation 6 - Current best internal CV score: 0.981081081081081\n",
            "\n",
            "Generation 7 - Current best internal CV score: 0.981081081081081\n",
            "\n",
            "Generation 8 - Current best internal CV score: 0.981081081081081\n",
            "\n",
            "Generation 9 - Current best internal CV score: 0.981081081081081\n",
            "\n",
            "Generation 10 - Current best internal CV score: 0.981081081081081\n",
            "\n",
            "Best pipeline: MLPClassifier(Normalizer(StandardScaler(input_matrix), norm=max), alpha=0.001, learning_rate_init=0.01)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TPOTClassifier(generations=10, n_jobs=2, population_size=20, random_state=42,\n",
              "               verbosity=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TPOTClassifier</label><div class=\"sk-toggleable__content\"><pre>TPOTClassifier(generations=10, n_jobs=2, population_size=20, random_state=42,\n",
              "               verbosity=2)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "TPOTClassifier(generations=10, n_jobs=2, population_size=20, random_state=42,\n",
              "               verbosity=2)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tpot.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zhqmFsmins-k"
      },
      "outputs": [],
      "source": [
        "tpot.export('tpot_hasil_pipeline.py')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdu4HUQ0jbyz"
      },
      "source": [
        "### Prediksi kolom diagnosis pada data test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtJt3b7Zjj2y",
        "outputId": "bafbfa2e-075e-4016-e442-3eaf839322a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       0, 0, 0, 1, 1])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_diagnosis_pred = tpot.predict(x_test)\n",
        "y_diagnosis_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCnlffY1jyp4"
      },
      "source": [
        "# Evaluasi dengan Confussion Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yE7d3X8oj0_e",
        "outputId": "c17dc90f-d827-434d-f113-8eb4469f47a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.97        54\n",
            "           1       1.00      0.92      0.96        39\n",
            "\n",
            "    accuracy                           0.97        93\n",
            "   macro avg       0.97      0.96      0.97        93\n",
            "weighted avg       0.97      0.97      0.97        93\n",
            "\n"
          ]
        }
      ],
      "source": [
        "nilaiEvaluasi = []\n",
        "\n",
        "nilaiEvaluasi.append(['Eksperimen', metrics.f1_score(y_test, y_diagnosis_pred), metrics.accuracy_score(y_test, y_diagnosis_pred)])\n",
        "\n",
        "print(metrics.classification_report(y_test, y_diagnosis_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GIJKGjQpkg_V"
      },
      "source": [
        "# Mengimplementasi Prediksi Kolom Diagnosis pada Dataset train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ew-qj296kmgZ"
      },
      "outputs": [],
      "source": [
        "diagnosis = tpot.predict(df_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "AjZqwA9NmJ1s",
        "outputId": "384bef5c-6d57-48c3-b13d-dd4ce6543e96"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-800bc6d2-3a0a-46ad-95da-cde111033bff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Diagnosis_Prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>911320501</td>\n",
              "      <td>11.600</td>\n",
              "      <td>18.36</td>\n",
              "      <td>73.88</td>\n",
              "      <td>412.7</td>\n",
              "      <td>0.08508</td>\n",
              "      <td>0.05855</td>\n",
              "      <td>0.03367</td>\n",
              "      <td>0.017770</td>\n",
              "      <td>0.1516</td>\n",
              "      <td>...</td>\n",
              "      <td>24.02</td>\n",
              "      <td>82.68</td>\n",
              "      <td>495.1</td>\n",
              "      <td>0.13420</td>\n",
              "      <td>0.18080</td>\n",
              "      <td>0.18600</td>\n",
              "      <td>0.08288</td>\n",
              "      <td>0.3210</td>\n",
              "      <td>0.07863</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>911320502</td>\n",
              "      <td>13.170</td>\n",
              "      <td>18.22</td>\n",
              "      <td>84.28</td>\n",
              "      <td>537.3</td>\n",
              "      <td>0.07466</td>\n",
              "      <td>0.05994</td>\n",
              "      <td>0.04859</td>\n",
              "      <td>0.028700</td>\n",
              "      <td>0.1454</td>\n",
              "      <td>...</td>\n",
              "      <td>23.89</td>\n",
              "      <td>95.10</td>\n",
              "      <td>687.6</td>\n",
              "      <td>0.12820</td>\n",
              "      <td>0.19650</td>\n",
              "      <td>0.18760</td>\n",
              "      <td>0.10450</td>\n",
