Trainer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTENKfUFs0IK"
      },
      "source": [
        "# 1) Pengumpulan Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkUw2s12s3Fp"
      },
      "source": [
        "[**Dataset Hungarian**](https://archive.ics.uci.edu/dataset/45/heart+disease) akan digunakan dalam eksperimen ini. Dataset ini berisi **76 atribut**, tetapi eksperimen ini hanya berfokus pada **14 atribut** yaitu *age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, dan target*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OTx_YQ7paYYw"
      },
      "source": [
        "# 2) Menelaah Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "BvBkAdIQXVF1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import itertools\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tpX0RYIAXtww"
      },
      "outputs": [],
      "source": [
        "dir = 'Datasets/hungarian.data'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-KWTzpapBcV",
        "outputId": "e6f14f24-d409-4012-d422-2cecfa4dbab6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['1254 0 40 1 1 0 0',\n",
              " '-9 2 140 0 289 -9 -9 -9',\n",
              " '0 -9 -9 0 12 16 84 0',\n",
              " '0 0 0 0 150 18 -9 7',\n",
              " '172 86 200 110 140 86 0 0',\n",
              " '0 -9 26 20 -9 -9 -9 -9',\n",
              " '-9 -9 -9 -9 -9 -9 -9 12',\n",
              " '20 84 0 -9 -9 -9 -9 -9',\n",
              " '-9 -9 -9 -9 -9 1 1 1',\n",
              " '1 1 -9. -9. name']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "with open(dir, encoding='Latin1') as file:\n",
        "  lines = [line.strip() for line in file]\n",
        "\n",
        "lines[0:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "YOBHnSibpSWD",
        "outputId": "151ca7f1-2fa0-47ce-ac9e-7a78f50802f0"
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              "      <th>7</th>\n",
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              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>66</th>\n",
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              "      <td>name</td>\n",
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              "      <td>-9.</td>\n",
              "      <td>-9.</td>\n",
              "      <td>name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1257</td>\n",
              "      <td>0</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>-9</td>\n",
              "      <td>4</td>\n",
              "      <td>138</td>\n",
              "      <td>...</td>\n",
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              "      <td>-9</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.</td>\n",
              "      <td>-9.</td>\n",
              "      <td>name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1258</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-9</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.</td>\n",
              "      <td>-9.</td>\n",
              "      <td>name</td>\n",
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              "<p>5 rows × 76 columns</p>\n",
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              "     0  1   2  3  4  5  6   7  8    9   ...  66  67 68 69 70 71 72   73   74  \\\n",
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              "\n",
              "     75  \n",
              "0  name  \n",
              "1  name  \n",
              "2  name  \n",
              "3  name  \n",
              "4  name  \n",
              "\n",
              "[5 rows x 76 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = itertools.takewhile(\n",
        "  lambda x: len(x) == 76,\n",
        "  (' '.join(lines[i:(i + 10)]).split() for i in range(0, len(lines), 10))\n",
        ")\n",
        "\n",
        "df = pd.DataFrame.from_records(data)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5D0bBrakrNk5",
        "outputId": "bdf0ae6e-07a3-43f9-900d-2ca21f59dd81"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 76 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   0       294 non-null    object\n",
            " 1   1       294 non-null    object\n",
            " 2   2       294 non-null    object\n",
            " 3   3       294 non-null    object\n",
            " 4   4       294 non-null    object\n",
            " 5   5       294 non-null    object\n",
            " 6   6       294 non-null    object\n",
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            " 10  10      294 non-null    object\n",
            " 11  11      294 non-null    object\n",
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            " 14  14      294 non-null    object\n",
            " 15  15      294 non-null    object\n",
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            " 19  19      294 non-null    object\n",
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            " 64  64      294 non-null    object\n",
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            " 69  69      294 non-null    object\n",
            " 70  70      294 non-null    object\n",
            " 71  71      294 non-null    object\n",
            " 72  72      294 non-null    object\n",
            " 73  73      294 non-null    object\n",
            " 74  74      294 non-null    object\n",
            " 75  75      294 non-null    object\n",
            "dtypes: object(76)\n",
            "memory usage: 174.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "u03LKkmWq9XB"
      },
      "outputs": [],
      "source": [
        "df = df.iloc[:, :-1] # menghapus kolom terakhir (name)\n",
        "df = df.drop(df.columns[0], axis=1) # drop id pasien"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iKY2mhVVsnzr"
      },
      "outputs": [],
      "source": [
        "df = df.astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ROWCUMDtxTp",
        "outputId": "0f1e53c7-aab0-4890-bf52-1532fc4b118a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 74 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   1       294 non-null    float64\n",
            " 1   2       294 non-null    float64\n",
            " 2   3       294 non-null    float64\n",
            " 3   4       294 non-null    float64\n",
            " 4   5       294 non-null    float64\n",
            " 5   6       294 non-null    float64\n",
            " 6   7       294 non-null    float64\n",
            " 7   8       294 non-null    float64\n",
            " 8   9       294 non-null    float64\n",
            " 9   10      294 non-null    float64\n",
            " 10  11      294 non-null    float64\n",
            " 11  12      294 non-null    float64\n",
            " 12  13      294 non-null    float64\n",
            " 13  14      294 non-null    float64\n",
            " 14  15      294 non-null    float64\n",
            " 15  16      294 non-null    float64\n",
            " 16  17      294 non-null    float64\n",
            " 17  18      294 non-null    float64\n",
            " 18  19      294 non-null    float64\n",
            " 19  20      294 non-null    float64\n",
            " 20  21      294 non-null    float64\n",
            " 21  22      294 non-null    float64\n",
            " 22  23      294 non-null    float64\n",
            " 23  24      294 non-null    float64\n",
            " 24  25      294 non-null    float64\n",
            " 25  26      294 non-null    float64\n",
            " 26  27      294 non-null    float64\n",
            " 27  28      294 non-null    float64\n",
            " 28  29      294 non-null    float64\n",
            " 29  30      294 non-null    float64\n",
            " 30  31      294 non-null    float64\n",
            " 31  32      294 non-null    float64\n",
            " 32  33      294 non-null    float64\n",
            " 33  34      294 non-null    float64\n",
            " 34  35      294 non-null    float64\n",
            " 35  36      294 non-null    float64\n",
            " 36  37      294 non-null    float64\n",
            " 37  38      294 non-null    float64\n",
            " 38  39      294 non-null    float64\n",
            " 39  40      294 non-null    float64\n",
            " 40  41      294 non-null    float64\n",
            " 41  42      294 non-null    float64\n",
            " 42  43      294 non-null    float64\n",
            " 43  44      294 non-null    float64\n",
            " 44  45      294 non-null    float64\n",
            " 45  46      294 non-null    float64\n",
            " 46  47      294 non-null    float64\n",
            " 47  48      294 non-null    float64\n",
            " 48  49      294 non-null    float64\n",
            " 49  50      294 non-null    float64\n",
            " 50  51      294 non-null    float64\n",
            " 51  52      294 non-null    float64\n",
            " 52  53      294 non-null    float64\n",
            " 53  54      294 non-null    float64\n",
            " 54  55      294 non-null    float64\n",
            " 55  56      294 non-null    float64\n",
            " 56  57      294 non-null    float64\n",
            " 57  58      294 non-null    float64\n",
            " 58  59      294 non-null    float64\n",
            " 59  60      294 non-null    float64\n",
            " 60  61      294 non-null    float64\n",
            " 61  62      294 non-null    float64\n",
            " 62  63      294 non-null    float64\n",
            " 63  64      294 non-null    float64\n",
            " 64  65      294 non-null    float64\n",
            " 65  66      294 non-null    float64\n",
            " 66  67      294 non-null    float64\n",
            " 67  68      294 non-null    float64\n",
            " 68  69      294 non-null    float64\n",
            " 69  70      294 non-null    float64\n",
            " 70  71      294 non-null    float64\n",
            " 71  72      294 non-null    float64\n",
            " 72  73      294 non-null    float64\n",
            " 73  74      294 non-null    float64\n",
            "dtypes: float64(74)\n",
            "memory usage: 170.1 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xI-lUT3-t1uh"
      },
      "source": [
        "# 3) Validasi Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "F763wI5Bt_v5",
        "outputId": "eb728cf5-9961-49ab-e400-ed06a825557e"
      },
      "outputs": [
        {
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>-9.0</td>\n",
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              "    <tr>\n",
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              "      <td>-9.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>-9.0</td>\n",
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              "      <td>-9.0</td>\n",
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              "    <tr>\n",
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              "      <td>-9.0</td>\n",
              "      <td>-9.0</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 74 columns</p>\n",
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              "    1     2    3    4    5    6    7    8      9    10  ...   65   66   67  \\\n",
              "0  0.0  40.0  1.0  1.0  0.0  0.0 -9.0  2.0  140.0  0.0  ... -9.0 -9.0 -9.0   \n",
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              "\n",
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              "\n",
              "[5 rows x 74 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "SwQKD4mOtzS4",
        "outputId": "af2f6040-2939-4528-ba49-61b1458fe368"
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      "outputs": [
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              "    1     2    3    4    5    6   7    8      9    10  ...  65   66  67   68  \\\n",
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          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "df.replace(-9, np.nan, inplace=True) # -9 = Null Values\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YspDlGqkuBYo",
        "outputId": "0e96229f-68ce-40d6-f554-3003c7295ec2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1       0\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "5       0\n",
              "     ... \n",
              "70      0\n",
              "71      0\n",
              "72      0\n",
              "73    266\n",
              "74    294\n",
              "Length: 74, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVD3tpj9uFNQ",
        "outputId": "437624b0-1059-405b-f314-cbd15260ee0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 74 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   1       294 non-null    float64\n",
            " 1   2       294 non-null    float64\n",
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            " 15  16      21 non-null     float64\n",
            " 16  17      1 non-null      float64\n",
            " 17  18      293 non-null    float64\n",
            " 18  19      294 non-null    float64\n",
            " 19  20      294 non-null    float64\n",
            " 20  21      294 non-null    float64\n",
            " 21  22      293 non-null    float64\n",
            " 22  23      292 non-null    float64\n",
            " 23  24      293 non-null    float64\n",
            " 24  25      293 non-null    float64\n",
            " 25  26      293 non-null    float64\n",
            " 26  27      285 non-null    float64\n",
            " 27  28      292 non-null    float64\n",
            " 28  29      104 non-null    float64\n",
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            " 31  32      293 non-null    float64\n",
            " 32  33      293 non-null    float64\n",
            " 33  34      293 non-null    float64\n",
            " 34  35      293 non-null    float64\n",
            " 35  36      293 non-null    float64\n",
            " 36  37      293 non-null    float64\n",
            " 37  38      292 non-null    float64\n",
            " 38  39      294 non-null    float64\n",
            " 39  40      104 non-null    float64\n",
            " 40  41      293 non-null    float64\n",
            " 41  42      294 non-null    float64\n",
            " 42  43      4 non-null      float64\n",
            " 43  44      0 non-null      float64\n",
            " 44  45      0 non-null      float64\n",
            " 45  46      0 non-null      float64\n",
            " 46  47      3 non-null      float64\n",
            " 47  48      0 non-null      float64\n",
            " 48  49      2 non-null      float64\n",
            " 49  50      28 non-null     float64\n",
            " 50  51      27 non-null     float64\n",
            " 51  52      17 non-null     float64\n",
            " 52  53      0 non-null      float64\n",
            " 53  54      294 non-null    float64\n",
            " 54  55      294 non-null    float64\n",
            " 55  56      294 non-null    float64\n",
            " 56  57      294 non-null    float64\n",
            " 57  58      19 non-null     float64\n",
            " 58  59      58 non-null     float64\n",
            " 59  60      48 non-null     float64\n",
            " 60  61      18 non-null     float64\n",
            " 61  62      59 non-null     float64\n",
            " 62  63      9 non-null      float64\n",
            " 63  64      23 non-null     float64\n",
            " 64  65      5 non-null      float64\n",
            " 65  66      50 non-null     float64\n",
            " 66  67      25 non-null     float64\n",
            " 67  68      294 non-null    float64\n",
            " 68  69      294 non-null    float64\n",
            " 69  70      294 non-null    float64\n",
            " 70  71      294 non-null    float64\n",
            " 71  72      294 non-null    float64\n",
            " 72  73      28 non-null     float64\n",
            " 73  74      0 non-null      float64\n",
            "dtypes: float64(74)\n",
            "memory usage: 170.1 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P7jn8Cw6udoI"
      },
      "source": [
        "# 4) Menentukan Object Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9Vqgk9zmubov",
        "outputId": "ad7c9b81-78aa-4832-e991-17a67c79d442"
      },
      "outputs": [
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              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>43</th>\n",
              "      <th>50</th>\n",
              "      <th>57</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>289.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     2    3    8      9      11   15   18     31   37   39   40  43  50   57\n",
              "0  40.0  1.0  2.0  140.0  289.0  0.0  0.0  172.0  0.0  0.0  NaN NaN NaN  0.0\n",
              "1  49.0  0.0  3.0  160.0  180.0  0.0  0.0  156.0  0.0  1.0  2.0 NaN NaN  1.0\n",
              "2  37.0  1.0  2.0  130.0  283.0  0.0  1.0   98.0  0.0  0.0  NaN NaN NaN  0.0\n",
              "3  48.0  0.0  4.0  138.0  214.0  0.0  0.0  108.0  1.0  1.5  2.0 NaN NaN  3.0\n",
              "4  54.0  1.0  3.0  150.0    NaN  0.0  0.0  122.0  0.0  0.0  NaN NaN NaN  0.0"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_selected = df.iloc[:, [1, 2, 7, 8, 10, 14, 17, 30, 36, 38, 39, 42, 49, 56]] # feature selection\n",
        "df_selected.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UyZJX8F2ujYf",
        "outputId": "9ccacc77-b561-44c6-f2e8-fa32a7044420"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\biman\\AppData\\Local\\Temp\\ipykernel_13920\\2533855582.py:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_selected.rename(columns=column_mapping, inplace=True)\n"
          ]
        }
      ],
      "source": [
        "column_mapping = {\n",
        "  2: 'age',\n",