              "      <td>0.2235</td>\n",
              "      <td>0.06925</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9113239</td>\n",
              "      <td>13.240</td>\n",
              "      <td>20.13</td>\n",
              "      <td>86.87</td>\n",
              "      <td>542.9</td>\n",
              "      <td>0.08284</td>\n",
              "      <td>0.12230</td>\n",
              "      <td>0.10100</td>\n",
              "      <td>0.028330</td>\n",
              "      <td>0.1601</td>\n",
              "      <td>...</td>\n",
              "      <td>25.50</td>\n",
              "      <td>115.00</td>\n",
              "      <td>733.5</td>\n",
              "      <td>0.12010</td>\n",
              "      <td>0.56460</td>\n",
              "      <td>0.65560</td>\n",
              "      <td>0.13570</td>\n",
              "      <td>0.2845</td>\n",
              "      <td>0.12490</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9113455</td>\n",
              "      <td>13.140</td>\n",
              "      <td>20.74</td>\n",
              "      <td>85.98</td>\n",
              "      <td>536.9</td>\n",
              "      <td>0.08675</td>\n",
              "      <td>0.10890</td>\n",
              "      <td>0.10850</td>\n",
              "      <td>0.035100</td>\n",
              "      <td>0.1562</td>\n",
              "      <td>...</td>\n",
              "      <td>25.46</td>\n",
              "      <td>100.90</td>\n",
              "      <td>689.1</td>\n",
              "      <td>0.13510</td>\n",
              "      <td>0.35490</td>\n",
              "      <td>0.45040</td>\n",
              "      <td>0.11810</td>\n",
              "      <td>0.2563</td>\n",
              "      <td>0.08174</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9113514</td>\n",
              "      <td>9.668</td>\n",
              "      <td>18.10</td>\n",
              "      <td>61.06</td>\n",
              "      <td>286.3</td>\n",
              "      <td>0.08311</td>\n",
              "      <td>0.05428</td>\n",
              "      <td>0.01479</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.1680</td>\n",
              "      <td>...</td>\n",
              "      <td>24.62</td>\n",
              "      <td>71.11</td>\n",
              "      <td>380.2</td>\n",
              "      <td>0.13880</td>\n",
              "      <td>0.12550</td>\n",
              "      <td>0.06409</td>\n",
              "      <td>0.02500</td>\n",
              "      <td>0.3057</td>\n",
              "      <td>0.07875</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>926424</td>\n",
              "      <td>21.560</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.138900</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.41070</td>\n",
              "      <td>0.22160</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>926682</td>\n",
              "      <td>20.130</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.097910</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.32150</td>\n",
              "      <td>0.16280</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>926954</td>\n",
              "      <td>16.600</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.053020</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.34030</td>\n",
              "      <td>0.14180</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>927241</td>\n",
              "      <td>20.600</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.152000</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.93870</td>\n",
              "      <td>0.26500</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>92751</td>\n",
              "      <td>7.760</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 32 columns</p>\n",
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            "text/plain": [
              "            id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    911320501       11.600         18.36           73.88      412.7   \n",
              "1    911320502       13.170         18.22           84.28      537.3   \n",
              "2      9113239       13.240         20.13           86.87      542.9   \n",
              "3      9113455       13.140         20.74           85.98      536.9   \n",
              "4      9113514        9.668         18.10           61.06      286.3   \n",
              "..         ...          ...           ...             ...        ...   \n",
              "101     926424       21.560         22.39          142.00     1479.0   \n",
              "102     926682       20.130         28.25          131.20     1261.0   \n",
              "103     926954       16.600         28.08          108.30      858.1   \n",
              "104     927241       20.600         29.33          140.10     1265.0   \n",
              "105      92751        7.760         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.08508           0.05855         0.03367             0.017770   \n",