        "  3: 'sex',\n",
        "  8: 'cp',\n",
        "  9: 'trestbps',\n",
        "  11: 'chol',\n",
        "  15: 'fbs',\n",
        "  18: 'restecg',\n",
        "  31: 'thalach',\n",
        "  37: 'exang',\n",
        "  39: 'oldpeak',\n",
        "  40: 'slope',\n",
        "  43: 'ca',\n",
        "  50: 'thal',\n",
        "  57: 'target'\n",
        "}\n",
        "\n",
        "df_selected.rename(columns=column_mapping, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzfzA0hlun6_",
        "outputId": "e7c1c422-4a32-4e97-9ed3-087ae56bc32b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       294 non-null    float64\n",
            " 1   sex       294 non-null    float64\n",
            " 2   cp        294 non-null    float64\n",
            " 3   trestbps  293 non-null    float64\n",
            " 4   chol      271 non-null    float64\n",
            " 5   fbs       286 non-null    float64\n",
            " 6   restecg   293 non-null    float64\n",
            " 7   thalach   293 non-null    float64\n",
            " 8   exang     293 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  slope     104 non-null    float64\n",
            " 11  ca        4 non-null      float64\n",
            " 12  thal      28 non-null     float64\n",
            " 13  target    294 non-null    float64\n",
            "dtypes: float64(14)\n",
            "memory usage: 32.3 KB\n"
          ]
        }
      ],
      "source": [
        "df_selected.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q75k0PxHu-2H",
        "outputId": "5f3e0d0c-c994-4817-8e45-647c32505a7d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age   sex  cp   trestbps  chol   fbs  restecg  thalach  exang  oldpeak  slope  ca   thal  target\n",
              "47.0  1.0  4.0  150.0     226.0  0.0  0.0      98.0     1.0    1.5      2.0    0.0  7.0   1.0       1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_selected.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kzi3MrbIvH1w"
      },
      "source": [
        "# 5) Membersihkan Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At1bCpYs-TAl"
      },
      "source": [
        "## Remove Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Tq21dTvBJn",
        "outputId": "bc40ac65-ffc3-4a7f-e3a8-817dee98c6f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age           0\n",
              "sex           0\n",
              "cp            0\n",
              "trestbps      1\n",
              "chol         23\n",
              "fbs           8\n",
              "restecg       1\n",
              "thalach       1\n",
              "exang         1\n",
              "oldpeak       0\n",
              "slope       190\n",
              "ca          290\n",
              "thal        266\n",
              "target        0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_selected.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "VjQsU8shvMH_"
      },
      "outputs": [],
      "source": [
        "columns_to_drop = ['ca', 'slope','thal'] # drop kolom yg null values > 50%\n",
        "df_selected = df_selected.drop(columns_to_drop, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNBa_qixvVGn",
        "outputId": "cf27fc92-88b7-4759-cce1-0247995c4cb3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age          0\n",
              "sex          0\n",
              "cp           0\n",
              "trestbps     1\n",
              "chol        23\n",
              "fbs          8\n",
              "restecg      1\n",
              "thalach      1\n",
              "exang        1\n",
              "oldpeak      0\n",
              "target       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_selected.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "lDykCN22vV4w"
      },
      "outputs": [],
      "source": [
        "meanTBPS = df_selected['trestbps'].dropna()\n",
        "meanChol = df_selected['chol'].dropna()\n",
        "meanfbs = df_selected['fbs'].dropna()\n",
        "meanRestCG = df_selected['restecg'].dropna()\n",
        "meanthalach = df_selected['thalach'].dropna()\n",
        "meanexang = df_selected['exang'].dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "_xaudsAXvkqn"
      },
      "outputs": [],
      "source": [
        "meanTBPS = meanTBPS.astype(float)\n",
        "meanChol = meanChol.astype(float)\n",
        "meanfbs = meanfbs.astype(float)\n",
        "meanthalach = meanthalach.astype(float)\n",
        "meanexang = meanexang.astype(float)\n",
        "meanRestCG = meanRestCG.astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "vFU7ucVQvlye"
      },
      "outputs": [],
      "source": [
        "meanTBPS = round(meanTBPS.mean())\n",
        "meanChol = round(meanChol.mean())\n",
        "meanfbs = round(meanfbs.mean())\n",
        "meanthalach = round(meanthalach.mean())\n",
        "meanexang = round(meanexang.mean())\n",
        "meanRestCG = round(meanRestCG.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "kRJHc5Fbvm84"
      },
      "outputs": [],
      "source": [
        "fill_values = {\n",
        "  'trestbps': meanTBPS,\n",
        "  'chol': meanChol,\n",
        "  'fbs': meanfbs,\n",
        "  'thalach':meanthalach,\n",
        "  'exang':meanexang,\n",
        "  'restecg':meanRestCG\n",
        "}\n",
        "\n",
        "df_clean = df_selected.fillna(value=fill_values) # fill missing values dengan rata-rata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8Qui9TFvuQP",
        "outputId": "3ee03049-9264-4ef2-e923-2e0aaf3d50ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 294 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       294 non-null    float64\n",
            " 1   sex       294 non-null    float64\n",
            " 2   cp        294 non-null    float64\n",
            " 3   trestbps  294 non-null    float64\n",
            " 4   chol      294 non-null    float64\n",
            " 5   fbs       294 non-null    float64\n",
            " 6   restecg   294 non-null    float64\n",
            " 7   thalach   294 non-null    float64\n",
            " 8   exang     294 non-null    float64\n",
            " 9   oldpeak   294 non-null    float64\n",
            " 10  target    294 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 25.4 KB\n"
          ]
        }
      ],
      "source": [
        "df_clean.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rd3klkFvvlf",
        "outputId": "732c0a59-050c-4ef9-d18c-e1de24a766c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcoQSerq-Wn_"
      },
      "source": [
        "## Remove Duplicated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "cATTlsh1vzte",
        "outputId": "c6684e6f-92ab-4cb3-a781-1bd1f848eb2d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "163  49.0  0.0  2.0     110.0  251.0  0.0      0.0    160.0    0.0      0.0   \n",
              "\n",
              "     target  \n",
              "163     0.0  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicate_rows = df_clean.duplicated()\n",
        "df_clean[duplicate_rows]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "QM6eB0zlw70f",
        "outputId": "53e5a0b9-5342-458d-cda1-6b3223764b0f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "90   49.0  0.0  2.0     110.0  251.0  0.0      0.0    160.0    0.0      0.0   \n",
              "163  49.0  0.0  2.0     110.0  251.0  0.0      0.0    160.0    0.0      0.0   \n",
              "\n",
              "     target  \n",
              "90      0.0  \n",
              "163     0.0  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean[df_clean.duplicated(keep=False)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "yhhjHjHJxGKf",
        "outputId": "163c0dae-02ea-450f-c3ff-db3aef9222aa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, target]\n",
              "Index: []"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.drop_duplicates(inplace=True)\n",
        "df_clean[df_clean.duplicated(keep=False)] # cek lagi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lPEa31q6xjo-",
        "outputId": "c790e91f-fc58-4ab6-e8f8-64f347dfdc3b"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>289.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  40.0  1.0  2.0     140.0  289.0  0.0      0.0    172.0    0.0      0.0   \n",
              "1  49.0  0.0  3.0     160.0  180.0  0.0      0.0    156.0    0.0      1.0   \n",
              "2  37.0  1.0  2.0     130.0  283.0  0.0      1.0     98.0    0.0      0.0   \n",
              "3  48.0  0.0  4.0     138.0  214.0  0.0      0.0    108.0    1.0      1.5   \n",
              "4  54.0  1.0  3.0     150.0  251.0  0.0      0.0    122.0    0.0      0.0   \n",
              "\n",
              "   target  \n",
              "0     0.0  \n",
              "1     1.0  \n",
              "2     0.0  \n",
              "3     3.0  \n",
              "4     0.0  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "SlWi-O_BxtKO"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "WLgayJCdyOcO",
        "outputId": "e24c56aa-5368-4093-ff4b-24ab42bba8b5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.014516</td>\n",
              "      <td>0.146616</td>\n",
              "      <td>0.246571</td>\n",
              "      <td>0.087101</td>\n",
              "      <td>0.181130</td>\n",
              "      <td>0.050672</td>\n",
              "      <td>-0.460514</td>\n",
              "      <td>0.239223</td>\n",
              "      <td>0.178172</td>\n",
              "      <td>0.210429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.014516</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.245769</td>\n",
              "      <td>0.082064</td>\n",
              "      <td>0.027695</td>\n",
              "      <td>0.044372</td>\n",
              "      <td>-0.108656</td>\n",
              "      <td>-0.106959</td>\n",
              "      <td>0.154925</td>\n",
              "      <td>0.115959</td>\n",
              "      <td>0.220732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0.146616</td>\n",
              "      <td>0.245769</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.081293</td>\n",
              "      <td>0.134697</td>\n",
              "      <td>0.031930</td>\n",
              "      <td>-0.016372</td>\n",
              "      <td>-0.367819</td>\n",
              "      <td>0.494674</td>\n",
              "      <td>0.351735</td>\n",
              "      <td>0.427536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.246571</td>\n",
              "      <td>0.082064</td>\n",
              "      <td>0.081293</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.080818</td>\n",
              "      <td>0.096222</td>\n",
              "      <td>0.011256</td>\n",
              "      <td>-0.181824</td>\n",
              "      <td>0.211507</td>\n",
              "      <td>0.204000</td>\n",
              "      <td>0.214898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.087101</td>\n",
              "      <td>0.027695</td>\n",
              "      <td>0.134697</td>\n",
              "      <td>0.080818</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.107686</td>\n",
              "      <td>0.048081</td>\n",
              "      <td>-0.122038</td>\n",
              "      <td>0.161055</td>\n",
              "      <td>0.106743</td>\n",
              "      <td>0.256027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.181130</td>\n",
              "      <td>0.044372</td>\n",
              "      <td>0.031930</td>\n",
              "      <td>0.096222</td>\n",
              "      <td>0.107686</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.047988</td>\n",
              "      <td>-0.069722</td>\n",
              "      <td>0.115503</td>\n",
              "      <td>0.063179</td>\n",
              "      <td>0.154319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>0.050672</td>\n",
              "      <td>-0.108656</td>\n",
              "      <td>-0.016372</td>\n",
              "      <td>0.011256</td>\n",
              "      <td>0.048081</td>\n",
              "      <td>0.047988</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006084</td>\n",
              "      <td>0.041290</td>\n",
              "      <td>0.042193</td>\n",
              "      <td>0.042643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.460514</td>\n",
              "      <td>-0.106959</td>\n",
              "      <td>-0.367819</td>\n",
              "      <td>-0.181824</td>\n",
              "      <td>-0.122038</td>\n",
              "      <td>-0.069722</td>\n",
              "      <td>0.006084</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.400508</td>\n",
              "      <td>-0.300458</td>\n",
              "      <td>-0.367525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.239223</td>\n",
              "      <td>0.154925</td>\n",
              "      <td>0.494674</td>\n",
              "      <td>0.211507</td>\n",
              "      <td>0.161055</td>\n",
              "      <td>0.115503</td>\n",
              "      <td>0.041290</td>\n",
              "      <td>-0.400508</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.624965</td>\n",
              "      <td>0.571710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.178172</td>\n",
              "      <td>0.115959</td>\n",
              "      <td>0.351735</td>\n",
              "      <td>0.204000</td>\n",
              "      <td>0.106743</td>\n",
              "      <td>0.063179</td>\n",
              "      <td>0.042193</td>\n",
              "      <td>-0.300458</td>\n",
              "      <td>0.624965</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.580732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.210429</td>\n",
              "      <td>0.220732</td>\n",
              "      <td>0.427536</td>\n",
              "      <td>0.214898</td>\n",
              "      <td>0.256027</td>\n",
              "      <td>0.154319</td>\n",
              "      <td>0.042643</td>\n",
              "      <td>-0.367525</td>\n",
              "      <td>0.571710</td>\n",
              "      <td>0.580732</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age       sex        cp  trestbps      chol       fbs  \\\n",
              "age       1.000000  0.014516  0.146616  0.246571  0.087101  0.181130   \n",
              "sex       0.014516  1.000000  0.245769  0.082064  0.027695  0.044372   \n",
              "cp        0.146616  0.245769  1.000000  0.081293  0.134697  0.031930   \n",
              "trestbps  0.246571  0.082064  0.081293  1.000000  0.080818  0.096222   \n",
              "chol      0.087101  0.027695  0.134697  0.080818  1.000000  0.107686   \n",
              "fbs       0.181130  0.044372  0.031930  0.096222  0.107686  1.000000   \n",
              "restecg   0.050672 -0.108656 -0.016372  0.011256  0.048081  0.047988   \n",
              "thalach  -0.460514 -0.106959 -0.367819 -0.181824 -0.122038 -0.069722   \n",
              "exang     0.239223  0.154925  0.494674  0.211507  0.161055  0.115503   \n",
              "oldpeak   0.178172  0.115959  0.351735  0.204000  0.106743  0.063179   \n",
              "target    0.210429  0.220732  0.427536  0.214898  0.256027  0.154319   \n",
              "\n",
              "           restecg   thalach     exang   oldpeak    target  \n",
              "age       0.050672 -0.460514  0.239223  0.178172  0.210429  \n",
              "sex      -0.108656 -0.106959  0.154925  0.115959  0.220732  \n",
              "cp       -0.016372 -0.367819  0.494674  0.351735  0.427536  \n",
              "trestbps  0.011256 -0.181824  0.211507  0.204000  0.214898  \n",
              "chol      0.048081 -0.122038  0.161055  0.106743  0.256027  \n",
              "fbs       0.047988 -0.069722  0.115503  0.063179  0.154319  \n",
              "restecg   1.000000  0.006084  0.041290  0.042193  0.042643  \n",
              "thalach   0.006084  1.000000 -0.400508 -0.300458 -0.367525  \n",
              "exang     0.041290 -0.400508  1.000000  0.624965  0.571710  \n",
              "oldpeak   0.042193 -0.300458  0.624965  1.000000  0.580732  \n",
              "target    0.042643 -0.367525  0.571710  0.580732  1.000000  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "id": "GbYs-deryRJv",
        "outputId": "4030afcd-1e57-4321-83a1-706d897d181d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cor_mat = df_clean.corr()\n",
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "sns.heatmap(cor_mat, annot=True, linewidths=0.5, fmt='.3f')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Qz9sXoqyyN2"
      },
      "source": [
        "# 6) Konstruksi Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNxXusB8yscw",
        "outputId": "6bc8ad1d-6715-41b5-d859-7e101f746967"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 293 entries, 0 to 293\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       293 non-null    float64\n",
            " 1   sex       293 non-null    float64\n",
            " 2   cp        293 non-null    float64\n",
            " 3   trestbps  293 non-null    float64\n",
            " 4   chol      293 non-null    float64\n",
            " 5   fbs       293 non-null    float64\n",
            " 6   restecg   293 non-null    float64\n",
            " 7   thalach   293 non-null    float64\n",
            " 8   exang     293 non-null    float64\n",
            " 9   oldpeak   293 non-null    float64\n",
            " 10  target    293 non-null    float64\n",
            "dtypes: float64(11)\n",
            "memory usage: 27.5 KB\n"
          ]
        }
      ],
      "source": [
        "df_clean.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zeXI5U9fy6fW",
        "outputId": "3d287bcd-f01f-4ed5-b83b-7953ade695f9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>289.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "0  40.0  1.0  2.0     140.0  289.0  0.0      0.0    172.0    0.0      0.0   \n",
              "1  49.0  0.0  3.0     160.0  180.0  0.0      0.0    156.0    0.0      1.0   \n",
              "2  37.0  1.0  2.0     130.0  283.0  0.0      1.0     98.0    0.0      0.0   \n",
              "3  48.0  0.0  4.0     138.0  214.0  0.0      0.0    108.0    1.0      1.5   \n",
              "4  54.0  1.0  3.0     150.0  251.0  0.0      0.0    122.0    0.0      0.0   \n",
              "\n",
              "   target  \n",
              "0     0.0  \n",
              "1     1.0  \n",
              "2     0.0  \n",
              "3     3.0  \n",
              "4     0.0  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSFfGJa6-ymK",
        "outputId": "bf00a7fd-19cf-4abe-82cb-cbae104b25f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.0    187\n",
              "1.0     37\n",
              "3.0     28\n",
              "2.0     26\n",
              "4.0     15\n",
              "Name: target, dtype: int64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "7EcFR831y8UV"
      },
      "outputs": [],
      "source": [
        "X = df_clean.drop('target', axis=1).values\n",
        "y = df_clean.iloc[:,-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "B-K1nIqhzFF-",
        "outputId": "4de91898-0669-4962-c7ec-5f5a7033bd0f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4]),\n",
              " [Text(0, 0, '0.0'),\n",
              "  Text(1, 0, '1.0'),\n",
              "  Text(2, 0, '3.0'),\n",
              "  Text(3, 0, '2.0'),\n",
              "  Text(4, 0, '4.0')])"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_clean['target'].value_counts().plot(kind='bar', figsize=(10,6))\n",
        "plt.xticks(rotation=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byw9x0pW-1ya"
      },
      "source": [
        "## Imbalance Data Handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "QZkDSimu0Frv"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "rUedLSb_zF32"
      },
      "outputs": [],
      "source": [
        "smote = SMOTE(random_state=42)\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ZZu17DRF0ZDW",
        "outputId": "6645984b-b566-4227-c978-c3496025e0f1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "new_df1 = pd.DataFrame(data=y)\n",
        "plt.subplot(1, 2, 1)\n",
        "new_df1.value_counts().plot(kind='bar',figsize=(10,6))\n",
        "plt.title(\"target before over sampling with SMOTE \")\n",
        "plt.xticks(rotation=0);\n",
        "plt.subplot(1, 2, 2)\n",
        "new_df2 = pd.DataFrame(data=y_resampled)\n",
        "new_df2.value_counts().plot(kind='bar',figsize=(10,6))\n",
        "plt.title(\"target after over sampling with SMOTE\")\n",
        "plt.xticks(rotation=0);\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "JBFVckTs1xH9",
        "outputId": "cdb08d19-5359-49dd-9a7f-f9902873bb4a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "      <td>293.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>47.822526</td>\n",
              "      <td>0.726962</td>\n",
              "      <td>2.986348</td>\n",
              "      <td>132.662116</td>\n",
              "      <td>250.860068</td>\n",
              "      <td>0.068259</td>\n",
              "      <td>0.218430</td>\n",
              "      <td>139.058020</td>\n",
              "      <td>0.303754</td>\n",
              "      <td>0.588055</td>\n",
              "      <td>0.795222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.824875</td>\n",
              "      <td>0.446282</td>\n",
              "      <td>0.965049</td>\n",
              "      <td>17.576793</td>\n",
              "      <td>65.059069</td>\n",
              "      <td>0.252622</td>\n",
              "      <td>0.460868</td>\n",
              "      <td>23.558003</td>\n",
              "      <td>0.460665</td>\n",
              "      <td>0.909554</td>\n",
              "      <td>1.238251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>248.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>277.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>66.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  293.000000  293.000000  293.000000  293.000000  293.000000  293.000000   \n",
              "mean    47.822526    0.726962    2.986348  132.662116  250.860068    0.068259   \n",
              "std      7.824875    0.446282    0.965049   17.576793   65.059069    0.252622   \n",
              "min     28.000000    0.000000    1.000000   92.000000   85.000000    0.000000   \n",
              "25%     42.000000    0.000000    2.000000  120.000000  211.000000    0.000000   \n",
              "50%     49.000000    1.000000    3.000000  130.000000  248.000000    0.000000   \n",
              "75%     54.000000    1.000000    4.000000  140.000000  277.000000    0.000000   \n",
              "max     66.000000    1.000000    4.000000  200.000000  603.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak      target  \n",
              "count  293.000000  293.000000  293.000000  293.000000  293.000000  \n",
              "mean     0.218430  139.058020    0.303754    0.588055    0.795222  \n",
              "std      0.460868   23.558003    0.460665    0.909554    1.238251  \n",
              "min      0.000000   82.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.000000  122.000000    0.000000    0.000000    0.000000  \n",
              "50%      0.000000  140.000000    0.000000    0.000000    0.000000  \n",
              "75%      0.000000  155.000000    1.000000    1.000000    1.000000  \n",
              "max      2.000000  190.000000    1.000000    5.000000    4.000000  "
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_clean.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "MJELdQwrGjAd"
      },
      "outputs": [],
      "source": [
        "df_clean.to_csv('Datasets/dfclean.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixavbPPf-8py"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJo5-kto2mbE",
        "outputId": "81204eba-106a-4b19-a813-4e044d30f222"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "935"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_smote_resampled_normal = scaler.fit_transform(X_resampled)\n",
        "len(X_smote_resampled_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UIRXS_K_21nN",
        "outputId": "075da8d1-6be6-4227-c619-30c2eb446307"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "      <td>935.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.563739</td>\n",
              "      <td>0.842507</td>\n",
              "      <td>0.818224</td>\n",
              "      <td>0.403413</td>\n",
              "      <td>0.341027</td>\n",
              "      <td>0.094277</td>\n",
              "      <td>0.117938</td>\n",
              "      <td>0.453354</td>\n",
              "      <td>0.598398</td>\n",
              "      <td>0.227015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.174873</td>\n",
              "      <td>0.332492</td>\n",
              "      <td>0.274211</td>\n",
              "      <td>0.147493</td>\n",
              "      <td>0.110990</td>\n",
              "      <td>0.252030</td>\n",
              "      <td>0.199527</td>\n",
              "      <td>0.197232</td>\n",
              "      <td>0.450288</td>\n",
              "      <td>0.201293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.473283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.305556</td>\n",
              "      <td>0.267954</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.312720</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.578947</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.387952</td>\n",
              "      <td>0.330240</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.440606</td>\n",
              "      <td>0.962447</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.683363</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.487481</td>\n",
              "      <td>0.393811</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.201473</td>\n",
              "      <td>0.593629</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.386166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                0           1           2           3           4           5  \\\n",
              "count  935.000000  935.000000  935.000000  935.000000  935.000000  935.000000   \n",
              "mean     0.563739    0.842507    0.818224    0.403413    0.341027    0.094277   \n",
              "std      0.174873    0.332492    0.274211    0.147493    0.110990    0.252030   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.473283    1.000000    0.666667    0.305556    0.267954    0.000000   \n",
              "50%      0.578947    1.000000    1.000000    0.387952    0.330240    0.000000   \n",
              "75%      0.683363    1.000000    1.000000    0.487481    0.393811    0.000000   \n",
              "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "                6           7           8           9  \n",
              "count  935.000000  935.000000  935.000000  935.000000  \n",
              "mean     0.117938    0.453354    0.598398    0.227015  \n",
              "std      0.199527    0.197232    0.450288    0.201293  \n",
              "min      0.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.312720    0.000000    0.000000  \n",
              "50%      0.000000    0.440606    0.962447    0.200000  \n",
              "75%      0.201473    0.593629    1.000000    0.386166  \n",
              "max      1.000000    1.000000    1.000000    1.000000  "
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dfcek1 = pd.DataFrame(X_smote_resampled_normal)\n",
        "dfcek1.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTj2n7b-_ALy"
      },
      "source": [
        "## Dataset Splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "hWUkj1aP26aV"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "# membagi fitur dan target menjadi data train dan test (untuk yang oversample saja)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42,stratify=y_resampled)\n",
        "\n",
        "# membagi fitur dan target menjadi data train dan test (untuk yang oversample + normalization)\n",
        "X_train_normal, X_test_normal, y_train_normal, y_test_normal = train_test_split(X_smote_resampled_normal, y_resampled, test_size=0.2, random_state=42,stratify = y_resampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjlHM6_P3m71"
      },
      "source": [
        "# 7) Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "VZHrbsO33fYl"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score,recall_score,f1_score,precision_score,confusion_matrix,precision_score\n",
        "\n",
        "def evaluation(Y_test,Y_pred):\n",
        "    acc = accuracy_score(Y_test,Y_pred)\n",
        "    rcl = recall_score(Y_test,Y_pred,average = 'weighted')\n",
        "    f1 = f1_score(Y_test,Y_pred,average = 'weighted')\n",
        "    ps = precision_score(Y_test,Y_pred,average = 'weighted')\n",
        "    metric_dict={\n",
        "        'accuracy': round(acc,3),\n",
        "        'recall': round(rcl,3),\n",
        "        'F1 score': round(f1,3),\n",
        "        'Precision score': round(ps,3)\n",
        "    }\n",
        "    return print(metric_dict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qCSnBu439Ym"
      },
      "source": [
        "## Oversample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "Y2P9jz0W4LMm"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyY79-1X3_L8"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5M--ARI_0mm"
      },
      "source": [
        "Nilai neighbors = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "ZCB8iG7c3z_F",
        "outputId": "ea45bb62-7989-4bf9-9ba0-8b957d2c8cd4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 0 ns\n",
            "Wall time: 2.99 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#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>KNeighborsClassifier(n_neighbors=3)</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "knn_model = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(knn_model, open('Model/knn_oversample.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZc_KEwZ4a8s",
        "outputId": "b3986b1f-d568-4962-9f7e-d35c0933c159"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "K-Nearest Neighbors (KNN) Model:\n",
            "Accuracy: 0.754\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.65      0.39      0.49        38\n",
            "         1.0       0.73      0.81      0.77        37\n",
            "         2.0       0.80      0.86      0.83        37\n",
            "         3.0       0.77      0.87      0.81        38\n",
            "         4.0       0.78      0.84      0.81        37\n",
            "\n",
            "    accuracy                           0.75       187\n",
            "   macro avg       0.75      0.76      0.74       187\n",
            "weighted avg       0.74      0.75      0.74       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_knn = knn_model.predict(X_test)\n",
        "\n",
        "# Evaluate the KNN model\n",
        "print(\"K-Nearest Neighbors (KNN) Model:\")\n",
        "accuracy_knn_smote = round(accuracy_score(y_test,y_pred_knn),3)\n",
        "print(\"Accuracy:\", accuracy_knn_smote)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_knn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bO3xo1I4hOM",
        "outputId": "7bb565c3-fac9-4334-8350-3b3dfe13fde6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.754, 'recall': 0.754, 'F1 score': 0.741, 'Precision score': 0.745}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test,y_pred_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "KI3ucPYr4qHE",
        "outputId": "2cb4e47f-cc11-431d-a6b4-da0013db7d49"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred_knn)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-4bu2kP48Ll"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifkFOLfK_yRV"
      },
      "source": [
        "n_estimators = 100, n_estimators berguna mengatur jumlah pohon keputusan yang akan dibangun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "T-eFo_6644SM",
        "outputId": "327b44d0-92d5-485c-bd74-ee7c565b2be5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 219 ms\n",
            "Wall time: 488 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(rf_model, open('Model/rf_oversample.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCsOjyxR5Hj8",
        "outputId": "13785e08-c149-470b-c13c-45e0cf638795"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Random Forest Model:\n",
            "Accuracy: 0.92\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.89      0.92        38\n",
            "         1.0       0.85      0.92      0.88        37\n",
            "         2.0       0.89      0.89      0.89        37\n",
            "         3.0       0.95      0.97      0.96        38\n",
            "         4.0       0.97      0.92      0.94        37\n",
            "\n",
            "    accuracy                           0.92       187\n",
            "   macro avg       0.92      0.92      0.92       187\n",
            "weighted avg       0.92      0.92      0.92       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_rf = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "print(\"\\nRandom Forest Model:\")\n",
        "accuracy_rf_smote = round(accuracy_score(y_test, y_pred_rf),3)\n",
        "print(\"Accuracy:\",accuracy_rf_smote)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_rf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beazhnyA5MSs",
        "outputId": "2678fc04-3260-4724-90aa-958961e01d6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.92, 'recall': 0.92, 'F1 score': 0.92, 'Precision score': 0.922}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test,y_pred_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "kKSCSfDj5MnE",
        "outputId": "dbd68d49-a050-4770-a5ae-09662ae43933"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred_rf)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCfHp2bd5Sif"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sbtd3ULb5XLc"
      },
      "source": [
        "Learning rate yaitu 0.1. learning rate berguna untuk mengontrol seberapa besar kita menyesuaikan bobot model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "AxLmNJ4X5Psk",
        "outputId": "78c38a7d-303a-428c-8c09-2e45f4246006"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 875 ms\n",
            "Wall time: 1.34 s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective='multi:softprob', ...)"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "xgb_model = XGBClassifier(learning_rate=0.1, n_estimators=100, random_state=42)\n",
        "xgb_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(xgb_model, open('Model/xgb_oversample.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEzSSxHp5cI0",
        "outputId": "c1fc8553-b91d-4a1a-bd99-ff5919240e28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "XGBoost Model:\n",
            "Accuracy: 0.904\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.92      0.89      0.91        38\n",
            "         1.0       0.94      0.84      0.89        37\n",
            "         2.0       0.85      0.89      0.87        37\n",
            "         3.0       0.88      0.97      0.93        38\n",
            "         4.0       0.94      0.92      0.93        37\n",
            "\n",
            "    accuracy                           0.90       187\n",