              "1            0.07466           0.05994         0.04859             0.028700   \n",
              "2            0.08284           0.12230         0.10100             0.028330   \n",
              "3            0.08675           0.10890         0.10850             0.035100   \n",
              "4            0.08311           0.05428         0.01479             0.005769   \n",
              "..               ...               ...             ...                  ...   \n",
              "101          0.11100           0.11590         0.24390             0.138900   \n",
              "102          0.09780           0.10340         0.14400             0.097910   \n",
              "103          0.08455           0.10230         0.09251             0.053020   \n",
              "104          0.11780           0.27700         0.35140             0.152000   \n",
              "105          0.05263           0.04362         0.00000             0.000000   \n",
              "\n",
              "     symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
              "0           0.1516  ...          24.02            82.68       495.1   \n",
              "1           0.1454  ...          23.89            95.10       687.6   \n",
              "2           0.1601  ...          25.50           115.00       733.5   \n",
              "3           0.1562  ...          25.46           100.90       689.1   \n",
              "4           0.1680  ...          24.62            71.11       380.2   \n",
              "..             ...  ...            ...              ...         ...   \n",
              "101         0.1726  ...          26.40           166.10      2027.0   \n",
              "102         0.1752  ...          38.25           155.00      1731.0   \n",
              "103         0.1590  ...          34.12           126.70      1124.0   \n",
              "104         0.2397  ...          39.42           184.60      1821.0   \n",
              "105         0.1587  ...          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.13420            0.18080          0.18600   \n",
              "1             0.12820            0.19650          0.18760   \n",
              "2             0.12010            0.56460          0.65560   \n",
              "3             0.13510            0.35490          0.45040   \n",
              "4             0.13880            0.12550          0.06409   \n",
              "..                ...                ...              ...   \n",
              "101           0.14100            0.21130          0.41070   \n",
              "102           0.11660            0.19220          0.32150   \n",
              "103           0.11390            0.30940          0.34030   \n",
              "104           0.16500            0.86810          0.93870   \n",
              "105           0.08996            0.06444          0.00000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
              "0                 0.08288          0.3210                  0.07863   \n",
              "1                 0.10450          0.2235                  0.06925   \n",
              "2                 0.13570          0.2845                  0.12490   \n",
              "3                 0.11810          0.2563                  0.08174   \n",
              "4                 0.02500          0.3057                  0.07875   \n",
              "..                    ...             ...                      ...   \n",
              "101               0.22160          0.2060                  0.07115   \n",
              "102               0.16280          0.2572                  0.06637   \n",
              "103               0.14180          0.2218                  0.07820   \n",
              "104               0.26500          0.4087                  0.12400   \n",
              "105               0.00000          0.2871                  0.07039   \n",
              "\n",
              "     Diagnosis_Prediction  \n",
              "0                       B  \n",
              "1                       B  \n",
              "2                       B  \n",
              "3                       B  \n",
              "4                       B  \n",
              "..                    ...  \n",
              "101                     M  \n",
              "102                     M  \n",
              "103                     M  \n",
              "104                     M  \n",
              "105                     B  \n",
              "\n",
              "[106 rows x 32 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test['Diagnosis_Prediction'] = diagnosis\n",
        "diagnosis_str = {1 : 'M', 0 : 'B'}\n",
        "df_test['Diagnosis_Prediction'] = df_test['Diagnosis_Prediction'].map(diagnosis_str)\n",
        "df_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lEdmZ2k_mvTP"
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      "outputs": [],
      "source": [
        "df_test.to_csv('testPrediksi.csv')"
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