            "   macro avg       0.91      0.90      0.90       187\n",
            "weighted avg       0.91      0.90      0.90       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_xgb = xgb_model.predict(X_test)\n",
        "\n",
        "# Evaluate the XGBoost model\n",
        "print(\"\\nXGBoost Model:\")\n",
        "accuracy_xgb_smote = round(accuracy_score(y_test, y_pred_xgb),3)\n",
        "print(\"Accuracy:\",accuracy_xgb_smote)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDr0ncqY5fRr",
        "outputId": "d14be246-7f03-4b20-9b28-1e570b2d19bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.904, 'recall': 0.904, 'F1 score': 0.904, 'Precision score': 0.906}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test,y_pred_xgb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "C8O6HRKw5gnr",
        "outputId": "97de04c8-acf8-4f0c-a01a-93bbd078639d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred_xgb)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7i8l-Lnd6yjz"
      },
      "source": [
        "## Oversample + Normalisasi\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucQ1_a_l6_0D"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "GgenE1tG5h17",
        "outputId": "e84f6631-6f76-4851-b1fd-b4d28111a9b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 0 ns\n",
            "Wall time: 0 ns\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
        "knn_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(knn_model, open('Model/knn_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slpJR0sS64GN",
        "outputId": "f517a1b8-72ee-4bd6-a3e0-485f09215976"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "K-Nearest Neighbors (KNN) Model:\n",
            "Accuracy: 0.861\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.88      0.76      0.82        38\n",
            "         1.0       0.78      0.84      0.81        37\n",
            "         2.0       0.87      0.92      0.89        37\n",
            "         3.0       0.92      0.87      0.89        38\n",
            "         4.0       0.87      0.92      0.89        37\n",
            "\n",
            "    accuracy                           0.86       187\n",
            "   macro avg       0.86      0.86      0.86       187\n",
            "weighted avg       0.86      0.86      0.86       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_knn = knn_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the KNN model\n",
        "print(\"K-Nearest Neighbors (KNN) Model:\")\n",
        "accuracy_knn_smote_normal = round(accuracy_score(y_test_normal,y_pred_knn),3)\n",
        "print(\"Accuracy:\", accuracy_knn_smote_normal)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_knn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8-Rb8AV66JD",
        "outputId": "668e517c-9c5c-4cc7-8f33-e25a251c9f40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.861, 'recall': 0.861, 'F1 score': 0.861, 'Precision score': 0.863}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "1bbSnuIN68Fj",
        "outputId": "a5528ab7-b535-4080-f767-6faf00946c83"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_knn)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1wEDK6w7BBD"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "nrRkA2TX6-AT",
        "outputId": "0f28dcef-d697-4aaf-a0e2-ad3b5576d41c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 188 ms\n",
            "Wall time: 422 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(rf_model, open('Model/rf_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIK59Nyh7Ku7",
        "outputId": "e313358d-15d0-40a9-d2b7-9aa6f8b19bc1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Random Forest Model:\n",
            "Accuracy: 0.92\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.89      0.92        38\n",
            "         1.0       0.85      0.92      0.88        37\n",
            "         2.0       0.89      0.89      0.89        37\n",
            "         3.0       0.95      0.97      0.96        38\n",
            "         4.0       0.97      0.92      0.94        37\n",
            "\n",
            "    accuracy                           0.92       187\n",
            "   macro avg       0.92      0.92      0.92       187\n",
            "weighted avg       0.92      0.92      0.92       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_rf = rf_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "print(\"\\nRandom Forest Model:\")\n",
        "accuracy_rf_smote_normal = round(accuracy_score(y_test_normal, y_pred_rf),3)\n",
        "print(\"Accuracy:\",accuracy_rf_smote_normal )\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_rf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAYlQnaO7MYT",
        "outputId": "e3015514-0bb3-47c3-b85f-9d02ef160ca0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.92, 'recall': 0.92, 'F1 score': 0.92, 'Precision score': 0.922}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "b67z9iI67Nlj",
        "outputId": "3b596442-d948-4e73-bd12-5d554539a233"
      },
      "outputs": [
        {
          "data": {
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r07ENGzaUpGw3gAwBAwAAeLGMjIxrzhnctm2bJCk8PDxb5yQBBAAA8JJPAcfExKhDhw4qV66czp07p4ULF2rt2rVasWKFDh06pIULF6pjx44qXry4duzYoWHDhqlZs2aKiIjI1nVoAAEAALzku4BPnDihvn376vjx4woJCVFERIRWrFihNm3a6Ndff9WqVas0ZcoUpaSkqGzZsurRo4deeOGFbF+HBhAAAMBLvPfee9e8r2zZslq3bl2uXIcGEAAAwEuGgPOKWc8WAAAAJIAAAADeMgcwr5AAAgAAGIYEEAAAgDmAAAAAyM9IAAEAAAybA0gDCAAAwBAwAAAA8jMaQAsNvLeufpjZT4lLn1Li0qe0dkoftW1Q8arHLnu5h1JXDlfnxlXyuEpzzX3/bT3ycE+1uru+Ora6W89HD9YvPx+xuiyjLVq4QB3atFSD22urT+8HtHPHDqtLMtKPWzZr2OAn1L5VM9WPqK61a1ZZXZKxeC1ykc3muc0L0QBa6Oipcxr53jo1jvpATQbP09ptv2jJ6G6qXr6423FDuteT02lRkQbbGrdFPXo+qHfmfqipM97V5cuXNfTJR5WaesHq0oy0/KsvNfG1WD3+ZJQWLVmqqlWr6YnHBygpKcnq0oyTmpqqW6tW1fMjRlpdivF4LZBTzAG00JcbD7ndHj3nWw28t67urF5ae3/5449aRKWSerpHAzUZPE8/L37SijKNNeWtt91uvzDmFXVsdbf27dmj2+vVt6gqc82bO1vd7++prt16SJJeGDVG69ev1bJPPtaAgY9ZXJ1ZmjRtpiZNm1ldBsRrkasMmwNoaQN46tQpvf/++9qwYYMSEhIkSWFhYWrcuLH69eunEiVKWFlenvLxsalHs6oKDPDTpj3HJEkF7QU0J6aThk5bpcTTKRZXiPPnzkmSgkNCLK7EPGmXLmnvnt0aMPBx1z4fHx/ddVdj7di+1cLKAODmZFkDuHnzZrVr106FChVS69atddttt0mSEhMT9cYbb2j8+PFasWKF6te/ftLicDjkcDjc9jkzLsvmc3OEmzUrhGrt1D4K8C+g86mX1GvMMu2L/yP9e21QS23cc0yfbzhocZXIyMjQlInjFVH3DlWucqvV5Rjn9JnTSk9PV/Hi7tMjihcvriNHDltUFYB8xUvn6nmKZV3SkCFD9MADD2jmzJmy/e2X7nQ6NWjQIA0ZMkQbNmy47nliY2M1ZswYt32+lVrLr3LbXK/ZE3767Xc1fGKuQgLt6tb0Nr0zvKPaPrtIlUsXUYu65XTXE3OtLhGSJo4fp8OHDmjW+/OtLgUAgH/MsgZw+/btmjNnTqbmT5JsNpuGDRum22+//YbniYmJUXR0tNu+kt3fyrU6PS3tcoYOHzsjSdp6IFH1bgtXVLd6uuhIU6XwIkpY+pTb8R+O7KLvdv2mdsMXW1CtmSaOf0nf/W+dZrz7gUqWCrO6HCMVLVJUvr6+mT7wkZSUpNDQUIuqApCvMAcwb4SFhemHH35QtWrVrnr/Dz/8oFKlSt3wPHa7XXa73W3fzTL8ezU+PpLdz1cvffCdZi/f6XZf3Nv99dysb/TF3z48As9wOp16/dWXte6bVZr+zhyVvqWM1SUZy8/fX9Vr1NSmjRvUslVrSX8My2/atEG9H3zY4uoA5As0gHnj2Wef1WOPPaa4uDi1atXK1ewlJiZq9erVeueddzRx4kSryssTYx9pqhWbj+jXE2cVVNBfvVpWV7OIcuo8YokST6dc9YMfv544q18Ski2o1jwTx4/Tyq++0KuTp6lQoUAlnTopSQosHKSAgACLqzPPvyL7a+SI51WzZi3Vqh2h+fPmKjU1VV27dbe6NONcuJCiX+PjXbePHv1N+/ftVUhIiMLCS1tYmXl4LZBTljWAUVFRCg0N1eTJkzV9+nSlp6dLknx9fVWvXj3NmTNHPXv2tKq8PFGiSCG9N7yjwooFKvmCQ7sOn1LnEUu05sdfrC4Nkj5ZskiSFDUw0m3/C6NfVqf7ullRktHad+io07//runT3tCpUydVtVp1TZ/1roozBJzn9uzerUED/v/7YvKEVyVJ997XVaNfirWqLCPxWuQiwz4EYnM6rV9iOC0tTadOnZIkhYaGys/P7x+dr2DbCblRFnLB0aXRNz4IeaaQ3dfqEnBFWnqG1SUAXifIbt0wbMH7Znjs3Kn/94THzp1TXjFZzs/PT+Hh4VaXAQAATGXYHECzni0AAAC8IwEEAACwlGFzAEkAAQAADEMCCAAAYNgcQBpAAAAAhoABAACQn5EAAgAA49lIAAEAAJCfkQACAADjkQACAAAgXyMBBAAAMCsAJAEEAAAwDQkgAAAwnmlzAGkAAQCA8UxrABkCBgAAMAwJIAAAMB4JIAAAAPI1EkAAAGA8EkAAAADkaySAAAAAZgWAJIAAAACmIQEEAADGYw4gAAAA8jUSQAAAYDzTEkAaQAAAYDzTGkCGgAEAAAxDAggAAIxHAggAAIB8jQQQAADArACQBBAAAMA0NIAAAMB4NpvNY1t2zJgxQxEREQoODlZwcLAaNWqkr776ynX/xYsXFRUVpeLFi6tw4cLq0aOHEhMTs/18aQABAAC8RJkyZTR+/HjFxcVpy5Ytatmypbp06aLdu3dLkoYNG6bPPvtMS5Ys0bp163Ts2DF1794929dhDiAAADCet3wKuHPnzm63X375Zc2YMUMbN25UmTJl9N5772nhwoVq2bKlJGn27NmqXr26Nm7cqLvuuivL16EBBAAAxvNkA+hwOORwONz22e122e326z4uPT1dS5YsUUpKiho1aqS4uDilpaWpdevWrmOqVaumcuXKacOGDdlqABkCBgAA8KDY2FiFhIS4bbGxsdc8fufOnSpcuLDsdrsGDRqkpUuXqkaNGkpISJC/v7+KFCnidnypUqWUkJCQrZpIAAEAADw4AhwTE6Po6Gi3fddL/6pWrapt27YpOTlZH330kSIjI7Vu3bpcrYkGEAAAwIOyMtz7V/7+/qpSpYokqV69etq8ebOmTp2qXr166dKlSzpz5oxbCpiYmKiwsLBs1cQQMAAAMJ63LANzNRkZGXI4HKpXr578/Py0evVq13379+9XfHy8GjVqlK1zkgACAAB4iZiYGHXo0EHlypXTuXPntHDhQq1du1YrVqxQSEiIBgwYoOjoaBUrVkzBwcEaMmSIGjVqlK0PgEj5tAE8/eVwq0vAFUU7TrC6BPzF0aXRNz4IecKvgHcsOQHJz5fBMHjPMjAnTpxQ3759dfz4cYWEhCgiIkIrVqxQmzZtJEmTJ0+Wj4+PevToIYfDoXbt2mn69OnZvo7N6XQ6c7t4q128bHUF+BMNoHehAfQeNIDegwbQewRYGEuFDfzIY+dOeOd+j507p/JlAggAAJAd3pIA5hUaQAAAYDzTGkBybwAAAMOQAAIAAJgVAJIAAgAAmIYEEAAAGI85gAAAAMjXSAABAIDxSAABAACQr5EAAgAA45mWANIAAgAAmNX/MQQMAABgGhJAAABgPNOGgEkAAQAADEMCCAAAjEcCCAAAgHyNBBAAABiPBBAAAAD5GgkgAAAwnmkJIA0gAACAWf0fQ8AAAACmIQEEAADGM20ImAQQAADAMCSAAADAeCSAAAAAyNdIAAEAgPEMCwBJAAEAAExDAggAAIxn2hxAGkAAAGA8w/o/hoABAABMQwIIAACMZ9oQMAkgAACAYUgAAQCA8QwLAEkAAQAATEMCCAAAjOfjY1YESAIIAABgGBJAAABgPNPmANIAAgAA47EMDAAAAPI1GkAvtGjhAnVo01INbq+tPr0f0M4dO6wuKd8beG9d/TCznxKXPqXEpU9p7ZQ+atug4lWPXfZyD6WuHK7OjavkcZXmmvv+23rk4Z5qdXd9dWx1t56PHqxffj5idVnG+nHLZg0b/ITat2qm+hHVtXbNKqtLMhp/M3KHzea5zRvRAHqZ5V99qYmvxerxJ6O0aMlSVa1aTU88PkBJSUlWl5avHT11TiPfW6fGUR+oyeB5WrvtFy0Z3U3Vyxd3O25I93pyOi0q0mBb47aoR88H9c7cDzV1xru6fPmyhj75qFJTL1hdmpFSU1N1a9Wqen7ESKtLMR5/M5BTNIBeZt7c2ep+f0917dZDlatU0QujxiggIEDLPvnY6tLytS83HtKKzUd06NgZHTx6WqPnfKvzqZd0Z/XSrmMiKpXU0z0aaNDryy2s1ExT3npbne7rpkqVb9Wtt1XTC2NeUULCce3bs8fq0ozUpGkzPTlkqO5p1cbqUozH34zcY7PZPLZ5IxpAL5J26ZL27tmtuxo1du3z8fHRXXc11o7tWy2szCw+PjY90KKaAgP8tGnPMUlSQXsBzYnppKHTVinxdIrFFeL8uXOSpOCQEIsrAazD3wz8E179KeBff/1Vo0aN0vvvv3/NYxwOhxwOh9s+p69ddrvd0+XlutNnTis9PV3Fi7sPOxYvXlxHjhy2qCpz1KwQqrVT+yjAv4DOp15SrzHLtC/+j2GU1wa11MY9x/T5hoMWV4mMjAxNmTheEXXvUOUqt1pdDmAZ/mbkLm9N6jzFqxPA33//XXPnzr3uMbGxsQoJCXHbJrwam0cVIj/56bff1fCJuWr21Hy98/k2vTO8o6qVK65Od1VWi7rlNHzGGqtLhKSJ48fp8KEDGhc70epSAOCmZWkC+H//93/Xvf/w4Rv/CyYmJkbR0dFu+5y+N1/6J0lFixSVr69vpsm7SUlJCg0Ntagqc6RdztDhY2ckSVsPJKrebeGK6lZPFx1pqhReRAlLn3I7/sORXfTdrt/UbvhiC6o108TxL+m7/63TjHc/UMlSYVaXA1iKvxm5y7AA0NoGsGvXrrLZbHJe52OVN4pk7fbMw70XL+dKeXnOz99f1WvU1KaNG9SyVWtJfwx3bdq0Qb0ffNji6szj4yPZ/Xz10gffafbynW73xb3dX8/N+kZfbDxkUXVmcTqdev3Vl7Xum1Wa/s4clb6ljNUlAZbjb0buMm0I2NIGMDw8XNOnT1eXLl2uev+2bdtUr169PK7KWv+K7K+RI55XzZq1VKt2hObPm6vU1FR17dbd6tLytbGPNNWKzUf064mzCiror14tq6tZRDl1HrFEiadTrvrBj19PnNUvCckWVGueiePHaeVXX+jVydNUqFCgkk6dlCQFFg5SQECAxdWZ58KFFP0aH++6ffTob9q/b69CQkIUFl76Oo9EbuNvBnLK0gawXr16iouLu2YDeKN0MD9q36GjTv/+u6ZPe0OnTp1U1WrVNX3WuypOnO9RJYoU0nvDOyqsWKCSLzi06/ApdR6xRGt+/MXq0iDpkyWLJElRAyPd9r8w+mV1uq+bFSUZbc/u3Ro04P+/FpMnvCpJuve+rhr9EnOw8xJ/M3KPYQGgbE4LO6z//e9/SklJUfv27a96f0pKirZs2aLmzZtn67w36xBwflS04wSrS8BfHF0afeODkCf8Chj218aL+fl69echjRJgYSx1x1jPfdDvxxdbeuzcOWVpAti0adPr3h8YGJjt5g8AACC7TJsDyD97AAAADEMDCAAAjGezeW7LjtjYWDVo0EBBQUEqWbKkunbtqv3797sd06JFi0xfNzdo0KBsXYcGEAAAwEusW7dOUVFR2rhxo77++mulpaWpbdu2SklxX41i4MCBOn78uGt77bXXsnUdr/4qOAAAgLzgLXMAly9f7nZ7zpw5KlmypOLi4tSsWTPX/kKFCiksLOcL4pMAAgAAeJDD4dDZs2fdNofDkaXHJif/sd5ssWLF3PYvWLBAoaGhqlWrlmJiYnThwoVs1UQDCAAAjOfJOYCxsbEKCQlx22Jjb7xmZkZGhoYOHaomTZqoVq1arv0PPfSQ5s+fr2+++UYxMTGaN2+eHn44e9/+whAwAAAwnieHgGNiYhQd7b4O69+/xvZqoqKitGvXLn377bdu+x977DHXz7Vr11Z4eLhatWqlQ4cOqXLlylmqiQYQAADAg+x2e5Yavr8aPHiwPv/8c61fv15lylz/+88bNmwoSTp48CANIAAAQFZ5yWdA5HQ6NWTIEC1dulRr165VxYoVb/iYbdu2SZLCw8OzfB0aQAAAAC8RFRWlhQsX6tNPP1VQUJASEhIkSSEhISpYsKAOHTqkhQsXqmPHjipevLh27NihYcOGqVmzZoqIiMjydWgAAQCA8bxlGZgZM2ZI+mOx57+aPXu2+vXrJ39/f61atUpTpkxRSkqKypYtqx49euiFF17I1nVoAAEAALyE0+m87v1ly5bVunXr/vF1aAABAIDxvCQAzDOsAwgAAGAYEkAAAGA8b5kDmFdoAAEAgPEM6/8YAgYAADANCSAAADCeaUPAJIAAAACGIQEEAADGIwEEAABAvkYCCAAAjGdYAEgCCAAAYBoSQAAAYDzT5gDSAAIAAOMZ1v8xBAwAAGAaEkAAAGA804aASQABAAAMQwIIAACMZ1gASAIIAABgGhJAAABgPB/DIkASQAAAAMOQAAIAAOMZFgDSAAIAALAMDAAAAPI1EkAAAGA8H7MCQBJAAAAA05AAAgAA4zEHEAAAAPkaCSAAADCeYQFg/mwA09IzrC4BVxz5aKjVJeAvbuk9y+oScMXppU9aXQIAg+XLBhAAACA7bDIrAqQBBAAAxmMZGAAAAORrJIAAAMB4LAMDAACAfI0EEAAAGM+wAJAEEAAAwDQkgAAAwHg+hkWAJIAAAACGIQEEAADGMywApAEEAABgGRgAAADkaySAAADAeIYFgCSAAAAApiEBBAAAxmMZGAAAAORrJIAAAMB4ZuV/JIAAAADGIQEEAADGM20dQBpAAABgPB+z+j+GgAEAAExDAggAAIxn2hAwCSAAAICXiI2NVYMGDRQUFKSSJUuqa9eu2r9/v9sxFy9eVFRUlIoXL67ChQurR48eSkxMzNZ1aAABAIDxbDbPbdmxbt06RUVFaePGjfr666+Vlpamtm3bKiUlxXXMsGHD9Nlnn2nJkiVat26djh07pu7du2frOjlqAB955BGdO3cu0/6UlBQ98sgjOTklAACA8ZYvX65+/fqpZs2aqlOnjubMmaP4+HjFxcVJkpKTk/Xee+9p0qRJatmyperVq6fZs2fr+++/18aNG7N8nRw1gHPnzlVqamqm/ampqfrggw9yckoAAADL2Gw2j20Oh0Nnz5512xwOR5bqSk5OliQVK1ZMkhQXF6e0tDS1bt3adUy1atVUrlw5bdiwIcvPN1sN4NmzZ5WcnCyn06lz5865PZHTp0/ryy+/VMmSJbNzSgAAgHwtNjZWISEhbltsbOwNH5eRkaGhQ4eqSZMmqlWrliQpISFB/v7+KlKkiNuxpUqVUkJCQpZrytangIsUKeLqZm+77bZM99tsNo0ZMyY7pwQAALCcJ9cBjImJUXR0tNs+u91+w8dFRUVp165d+vbbb3O9pmw1gN98842cTqdatmypjz/+2BVHSpK/v7/Kly+v0qVL53qRAAAAnuTJZWDsdnuWGr6/Gjx4sD7//HOtX79eZcqUce0PCwvTpUuXdObMGbcUMDExUWFhYVk+f7YawObNm0uSjhw5onLlyhm3Zg4AAIAnOZ1ODRkyREuXLtXatWtVsWJFt/vr1asnPz8/rV69Wj169JAk7d+/X/Hx8WrUqFGWr5OjhaDXrFmjwoUL64EHHnDbv2TJEl24cEGRkZE5OS0AAIAlvCXSioqK0sKFC/Xpp58qKCjINa8vJCREBQsWVEhIiAYMGKDo6GgVK1ZMwcHBGjJkiBo1aqS77rory9fJ0aeAY2NjFRoamml/yZIl9corr+TklAAAAMabMWOGkpOT1aJFC4WHh7u2xYsXu46ZPHmy7r33XvXo0UPNmjVTWFiYPvnkk2xdJ0cJYHx8fKZIUpLKly+v+Pj4nJwSAADAMj5eMq3N6XTe8JiAgAC99dZbeuutt3J8nRwlgCVLltSOHTsy7d++fbuKFy+e42IAAADgeTlKAB988EE99dRTCgoKUrNmzST98dUlTz/9tHr37p2rBQIAAHialwSAeSZHDeC4ceP0888/q1WrVipQ4I9TZGRkqG/fvswBBAAA8HI5agD9/f21ePFijRs3Ttu3b1fBggVVu3ZtlS9fPrfrAwAA8DjTlrbLUQP4p9tuu+2q3wgCAAAA75XlBjA6Olrjxo1TYGBgpq8z+btJkyb948IAAADyimEBYNYbwK1btyotLc3187WYFqHmth+3bNa8Oe9r797dOnXypCZOeVMtWra2uiwjLftokT79eLESjh+TJFWoVEWRAwbpriZNLa4s/xvYoaYGdqil8qWCJEl743/XK4u2aGXcH8tMvRnVXC3rlFF4sUCdv5imjXsT9MLcDfrptzMWVm2WRQsXaO7s93Tq1EndVrWa/j1ipGpHRFhdlpF4LXKHtywDk1ey3AB+8803V/0ZuSs1NVW3Vq2q+7p11/BhT1ldjtFKlAzT44OHqUzZ8nI6nVr+xaf6z7ND9O78j1SxchWry8vXjp46r5FzN+jgsWTZbNLDrappyX866K6h/9Xe+NPaevCkFq39Sb+ePK9iQXb958EG+nxsZ1V7dL4yMm68hhb+meVffamJr8XqhVFjVLt2HS2YN1dPPD5An36+nKXA8hivBXIqR+sAwnOaNG2mJ4cM1T2t2lhdivGaNGuhu5o0U5ly5VW2fAUNfPJpFSxUSHt2bbe6tHzvy82/aEVcvA4dT9bBY8kaPW+Tzl9M051V//ii8/dX7NF3u48r/sQ5bTt0SmPm/6CyJYJUvmSQxZWbYd7c2ep+f0917dZDlatU0QujxiggIEDLPvnY6tKMw2uRe2w2z23eKMsJYPfu3bN80ux+HQng7dLT07V29QpdTE1Vzdp1rS7HKD4+NvVoUlmBAX7atC8h0/2F7AXUt3U1HUlI1m+nzltQoVnSLl3S3j27NWDg4659Pj4+uuuuxtqx/drTg5D7eC3wT2S5AQwJCXH97HQ6tXTpUoWEhKh+/fqSpLi4OJ05cyZbjSLg7Q4d/ElRj/TRpUuXVLBgIb00YaoqVKpsdVlGqFm+mNZO6KEAf1+dT01Tr5e/0r5fT7vuf6xjTb3cr7EKF/TT/t9Oq9PIz5R2OcPCis1w+sxppaenZxpeLF68uI4cOWxRVWbitchdpn2GIcsN4OzZs10/P//88+rZs6dmzpwpX19fSX8kJE8++aSCg4OzVUBqaqri4uJUrFgx1ahRw+2+ixcv6r///a/69u17zcc7HA45HA63fZfkJ7vdnq06gKspV76i3l3wsVLOn9O61Sv1yuj/6I1Zc2gC88BPR8+o4dOLFVLIrm5NKuudYa3UNmaZqwlctPaAVm/9TWHFCmlot7qa/3xbtXxuqRxp6RZXDgDeL0dzAN9//309++yzruZPknx9fRUdHa33338/y+f56aefVL16dTVr1ky1a9dW8+bNdfz4cdf9ycnJ6t+//3XPERsbq5CQELft9dfGZ/9JAVfh5+enMmXLqWr1mnps8DBVubWqPlo03+qyjJB2OUOHj5/V1kMn9eIHG7XzyClF3ff/P9l49sIlHTqerO92H9dD41eoapmi6tKoooUVm6FokaLy9fVVUlKS2/6kpCSFhoZaVJWZeC1yl48HN2+Uo7ouX76sffv2Zdq/b98+ZWRkfQjm+eefV61atXTixAnt379fQUFBatKkieLj47N8jpiYGCUnJ7ttzzz37yw/HsiODGeG0i5dsroMI/nYbLL7+V71Ppv+mGjtf437kXv8/P1VvUZNbdq4wbUvIyNDmzZtUESd2y2szDy8FvgncvRNIP3799eAAQN06NAh3XnnnZKkTZs2afz48TdM7P7q+++/16pVqxQaGqrQ0FB99tlnevLJJ9W0aVN98803CgwMvOE57HZ7puHec46bdx7QhQsp+vUvDfDRo79p/769CgkJUVh4aQsrM8/b0yarYeOmKhkWrgsXUrR6+RfaFrdZE96cZXVp+d7YvndpRdwv+vXkeQUV9FOv5repWe1b1HnUZ6pQKlj3N62i1Vt/1amzqbqleGE9c//tSnWka8WWrP/jETn3r8j+GjniedWsWUu1akdo/ry5Sk1NVdduzAHPa7wWuYc5gFkwceJEhYWF6fXXX3cN2YaHh2v48OF65plnsnye1NRUFSjw/0uw2WyaMWOGBg8erObNm2vhwoU5Ke+mtmf3bg0aEOm6PXnCq5Kke+/rqtEvxVpVlpFOn/5dr4weoaRTJxVYOEiVq9ymCW/OUoOGja0uLd8rEVJQ7w1rpbBigUpOcWjXz0nqPOozrdn2m8KLFVKTmuEafF+Eiha268SZVH27+5juee4TnUxOtbp0I7Tv0FGnf/9d06e9oVOnTqpqteqaPutdFWfYMc/xWuQeH7P6P9mcTuc/WjX17NmzkpTtD39I0p133qkhQ4boX//6V6b7Bg8erAULFujs2bNKT8/epO6bOQHMb1IcTMj3JhX7vGN1Cbji9NInrS4B8DoBOYqlcsfQTzNPbcstU7pU89i5cyrHcxMvX76sVatW6cMPP3TFpseOHdP581lfh6tbt2768MMPr3rftGnT9OCDD+of9qcAAAA35GPz3OaNcpQA/vLLL2rfvr3i4+PlcDj0008/qVKlSnr66aflcDg0c+ZMT9SaZSSA3oME0LuQAHoPEkAgMysTwOj/81wCOOm+fJIAPv3006pfv75Onz6tggULuvZ369ZNq1evzrXiAAAA8oLNZvPY5o1y1Gv/73//0/fffy9/f3+3/RUqVNDRo0dzpTAAAAB4Ro4awIyMjKt+MOO3335TUBBfxg4AAG4u3jpXz1NyNATctm1bTZkyxXXbZrPp/PnzGjVqlDp27JhbtQEAAMADcrwOYPv27VWjRg1dvHhRDz30kA4cOKDQ0NBrfqoXAADAW3npVD2PyVEDWLZsWW3fvl2LFy/W9u3bdf78eQ0YMEB9+vRx+1AIAADAzcDHsA4w2w1gWlqaqlWrps8//1x9+vRRnz59PFEXAAAAPCTbDaCfn58uXrzoiVoAAAAskeNvxrhJ5ej5RkVF6dVXX9Xly5dzux4AAAB4WI7mAG7evFmrV6/WypUrVbt2bQUGBrrd/8knn+RKcQAAAHnBsCmAOWsAixQpoh49euR2LQAAAMgD2WoAMzIyNGHCBP3000+6dOmSWrZsqdGjR/PJXwAAcFMz7VPA2ZoD+PLLL2vEiBEqXLiwbrnlFr3xxhuKioryVG0AAADwgGw1gB988IGmT5+uFStWaNmyZfrss8+0YMECZWRkeKo+AAAAj7PZPLd5o2w1gPHx8W5f9da6dWvZbDYdO3Ys1wsDAADIKz42z23eKFsN4OXLlxUQEOC2z8/PT2lpablaFAAAADwnWx8CcTqd6tevn+x2u2vfxYsXNWjQILelYFgGBgAA3ExM+xBIthrAyMjITPsefvjhXCsGAAAAnpetBnD27NmeqgMAAMAyhgWAxn31HQAAgPFy9E0gAAAA+Ym3flrXU0gAAQAADEMCCAAAjGeTWREgDSAAADAeQ8AAAADI10gAAQCA8UgAAQAAkK+RAAIAAOPZDFsJmgQQAADAMCSAAADAeMwBBAAAQL5GAggAAIxn2BRAGkAAAAAfwzpAhoABAAC8yPr169W5c2eVLl1aNptNy5Ytc7u/X79+stlsblv79u2zdQ0SQAAAYDxv+hBISkqK6tSpo0ceeUTdu3e/6jHt27fX7NmzXbftdnu2rkEDCAAA4EEOh0MOh8Ntn91uv2bT1qFDB3Xo0OG657Tb7QoLC8txTQwBAwAA49lsnttiY2MVEhLitsXGxv6jeteuXauSJUuqatWqeuKJJ5SUlJStx5MAAgAAeFBMTIyio6Pd9mV3yPav2rdvr+7du6tixYo6dOiQRowYoQ4dOmjDhg3y9fXN0jloAAEAgPF85LlJgNcb7s2J3r17u36uXbu2IiIiVLlyZa1du1atWrXK0jnyZQPo58vItrcIzL3/vyMXnF76pNUl4IqiDQZbXQKuOL15mtUlAP9IpUqVFBoaqoMHD5rdAAIAAGTHzbwM4G+//aakpCSFh4dn+TE0gAAAwHjetAzM+fPndfDgQdftI0eOaNu2bSpWrJiKFSumMWPGqEePHgoLC9OhQ4f03HPPqUqVKmrXrl2Wr0EDCAAA4EW2bNmie+65x3X7zw+QREZGasaMGdqxY4fmzp2rM2fOqHTp0mrbtq3GjRuXrXmGNIAAAMB43vRVcC1atJDT6bzm/StWrPjH1+DTEgAAAIYhAQQAAMbzogAwT5AAAgAAGIYEEAAAGM+b5gDmBRJAAAAAw5AAAgAA4xkWANIAAgAAmDYkatrzBQAAMB4JIAAAMJ7NsDFgEkAAAADDkAACAADjmZX/kQACAAAYhwQQAAAYj4WgAQAAkK+RAAIAAOOZlf/RAAIAABj3TSAMAQMAABiGBBAAABiPhaABAACQr5EAAgAA45mWiJn2fAEAAIxHAggAAIzHHEAAAADkaySAAADAeGblfySAAAAAxiEBBAAAxjNtDiANIAAAMJ5pQ6KmPV8AAADjkQACAADjmTYETAIIAABgGBJAAABgPLPyPxJAAAAA45AAAgAA4xk2BZAEEAAAwDQkgAAAwHg+hs0CpAEEAADGYwgYllu0cIE6tGmpBrfXVp/eD2jnjh1Wl2SkH7ds1rDBT6h9q2aqH1Fda9essrok4/HeyHsDH7hbPyyOUeL/JijxfxO0du4zatukhiSpXHgxpW6ddtWte+vbLa7cHLwvkBM0gF5m+VdfauJrsXr8ySgtWrJUVatW0xOPD1BSUpLVpRknNTVVt1atqudHjLS6FIj3hlWOJp7RyDc/VeM+r6lJnwla+8NPWjL5MVWvFKbfEk+rQusYt23sjM91LuWiVny32+rSjcD7IvfYPPg/b0QD6GXmzZ2t7vf3VNduPVS5ShW9MGqMAgICtOyTj60uzThNmjbTk0OG6p5WbawuBeK9YZUv1+/Sim/36FD8SR2MP6HRb32m8xccujOiojIynEpMOue23XdPHX389Y9KSb1kdelG4H2BnKIB9CJply5p757duqtRY9c+Hx8f3XVXY+3YvtXCygBr8d7wDj4+Nj3Qrp4CC/pr044jme6/vXpZ1a1WVnOXbbCgOvPwvshdNpvnNm9k+YdA9u7dq40bN6pRo0aqVq2a9u3bp6lTp8rhcOjhhx9Wy5Ytr/t4h8Mhh8Phts/pa5fdbvdk2R5x+sxppaenq3jx4m77ixcvriNHDltUFWA93hvWqlmltNbOfUYB/gV0PtWhXs+8o32HEzIdF9m1kfYePq6N2zM3h8h9vC/wT1iaAC5fvlx169bVs88+q9tvv13Lly9Xs2bNdPDgQf3yyy9q27at1qxZc91zxMbGKiQkxG2b8GpsHj0DAMj/fvo5UQ17x6pZ34l6Z8m3emfsv1StUpjbMQF2P/XqUJ/0DzctH9k8tnkjSxvAsWPHavjw4UpKStLs2bP10EMPaeDAgfr666+1evVqDR8+XOPHj7/uOWJiYpScnOy2DX8+Jo+eQe4qWqSofH19M03eTUpKUmhoqEVVAdbjvWGttMvpOvzrKW3d+6tefPP/tPOno4p6sIXbMd1a11WhAH8t+PwHa4o0EO8L/BOWNoC7d+9Wv379JEk9e/bUuXPndP/997vu79Onj3bc4OPsdrtdwcHBbtvNOPwrSX7+/qpeo6Y2bfz//4LOyMjQpk0bFFGHJRVgLt4b3sXHZpPd330GUb+ujfXFup06dfq8RVWZh/dF7mIOYB6zXfnN+Pj4KCAgQCEhIa77goKClJycbFVplvhXZH+NHPG8ataspVq1IzR/3lylpqaqa7fuVpdmnAsXUvRrfLzr9tGjv2n/vr0KCQlRWHhpCyszE+8Na4wdcp9WfLdbvx4/raDAAPXqUF/N6t+qzk9Odx1TqWyo7r6jsroOmWFhpWbifZF7vLVR8xRLG8AKFSrowIEDqly5siRpw4YNKleunOv++Ph4hYeHW1WeJdp36KjTv/+u6dPe0KlTJ1W1WnVNn/WuihPn57k9u3dr0IBI1+3JE16VJN17X1eNfol5pnmN94Y1ShQrrPfG9VVYaLCSz1/UrgNH1fnJ6VqzaZ/rmMgujXQ08YxWbdh3nTPBE3hfIKdsTqfTadXFZ86cqbJly6pTp05XvX/EiBE6ceKE3n333Wyd9+Ll3KgOuSEtPcPqEvAXfr6s/OQtijYYbHUJuOL05mlWl4ArAiyMpb7ee8pj525T3fsacksbQE+hAfQeNIDehQbQe9AAeg8aQO9BA5h3LJ8DCAAAYDUfw+YAEgcAAAAYhgQQAAAYz+alCzZ7CgkgAACAF1m/fr06d+6s0qVLy2azadmyZW73O51OvfjiiwoPD1fBggXVunVrHThwIFvXoAEEAADG86aFoFNSUlSnTh299dZbV73/tdde0xtvvKGZM2dq06ZNCgwMVLt27XTx4sUsX4MhYAAAYDxvGgLu0KGDOnTocNX7nE6npkyZohdeeEFdunSRJH3wwQcqVaqUli1bpt69e2fpGiSAAAAAHuRwOHT27Fm3zeFw5OhcR44cUUJCglq3bu3aFxISooYNG2rDhg3XeaQ7GkAAAGA8H5vnttjYWIWEhLhtsbE5+0aphIQESVKpUqXc9pcqVcp1X1YwBAwAAOBBMTExio6Odttnt9stquYPNIAAAMB4npwDaLfbc63hCwsLkyQlJiYqPDzctT8xMVF169bN8nkYAgYAALhJVKxYUWFhYVq9erVr39mzZ7Vp0yY1atQoy+chAQQAAMbLyXItnnL+/HkdPHjQdfvIkSPatm2bihUrpnLlymno0KF66aWXdOutt6pixYoaOXKkSpcura5du2b5GjSAAAAAXmTLli265557XLf/nD8YGRmpOXPm6LnnnlNKSooee+wxnTlzRnfffbeWL1+ugICALF/D5nQ6nbleucUuXra6AvwpLT3D6hLwF36+zPrwFkUbDLa6BFxxevM0q0vAFQEWxlLfHTjtsXM3ubWox86dUySAAADAeD7eNAacB4gDAAAADEMCCAAAjGdW/kcCCAAAYBwSQAAAAMMiQBJAAAAAw5AAAgAA43nyq+C8EQkgAACAYUgAAQCA8QxbBpAGEAAAwLD+jyFgAAAA05AAAgAAGBYBkgACAAAYhgQQAAAYj2VgAAAAkK+RAAIAAOOZtgwMCSAAAIBhSAABAIDxDAsAaQABAABM6wAZAgYAADAMCSAAADAey8AAAAAgXyMBBAAAxmMZGAAAAORrJIAAAMB4hgWA+bMBTEvPsLoEXOHnS8jsTS440q0uAVec3jzN6hJwRdGOE6wuAVekrhxudQnGyJcNIAAAQLYYFgHSAAIAAOOxDAwAAADyNRJAAABgPJaBAQAAQL5GAggAAIxnWABIAggAAGAaEkAAAADDIkASQAAAAMOQAAIAAOOxDiAAAADyNRJAAABgPNPWAaQBBAAAxjOs/2MIGAAAwDQkgAAAAIZFgCSAAAAAhiEBBAAAxmMZGAAAAORrJIAAAMB4pi0DQwIIAABgGBJAAABgPMMCQBpAAAAA0zpAhoABAAAMQwIIAACMxzIwAAAAyNdoAAEAgPFsNs9t2TF69GjZbDa3rVq1arn+fBkCBgAA8CI1a9bUqlWrXLcLFMj9do0GEAAAGM+bZgAWKFBAYWFhHr0GQ8AAAAAe5HA4dPbsWbfN4XBc8/gDBw6odOnSqlSpkvr06aP4+Phcr4kGEAAAwOa5LTY2ViEhIW5bbGzsVcto2LCh5syZo+XLl2vGjBk6cuSImjZtqnPnzuXu03U6nc5cPaMXOOfIsLoEXOHny78xvMkFR7rVJeCKQnZfq0vAFUU7TrC6BFyRunK4Zdc+fPKix859S7AtU+Jnt9tlt9tv+NgzZ86ofPnymjRpkgYMGJBrNTEHEAAAwIOy2uxdTZEiRXTbbbfp4MGDuVoT8QwAADCetywD83fnz5/XoUOHFB4enjtP9AoaQAAAAC/x7LPPat26dfr555/1/fffq1u3bvL19dWDDz6Yq9dhCBgAABjPW5aB+e233/Tggw8qKSlJJUqU0N13362NGzeqRIkSuXodGkAAAAAvsWjRojy5Dg0gAACAt0SAeYQ5gAAAAIYhAQQAAMazGRYB0gACAADj/dPlWm42DAF7mR+3bNawwU+ofatmqh9RXWvXrLK6JOMtWrhAHdq0VIPba6tP7we0c8cOq0syztz339YjD/dUq7vrq2Oru/V89GD98vMRq8syGu+LvDfw3rr6YWY/JS59SolLn9LaKX3UtkHFqx677OUeSl05XJ0bV8njKnGzoAH0Mqmpqbq1alU9P2Kk1aVA0vKvvtTE12L1+JNRWrRkqapWraYnHh+gpKQkq0szyta4LerR80G9M/dDTZ3xri5fvqyhTz6q1NQLVpdmJN4X1jh66pxGvrdOjaM+UJPB87R22y9aMrqbqpcv7nbckO71lP++5NXzPPhVwF6JBtDLNGnaTE8OGap7WrWxuhRImjd3trrf31Ndu/VQ5SpV9MKoMQoICNCyTz62ujSjTHnrbXW6r5sqVb5Vt95WTS+MeUUJCce1b88eq0szEu8La3y58ZBWbD6iQ8fO6ODR0xo951udT72kO6uXdh0TUamknu7RQINeX25hpbgZeF0D6OSfLfASaZcuae+e3bqrUWPXPh8fH911V2Pt2L7Vwspw/tw5SVJwSIjFlZiH94V38PGx6YEW1RQY4KdNe45JkgraC2hOTCcNnbZKiadTLK7w5uOtXwXnKV73IRC73a7t27erevXqVpcCw50+c1rp6ekqXtx9eKV48eI6cuSwRVUhIyNDUyaOV0TdO1S5yq1Wl2Mc3hfWqlkhVGun9lGAfwGdT72kXmOWaV/8H0Pvrw1qqY17junzDQctrhI3A8sawOjo6KvuT09P1/jx413/cZk0adJ1z+NwOORwONz2XZKf7HZ77hQKwKtMHD9Ohw8d0Kz351tdCpDnfvrtdzV8Yq5CAu3q1vQ2vTO8o9o+u0iVSxdRi7rldNcTc60u8SbmpVGdh1jWAE6ZMkV16tRRkSJF3PY7nU7t3btXgYGBsmUhN42NjdWYMWPc9v37Py9qxMhRuVkuDFS0SFH5+vpmmtielJSk0NBQi6oy28TxL+m7/63TjHc/UMlSYVaXYyTeF9ZKu5yhw8fOSJK2HkhUvdvCFdWtni460lQpvIgSlj7ldvyHI7vou12/qd3wxRZUC29mWQP4yiuv6O2339brr7+uli1buvb7+flpzpw5qlGjRpbOExMTkylNvCS/XK0VZvLz91f1GjW1aeMGtWzVWtIfw4+bNm1Q7wcftrg6szidTr3+6sta980qTX9njkrfUsbqkozF+8K7+PhIdj9fvfTBd5q9fKfbfXFv99dzs77RFxsPWVTdzcVb5+p5imUN4L///W+1atVKDz/8sDp37qzY2Fj5+WW/cbPb7ZmGe885MnKrzDx34UKKfo2Pd90+evQ37d+3VyEhIQoLL32dR8IT/hXZXyNHPK+aNWupVu0IzZ83V6mpqerarbvVpRll4vhxWvnVF3p18jQVKhSopFMnJUmBhYMUEBBgcXXm4X1hjbGPNNWKzUf064mzCiror14tq6tZRDl1HrFEiadTrvrBj19PnNUvCckWVHvzMaz/s/ZDIA0aNFBcXJyioqJUv359LViwIEvDvvnZnt27NWhApOv25AmvSpLuva+rRr8Ua1VZxmrfoaNO//67pk97Q6dOnVTVatU1fda7Ks5QV576ZMkiSVLUwEi3/S+Mflmd7utmRUlG431hjRJFCum94R0VVixQyRcc2nX4lDqPWKI1P/5idWm4CdmcXrLuyqJFizR06FCdPHlSO3fuzPIQ8NXczAlgfuPn63UrDRntgiPd6hJwRSG7r9Ul4IqiHSdYXQKuSF053LJrH0++5LFzh4f4e+zcOeU1y8D07t1bd999t+Li4lS+fHmrywEAAMi3vKYBlKQyZcqoTBkmdwMAgLxlM2wWIONzAAAAhvGqBBAAAMASZgWAJIAAAACmIQEEAADGMywApAEEAAAwbRlihoABAAAMQwIIAACMxzIwAAAAyNdIAAEAAMwKAEkAAQAATEMCCAAAjGdYAEgCCAAAYBoSQAAAYDzT1gGkAQQAAMZjGRgAAADkaySAAADAeKYNAZMAAgAAGIYGEAAAwDA0gAAAAIZhDiAAADAecwABAACQr5EAAgAA45m2DiANIAAAMB5DwAAAAMjXSAABAIDxDAsASQABAABMQwIIAABgWARIAggAAGAYEkAAAGA805aBIQEEAAAwDAkgAAAwHusAAgAAIF8jAQQAAMYzLACkAQQAADCtA2QIGAAAwDA0gAAAwHg2D/4vJ9566y1VqFBBAQEBatiwoX744Ydcfb40gAAAAF5k8eLFio6O1qhRo/Tjjz+qTp06ateunU6cOJFr16ABBAAAxrPZPLdl16RJkzRw4ED1799fNWrU0MyZM1WoUCG9//77ufZ8aQABAAA8yOFw6OzZs26bw+G46rGXLl1SXFycWrdu7drn4+Oj1q1ba8OGDblWU778FHCQ/ebvax0Oh2JjYxUTEyO73W51OUbLT69FQAFfq0v4R/LTa3Gzy0+vRerK4VaX8I/lp9fDKgEe7IhGvxSrMWPGuO0bNWqURo8enenYU6dOKT09XaVKlXLbX6pUKe3bty/XarI5nU5nrp0Nuebs2bMKCQlRcnKygoODrS7HaLwW3oPXwnvwWngXXg/v5nA4MiV+drv9qs36sWPHdMstt+j7779Xo0aNXPufe+45rVu3Tps2bcqVmvJlAggAAOAtrtXsXU1oaKh8fX2VmJjotj8xMVFhYWG5VtPNP1YKAACQT/j7+6tevXpavXq1a19GRoZWr17tlgj+UySAAAAAXiQ6OlqRkZGqX7++7rzzTk2ZMkUpKSnq379/rl2DBtBL2e12jRo1ism8XoDXwnvwWngPXgvvwuuRv/Tq1UsnT57Uiy++qISEBNWtW1fLly/P9MGQf4IPgQAAABiGOYAAAACGoQEEAAAwDA0gAACAYWgAAQAADEMD6IXeeustVahQQQEBAWrYsKF++OEHq0sy0vr169W5c2eVLl1aNptNy5Yts7okY8XGxqpBgwYKCgpSyZIl1bVrV+3fv9/qsow0Y8YMRUREKDg4WMHBwWrUqJG++uorq8uCpPHjx8tms2no0KFWl4KbAA2gl1m8eLGio6M1atQo/fjjj6pTp47atWunEydOWF2acVJSUlSnTh299dZbVpdivHXr1ikqKkobN27U119/rbS0NLVt21YpKSlWl2acMmXKaPz48YqLi9OWLVvUsmVLdenSRbt377a6NKNt3rxZs2bNUkREhNWl4CbBMjBepmHDhmrQoIGmTZsm6Y/Vv8uWLashQ4bo3//+t8XVmctms2np0qXq2rWr1aVA0smTJ1WyZEmtW7dOzZo1s7oc4xUrVkwTJkzQgAEDrC7FSOfPn9cdd9yh6dOn66WXXlLdunU1ZcoUq8uClyMB9CKXLl1SXFycWrdu7drn4+Oj1q1ba8OGDRZWBniX5ORkSX80HrBOenq6Fi1apJSUlFz9iipkT1RUlDp16uT2twO4Eb4JxIucOnVK6enpmVb6LlWqlPbt22dRVYB3ycjI0NChQ9WkSRPVqlXL6nKMtHPnTjVq1EgXL15U4cKFtXTpUtWoUcPqsoy0aNEi/fjjj9q8ebPVpeAmQwMI4KYSFRWlXbt26dtvv7W6FGNVrVpV27ZtU3Jysj766CNFRkZq3bp1NIF57Ndff9XTTz+tr7/+WgEBAVaXg5sMDaAXCQ0Nla+vrxITE932JyYmKiwszKKqAO8xePBgff7551q/fr3KlCljdTnG8vf3V5UqVSRJ9erV0+bNmzV16lTNmjXL4srMEhcXpxMnTuiOO+5w7UtPT9f69es1bdo0ORwO+fr6WlghvBlzAL2Iv7+/6tWrp9WrV7v2ZWRkaPXq1cyvgdGcTqcGDx6spUuXas2aNapYsaLVJeEvMjIy5HA4rC7DOK1atdLOnTu1bds211a/fn316dNH27Zto/nDdZEAepno6GhFRkaqfv36uvPOOzVlyhSlpKSof//+VpdmnPPnz+vgwYOu20eOHNG2bdtUrFgxlStXzsLKzBMVFaWFCxfq008/VVBQkBISEiRJISEhKliwoMXVmSUmJkYdOnRQuXLldO7cOS1cuFBr167VihUrrC7NOEFBQZnmwQYGBqp48eLMj8UN0QB6mV69eunkyZN68cUXlZCQoLp162r58uWZPhgCz9uyZYvuuece1+3o6GhJUmRkpObMmWNRVWaaMWOGJKlFixZu+2fPnq1+/frlfUEGO3HihPr27avjx48rJCREERERWrFihdq0aWN1aQCygXUAAQAADMMcQAAAAMPQAAIAABiGBhAAAMAwNIAAAACGoQEEAAAwDA0gAACAYWgAAQAADEMDCAAAYBgaQAAAAMPQAAKwhM1mu+42evRoq0sEgHyL7wIGYInjx4+7fl68eLFefPFF7d+/37WvcOHCrp+dTqfS09NVoAD/yQKA3EACCMASYWFhri0kJEQ2m811e9++fQoKCtJXX32levXqyW6369tvv1W/fv3UtWtXt/MMHTpULVq0cN3OyMhQbGysKlasqIIFC6pOnTr66KOP8vbJAYCX45/TALzWv//9b02cOFGVKlVS0aJFs/SY2NhYzZ8/XzNnztStt96q9evX6+GHH1aJEiXUvHlzD1cMADcHGkAAXmvs2LFq06ZNlo93OBx65ZVXtGrVKjVq1EiSVKlSJX377beaNWsWDSAAXEEDCMBr1a9fP1vHHzx4UBcuXMjUNF66dEm33357bpYGADc1GkAAXiswMNDtto+Pj5xOp9u+tLQ018/nz5+XJH3xxRe65ZZb3I6z2+0eqhIAbj40gABuGiVKlNCuXbvc9m3btk1+fn6SpBo1ashutys+Pp7hXgC4DhpAADeNli1basKECfrggw/UqFEjzZ8/X7t27XIN7wYFBenZZ5/VsGHDlJGRobvvvlvJycn67rvvFBwcrMjISIufAQB4BxpAADeNdu3aaeTIkXruued08eJFPfLII+rbt6927tzpOmbcuHEqUaKEYmNjdfjwYRUpUkR33HGHRowYYWHlAOBdbM6/T6gBAABAvsZC0AAAAIahAQQAADAMDSAAAIBhaAABAAAMQwMIAABgGBpAAAAAw9AAAgAAGIYGEAAAwDA0gAAAAIahAQQAADAMDSAAAIBh/h/h1qoNdlwRrAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_rf)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBug_4MM7TCs"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "SuaBfeHB7PXj",
        "outputId": "44567fc1-4e9d-49b6-aa73-37b426d3ddf2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 859 ms\n",
            "Wall time: 334 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, objective='multi:softprob', ...)"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "xgb_model = XGBClassifier(learning_rate=0.1, n_estimators=100, random_state=42)\n",
        "xgb_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(xgb_model, open('Model/xgb_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kOK5G1g7Vmc",
        "outputId": "d20ad404-c5ac-454a-bcda-4a14fa8d695e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "XGBoost Model:\n",
            "Accuracy: 0.904\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.92      0.89      0.91        38\n",
            "         1.0       0.94      0.84      0.89        37\n",
            "         2.0       0.85      0.89      0.87        37\n",
            "         3.0       0.88      0.97      0.93        38\n",
            "         4.0       0.94      0.92      0.93        37\n",
            "\n",
            "    accuracy                           0.90       187\n",
            "   macro avg       0.91      0.90      0.90       187\n",
            "weighted avg       0.91      0.90      0.90       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_xgb = xgb_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the XGBoost model\n",
        "print(\"\\nXGBoost Model:\")\n",
        "accuracy_xgb_smote_normal = round(accuracy_score(y_test_normal, y_pred_xgb),3)\n",
        "print(\"Accuracy:\",accuracy_xgb_smote_normal)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kyhqtQjM7dD7",
        "outputId": "5bfd2fc9-7e14-4086-e5db-e24bb948a490"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.904, 'recall': 0.904, 'F1 score': 0.904, 'Precision score': 0.906}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_xgb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "z019Qh4C7ed6",
        "outputId": "b88c2414-685f-4f16-d6b8-8d35c41bd8a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_xgb)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J40IVRtf7her"
      },
      "source": [
        "## Tunning + Normalization + Oversample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQaqb4dD7lmb"
      },
      "source": [
        "Setiap parameter tunnning tidak selalu sama karena bergantung pada algoritma yang digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "14rQh4l67r96"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6cDVVBI7ijq"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "1Y6w0cOE7ffi"
      },
      "outputs": [],
      "source": [
        "knn_model = KNeighborsClassifier()\n",
        "param_grid = {\n",
        " \"n_neighbors\": range(3, 21),\n",
        " \"metric\": [\"euclidean\", \"manhattan\", \"chebyshev\"],\n",
        " \"weights\": [\"uniform\", \"distance\"],\n",
        " \"algorithm\": [\"auto\", \"ball_tree\", \"kd_tree\"],\n",
        " \"leaf_size\": range(10, 61),\n",
        "}\n",
        "knn_model = RandomizedSearchCV(estimator=knn_model, param_distributions=param_grid, n_iter=100, scoring=\"accuracy\", cv=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(knn_model, open('Model/knn_tunning_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "XFIlybzx7pyz",
        "outputId": "e1184853-94dd-486b-d47f-617c7250ccd2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 1.58 s\n",
            "Wall time: 4.31 s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
              "                   param_distributions={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;,\n",
              "                                                      &#x27;kd_tree&#x27;],\n",
              "                                        &#x27;leaf_size&#x27;: range(10, 61),\n",
              "                                        &#x27;metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;,\n",
              "                                                   &#x27;chebyshev&#x27;],\n",
              "                                        &#x27;n_neighbors&#x27;: range(3, 21),\n",
              "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "                   scoring=&#x27;accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
              "                   param_distributions={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;,\n",
              "                                                      &#x27;kd_tree&#x27;],\n",
              "                                        &#x27;leaf_size&#x27;: range(10, 61),\n",
              "                                        &#x27;metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;,\n",
              "                                                   &#x27;chebyshev&#x27;],\n",
              "                                        &#x27;n_neighbors&#x27;: range(3, 21),\n",
              "                                        &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "                   scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=100,\n",
              "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
              "                                                      'kd_tree'],\n",
              "                                        'leaf_size': range(10, 61),\n",
              "                                        'metric': ['euclidean', 'manhattan',\n",
              "                                                   'chebyshev'],\n",
              "                                        'n_neighbors': range(3, 21),\n",
              "                                        'weights': ['uniform', 'distance']},\n",
              "                   scoring='accuracy')"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "knn_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcPFCDsr8Cl6",
        "outputId": "2f98d556-62a9-403d-b19f-fab629658247"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'weights': 'distance', 'n_neighbors': 3, 'metric': 'manhattan', 'leaf_size': 59, 'algorithm': 'ball_tree'}\n"
          ]
        }
      ],
      "source": [
        "best_params = knn_model.best_params_\n",
        "print(f\"Best parameters: {best_params}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyOgUdH68HJK",
        "outputId": "c2a617f4-8321-4604-90ce-038ebedfb043"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "K-Nearest Neighbors (KNN) Model:\n",
            "Accuracy: 0.92\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      0.84      0.90        38\n",
            "         1.0       0.80      0.86      0.83        37\n",
            "         2.0       0.92      0.95      0.93        37\n",
            "         3.0       0.97      0.95      0.96        38\n",
            "         4.0       0.95      1.00      0.97        37\n",
            "\n",
            "    accuracy                           0.92       187\n",
            "   macro avg       0.92      0.92      0.92       187\n",
            "weighted avg       0.92      0.92      0.92       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_knn = knn_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the KNN model\n",
        "print(\"K-Nearest Neighbors (KNN) Model:\")\n",
        "accuracy_knn_smote_normal_Tun = round(accuracy_score(y_test_normal,y_pred_knn),3)\n",
        "print(\"Accuracy:\", accuracy_knn_smote_normal_Tun)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_knn))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSshjQc08K56",
        "outputId": "7c0a0d98-1fa2-4e01-c7ef-7f3d4cbc4109"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.92, 'recall': 0.92, 'F1 score': 0.92, 'Precision score': 0.923}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "unkk3zKW8Mpq",
        "outputId": "31724768-8357-4e63-9b49-4eda243a6a5b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_knn)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugo7Js068PGb"
      },
      "source": [
        "### RandomForest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "WFrOXBsD8OE7"
      },
      "outputs": [],
      "source": [
        "rf_model = RandomForestClassifier()\n",
        "param_grid = {\n",
        " \"n_estimators\": [100, 200],\n",
        " \"max_depth\": [ 10, 15],\n",
        " \"min_samples_leaf\": [1, 2],\n",
        " \"min_samples_split\": [2, 5],\n",
        " \"max_features\": [\"sqrt\", \"log2\"],\n",
        " # \"random_state\": [42, 100, 200]\n",
        "}\n",
        "rf_model = RandomizedSearchCV(rf_model, param_grid, n_iter=100, cv=5, n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "IQY5oFw28SGt",
        "outputId": "1ce82d84-e255-4a81-fbc3-1dcd7492f9f8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\biman\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\model_selection\\_search.py:307: UserWarning: The total space of parameters 32 is smaller than n_iter=100. Running 32 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 219 ms\n",
            "Wall time: 29.3 s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=100,\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [10, 15],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 5],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=100,\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [10, 15],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [1, 2],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 5],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=100,\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={'max_depth': [10, 15],\n",
              "                                        'max_features': ['sqrt', 'log2'],\n",
              "                                        'min_samples_leaf': [1, 2],\n",
              "                                        'min_samples_split': [2, 5],\n",
              "                                        'n_estimators': [100, 200]})"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "rf_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fn0kjMF08UZ6",
        "outputId": "172a24dd-b9a5-422a-aa15-d36ea9d4b4bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 15}\n"
          ]
        }
      ],
      "source": [
        "best_params = rf_model.best_params_\n",
        "print(f\"Best parameters: {best_params}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(rf_model, open('Model/rf_tunning_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyvq7ayR9HQE",
        "outputId": "e1931a02-4499-4b8c-b5b6-b28881222fe8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Random Forest Model:\n",
            "Accuracy: 0.925\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.92      0.93        38\n",
            "         1.0       0.89      0.89      0.89        37\n",
            "         2.0       0.89      0.89      0.89        37\n",
            "         3.0       0.93      0.97      0.95        38\n",
            "         4.0       0.97      0.95      0.96        37\n",
            "\n",
            "    accuracy                           0.93       187\n",
            "   macro avg       0.93      0.92      0.92       187\n",
            "weighted avg       0.93      0.93      0.93       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_rf = rf_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the Random Forest model\n",
        "print(\"\\nRandom Forest Model:\")\n",
        "accuracy_rf_smote_normal_Tun = round(accuracy_score(y_test_normal, y_pred_rf),3)\n",
        "print(\"Accuracy:\",accuracy_rf_smote_normal_Tun)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_rf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOsfPqIy9Juq",
        "outputId": "1e7117d4-75b8-4465-c7cb-b8257840debe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.925, 'recall': 0.925, 'F1 score': 0.925, 'Precision score': 0.925}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WJ1Pd9ym9LsS",
        "outputId": "d94f36bc-9b7e-453c-c30e-2d7073206018"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_knn)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uAv_6mTP9NnD"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "pwpnNrYx9Mxa"
      },
      "outputs": [],
      "source": [
        "xgb_model = XGBClassifier()\n",
        "param_grid = {\n",
        " \"max_depth\": [3, 5, 7],\n",
        " \"learning_rate\": [0.01, 0.1],\n",
        " \"n_estimators\": [100, 200],\n",
        " \"gamma\": [0, 0.1],\n",
        " \"colsample_bytree\": [0.7, 0.8],\n",
        "}\n",
        "xgb_model = RandomizedSearchCV(xgb_model, param_grid, n_iter=10, cv=5, n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "PfuzJNBF9QMV",
        "outputId": "bf6d893c-17aa-4132-bfe0-6d9b0d292b0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: total: 1 s\n",
            "Wall time: 8.24 s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.7, 0.8],\n",
              "                                        &#x27;gamma&#x27;: [0, 0.1],\n",
              "                                        &#x27;learning_rate&#x27;: [0.01, 0.1],\n",
              "                                        &#x27;max_depth&#x27;: [3, 5, 7],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={&#x27;colsample_bytree&#x27;: [0.7, 0.8],\n",
              "                                        &#x27;gamma&#x27;: [0, 0.1],\n",
              "                                        &#x27;learning_rate&#x27;: [0.01, 0.1],\n",
              "                                        &#x27;max_depth&#x27;: [3, 5, 7],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 200]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           callbacks=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, device=None,\n",
              "                                           early_stopping_rounds=None,\n",
              "                                           enable_categorical=False,\n",
              "                                           eval_metric=None, feature_types=None,\n",
              "                                           gamma=None, grow_policy=None,\n",
              "                                           importance_type=None,\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate...\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           max_leaves=None,\n",
              "                                           min_child_weight=None, missing=nan,\n",
              "                                           monotone_constraints=None,\n",
              "                                           multi_strategy=None,\n",
              "                                           n_estimators=None, n_jobs=None,\n",
              "                                           num_parallel_tree=None,\n",
              "                                           random_state=None, ...),\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={'colsample_bytree': [0.7, 0.8],\n",
              "                                        'gamma': [0, 0.1],\n",
              "                                        'learning_rate': [0.01, 0.1],\n",
              "                                        'max_depth': [3, 5, 7],\n",
              "                                        'n_estimators': [100, 200]})"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "xgb_model.fit(X_train_normal, y_train_normal)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {},
      "outputs": [],
      "source": [
        "pickle.dump(xgb_model, open('Model/xgb_tunning_oversample_normalization.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROuyYPA49R5C",
        "outputId": "083d1530-b764-4c2f-9620-705f1be2f03a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best parameters: {'n_estimators': 100, 'max_depth': 7, 'learning_rate': 0.1, 'gamma': 0, 'colsample_bytree': 0.7}\n"
          ]
        }
      ],
      "source": [
        "best_params = xgb_model.best_params_\n",
        "print(f\"Best parameters: {best_params}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vq3X7z7A9Tbq",
        "outputId": "f64f5ce9-31fa-478d-a6c1-b71866e40f1d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "XGBoost Model:\n",
            "Accuracy: 0.92\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.90      0.95      0.92        38\n",
            "         1.0       0.89      0.84      0.86        37\n",
            "         2.0       0.89      0.86      0.88        37\n",
            "         3.0       0.95      1.00      0.97        38\n",
            "         4.0       0.97      0.95      0.96        37\n",
            "\n",
            "    accuracy                           0.92       187\n",
            "   macro avg       0.92      0.92      0.92       187\n",
            "weighted avg       0.92      0.92      0.92       187\n",
            "\n"
          ]
        }
      ],
      "source": [
        "y_pred_xgb = xgb_model.predict(X_test_normal)\n",
        "\n",
        "# Evaluate the XGBoost model\n",
        "print(\"\\nXGBoost Model:\")\n",
        "accuracy_xgb_smote_normal_Tun = round(accuracy_score(y_test_normal, y_pred_xgb),3)\n",
        "print(\"Accuracy:\",accuracy_xgb_smote_normal_Tun)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test_normal, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mlVlqQT9VUB",
        "outputId": "9159c48c-0160-4a31-c1bd-d33d791f3594"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'accuracy': 0.92, 'recall': 0.92, 'F1 score': 0.919, 'Precision score': 0.919}\n"
          ]
        }
      ],
      "source": [
        "evaluation(y_test_normal,y_pred_xgb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "HGn5YbP99Wf6",
        "outputId": "f8136931-f8d1-4daf-a61a-fff034b106ca"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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mzbV//36VL18+yzExBxAAAMCDcwCvlexdib+/vypUqCBJql27tjZs2KCxY8dq8uTJmY6tV6+eJJEAAgAAZJsXfxNIRkbGVecMbt68WZJUrFixbPVJAggAAOAl4uLi1LZtW5UqVUrnzp3T7NmztXLlSi1atEgHDhzQ7Nmzde+99yo8PFxbt25Vv3791LhxY9WoUSNb5/HedNcAvdrV0Q9Tn1XCgjglLIjTygk91apeBbdj6lUtoQVjonVq0QAlLIjTkg96KMCfvP1mmjN7ltq2bKa6t1dX1y4Pa9vWrVaHZCzuhXf4ceMG9evzrNo0b6w6NSpr5fKlVodkLO5FLrLZPLdlw4kTJ9StWzdFRUWpefPm2rBhgxYtWqSWLVvK399fS5cuVatWrVSpUiW99NJL6tSpk7755ptsXy6ZhIWOnEzWG5OXav9vibLJpsfb1NTcdx5V/Z6TtOvnk6pXtYS+HvG4Rs5ao9gx3+lyeoZqVIhURt57bsdrLVzwnUYOj9frAwerevWamjVzup59pqe+/nahwsPDrQ7PKNwL75GamqrboqL0YIeO6t/veavDMRr3Iu+ZMmXKVfeVLFlSq1atypXzkABa6Lvv97q9HvTxcvVqX1d3Vi2hXT+f1PA+bTThy/UaOWuN65h9vybe7DCNNnP6VHV86BG179BJkvT6wMFavXql5n/1pXr2etri6MzCvfAeDRs1VsNGja0OA+Je5CovngPoCZYmgKdOndInn3yitWvX6vjx45KkyMhI3XXXXerevbsKFy5sZXg3lY+PTZ2aVlVQgJ/Wb/9NhcOCdGfVEpqzZKtWTOipssULau/hUxr00XJ9v+2w1eEaIe3SJe3auUM9ez3javPx8VH9+ndp65afLIzMPNwLAMhdliWAGzZsUOvWrZU/f361aNFCFStWlCQlJCTo/fff17Bhw7Ro0SLVqVPnmv04HI5MT8Y4My7L5nNrFDerliuilROeUoB/Pp1PvaTOr3+m3b+c1J1VSkiS/tmjqeImLNbW/cfVtXVNfTe6m2p3n6ADv522NnADnDl7Runp6ZmGF8PDw3Xo0EGLojIT9wKAx3nJV8HdLJZlSX379tXDDz+sSZMmyfa3X7rT6VTv3r3Vt29frV279pr9xMfHa/DgwW5tvqWayK9009wO2SP2Hk5UvZ6TFBpkV4emVfTRgPZq1XeafHx+/51M+fcmzVywWZK0Zd9xNa1dTtH33q43P1xmYdQAAOBWZtmA95YtW9SvX79MyZ8k2Ww29evXz7W2zbXExcUpKSnJbctX8m4PROwZaZfTdfDIaf2095je/HCZtu1PUMzD9XQs8ZwkadfPJ92O3/PLSZUsGmpFqMYpGFZQvr6+Skx0n3eZmJioiIgIi6IyE/cCgMd58KvgvJFlUUVGRuqHH3646v4ffvhBRYsWvW4/drtdISEhbtutMvx7JT4+Ntn98umXY2d19GSyKpZyH/KqUCJch48nWRSdWfz8/VW5SlWtX/f/VeiMjAytX79WNWrebmFk5uFeAPA4wxJAyzKll19+WU8//bQ2bdqk5s2bu5K9hIQELVu2TB999JFGjhxpVXg3xZCnm2vR+v36NSFJwfn91blFdTWuVUYPvDxTkjR6zvd6vUdTbdufoC37j+vxNjUVVTpCj735ubWBG+SJ6B56Y8Crqlq1mqpVr6FPZ05Xamqq2nfoaHVoxuFeeI8LF1L06+H/fxjtyJHftGf3LoWGhiqyWHELIzMP9wI5ZVkCGBMTo4iICI0ePVoTJkxQenq6JMnX11e1a9fWtGnT9Mgjj1gV3k1RuGCQpgzooMjwAkpKcWj7gQQ98PJMLd/4+6T2cXPXKcA/n4b3ba2CwYHadiBB98fO1KGjZyyO3Bxt2t6rM6dPa8K493Xq1ElFVaqsCZM/VjjDjjcd98J77NyxQ717Rrtejx7xriTp/gfba9Bb8VaFZSTuRS4y7CEQm9Np/arCaWlpOnXqlCQpIiJCfn5+N9RfYONBuRAVcsOZ5YOsDgHwSmnpGVaHAHidYLt1w6WBD070WN+p/37WY33nlFdMlvPz88v2lxgDAADkGi+dq+cpZl0tAAAAvKMCCAAAYCnD5gBSAQQAADAMFUAAAADD5gCSAAIAADAEDAAAgLyMCiAAADCejQogAAAA8jIqgAAAwHhUAAEAAJCnUQEEAAAwqwBIBRAAAMA0VAABAIDxTJsDSAIIAACMZ1oCyBAwAACAYagAAgAA41EBBAAAQJ5GBRAAABiPCiAAAADyNCqAAAAAZhUAqQACAACYhgogAAAwHnMAAQAAkKdRAQQAAMYzrQJIAggAAIxnWgLIEDAAAIBhqAACAADjUQEEAABAnkYFEAAAwKwCIBVAAAAA01ABBAAAxmMOIAAAAPI0KoAAAMB4plUASQABAIDxTEsAGQIGAAAwDBVAAAAAswqAVAABAABMQwUQAAAYjzmAAAAAyNPyZAXwyII3rA4BfyjZ6zOrQ8BfbBvb0eoQ8Icgu6/VIeAPfr7UQkAFEAAAABaZOHGiatSooZCQEIWEhKhBgwZasGCBa//FixcVExOj8PBwFShQQJ06dVJCQkK2z0MCCAAAjGez2Ty2ZUeJEiU0bNgwbdq0SRs3blSzZs3Url077dixQ5LUr18/ffPNN5o7d65WrVqlo0ePqmPH7I/u5MkhYAAAgOzwliHgBx54wO3122+/rYkTJ2rdunUqUaKEpkyZotmzZ6tZs2aSpKlTp6py5cpat26d6tevn+XzUAEEAADwIIfDoeTkZLfN4XBc933p6emaM2eOUlJS1KBBA23atElpaWlq0aKF65hKlSqpVKlSWrt2bbZiIgEEAACweW6Lj49XaGio2xYfH3/VULZt26YCBQrIbrerd+/emjdvnqpUqaLjx4/L399fYWFhbscXLVpUx48fz9blMgQMAADgQXFxcYqNjXVrs9vtVz0+KipKmzdvVlJSkr744gtFR0dr1apVuRoTCSAAADCeJ+cA2u32ayZ8f+fv768KFSpIkmrXrq0NGzZo7Nix6ty5sy5duqSzZ8+6VQETEhIUGRmZrZgYAgYAAPBiGRkZcjgcql27tvz8/LRs2TLXvj179ujw4cNq0KBBtvqkAggAAIznLU8Bx8XFqW3btipVqpTOnTun2bNna+XKlVq0aJFCQ0PVs2dPxcbGqlChQgoJCVHfvn3VoEGDbD0BLJEAAgAAeI0TJ06oW7duOnbsmEJDQ1WjRg0tWrRILVu2lCSNHj1aPj4+6tSpkxwOh1q3bq0JEyZk+zwkgAAAwHjeUgGcMmXKNfcHBARo/PjxGj9+/A2dhwQQAADAO/K/m4aHQAAAAAxDBRAAABjPW4aAbxYqgAAAAIahAggAAIxHBRAAAAB5GhVAAABgPCqAAAAAyNOoAAIAAOOZVgEkAQQAADAr/2MIGAAAwDRUAAEAgPFMGwKmAggAAGAYKoAAAMB4VAABAACQp1EBBAAAxjOsAEgFEAAAwDRUAAEAgPFMmwNIAggAAIxnWP7HEDAAAIBpqAACAADjmTYETAUQAADAMFQAAQCA8QwrAFIBBAAAMA0VQAAAYDwfH7NKgFQAAQAADEMFEAAAGM+0OYAkgAAAwHgsAwMAAIA8jQqgl5n+yYdatXypfvn5oOz2AFWvWUvPPf+SSpcpa3VoeVr3e8qr+z0VVCoiSJK0+0iS3vv3Di3bdlyS9ESTcupUv7RqlC6o4EA/lX/uKyWnplkZslHmfzFHX3/5mY4fOypJKlOugqJ79lb9ho0sjsxMP27coJnTPtGuXTt06uRJjRzzgZo2a2F1WMaaM3uWpk+dolOnTqpiVCW9NuANVa9Rw+qwbjmGFQCpAHqbnzZtVKdHHtVH0/+lsRM/1uXLl/Xic08pNfWC1aHlaUdPp+qtL7aqxeDFajF4sdbsOqEZz9+tqOIhkqT8/vm0fNsxjfl2p8WRmqlwkUg906efPprxuT6c/pnuqHOn/vlyXx06sN/q0IyUmpqq26Ki9OqAN6wOxXgLF3ynkcPj9cxzMZozd56ioirp2Wd6KjEx0erQ4OWoAHqZMeM/dHv9+uB3dG/zu7V7507dXruORVHlfYu3HHV7/c5X29T9nvKqUz5ce44ma/KSvZKku6IKWxGe8Ro2bur2utdzL+jrLz/Tzu1bVLZ8BWuCMljDRo3VsFFjq8OApJnTp6rjQ4+ofYdOkqTXBw7W6tUrNf+rL9Wz19MWR3drYQ4gvMr5c+ckSSGhoRZHYg4fm03t7yyp/PZ82nCAf0V7m/T0dC1b/J0upqaqavVaVocDWCbt0iXt2rlD9Rvc5Wrz8fFR/fp3aeuWnyyMDLcCr64A/vrrrxo4cKA++eSTqx7jcDjkcDjc2y7nk91u93R4HpeRkaExI4epRq07VL7CbVaHk+dVLhGqBf9sLrufr1Icl9V93P+092iy1WHhDwf271XMk1116dIlBQbm11sjxqpMufJWhwVY5szZM0pPT1d4eLhbe3h4uA4dOmhRVLcuKoBe5PTp05o+ffo1j4mPj1doaKjbNmbksJsUoWeNHDZUBw/s09D4kVaHYoT9x87pnoGL1XroUk1bsV8fPHWnKv4xBxDWK1W6rD6e9aUmTp2tdp0e0TuD/qmfDx6wOiwAuCVZWgH897//fc39Bw9e/18wcXFxio2NdWtLuezVhc0sGTnsLf3vv6s08eMZKlI00upwjJCWnqFDJ85Lkrb+cka1yhTS0y0r6uXpGy2ODJLk5+enEiVLSZKiKlfV7p079MWcT/XygIEWRwZYo2BYQfn6+mZ64CMxMVEREREWRXXrMqwAaG0C2L59e9lsNjmdzqsec72SrN1uzzTcezklPVfis4LT6dR7776tVSuWasJH01T8HyWsDslYPj422fN5dZHcaBnODKVdumR1GIBl/Pz9VblKVa1ft1bNmv++DE9GRobWr1+rLo8+bnF0tx6GgG+iYsWK6auvvlJGRsYVtx9//NHK8CwxcthQLfruGw1+Z4Ty5w9S4qmTSjx1UhcvXrQ6tDzt9Yeqq0HFwioZnl+VS4Tq9Yeqq2FUEX2x9hdJUpGQAFUrGaZyRYMlSVVKhKpayTCFBflbGbYxPhw3Wlt+3KhjR4/owP69+nDcaG3etEEt2t5ndWhGunAhRXt279Ke3bskSUeO/KY9u3e51mnEzfNEdA999cXn+vf8eTp44IDeGjJIqampat+ho9WhwctZWgGsXbu2Nm3apHbt2l1x//Wqg3nRV3PnSJJiekW7tb8+6G3d92AHK0IyQkRwgMb1qqeioQFKTk3Tzl/P6pH3VmnVzgRJUvQ95fVK+2qu478Z0FyS1Pfj9Zrzv5+tCNkoZ86c1juDBijx1EkFFQhW+QoVNeKDyapb767rvxm5bueOHerd8///jho94l1J0v0Pttegt+KtCstIbdreqzOnT2vCuPd16tRJRVWqrAmTP1Y4Q8DZZlgBUDanhRnWf//7X6WkpKhNmzZX3J+SkqKNGzeqSZMm2er39C08BJzXRPX5wuoQ8BfbxlIV8BZBdl+rQ8Af/HyZ6uEtAiwsS90xZLnH+v7xzWYe6zunLK0ANmp07a9xCgoKynbyBwAAkF3MAQQAAECeduuvlwIAAHCDDCsAUgEEAAAwDRVAAABgPOYAAgAAIE+jAggAAIxnWAGQBBAAAIAhYAAAAORpVAABAIDxDCsAUgEEAADwFvHx8apbt66Cg4NVpEgRtW/fXnv27HE7pmnTprLZbG5b7969s3UeEkAAAGC8vydUubllx6pVqxQTE6N169ZpyZIlSktLU6tWrZSSkuJ2XK9evXTs2DHXNnz48GydhyFgAAAAL7Fw4UK319OmTVORIkW0adMmNW7c2NWeP39+RUZG5vg8VAABAIDxbDbPbQ6HQ8nJyW6bw+HIUlxJSUmSpEKFCrm1z5o1SxEREapWrZri4uJ04cKFbF0vCSAAAIAHxcfHKzQ01G2Lj4+/7vsyMjL04osvqmHDhqpWrZqr/bHHHtOnn36qFStWKC4uTjNnztTjjz+erZgYAgYAAMbz5DqAcXFxio2NdWuz2+3XfV9MTIy2b9+uNWvWuLU//fTTrp+rV6+uYsWKqXnz5jpw4IDKly+fpZhIAAEAgPE8uQyM3W7PUsL3V3369NG3336r1atXq0SJEtc8tl69epKk/fv3kwACAADcapxOp/r27at58+Zp5cqVKlu27HXfs3nzZklSsWLFsnweEkAAAGA8b/kquJiYGM2ePVtff/21goODdfz4cUlSaGioAgMDdeDAAc2ePVv33nuvwsPDtXXrVvXr10+NGzdWjRo1snweEkAAAAAvMXHiREm/L/b8V1OnTlX37t3l7++vpUuXasyYMUpJSVHJkiXVqVMnvf7669k6DwkgAAAwnrdUAJ1O5zX3lyxZUqtWrbrh87AMDAAAgGGoAAIAAON5SQHwpqECCAAAYBgqgAAAwHjeMgfwZiEBBAAAxjMs/2MIGAAAwDRUAAEAgPFMGwKmAggAAGAYKoAAAMB4hhUAqQACAACYhgogAAAwno9hJUAqgAAAAIahAggAAIxnWAGQBBAAAIBlYAAAAJCnUQEEAADG8zGrAEgFEAAAwDRUAAEAgPGYAwgAAIA8jQogAAAwnmEFwLyZAPrlM+wuerGfRrW3OgT8RVTvOVaHgD8kzHjC6hAAGCxPJoAAAADZYZNZxSMSQAAAYDyWgQEAAECeRgUQAAAYj2VgAAAAkKdRAQQAAMYzrABIBRAAAMA0VAABAIDxfAwrAVIBBAAAMAwVQAAAYDzDCoAkgAAAACwDAwAAgDyNCiAAADCeYQVAKoAAAACmoQIIAACMxzIwAAAAyNOoAAIAAOOZVf+jAggAAGAcKoAAAMB4pq0DSAIIAACM52NW/scQMAAAgGmoAAIAAOOZNgRMBRAAAMAwVAABAIDxDCsA5qwC+OSTT+rcuXOZ2lNSUvTkk0/ecFAAAADwnBwlgNOnT1dqamqm9tTUVM2YMeOGgwIAALiZbDabxzZvlK0h4OTkZDmdTjmdTp07d04BAQGufenp6fruu+9UpEiRXA8SAAAAuSdbCWBYWJgrm61YsWKm/TabTYMHD8614AAAAG4G09YBzFYCuGLFCjmdTjVr1kxffvmlChUq5Nrn7++v0qVLq3jx4rkeJAAAgCd561Ctp2QrAWzSpIkk6dChQypVqpRxvywAAABPio+P11dffaXdu3crMDBQd911l959911FRUW5jrl48aJeeuklzZkzRw6HQ61bt9aECRNUtGjRLJ8nRw+BLF++XF988UWm9rlz52r69Ok56RIAAMAyNg9u2bFq1SrFxMRo3bp1WrJkidLS0tSqVSulpKS4junXr5+++eYbzZ07V6tWrdLRo0fVsWPHbJ0nR+sAxsfHa/LkyZnaixQpoqefflrR0dE56RYAAMBoCxcudHs9bdo0FSlSRJs2bVLjxo2VlJSkKVOmaPbs2WrWrJkkaerUqapcubLWrVun+vXrZ+k8OUoADx8+rLJly2ZqL126tA4fPpyTLgEAACzj48FpbQ6HQw6Hw63NbrfLbrdf971JSUmS5HruYtOmTUpLS1OLFi1cx1SqVEmlSpXS2rVrs5wA5mgIuEiRItq6dWum9i1btig8PDwnXQIAAORJ8fHxCg0Nddvi4+Ov+76MjAy9+OKLatiwoapVqyZJOn78uPz9/RUWFuZ2bNGiRXX8+PEsx5SjCuCjjz6q559/XsHBwWrcuLGk38esX3jhBXXp0iUnXQIAAFjGk8+1xsXFKTY21q0tK9W/mJgYbd++XWvWrMn1mHKUAA4dOlQ///yzmjdvrnz5fu8iIyND3bp10zvvvJOrAQIAANzKsjrc+1d9+vTRt99+q9WrV6tEiRKu9sjISF26dElnz551qwImJCQoMjIyy/3nKAH09/fXZ599pqFDh2rLli0KDAxU9erVVbp06Zx0BwAAYClvWdrO6XSqb9++mjdvnlauXJnpmYvatWvLz89Py5YtU6dOnSRJe/bs0eHDh9WgQYMsnydHCeCfKlaseMVvBAEAAED2xcTEaPbs2fr6668VHBzsmtcXGhqqwMBAhYaGqmfPnoqNjVWhQoUUEhKivn37qkGDBll+AETKRgIYGxuroUOHKigoKNM49t+NGjUqywEAAABYzUsKgJo4caIkqWnTpm7tU6dOVffu3SVJo0ePlo+Pjzp16uS2EHR2ZDkB/Omnn5SWlub6+Wq8pYR6q/px4wbNnPaJdu3aoVMnT2rkmA/UtFmL678RHvWvGVP08YSx6ti5q2L6vWp1OHlezxYV9WSLiioVESRJ2n0kSe9+tVVLtxxVwSB/xT1UU82qF1OJiCCdSnboPxt/1dtzNys5Nc3iyM0xZ/YsTZ86RadOnVTFqEp6bcAbql6jhtVhGYl7kTs8uQxMdjidzuseExAQoPHjx2v8+PE5Pk+WE8AVK1Zc8WfkrtTUVN0WFaUHO3RU/37PWx0OJO3euV3fzpurchWY7nCzHDl9QYPm/KgDx8/JJumxxuX1r5eaqlHcf2Sz2VSsYKBen/2j9vx2ViUjCmh0z3oqVjBQ3cautjp0Iyxc8J1GDo/X6wMHq3r1mpo1c7qefaanvv52IUuB3WTcC+RUjtYBhOc0bNRYz/V9Ufc0b2l1KJCUeuGC3hkYp9i4QQoODrE6HGMs/PE3Ldl8VAePn9OB4+c09PPNSrl4WXVvK6xdv53VE2NWa+GPv+nQifNavfO4hn7+k9rcUUK+Pt7xL/i8bub0qer40CNq36GTyleooNcHDlZAQIDmf/Wl1aEZh3uRe2w2z23eKMsVwOx8x9xXX32Vo2AAbzN25Nuq37CRat9ZX7Omfmh1OEbysdnUoX5p5bfn0w/7Tl7xmJBAf51LTVN6xvWHTnBj0i5d0q6dO9Sz1zOuNh8fH9Wvf5e2brn69CDkPu4FbkSWE8DQ0FDXz06nU/PmzVNoaKjq1Kkj6fevJjl79my2v4wY8FbLlyzQ/j27NOGTf1kdipGqlAzTksFtFODnq/MXL6vr6JXacyQp03GFgu3q36G6pi3fZ0GU5jlz9ozS09MzDS+Gh4fr0KGDFkVlJu5F7jLtGYYsJ4BTp051/fzqq6/qkUce0aRJk+Tr6ytJSk9P13PPPaeQkOwNk6WmpmrTpk0qVKiQqlSp4rbv4sWL+vzzz9WtW7ervv9K3693SX7ZXnAR+KsTCcc1ftS7Gv7+h/LnvyVL7DuarEZx/1FIfj+1u7O0JvVuqHuHLnZLAoMD/TS3fzPtOZKk+C+3WBgtANxacjQH8JNPPtHLL7/sSv4kydfXV7Gxsfrkk0+y3M/evXtVuXJlNW7cWNWrV1eTJk107Ngx1/6kpCT16NHjmn1c6fv13hs+LPsXBfzF3t07dfbMafXu3lktG96ulg1v15afNmre57PVsuHtSk9PtzrEPC8tPUMHE85p86HTGvzZT9p++IyebVPJtb9AQD59+Woznb+Ypq6jV+pyOsO/N0PBsILy9fVVYmKiW3tiYqIiIiIsispM3Ivc5ePBzRvlKK7Lly9r9+7dmdp3796tjIyMLPfz6quvqlq1ajpx4oT27Nmj4OBgNWzYUIcPH85yH3FxcUpKSnLbXnrltSy/H7iSO+rU08ezvtSHMz53bVGVq6p56/v04YzP3f7xg5vDx2aTf77ff+/BgX6aF9dCly5nqMvIFXKkZf3vHdwYP39/Va5SVevXrXW1ZWRkaP36tapR83YLIzMP9wI3IkffBNKjRw/17NlTBw4c0J133ilJWr9+vYYNG3bdit1fff/991q6dKkiIiIUERGhb775Rs8995waNWqkFStWKCgo6Lp9XOn79c45bt3/GVy4kKJf/5IAHznym/bs3qXQ0FBFFituYWRmyR8UpLLlb3NrCwgIVEhoaKZ25L6BnW/Xki1H9NupFBUI9NPDd5XV3ZWLquOwZb8nf681V6A9n54ev0bBgX4KDvSTJJ1KdigjC2to4cY8Ed1Dbwx4VVWrVlO16jX06czpSk1NVfsOzAG/2bgXuYc5gFkwcuRIRUZG6r333nMN2RYrVkz9+/fXSy+9lOV+UlNTlS/f/4dgs9k0ceJE9enTR02aNNHs2bNzEt4tbeeOHerdM9r1evSIdyVJ9z/YXoPeircqLOCmKhwSoEnPNlRkWKCSL6Rpx69n1HHYMq3Yfkx3Vy6qurcVliRtHtPB7X3Vn/9Kh0+lWBGyUdq0vVdnTp/WhHHv69Spk4qqVFkTJn+scIYdbzruRe4xbRUpmzMrS05fQ3JysiRl++EPSbrzzjvVt29fPfHEE5n29enTR7NmzVJycnK251vdyhXAvCbpAt/M4E2qxnxudQj4Q8KMzH/vAaYLyFFZKne8+HXmqW25ZUy7Stc/6CbL8dzEy5cva+nSpfrXv/7lKpsePXpU58+fz3IfHTp00L/+deUlNsaNG6dHH300S1+JAgAAcCN8bJ7bvFGOKoC//PKL2rRpo8OHD8vhcGjv3r0qV66cXnjhBTkcDk2aNMkTsWYZFUDvQQXQu1AB9B5UAIHMrKwAxv7bcxXAUQ/mkQrgCy+8oDp16ujMmTMKDAx0tXfo0EHLli3LteAAAABuBpvN5rHNG+Uo1/7vf/+r77//Xv7+/m7tZcqU0ZEjR3IlMAAAAHhGjhLAjIyMKz6Y8dtvvyk4OPiGgwIAALiZvHWunqfkaAi4VatWGjNmjOu1zWbT+fPnNXDgQN177725FRsAAAA8IMfrALZp00ZVqlTRxYsX9dhjj2nfvn2KiIi46lO9AAAA3spLp+p5TI4SwJIlS2rLli367LPPtGXLFp0/f149e/ZU165d3R4KAQAAuBX4GJYBZjsBTEtLU6VKlfTtt9+qa9eu6tq1qyfiAgAAgIdkOwH08/PTxYsXPRELAACAJXL8zRi3qBxdb0xMjN59911dvnw5t+MBAACAh+VoDuCGDRu0bNkyLV68WNWrV1dQUJDb/q+++ipXggMAALgZDJsCmLMEMCwsTJ06dcrtWAAAAHATZCsBzMjI0IgRI7R3715dunRJzZo106BBg3jyFwAA3NJMewo4W3MA3377bQ0YMEAFChTQP/7xD73//vuKiYnxVGwAAADwgGwlgDNmzNCECRO0aNEizZ8/X998841mzZqljIwMT8UHAADgcTab5zZvlK0E8PDhw25f9daiRQvZbDYdPXo01wMDAAC4WXxsntu8UbYSwMuXLysgIMCtzc/PT2lpabkaFAAAADwnWw+BOJ1Ode/eXXa73dV28eJF9e7d220pGJaBAQAAtxLTHgLJVgIYHR2dqe3xxx/PtWAAAADgedlKAKdOneqpOAAAACxjWAHQuK++AwAAMF6OvgkEAAAgL/HWp3U9hQogAACAYagAAgAA49lkVgmQBBAAABiPIWAAAADkaVQAAQCA8agAAgAAIE+jAggAAIxnM2wlaCqAAAAAhqECCAAAjMccQAAAAORpVAABAIDxDJsCSAIIAADgY1gGyBAwAACAYagAAgAA4/EQCAAAAPI0KoAAAMB4hk0BpAIIAADgTVavXq0HHnhAxYsXl81m0/z58932d+/eXTabzW1r06ZNts5BBRAAABjPR95TAkxJSVHNmjX15JNPqmPHjlc8pk2bNpo6darrtd1uz9Y58mQC6OdLYdNbRARn7z9IeFbCjCesDgF/KFi3j9Uh4A9nNoyzOgTATdu2bdW2bdtrHmO32xUZGZnjc5ApAQAA49lsntscDoeSk5PdNofDcUPxrly5UkWKFFFUVJSeffZZJSYmZuv9JIAAAMB4PjbPbfHx8QoNDXXb4uPjcxxrmzZtNGPGDC1btkzvvvuuVq1apbZt2yo9PT3LfeTJIWAAAABvERcXp9jYWLe27M7Z+6suXbq4fq5evbpq1Kih8uXLa+XKlWrevHmW+iABBAAAxvPkV8HZ7fYbSviup1y5coqIiND+/fuznAAyBAwAAHAL++2335SYmKhixYpl+T1UAAEAgPG8aSHo8+fPa//+/a7Xhw4d0ubNm1WoUCEVKlRIgwcPVqdOnRQZGakDBw7olVdeUYUKFdS6dessn4MEEAAAwIts3LhR99xzj+v1n/MHo6OjNXHiRG3dulXTp0/X2bNnVbx4cbVq1UpDhw7N1jAzCSAAADCeJ+cAZlfTpk3ldDqvun/RokU3fA7mAAIAABiGCiAAADCeFxUAbwoSQAAAYDzThkRNu14AAADjUQEEAADGsxk2BkwFEAAAwDBUAAEAgPHMqv9RAQQAADAOFUAAAGA8b1oI+magAggAAGAYKoAAAMB4ZtX/SAABAACM+yYQhoABAAAMQwUQAAAYj4WgAQAAkKdRAQQAAMYzrSJm2vUCAAAYjwogAAAwHnMAAQAAkKdRAQQAAMYzq/5HBRAAAMA4VAABAIDxTJsDSAIIAACMZ9qQqGnXCwAAYDwqgAAAwHimDQFTAQQAADAMFUAAAGA8s+p/VAABAACMQwUQAAAYz7ApgFQAAQAATEMFEAAAGM/HsFmAJIAAAMB4DAHDcnNmz1Lbls1U9/bq6trlYW3butXqkIzG/fAe3Iubr9fDd+uHz+KU8N8RSvjvCK2c/pJaNazi2l80PFhThnbToSXv6NT37+n72a+qffNa1gVsID4XyAkSQC+zcMF3Gjk8Xs88F6M5c+cpKqqSnn2mpxITE60OzUjcD+/BvbDGkYSzeuODr3VX1+Fq2HWEVv6wV3NHP63K5SIlSR8P7aaKZYro4Rcnq87D7+jr5Zv16btPqmZUCYsjNwOfi9xj8+Afb0QC6GVmTp+qjg89ovYdOql8hQp6feBgBQQEaP5XX1odmpG4H96De2GN71Zv16I1O3Xg8EntP3xCg8Z/o/MXHLqzRllJUv2a5TRhzipt3PGLfj6SqHc/XqSz51J1e5WSFkduBj4XyCkSQC+SdumSdu3cofoN7nK1+fj4qH79u7R1y08WRmYm7of34F54Bx8fmx5uXVtBgf5av/WQJGndloN6qFVtFQzJL5vt9/0B9nxavXGfxdHmfXwucpfN5rnNG1n+EMiuXbu0bt06NWjQQJUqVdLu3bs1duxYORwOPf7442rWrNk13+9wOORwONzanL522e12T4btEWfOnlF6errCw8Pd2sPDw3Xo0EGLojIX98N7cC+sVbVCca2c/pIC/PPpfKpDnV/6SLsPHpckPf7KJ5r57pM6umq40tLSdeHiJXWO/UgHfz1lcdR5H58L3AhLK4ALFy5UrVq19PLLL+v222/XwoUL1bhxY+3fv1+//PKLWrVqpeXLl1+zj/j4eIWGhrptI96Nv0lXAAB5396fE1SvS7wadxupj+au0UdDnlClP+YADoy5X2HBgWr7zPtq+Phwvf/pcn06/ElVrVDc4qiB7PGRzWObN7I0ARwyZIj69++vxMRETZ06VY899ph69eqlJUuWaNmyZerfv7+GDRt2zT7i4uKUlJTktvV/Ne4mXUHuKhhWUL6+vpkm7yYmJioiIsKiqMzF/fAe3AtrpV1O18FfT+mnXb/qzQ/+rW17jyjm0aYqWyJCz3ZpomcGfaqVP+zVtr1H9M6HC/TjzsN6pnNjq8PO8/hc4EZYmgDu2LFD3bt3lyQ98sgjOnfunB566CHX/q5du2rrdR5nt9vtCgkJcdtuxeFfSfLz91flKlW1ft1aV1tGRobWr1+rGjVvtzAyM3E/vAf3wrv42Gyy++dT/gB/SVKG0+m2Pz3dKR9vnfiUh/C5yF3MAbzJbH/8Znx8fBQQEKDQ0FDXvuDgYCUlJVkVmiWeiO6hNwa8qqpVq6la9Rr6dOZ0paamqn2HjlaHZiTuh/fgXlhjSN8Hteh/O/TrsTMKDgpQ57Z11LjObXrguQna8/Nx7T98QuNef1Rxo+YpMSlFD95TQ83rR6njC5OsDt0IfC5yj7cmap5iaQJYpkwZ7du3T+XLl5ckrV27VqVKlXLtP3z4sIoVK2ZVeJZo0/ZenTl9WhPGva9Tp04qqlJlTZj8scIp51uC++E9uBfWKFyogKYM7abIiBAlnb+o7fuO6IHnJmj5+t2SpPZ9J+qt59vpi7HPqEB+uw78elJPvTlTi9bstDhyM/C5QE7ZnM6/1e5vokmTJqlkyZK67777rrh/wIABOnHihD7++ONs9Xvxcm5EBwCeU7BuH6tDwB/ObBhndQj4Q4CFZakluzz35HrLyt6XkFuaAHoKCSAAb0cC6D1IAL0HCeDNY/kcQAAAAKv5GDYHkG8CAQAAMAwVQAAAYDybly7Y7ClUAAEAAAxDBRAAABiPdQABAAAMwxAwAAAALLN69Wo98MADKl68uGw2m+bPn++23+l06s0331SxYsUUGBioFi1aaN++fdk6BwkgAAAwno/Nc1t2paSkqGbNmho/fvwV9w8fPlzvv/++Jk2apPXr1ysoKEitW7fWxYsXs3wOhoABAAC8SNu2bdW2bdsr7nM6nRozZoxef/11tWvXTpI0Y8YMFS1aVPPnz1eXLl2ydA4qgAAAwHg2D/5xOBxKTk522xwOR47iPHTokI4fP64WLVq42kJDQ1WvXj2tXbs2y/2QAAIAAHhQfHy8QkND3bb4+Pgc9XX8+HFJUtGiRd3aixYt6tqXFQwBAwAA43lyGZi4uDjFxsa6tdntds+dMAtIAAEAADzIbrfnWsIXGRkpSUpISFCxYsVc7QkJCapVq1aW+2EIGAAAGM/mwS03lS1bVpGRkVq2bJmrLTk5WevXr1eDBg2y3A8VQAAAYDwfL/oqkPPnz2v//v2u14cOHdLmzZtVqFAhlSpVSi+++KLeeust3XbbbSpbtqzeeOMNFS9eXO3bt8/yOUgAAQAAvMjGjRt1zz33uF7/OX8wOjpa06ZN0yuvvKKUlBQ9/fTTOnv2rO6++24tXLhQAQEBWT6Hzel0OnM9cotdvGx1BABwbQXr9rE6BPzhzIZxVoeAPwRYWJZat/+sx/quXyHMY33nFHMAAQAADMMQMAAAgPdMAbwpqAACAAAYhgogAAAwns2wEiAVQAAAAMNQAQQAAMbzomUAbwoSQAAAYDzD8j+GgAEAAExDBRAAAMCwEiAVQAAAAMNQAQQAAMZjGRgAAADkaVQAAQCA8UxbBoYKIAAAgGGoAAIAAOMZVgAkAQQAADAtA2QIGAAAwDBUAAEAgPFYBgYAAAB5GhVAAABgPJaBAQAAQJ5GBRAAABjPsAJg3kwA09IzrA4Bf/DzpcjsTfhseI8zG8ZZHQL+UPDeEVaHgD+kLu5vdQjGyJMJIAAAQLYYVgIkAQQAAMZjGRgAAADkaVQAAQCA8VgGBgAAAHkaFUAAAGA8wwqAVAABAABMQwUQAADAsBIgFUAAAADDUAEEAADGYx1AAAAA5GlUAAEAgPFMWweQBBAAABjPsPyPIWAAAADTUAEEAAAwrARIBRAAAMAwVAABAIDxWAYGAAAAeRoVQAAAYDzTloGhAggAAGAYKoAAAMB4hhUASQABAABMywAZAgYAADAMFUAAAGA8loEBAABAnkYFEAAAGI9lYAAAAJCnkQACAADj2Ty4ZcegQYNks9nctkqVKt3g1WXGEDAAAIAXqVq1qpYuXep6nS9f7qdrJIAAAABeNAcwX758ioyM9Og5GAIGAADGs3nwj8PhUHJystvmcDiuGsu+fftUvHhxlStXTl27dtXhw4dz/XpJAAEAADwoPj5eoaGhblt8fPwVj61Xr56mTZumhQsXauLEiTp06JAaNWqkc+fO5WpMNqfT6czVHr3AOUeG1SHgD36+/BvDm6Sl89nwFnw2vEfBe0dYHQL+kLq4v2XnPnTqosf6Lh5sy1Txs9vtstvt133v2bNnVbp0aY0aNUo9e/bMtZiYAwgAAOBBWU32riQsLEwVK1bU/v37czUm/gkKAACM5y3LwPzd+fPndeDAARUrVuwGe3JHAggAAOAlXn75Za1atUo///yzvv/+e3Xo0EG+vr569NFHc/U8DAEDAAB4yTIwv/32mx599FElJiaqcOHCuvvuu7Vu3ToVLlw4V89DAggAAOAl5syZc1POQwIIAACMZ/OWEuBNQgIIAACMZzMr/+MhEG/z48YN6tfnWbVp3lh1alTWyuVLr/8meNSc2bPUtmUz1b29urp2eVjbtm61OiTj8LnwPnwubr5e99fSD5O6K2He80qY97xWjumqVnXLuvYvGtFZqYv7u23vP9/SwojhzUgAvUxqaqpui4rSqwPesDoUSFq44DuNHB6vZ56L0Zy58xQVVUnPPtNTiYmJVodmFD4X3oXPhTWOnDqnN6as0l0xM9Swz0yt3PyL5g7qoMqlw13HTPlui8p0nuDa/vnxKgsjvrV46zIwnsIQsJdp2KixGjZqbHUY+MPM6VPV8aFH1L5DJ0nS6wMHa/XqlZr/1Zfq2etpi6MzB58L78LnwhrfrTvg9nrQtDXqdX8t3Vm5uHb98nvynXoxTQlnUqwID7cYr6sA5sFvpsMtKu3SJe3auUP1G9zlavPx8VH9+ndp65afLIwMsA6fC+/g42PTw00rKSjAT+t3HnW1d25WRb/OjdHGD7tryJONFGinzpNVNpvnNm/kdf9l2O12bdmyRZUrV7Y6FBjuzNkzSk9PV3h4uFt7eHi4Dh06aFFUgLX4XFirapkIrRzbVQH++XQ+9ZI6D56v3Yd/r/59tmKXDick61jieVUvV1hv9WyiiiUKqcuQry2OGt7IsgQwNjb2iu3p6ekaNmyY6y+XUaNGXbMfh8OR6QuWL8kvx9+5BwCAt9r722nVe3a6QoPs6tCooj7qf69avTxHuw8n6pPv/v9BnB0/n9Kx0ylaOLyzyhYL06FjZ60L+pbhpaU6D7EsARwzZoxq1qypsLAwt3an06ldu3YpKChItizUTePj4zV48GC3ttf++aYGvDEwN8OFgQqGFZSvr2+mie2JiYmKiIiwKCrAWnwurJV2OUMHj56VJP20L0G1KxZTTIfa6jt2caZjN+w+JkkqX5wEEJlZNgfwnXfeUVJSkt544w2tWLHCtfn6+mratGlasWKFli9fft1+4uLilJSU5La99MprN+EKkNf5+furcpWqWr9urastIyND69evVY2at1sYGWAdPhfexcdHsvv5XnFfzXJFJEnHT/NQSFYwB/Amee2119S8eXM9/vjjeuCBBxQfHy8/P79s92O32zMN955zZORWmDfdhQsp+vXwYdfrI0d+057duxQaGqrIYsUtjMxMT0T30BsDXlXVqtVUrXoNfTpzulJTU9W+Q0erQzMKnwvvwufCGkOebKRFGw7p1xPJCg70V+dmldW4Rik9MGCuyhYLU+dmlbXoh4NKTE5V9bKFNbx3M/1366/afuik1aHfErw0T/MYSx8CqVu3rjZt2qSYmBjVqVNHs2bNytKwb162c8cO9e4Z7Xo9esS7kqT7H2yvQW/FWxWWsdq0vVdnTp/WhHHv69Spk4qqVFkTJn+scIa6bio+F96Fz4U1Cofl15T+9yqyUJCSLji0/eApPTBgrpb/+ItKFA5Ws9tLq0+H2goK8NNvJ89p/pq9GjZ77fU7hpFsTi9Zd2XOnDl68cUXdfLkSW3btk1VqlTJcV+3cgUwr/Hz9bqVhoyWls5nw1vw2fAeBe8dYXUI+EPq4v6WnftY0iWP9V0s1N9jfeeU1ywD06VLF919993atGmTSpcubXU4AAAAeZbXJICSVKJECZUoUcLqMAAAgGFshs0CZAwCAADAMF5VAQQAALCEWQVAKoAAAACmoQIIAACMZ1gBkAQQAADAtGWIGQIGAAAwDBVAAABgPJaBAQAAQJ5GBRAAAMCsAiAVQAAAANNQAQQAAMYzrABIBRAAAMA0VAABAIDxTFsHkAQQAAAYj2VgAAAAkKdRAQQAAMYzbQiYCiAAAIBhSAABAAAMQwIIAABgGOYAAgAA4zEHEAAAAHkaFUAAAGA809YBJAEEAADGYwgYAAAAeRoVQAAAYDzDCoBUAAEAAExDBRAAAMCwEiAVQAAAAMNQAQQAAMYzbRkYKoAAAACGoQIIAACMxzqAAAAAyNOoAAIAAOMZVgAkAQQAADAtA2QIGAAAwDAkgAAAwHg2D/7JifHjx6tMmTIKCAhQvXr19MMPP+Tq9ZIAAgAAeJHPPvtMsbGxGjhwoH788UfVrFlTrVu31okTJ3LtHCSAAADAeDab57bsGjVqlHr16qUePXqoSpUqmjRpkvLnz69PPvkk166XBBAAAMCDHA6HkpOT3TaHw3HFYy9duqRNmzapRYsWrjYfHx+1aNFCa9euzbWY8uRTwMH2Wz+vdTgcio+PV1xcnOx2u9XhGC0v3YuAfLf2ZyMv3YtbXV66F6mL+1sdwg3LS/fDKgEezIgGvRWvwYMHu7UNHDhQgwYNynTsqVOnlJ6erqJFi7q1Fy1aVLt37861mGxOp9OZa70h1yQnJys0NFRJSUkKCQmxOhyjcS+8B/fCe3AvvAv3w7s5HI5MFT+73X7FZP3o0aP6xz/+oe+//14NGjRwtb/yyitatWqV1q9fnysx5ckKIAAAgLe4WrJ3JREREfL19VVCQoJbe0JCgiIjI3Mtplt7PAgAACAP8ff3V+3atbVs2TJXW0ZGhpYtW+ZWEbxRVAABAAC8SGxsrKKjo1WnTh3deeedGjNmjFJSUtSjR49cOwcJoJey2+0aOHAgk3m9APfCe3AvvAf3wrtwP/KWzp076+TJk3rzzTd1/Phx1apVSwsXLsz0YMiN4CEQAAAAwzAHEAAAwDAkgAAAAIYhAQQAADAMCSAAAIBhSAC90Pjx41WmTBkFBASoXr16+uGHH6wOyUirV6/WAw88oOLFi8tms2n+/PlWh2Ss+Ph41a1bV8HBwSpSpIjat2+vPXv2WB2WkSZOnKgaNWooJCREISEhatCggRYsWGB1WJA0bNgw2Ww2vfjii1aHglsACaCX+eyzzxQbG6uBAwfqxx9/VM2aNdW6dWudOHHC6tCMk5KSopo1a2r8+PFWh2K8VatWKSYmRuvWrdOSJUuUlpamVq1aKSUlxerQjFOiRAkNGzZMmzZt0saNG9WsWTO1a9dOO3bssDo0o23YsEGTJ09WjRo1rA4FtwiWgfEy9erVU926dTVu3DhJv6/+XbJkSfXt21evvfaaxdGZy2azad68eWrfvr3VoUDSyZMnVaRIEa1atUqNGze2OhzjFSpUSCNGjFDPnj2tDsVI58+f1x133KEJEyborbfeUq1atTRmzBirw4KXowLoRS5duqRNmzapRYsWrjYfHx+1aNFCa9eutTAywLskJSVJ+j3xgHXS09M1Z84cpaSk5OpXVCF7YmJidN9997n9vwO4Hr4JxIucOnVK6enpmVb6Llq0qHbv3m1RVIB3ycjI0IsvvqiGDRuqWrVqVodjpG3btqlBgwa6ePGiChQooHnz5qlKlSpWh2WkOXPm6Mcff9SGDRusDgW3GBJAALeUmJgYbd++XWvWrLE6FGNFRUVp8+bNSkpK0hdffKHo6GitWrWKJPAm+/XXX/XCCy9oyZIlCggIsDoc3GJIAL1IRESEfH19lZCQ4NaekJCgyMhIi6ICvEefPn307bffavXq1SpRooTV4RjL399fFSpUkCTVrl1bGzZs0NixYzV58mSLIzPLpk2bdOLECd1xxx2utvT0dK1evVrjxo2Tw+GQr6+vhRHCmzEH0Iv4+/urdu3aWrZsmastIyNDy5YtY34NjOZ0OtWnTx/NmzdPy5cvV9myZa0OCX+RkZEhh8NhdRjGad68ubZt26bNmze7tjp16qhr167avHkzyR+uiQqgl4mNjVV0dLTq1KmjO++8U2PGjFFKSop69OhhdWjGOX/+vPbv3+96fejQIW3evFmFChVSqVKlLIzMPDExMZo9e7a+/vprBQcH6/jx45Kk0NBQBQYGWhydWeLi4tS2bVuVKlVK586d0+zZs7Vy5UotWrTI6tCMExwcnGkebFBQkMLDw5kfi+siAfQynTt31smTJ/Xmm2/q+PHjqlWrlhYuXJjpwRB43saNG3XPPfe4XsfGxkqSoqOjNW3aNIuiMtPEiRMlSU2bNnVrnzp1qrp3737zAzLYiRMn1K1bNx07dkyhoaGqUaOGFi1apJYtW1odGoBsYB1AAAAAwzAHEAAAwDAkgAAAAIYhAQQAADAMCSAAAIBhSAABAAAMQwIIAABgGBJAAAAAw5AAAgAAGIYEEAAAwDAkgAAsYbPZrrkNGjTI6hABIM/iu4ABWOLYsWOunz/77DO9+eab2rNnj6utQIECrp+dTqfS09OVLx9/ZQFAbqACCMASkZGRri00NFQ2m831evfu3QoODtaCBQtUu3Zt2e12rVmzRt27d1f79u3d+nnxxRfVtGlT1+uMjAzFx8erbNmyCgwMVM2aNfXFF1/c3IsDAC/HP6cBeK3XXntNI0eOVLly5VSwYMEsvSc+Pl6ffvqpJk2apNtuu02rV6/W448/rsKFC6tJkyYejhgAbg0kgAC81pAhQ9SyZcssH+9wOPTOO+9o6dKlatCggSSpXLlyWrNmjSZPnkwCCAB/IAEE4LXq1KmTreP379+vCxcuZEoaL126pNtvvz03QwOAWxoJIACvFRQU5Pbax8dHTqfTrS0tLc318/nz5yVJ//nPf/SPf/zD7Ti73e6hKAHg1kMCCOCWUbhwYW3fvt2tbfPmzfLz85MkValSRXa7XYcPH2a4FwCugQQQwC2jWbNmGjFihGbMmKEGDRro008/1fbt213Du8HBwXr55ZfVr18/ZWRk6O6771ZSUpL+97//KSQkRNHR0RZfAQB4BxJAALeM1q1b64033tArr7yiixcv6sknn1S3bt20bds21zFDhw5V4cKFFR8fr4MHDyosLEx33HGHBgwYYGHkAOBdbM6/T6gBAABAnsZC0AAAAIYhAQQAADAMCSAAAIBhSAABAAAMQwIIAABgGBJAAAAAw5AAAgAAGIYEEAAAwDAkgAAAAIYhAQQAADAMCSAAAIBh/g+GgAhmwb+qnQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test_normal, y_pred_xgb)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('True')\n",
        "plt.ylabel('Predict')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c07800Q19ZJL"
      },
      "source": [
        "# 8) Evaluasi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1hzfNi3AUGg"
      },
      "source": [
        "## Oversample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ndq_wDO39X1C",
        "outputId": "de2f9b05-58b6-4935-c7e3-ecfb44746c2e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbour</td>\n",
              "      <td>75.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>90.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Model  Accuracy\n",
              "0  K-Nearest Neighbour      75.4\n",
              "1        Random Forest      92.0\n",
              "2              XGBoost      90.4"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_comp1 = pd.DataFrame({\n",
        "    'Model': ['K-Nearest Neighbour','Random Forest','XGBoost'],\n",
        "    'Accuracy': [accuracy_knn_smote*100,accuracy_rf_smote*100,accuracy_xgb_smote*100]\n",
        "    })\n",
        "model_comp1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "OmvzOaYg9eKi",
        "outputId": "b7d52be1-fcf8-44ee-ad53-1f8927f0698b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Membuat bar plot dengan keterangan jumlah\n",
        "fig, ax = plt.subplots()\n",
        "bars = plt.bar(model_comp1['Model'], model_comp1['Accuracy'])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Oversample')\n",
        "plt.xticks(rotation=45, ha='right') # Untuk memutar label sumbu x agar lebih mudah dibaca\n",
        "# Menambahkan keterangan jumlah di atas setiap bar\n",
        "for bar in bars:\n",
        " yval = bar.get_height()\n",
        " plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8-8ILUiAXdX"
      },
      "source": [
        "## Oversample + Normalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "4_vHs2wh9gfK",
        "outputId": "0446da9e-0e90-442c-ac6f-2064ee0593c9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbour</td>\n",
              "      <td>86.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>90.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Model  Accuracy\n",
              "0  K-Nearest Neighbour      86.1\n",
              "1        Random Forest      92.0\n",
              "2              XGBoost      90.4"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_comp2 = pd.DataFrame({\n",
        "    'Model': ['K-Nearest Neighbour','Random Forest','XGBoost'],\n",
        "    'Accuracy': [accuracy_knn_smote_normal*100,accuracy_rf_smote_normal*100,accuracy_xgb_smote_normal*100]\n",
        "    })\n",
        "model_comp2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "7iJHuvKQ9iby",
        "outputId": "3e4d5acc-775e-425a-85f3-7ed9fd4011bb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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dOzBy5Ei4uLhAT09PoefhY/Pnz0eLFi1Qq1Yt9OvXT5x+bWhoiKlTp+bpOL6X+vXrY8GCBRg5ciSqVq2KPn36wMLCAvfv38fatWshl8tx6NChHIHQ1NQUjRo1wqJFi5CUlITOnTsrLFdRUcG6devQokULVKpUCV5eXrCyssLz589x8uRJGBgY4ODBg1+t7e+//0aTJk3g6emJihUrQk1NDXv37kVMTIw4YDcvP8v88uTJE7Rp0wbNmzfHhQsXsGXLFnTr1g3VqlUrkP0NGDAAy5cvR9euXTF8+HBYWFhg69at0NLSAvDfTm1R0cIgQ0VamTJl0KNHDwQGBuZYtm7dOpQuXRobN27E3r17YW5uDn9/f0yZMuW71FauXDn88ccfmDhxIkaPHg1zc3MMGjQIJUqUyDHj6d9kf9PfvXs3du/erbDMxsYG7dq1g4mJCf7880+MGjUKEydORLFixdCjRw80adIkx4yswYMH48aNG9iwYQMWL14MGxubLwYZNzc3BAcHY8qUKZg8eTLU1dXRoEEDzJ0796uDpJVtxIgRqF69OhYuXIglS5YgISEBFhYW6NSpEyZMmPDF04udO3fG8ePHoa+v/9kep4YNG+LChQuYMWMGli9fjnfv3sHc3Byurq45eng+x9raGl27dkVISAg2b94MNTU1lC9fHjt37hQHb+flZ5lfduzYgcmTJ2PcuHFQU1PDkCFDclyvJj9lXxtn6NCh+PXXX6Gnp4devXqhdu3a8PDwEAMNkUzg6CoiIvqCqVOnYtq0aXj16hWKFy+u7HKwZMkSjBgxAs+ePYOVlZWyy6FCgGNkiIioUPp0/FJqaipWr16NsmXLMsSQiKeWiIioUOrQoQNKlSoFBwcHJCQkYMuWLbh//z62bt2q7NKoEGGQISKiQsnd3R3r1q3D1q1bkZWVhYoVK2L79u05BlnTj41jZIiIiEiyOEaGiIiIJItBhoiIiCSryI+RkcvlePHiBfT19XkBJSIiIokQBAFJSUmwtLTMccuVjxX5IPPixYscd3glIiIiaXj69ClKliz5xeVFPsjo6+sD+PBGGBgYKLkaIiIiyo3ExERYW1uLf8e/pMgHmezTSQYGBgwyREREEvNvw0I42JeIiIgki0GGiIiIJItBhogKhaSkJPj6+sLGxgba2tqoXbs2rly5AgDIyMiAn58fqlSpAl1dXVhaWqJXr1548eLFv253xYoVsLW1hZaWFlxdXXH58uWCPhQi+o4YZIioUOjfvz+OHTuGzZs34/bt22jWrBnc3Nzw/PlzpKSkICwsDJMmTUJYWBj27NmDiIgItGnT5qvb3LFjB0aOHIkpU6YgLCwM1apVg7u7O2JjY7/TURFRQSvytyhITEyEoaEhEhISONiXqJB6//499PX1sX//frRs2VJsd3Z2RosWLTBz5swcr7ly5Qpq1KiByMhIlCpV6rPbdXV1hYuLC5YvXw7gw3WlrK2tMXToUIwbN65gDoaI8kVu/36zR4aIlC4zMxNZWVnQ0tJSaNfW1sbZs2c/+5qEhATIZDIYGRl9dnl6ejquXbsGNzc3sU1FRQVubm64cOFCvtVORMrFIENESqevr49atWphxowZePHiBbKysrBlyxZcuHABL1++zLF+amoq/Pz80LVr1y9+U3v9+jWysrJgZmam0G5mZobo6OgCOQ4i+v4YZIioUNi8eTMEQYCVlRU0NTWxdOlSdO3aNcelyTMyMuDp6QlBELBy5UolVUtEhQWDDBEVCvb29jh16hTevXuHp0+f4vLly8jIyEDp0qXFdbJDTGRkJI4dO/bV8+bFixeHqqoqYmJiFNpjYmJgbm5eYMdB0vW1mXPAh3v/TJ48GRYWFtDW1oabmxsePHiQ6+3PmTMHMpkMvr6+BVD9j4tBhogKFV1dXVhYWCAuLg5HjhxB27ZtAfwvxDx48ADHjx+HiYnJV7ejoaEBZ2dnhISEiG1yuRwhISGoVatWgR4DSdPXZs4BwLx587B06VKsWrUKly5dgq6uLtzd3ZGamvqv275y5QpWr16NqlWrFvRh/HiEIi4hIUEAICQkJCi7FCL6iuDgYOHw4cPC48ePhaNHjwrVqlUTXF1dhfT0dCE9PV1o06aNULJkSeHGjRvCy5cvxUdaWpq4jcaNGwvLli0Tn2/fvl3Q1NQUNm7cKNy7d0/w9vYWjIyMhOjoaGUcIhViKSkpgqqqqvDnn38qtDs5OQkTJkwQ5HK5YG5uLsyfP19cFh8fL2hqagrbtm376raTkpKEsmXLCseOHRMaNGggDB8+vCAOocjJ7d9v9sgQUaGQkJAAHx8flC9fHr169ULdunVx5MgRqKur4/nz5zhw4ACePXsGBwcHWFhYiI/z58+L23j06BFev34tPu/cuTMWLFiAyZMnw8HBATdu3EBwcHCOAcBE/zZz7smTJ4iOjlaYBWdoaAhXV9d/nQXn4+ODli1bKryW8k+Rv2kkEUmDp6cnPD09P7vM1tYWQi4uefXPP//kaBsyZAiGDBnyX8ujIu7jmXMVKlSAmZkZtm3bhgsXLqBMmTLiTLe8zoLbvn07wsLCFMbaUP5ijwwRERFyP3Mut54+fYrhw4dj69atOXp6KP8wyBAREeHrM+eyZ7rlZRbctWvXEBsbCycnJ6ipqUFNTQ2nTp3C0qVLoaamhqysrAI/ph8BTy0RSZjtuL+UXQIp2T9zWv77SpQnurq60NXVFWfOzZs3D3Z2djA3N0dISAgcHBwAfLiE/qVLlzBo0KDPbqdJkya4ffu2QpuXlxfKly8PPz8/qKqqFvSh/BAYZIiIiAAcOXIEgiCgXLlyePjwIcaMGYPy5cvDy8tLvP7LzJkzUbZsWdjZ2WHSpEmwtLREu3btxG00adIE7du3x5AhQ6Cvr4/KlSsr7ENXVxcmJiY52unbMcgQERHhw8w5f39/PHv2DMbGxvDw8MCsWbOgrq4OABg7diySk5Ph7e2N+Ph41K1bF8HBwQrjXz6dOUcFj3e/JpIwnloinlqioop3vyYiIqIij0GGiIiIJItjZIiI6D/hKc4fm7JPb7JHhoiIiCSLQYaIiIgki0GGiIiIJItBhpCVlYVJkybBzs4O2trasLe3x4wZM3LcpC88PBxt2rSBoaEhdHV14eLigqioqC9u9+7du/Dw8ICtrS1kMhmWLFlSwEdCREQ/Gg72JcydOxcrV65EYGAgKlWqhKtXr8LLywuGhoYYNmwYgA8Xeapbty769euHadOmwcDAAHfv3v3qjdBSUlJQunRpdOrUCSNGjPheh0NERD8QBhnC+fPn0bZtW7Rs+WHkua2tLbZt24bLly+L60yYMAE///wz5s2bJ7bZ29t/dbsuLi5wcXEBAIwbN64AKicioh8dTy0RateujZCQEPz9998AgJs3b+Ls2bNo0aIFAEAul+Ovv/7CTz/9BHd3d5iamsLV1RX79u1TYtVEREQMMoQPvSVdunRB+fLloa6uDkdHR/j6+qJ79+4AgNjYWLx79w5z5sxB8+bNcfToUbRv3x4dOnTAqVOnlFw9ERH9yHhqibBz505s3boVQUFBqFSpEm7cuAFfX19YWlqid+/ekMvlAIC2bduKY10cHBxw/vx5rFq1Cg0aNFBm+URE9ANjkCGMGTNG7JUBgCpVqiAyMhIBAQHo3bs3ihcvDjU1NVSsWFHhdRUqVMDZs2eVUTIREREAnloifJhdpKKi+FFQVVUVe2I0NDTg4uKCiIgIhXX+/vtv2NjYfLc6iYiIPsUeGULr1q0xa9YslCpVCpUqVcL169exaNEi9O3bV1xnzJgx6Ny5M+rXr49GjRohODgYBw8eRGhoqLhOr169YGVlhYCAAABAeno67t27J/77+fPnuHHjBvT09FCmTJnveoxERFQ0McgQli1bhkmTJmHw4MGIjY2FpaUlBgwYgMmTJ4vrtG/fHqtWrUJAQACGDRuGcuXKYffu3ahbt664TlRUlELPzosXL+Do6Cg+X7BgARYsWIAGDRooBCAiIqJvJRM+vXxrEZOYmAhDQ0MkJCTAwMBA2eUQ5SvedZiUfedhgJ/DH11BfQZz+/ebY2SIiIhIsnhq6T/gtxAqDN+GiYh+ZOyRISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyWKQISIiIslikCEiIiLJYpAhIiIiyVJqkMnKysKkSZNgZ2cHbW1t2NvbY8aMGRAEQVxHEARMnjwZFhYW0NbWhpubGx48eKDEqomIiKiwUGqQmTt3LlauXInly5cjPDwcc+fOxbx587Bs2TJxnXnz5mHp0qVYtWoVLl26BF1dXbi7uyM1NVWJlRMREVFhoKbMnZ8/fx5t27ZFy5YtAQC2trbYtm0bLl++DOBDb8ySJUswceJEtG3bFgCwadMmmJmZYd++fejSpYvSaiciIiLlU2qPTO3atRESEoK///4bAHDz5k2cPXsWLVq0AAA8efIE0dHRcHNzE19jaGgIV1dXXLhwQSk1ExERUeGh1B6ZcePGITExEeXLl4eqqiqysrIwa9YsdO/eHQAQHR0NADAzM1N4nZmZmbjsU2lpaUhLSxOfJyYmFlD1REREpGxK7ZHZuXMntm7diqCgIISFhSEwMBALFixAYGDgN28zICAAhoaG4sPa2jofKyYiIqLCRKlBZsyYMRg3bhy6dOmCKlWqoGfPnhgxYgQCAgIAAObm5gCAmJgYhdfFxMSIyz7l7++PhIQE8fH06dOCPQgiIiJSGqUGmZSUFKioKJagqqoKuVwOALCzs4O5uTlCQkLE5YmJibh06RJq1ar12W1qamrCwMBA4UFERERFk1LHyLRu3RqzZs1CqVKlUKlSJVy/fh2LFi1C3759AQAymQy+vr6YOXMmypYtCzs7O0yaNAmWlpZo166dMksnIiKiQkCpQWbZsmWYNGkSBg8ejNjYWFhaWmLAgAGYPHmyuM7YsWORnJwMb29vxMfHo27duggODoaWlpYSKyciIqLCQKlBRl9fH0uWLMGSJUu+uI5MJsP06dMxffr071cYERERSQLvtURERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJLFIENERESSxSBDREREksUgQ0RERJKl9CDz/Plz9OjRAyYmJtDW1kaVKlVw9epVcbkgCJg8eTIsLCygra0NNzc3PHjwQIkVExERUWGh1CATFxeHOnXqQF1dHYcPH8a9e/ewcOFCFCtWTFxn3rx5WLp0KVatWoVLly5BV1cX7u7uSE1NVWLlREREVBioKXPnc+fOhbW1NTZs2CC22dnZif8WBAFLlizBxIkT0bZtWwDApk2bYGZmhn379qFLly7fvWYiIiIqPJTaI3PgwAFUr14dnTp1gqmpKRwdHbF27Vpx+ZMnTxAdHQ03NzexzdDQEK6urrhw4cJnt5mWlobExESFBxERERVNSg0yjx8/xsqVK1G2bFkcOXIEgwYNwrBhwxAYGAgAiI6OBgCYmZkpvM7MzExc9qmAgAAYGhqKD2tr64I9CCIiIlKaPJ1aksvlOHXqFM6cOYPIyEikpKSgRIkScHR0hJubW55Dg1wuR/Xq1TF79mwAgKOjI+7cuYNVq1ahd+/eedpWNn9/f4wcOVJ8npiYyDBDRERUROWqR+b9+/eYOXMmrK2t8fPPP+Pw4cOIj4+HqqoqHj58iClTpsDOzg4///wzLl68mOudW1hYoGLFigptFSpUQFRUFADA3NwcABATE6OwTkxMjLjsU5qamjAwMFB4EBERUdGUqx6Zn376CbVq1cLatWvRtGlTqKur51gnMjISQUFB6NKlCyZMmIBffvnlX7dbp04dREREKLT9/fffsLGxAfBh4K+5uTlCQkLg4OAA4EMPy6VLlzBo0KDclE5ERERFWK6CzNGjR1GhQoWvrmNjYwN/f3+MHj1a7FH5NyNGjEDt2rUxe/ZseHp64vLly1izZg3WrFkDAJDJZPD19cXMmTNRtmxZ2NnZYdKkSbC0tES7du1ytQ8iIiIqunIVZP4txHxMXV0d9vb2uVrXxcUFe/fuhb+/P6ZPnw47OzssWbIE3bt3F9cZO3YskpOT4e3tjfj4eNStWxfBwcHQ0tLKdU1ERERUNH3zdWQyMzOxevVqhIaGIisrC3Xq1IGPj0+eA0arVq3QqlWrLy6XyWSYPn06pk+f/q2lEhERURH1zUFm2LBh+Pvvv9GhQwdkZGRg06ZNuHr1KrZt25af9RERERF9Ua6DzN69e9G+fXvx+dGjRxEREQFVVVUAgLu7O2rWrJn/FRIRERF9Qa4viLd+/Xq0a9cOL168AAA4OTlh4MCBCA4OxsGDBzF27Fi4uLgUWKFEREREn8p1kDl48CC6du2Khg0bYtmyZVizZg0MDAwwYcIETJo0CdbW1ggKCirIWomIiIgU5GmMTOfOneHu7o6xY8fC3d0dq1atwsKFCwuqNiIiIqKvyvO9loyMjLBmzRrMnz8fvXr1wpgxY5CamloQtRERERF9Va6DTFRUFDw9PVGlShV0794dZcuWxbVr16Cjo4Nq1arh8OHDBVknERERUQ65DjK9evWCiooK5s+fD1NTUwwYMAAaGhqYNm0a9u3bh4CAAHh6ehZkrUREREQKcj1G5urVq7h58ybs7e3h7u4OOzs7cVmFChVw+vRp8dYCRERERN9DroOMs7MzJk+ejN69e+P48eOoUqVKjnW8vb3ztTgiIiKir8n1qaVNmzYhLS0NI0aMwPPnz7F69eqCrIuIiIjoX+W6R8bGxgZ//PFHQdZCRERElCe56pFJTk7O00bzuj4RERHRt8hVkClTpgzmzJmDly9ffnEdQRBw7NgxtGjRAkuXLs23AomIiIi+JFenlkJDQzF+/HhMnToV1apVQ/Xq1WFpaQktLS3ExcXh3r17uHDhAtTU1ODv748BAwYUdN1EREREuQsy5cqVw+7duxEVFYVdu3bhzJkzOH/+PN6/f4/ixYvD0dERa9euRYsWLcS7YRMREREVtDzda6lUqVIYNWoURo0aVVD1EBEREeVanu+1RERERFRYMMgQERGRZDHIEBERkWQxyBAREZFkMcgQERGRZOU5yNja2mL69OmIiooqiHqIiIiIci3PQcbX1xd79uxB6dKl0bRpU2zfvh1paWkFURsRERHRV31TkLlx4wYuX76MChUqYOjQobCwsMCQIUMQFhZWEDUSERERfdY3j5FxcnLC0qVL8eLFC0yZMgXr1q2Di4sLHBwcsH79egiCkJ91EhEREeWQpyv7fiwjIwN79+7Fhg0bcOzYMdSsWRP9+vXDs2fPMH78eBw/fhxBQUH5WSsRERGRgjwHmbCwMGzYsAHbtm2DiooKevXqhcWLF6N8+fLiOu3bt4eLi0u+FkpERET0qTwHGRcXFzRt2hQrV65Eu3btoK6unmMdOzs7dOnSJV8KJCIiIvqSPAeZx48fw8bG5qvr6OrqYsOGDd9cFBEREVFu5Hmwb2xsLC5dupSj/dKlS7h69Wq+FEVERESUG3kOMj4+Pnj69GmO9ufPn8PHxydfiiIiIiLKjTwHmXv37sHJySlHu6OjI+7du5cvRRERERHlRp6DjKamJmJiYnK0v3z5Empq3zybm4iIiCjP8hxkmjVrBn9/fyQkJIht8fHxGD9+PJo2bZqvxRERERF9TZ67UBYsWID69evDxsYGjo6OAIAbN27AzMwMmzdvzvcCiYiIiL4kz0HGysoKt27dwtatW3Hz5k1oa2vDy8sLXbt2/ew1ZYiIiIgKyjcNatHV1YW3t3d+10JERESUJ988OvfevXuIiopCenq6QnubNm3+c1FEREREufFNV/Zt3749bt++DZlMJt7lWiaTAQCysrLyt0IiIiKiL8jzrKXhw4fDzs4OsbGx0NHRwd27d3H69GlUr14doaGhBVAiERER0efluUfmwoULOHHiBIoXLw4VFRWoqKigbt26CAgIwLBhw3D9+vWCqJOIiIgohzz3yGRlZUFfXx8AULx4cbx48QIAYGNjg4iIiPytjoiIiOgr8twjU7lyZdy8eRN2dnZwdXXFvHnzoKGhgTVr1qB06dIFUSMRERHRZ+U5yEycOBHJyckAgOnTp6NVq1aoV68eTExMsGPHjnwvkIiIiOhL8hxk3N3dxX+XKVMG9+/fx9u3b1GsWDFx5hIRERHR95CnMTIZGRlQU1PDnTt3FNqNjY0ZYoiIiOi7y1OQUVdXR6lSpXitGCIiIioU8jxracKECRg/fjzevn1bEPUQERER5Vqex8gsX74cDx8+hKWlJWxsbKCrq6uwPCwsLN+KIyIiIvqaPAeZdu3aFUAZRERERHmX5yAzZcqUgqiDiIiIKM/yPEaGiIiIqLDIc4+MiorKV6dac0YTERERfS95DjJ79+5VeJ6RkYHr168jMDAQ06ZNy7fCiIiIiP5NnoNM27Ztc7R17NgRlSpVwo4dO9CvX798KYyIiIjo3+TbGJmaNWsiJCQkvzZHRERE9K/yJci8f/8eS5cuhZWVVX5sjoiIiChX8nxq6dObQwqCgKSkJOjo6GDLli35WhwRERHR1+Q5yCxevFghyKioqKBEiRJwdXVFsWLF8rU4IiIioq/Jc5Dp06dPAZRBRERElHd5HiOzYcMG7Nq1K0f7rl27EBgYmC9FEREREeVGnoNMQEAAihcvnqPd1NQUs2fPzpeiiIiIiHIjz0EmKioKdnZ2OdptbGwQFRWVL0URERER5Uaeg4ypqSlu3bqVo/3mzZswMTHJl6KIiIiIciPPQaZr164YNmwYTp48iaysLGRlZeHEiRMYPnw4unTp8s2FzJkzBzKZDL6+vmJbamoqfHx8YGJiAj09PXh4eCAmJuab90FERERFS56DzIwZM+Dq6oomTZpAW1sb2traaNasGRo3bvzNY2SuXLmC1atXo2rVqgrtI0aMwMGDB7Fr1y6cOnUKL168QIcOHb5pH0RERFT05Hn6tYaGBnbs2IGZM2fixo0b0NbWRpUqVWBjY/NNBbx79w7du3fH2rVrMXPmTLE9ISEBv//+O4KCgtC4cWMAH2ZMVahQARcvXkTNmjW/aX9ERERUdHzzLQrKli2LTp06oVWrVt8cYgDAx8cHLVu2hJubm0L7tWvXkJGRodBevnx5lCpVChcuXPjm/REREVHRkecg4+Hhgblz5+ZonzdvHjp16pSnbW3fvh1hYWEICAjIsSw6OhoaGhowMjJSaDczM0N0dPQXt5mWlobExESFBxERERVNeQ4yp0+fxs8//5yjvUWLFjh9+nSut/P06VMMHz4cW7duhZaWVl7L+KKAgAAYGhqKD2tr63zbNhERERUueQ4y7969g4aGRo52dXX1PPV+XLt2DbGxsXBycoKamhrU1NRw6tQpLF26FGpqajAzM0N6ejri4+MVXhcTEwNzc/Mvbtff3x8JCQni4+nTp7muiYiIiKQlz0GmSpUq2LFjR4727du3o2LFirneTpMmTXD79m3cuHFDfFSvXh3du3cX/62uro6QkBDxNREREYiKikKtWrW+uF1NTU0YGBgoPIiIiKhoyvOspUmTJqFDhw549OiROJsoJCQE27Zt++w9mL5EX18flStXVmjT1dWFiYmJ2N6vXz+MHDkSxsbGMDAwwNChQ1GrVi3OWCIiIiIA3xBkWrdujX379mH27Nn4448/oK2tjapVq+L48eNo0KBBvha3ePFiqKiowMPDA2lpaXB3d8dvv/2Wr/sgIiIi6cpzkAGAli1bomXLljna79y5k6OXJS9CQ0MVnmtpaWHFihVYsWLFN2+TiIiIiq5vvo5MtqSkJKxZswY1atRAtWrV8qMmIiIiolz55iBz+vRp9OrVCxYWFliwYAEaN26Mixcv5mdtRERERF+Vp1NL0dHR2LhxI37//XckJibC09MTaWlp2LdvX55mLBERERHlh1z3yLRu3RrlypXDrVu3sGTJErx48QLLli0ryNqIiIiIvirXPTKHDx/GsGHDMGjQIJQtW7YgayIiIiLKlVz3yJw9exZJSUlwdnaGq6srli9fjtevXxdkbURERERflesgU7NmTaxduxYvX77EgAEDsH37dlhaWkIul+PYsWNISkoqyDqJiIiIcsjzrCVdXV307dsXZ8+exe3btzFq1CjMmTMHpqamaNOmTUHUSERERPRZ/+k6MuXKlcO8efPw7NkzbNu2Lb9qIiIiIsqV/3xBPABQVVVFu3btcODAgfzYHBEREVGu5EuQISIiIlIGBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLAYZIiIikiwGGSIiIpIsBhkiIiKSLKUGmYCAALi4uEBfXx+mpqZo164dIiIiFNZJTU2Fj48PTExMoKenBw8PD8TExCipYiIiIipMlBpkTp06BR8fH1y8eBHHjh1DRkYGmjVrhuTkZHGdESNG4ODBg9i1axdOnTqFFy9eoEOHDkqsmoiIiAoLNWXuPDg4WOH5xo0bYWpqimvXrqF+/fpISEjA77//jqCgIDRu3BgAsGHDBlSoUAEXL15EzZo1lVE2ERERFRKFaoxMQkICAMDY2BgAcO3aNWRkZMDNzU1cp3z58ihVqhQuXLjw2W2kpaUhMTFR4UFERERFU6EJMnK5HL6+vqhTpw4qV64MAIiOjoaGhgaMjIwU1jUzM0N0dPRntxMQEABDQ0PxYW1tXdClExERkZIUmiDj4+ODO3fuYPv27f9pO/7+/khISBAfT58+zacKiYiIqLBR6hiZbEOGDMGff/6J06dPo2TJkmK7ubk50tPTER8fr9ArExMTA3Nz889uS1NTE5qamgVdMhERERUCSu2REQQBQ4YMwd69e3HixAnY2dkpLHd2doa6ujpCQkLEtoiICERFRaFWrVrfu1wiIiIqZJTaI+Pj44OgoCDs378f+vr64rgXQ0NDaGtrw9DQEP369cPIkSNhbGwMAwMDDB06FLVq1eKMJSIiIlJukFm5ciUAoGHDhgrtGzZsQJ8+fQAAixcvhoqKCjw8PJCWlgZ3d3f89ttv37lSIiIiKoyUGmQEQfjXdbS0tLBixQqsWLHiO1REREREUlJoZi0RERER5RWDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERERSRaDDBEREUmWJILMihUrYGtrCy0tLbi6uuLy5cvKLomIiIgKgUIfZHbs2IGRI0diypQpCAsLQ7Vq1eDu7o7Y2Fhll0ZERERKVuiDzKJFi/DLL7/Ay8sLFStWxKpVq6Cjo4P169cruzQiIiJSskIdZNLT03Ht2jW4ubmJbSoqKnBzc8OFCxeUWBkREREVBmrKLuBrXr9+jaysLJiZmSm0m5mZ4f79+599TVpaGtLS0sTnCQkJAIDExMR8r0+elpLv2yRpKYjPVV7wM0jK/gwC/Bz+6ArqM5i9XUEQvrpeoQ4y3yIgIADTpk3L0W5tba2EaqioM1yi7AroR8fPIClbQX8Gk5KSYGho+MXlhTrIFC9eHKqqqoiJiVFoj4mJgbm5+Wdf4+/vj5EjR4rP5XI53r59CxMTE8hksgKt90eTmJgIa2trPH36FAYGBsouh35A/AySsvEzWHAEQUBSUhIsLS2/ul6hDjIaGhpwdnZGSEgI2rVrB+BDMAkJCcGQIUM++xpNTU1oamoqtBkZGRVwpT82AwMD/gcmpeJnkJSNn8GC8bWemGyFOsgAwMiRI9G7d29Ur14dNWrUwJIlS5CcnAwvLy9ll0ZERERKVuiDTOfOnfHq1StMnjwZ0dHRcHBwQHBwcI4BwERERPTjKfRBBgCGDBnyxVNJpDyampqYMmVKjlN5RN8LP4OkbPwMKp9M+Ld5TURERESFVKG+IB4RERHR1zDIEBERkWQxyBAREZFkMcgQERGRZDHIUA5ZWVnKLoF+QHK5PEdbUlKSEiohIilhkKEcVFVVAQArV67McXsIooKioqKCyMhILFmyBACwa9cu9OrVS7zxK9H3xC900sEgQ6KPvxGvWLECPj4+iI6OVmJF9CPJzMzEypUrsWHDBvTu3RudO3dG27Ztc3WJcqL8kt0LqKqqiqtXryItLU3JFdG/YZAhkYrKh4/DqVOnIJPJsHv3blSrVk3JVdGPQk1NDVOmTIGNjQ02b94MT09P9OnTBwC/HdP38ezZM/Tp0wdHjx7F7t27UaNGDYSFhSm7LPoXDDKk4PLly2jcuHGOO4gTFaTs63JqaGjAyMgITZs2xbNnzxAQEADgw7djhhkqaCkpKXj79i38/PzQvXt3BAYGolatWvwdWMgxyPzgPr2ws42NDRYsWABdXV2cPHkSwIeeGv5HpoIiCAJkMhmuXbuG58+fIzAwEDt27ICjoyP279+vEGYA4PXr18osl4ooQRDw008/oV+/frh9+zZKly4NExMTAPwdWNgxyPzgZDIZgA9jYiIiImBmZoaePXti/PjxWLNmDaZOnQqA/5GpYGSHmL179+Lnn3/GsmXL8ObNGxgZGWHChAlwcXHBgQMHMHv2bADA5MmTMWjQII5boHyV/TnMysqCra0tVq1ahdKlS2Px4sXYtWsXAP4OLNQE+uG9e/dOqFatmlCyZEnh4cOHgiAIwuvXr4UFCxYIRkZGwtSpU8V15XK5ssqkIurQoUOCtra28PvvvwuvXr1SWBYTEyOMHj1asLe3FypUqCAYGxsLFy9eVFKlVBRl/047cuSIMHjwYOHt27eCIAhCeHi44O7uLjRp0kT4448/xPX//PNPITU1VSm10ufxppE/ILlcLg7szRYdHY3u3bvj8ePHOH78OOzt7fH69Wts2rQJs2fPRu/evbFw4UIlVUxFVXp6Ory9vWFqaop58+YhOTkZUVFR2LJlC+zs7NCyZUvo6+vjwoULiIiIQPPmzVGmTBlll01FzO7du9G/f3/069cPnp6eqFGjBgDg3r17GDlyJLKystC8eXMkJSVh+vTpiIyMhLW1tZKrpmwMMj+wzMxMqKmpid2q0dHR6NKlCyIjI8Uw8+bNGyxfvhznz59HcHCweCqKKD9kZGSgadOmMDU1xbJlyzBp0iQ8ePAAL168QEJCArp06SJeV4aoIFy/fh3NmjXDrFmz4O3tLba/ffsWxsbGePLkCSZOnIiIiAikpKRgy5YtcHJyUmLF9CkGmR/UmjVrMHfuXNy6dQu6urpimHn58iU6dOiAhIQEHDx4EPb29khISICBgQFkMpm4HtG3+Nzn56+//kKPHj2QmZmJZs2aoWPHjujatSvmzJmDffv2ITQ0FFpaWkqqmIq6rVu3YtWqVThz5gzi4uIQHByMLVu24ObNmxgyZAjGjRuH+Ph4pKamQk1NDcWLF1d2yfQJNWUXQMpRvXp1qKmpoXHjxjhx4gR0dXUhl8thYWGBwYMHo3fv3nBxccGtW7dQsmRJAJ//I0SUW9mfn3PnzuHMmTN49eoV3Nzc0LJlS9y9exePHz9G3bp1xZl0L1++RKlSpZRcNRVFH/8us7CwwLlz5zBx4kSEhobC2NgYpUqVQp06dTB+/Hi4u7vD0dFRyRXT1zDI/AA+NybGyckJe/bsQceOHdGgQQOcOnUKurq6AIDixYvDx8cHmpqasLCwEF/DEEP/hUwmw549e+Dt7Y3atWujRIkSaNmyJfz8/DB16lRYWloCAG7fvo3t27cjMDAQp0+fZm8M5ZvsAJOeng5NTU3I5XI0btwYCxYswKZNm1C/fn306dNHDC779u1Damqqkqumf8MgU8R9HGKOHj2KFy9ewMzMDOXLl0elSpWwe/dueHh4oH79+li7di0MDAywbt06lClTBnPnzgXw4aqq2dfwIPpWERERGDlyJGbPng1vb28kJydj8+bNAABNTU0AwM2bN7Fo0SKEhYXh1KlTqFq1qjJLpiIkO8QEBwdj69atePnyJapWrQovLy+MHDkSv/zyC/T19cX1x48fj7dv38LOzk6JVVNucIzMD2Ls2LEIDAyEubk5IiMjUblyZfTv3x99+vTBo0eP0LNnT9y4cQOmpqYwMjLClStXoK6uruyyqQi5fPkyxowZg1OnTuHRo0do0KABWrZsidWrVwMAnj59Cmtra1y9ehUWFhawsrJScsVU1Bw4cACenp7w8fHBq1evEBsbi/Pnz+PAgQNo2LAhgA9f+LZv346DBw/i6NGjPK0kAeyR+QFs27YNgYGB2Lt3L2rWrIlbt25h1apVWLZsGbS0tNClSxecP38ehw8fhqamJho0aABVVVVxVhPRt8j+Bnz06FEYGRkhKysLT58+xaVLl9C1a1e0bNkSv/32G4AP9/eaP38+Vq9ejerVqyu5ciqKEhMTsXDhQkyYMAGTJk0CAERFRWHWrFlo164dTp8+jbJlyyIyMhIpKSkIDQ1FpUqVlFw15Qav7PsDuHPnDhwdHVG7dm2oqKjAwcEBw4cPR+nSpbF3715kZGQAAFq0aIHGjRuL97VhiKH/QiaT4ezZs+jQoQMiIiJQtmxZVKpUCY0bN0bNmjWxevVq8bRncHAwUlJSxFNMRPktLS0Njx49EicvAIC1tTX8/f1RvXp17N27F9ra2vD09MTvv//OECMh/EtVhGWPj9HX10dCQgLi4uJQrFgxAECFChXQqVMndO/eHc+fP4etra3Cazkmhv6ryMhIHDp0COPHj0fPnj0BAK1bt8bjx4+hoaGBu3fv4v3799i5cyfWrl2L06dPc2or5bvsnsESJUrAwcEB586dQ6dOnaCnpweZTAZbW1vo6Ojg9u3bAABDQ0MlV0x5xR6ZIkIQhBz3Acn+tlu5cmXcuHEDu3fvVriDsJWVFapUqcLQQvnu/v376Nq1K4KCgsTwDADe3t7o27cvIiMjUbVqVfTv3x8hISE4efIkqlSposSKqSjJHvopl8sVfi82aNAAly5dwrZt25CSkiK2GxgYwMLCAllZWTlupEuFHwf7FkG7d+9GQkICAKBr167Q1tbGzJkzMXXqVMyfPx+1a9eGhYUFfvnlF6SnpyMkJCTH9Gyi/8rX11ec0hoYGKjwTTcpKQn37t2DhYUFdHV1xbsME/1X2T0wR44cwebNm/H8+XM4Ojril19+QYUKFTBkyBCcOnUKlStXhouLC+7fv4/t27fj4sWLqFixorLLp2/AICNxvr6+iIuLQ2BgIABg+PDh2Lx5M8zMzJCcnAyZTIYtW7agXr16mDt3LlasWIHk5GSYm5tDT08PZ8+ehbq6+mevNUOUW1+6WKKfnx/+/PNPdO7cGcOGDYORkdH3L45+OAcOHECnTp3Qs2dPGBgYYO/evShZsiT8/f3x888/Y+nSpThz5gzCw8NhZ2eHWbNmcaq/hDHISFhycjLmz5+PPXv2oEmTJhg6dCj69u2LX3/9FdbW1pDL5fDy8sLly5dx/PhxVKlSBXfu3MG7d++QlpaGevXqQUVFhbOT6D/JDjGXLl3CuXPnoKGhId7wEQBGjRqF0NBQtGvXDkOHDoWRkRGvEk0FQhAExMXFoWXLlmjXrh38/PwAADExMfjll1/w9u1bbNq0CaVLlwbwoWdQQ0ODg8wljkFG4uLi4rB+/Xps2bIFpqamACCOvs/+Q9G4cWMkJSXhypUrOV7Pi93Rf5EdSHbv3g0vLy84ODggPj4e4eHhGDp0KBYtWgQAGDFiBC5cuICGDRvC39+fAyqpwKSkpMDV1RVDhw6Ft7c3MjIyoK6ujtjYWDg5OcHLywszZsxQdpmUj3guQcIEQUCxYsXg5eWFbt264dmzZ3j06BF0dHQgk8nES2uPGzcOsbGxiIiIyLENhhjKi08HlMtkMjx8+BBDhw7F3Llzcfr0aZw6dQpbtmzB6tWrMXr0aADA4sWLUa1aNVy6dEmc7k/0XyUlJeHp06cKtxHIzMyEXC7HgwcPAHz4HZeRkQFTU1O4ubl99vcgSRuDjARl/zHJ7nExNjZGnz590LdvX7x58wb9+/cHAPEeNdra2gDA0fj0n2SPo7p9+zaOHDkitr958wb6+vpo3bo1AKBYsWLo3Lkz1q5di99++w0nTpwAAKxevRo7duzgFGvKF3fv3kXLli3RrFkzODs749ixYwA+zEAaP348Fi1ahPXr10NFRUW8SnlcXBzMzMyUWTYVAA6MkJj09HRoaGgAAK5cuQIVFRVYWFjA0tISXl5eEAQBq1evRvfu3TF16lQkJiZi9uzZsLKywk8//aTk6kmqskPMrVu34ODggGnTpsHd3R0AoKOjg0ePHuHvv/9GyZIlxdNNDRs2hIWFBV6+fCluJ/v0J9F/cfPmTdSrVw+9evVCq1atsGDBAgwbNgz37t2DTCZD+/btMX78ePTv3x9hYWGwtrbGs2fPcOLECVy6dEnZ5VN+E0gSevXqJVy9elV87ufnJxgbGwulSpUSzM3NhdOnTwuCIAhv3rwRFixYIBgaGgr6+vpCz549hT59+gjp6emCIAhCVlaWUuon6cr+zFy/fl3Q1tYWJkyYoLA8PT1daNWqldChQwfh2rVrYntaWppQvXp1YcOGDd+zXCribt26Jejo6AhTpkwR28LDw4X69esLly9fFm7duiXEx8cLgiAI27ZtE1xcXIRatWoJ7u7uws2bN5VUNRUkBhkJePv2rVC/fn3BxMREuH37tnDz5k2hdOnSwunTp4UTJ04I/fr1EzQ1NYUDBw4IgiAIcXFxwsKFCwUrKyth5syZ4nYyMjKUdQgkcffv3xc0NTWFWbNmKbQfPHhQSEpKEvbt2yfUr19faN26tXDw4EHhzp07wtixY4USJUoIT548UU7RVOQkJCQILi4ugrW1tUL7mDFjBC0tLcHOzk4wNTUVateuLTx8+FAQBEFITk4W5HK5kJycrIyS6TtgkJGIFy9eCB06dBBMTEyEJUuWCDNmzBCXvX//XvDx8RE0NTWFgwcPCoIgCLGxscLWrVuFzMxMQRAEQS6XK6Vukr73798LXbp0EUxMTIQTJ06I7TNnzhRKliwphIeHC4IgCHv27BE6deokqKqqChUqVBDKli0rhIWFKatsKoISEhKE3377TbCyshIGDBggCIIg9kBv27ZNiIqKElavXi3Y2toKw4YNE1JTU/k78AfAIFPIfXwq6MWLF0LHjh0FmUwm9OvXTxCE//3nzA4zurq6ws6dOxW2kf0fmehbnThxQujQoYPQqFEj4fLly8LSpUsFY2Nj4fDhwwrrpaenC3///bcQHh4uxMbGKqlaKsri4+OF9evXCyVKlBCqVasmlChRQggNDVVYp169ekLr1q2VVCF9b7yOjMQ8e/YM/v7+OHDgAE6ePAknJydxcGVaWhr69euH58+f4+TJk8oulYqY06dPY9GiRQgPD0dkZCRCQ0NRs2ZNcTacTCbjhe4o3z179gynTp1CeHg4/Pz8oK+vj+TkZOzatQszZsyAvb09jh49CuDDHa41NTXRtWtXlChRAosWLYKqqio/k0UcZy0VUh/fMmDevHl4/PgxlixZgpIlS2LevHl49+4dmjZtipMnT6Jq1aoQBAGamprYsGEDrw1D+So7nNSvXx8qKiqYM2cOdHV1kZycDOB/ASb730T55c6dO+jduzecnZ1RvHhx6OvrAwB0dXXRtm1bAB+uk+Xt7Y01a9ZAU1MTkyZNwrFjx3D27FlesfwHwZ9yIfRxiLl+/TpiYmKwZs0amJqaYtKkSbCwsMBvv/2GQYMGoXHjxuKdgwVBEK+XwHsnUX75uKelbt268PPzw8KFCzFr1iykp6ejRYsW7I2hfHfv3j3Uq1cPQ4YMga+vr3hj0aCgIFSvXh0//fQT2rdvD+BDmNHW1oalpSUWLFiAc+fOoXz58sosn74nJZ3SolwYM2aMULZsWWHIkCFC3bp1BVVVVcHX11ecfZQ9AFgmk4kj9IkKyseDJU+dOiW0bdtWaNq0qbBv3z4lVkVF0du3b4V69eoJv/zyi0J7QECAIJPJBBMTE3GQeXx8vBAYGCjo6uoKMplM4TIV9GNgj0whIXzybfbYsWNYvXo1Dh8+jNq1ayM1NRV//PEH+vbtC5lMhjlz5sDCwgK//vorfvrpJ9ja2iqveCqyPv5cftzrkn2aafLkyVi/fj3c3Nygq6ur5GqpqIiKisLbt2/RtWtXsW337t2YM2cONm3ahF27dqFBgwYIDQ1FhQoV0Lp1a6irq6NGjRqwt7dXYuWkDAwyhUC3bt3g7++PKlWqiG0JCQkwMzNDtWrVAHy43UCPHj2QnJyMQYMGwdDQEBMmTEDJkiUxe/ZsyGQy3sWa/pPskPLkyRO8ffsWVatWFU9VZvv0NNOsWbNgbW3NEEP5IvvK5eHh4YiKilIIJWZmZjhz5gyqVKmCpk2bon///nB2dsbjx49hbm6OLl268NTmD4qDKAqBrKwslCtXTqHN0tISjx49wtWrVwH87z5J9erVg6GhIaZNm4YpU6YA+N8AS4YY+i9kMhn27NmDWrVqoXXr1qhatSr27dsnDur9eL3sz2OtWrVQsmRJZZRLRcyDBw8wc+ZMAICenh7evXuHqKgocXndunXFL3tmZmbo2rUrypUrh6ysLAAcaP4jY5BRouz/gDt27ICGhgZWrlyJM2fOIDMzE05OTujYsSOmTJmC8+fPi/9JixUrhq5du2LdunWYO3euws37iL6VIAh48eIFZs2ahYkTJyI4OBgVK1aEn58ftm/fjnfv3imszz8alN82b96MLVu2AADq1KkDJycnDBs2TAwz6enpAP5309wrV66gdOnSMDQ0VE7BVGgwyCjRp9Ok586dCy8vL4SFhUFLSwsDBw6EgYEBBg0ahHXr1uHPP/9Enz598PDhQ7Rs2RKlS5dGeHi4kqqnoiC7Z0UQBBQrVgz16tWDl5cXqlatit27d6NWrVqYN28eduzYkSPMEOWH7M9g7dq1oaWlhbS0NBQrVgw9e/ZEbGws+vXrh2fPnok3y42Li4O/vz8CAwMxffp06OnpKbN8KgR4LkJJPh3cCwBPnjxB9erV0bNnT2zduhWNGjWCtrY2goKCMHz4cNjZ2cHY2BgnTpyAmpoaDAwMxOsqEH0LmUyGv/76Cxs3bkRUVBS0tLSQmZkpLt+4cSN69+6NxYsXIzU1FX369OF4GMpX2b8H7ezs8M8//+D06dNo2rQphg8fjoSEBKxduxaVK1dG3759ERsbi8TERFy7dg0hISGoVKmSkqunwoBX9lWCj6/x8vjxY6irqyMtLQ1lypSBIAhwdHRESkoKtm7dChcXFwDAixcvoK2tjWLFigEAxo4di127diE0NBQ2NjZKOxaStosXL6Ju3bro27cv7ty5g/DwcAwePBijR48WP2sA0KFDBzx79gzHjh1jVz7li3/++QcnTpwQv7AZGxvDxcUFM2bMQJs2bcT1Dh8+jH379uHatWvQ1tZG48aN0bNnT5QpU0aJ1VNhwiDznX3cEzNlyhQEBwcjNjYWpqam6NSpE0aPHg1BEODs7Iz3799j48aNcHZ2Fgfynj9/Hlu3bsWuXbtw5MgRODo6KvNwSMIiIiKwZ88eaGpqYuTIkQCAkSNH4uzZs2jTpg2GDh2qEFpevHgBS0tLZZVLRUh6ejo8PDwQFhYGFRUVpKamolmzZti2bRvatm2L+fPnQ1VVFXZ2duJrMjIyoK6uzgsvUg4MMkoyffp0/Prrr9i5cyesrKwwY8YMbNu2Dbdv30alSpUgCAJcXFzw+PFjnDlzRuxCjY6Oxr59++Dm5sZvJPTNHj9+jL59+yIiIgITJ06Ej4+PuGzkyJE4ffo0OnTogEGDBin0zBDll6SkJOjr6+P69eu4f/8+nj17ho0bNyI8PBxWVlbIzMxEpUqVYGlpiRo1aqBWrVpwdnZmkKEcOEbmO8rKyoKqqioSEhJw/vx5bNiwAU2aNMGff/6JQ4cOYeXKlahUqRJSUlKgo6ODK1euoG/fvgqX2jY3N4e3tzdvP0D/SalSpdC4cWNERkZi//79CmNfFi1ahDFjxuD333+HhoYGRo0axT8clO+yB+k6Ojoq9CzfunULo0aNwqtXrxAaGorr169j69atcHd3B8AZc5QTe2S+gxs3bsDBwUF8HhsbiypVquDw4cN49eoVOnbsiPnz52PgwIFIS0vDkiVL0KhRI9SoUUN8TXYIIvoWn/sWm5mZicWLF2Pbtm2oXbs2Zs+eDQMDA3H5hAkT0L9/f4XufaKCtGvXLnh7e+POnTuwsrIS25OTkznInL6IX+sL2MOHD9GiRQsMGjRIbDMwMEDTpk3x22+/wdPTEwsXLsTAgQMBAM+fP8e5c+fw7Nkzhe0wxNC3yg4x58+fx+zZszF9+nTs3bsXampqGDlyJDp37oyrV6/C398fiYmJ4utmzZrFEEPfjSAIqFKlCvT19ZGamgrgf9fa0tHRUWZpVMgxyBQwY2NjjBkzBiEhIRg+fDiAD7cbqFy5MtavX4+2bduiV69eAID4+HgMHToU7969E29RT/RfyWQy7N69G+7u7jh+/DgOHz4MDw8PDBo0CGlpaRg9ejTatGmDW7duYejQoUhKSlJ2yfQDkslkKF++PHR0dHDy5EkA//sCx9NJ9DUcI1PAjI2N0adPH6irq2Px4sWQy+VYtmwZxo0bh5iYGGzatAkdOnSArq4uoqOjkZSUhCtXrkBVVVVhmjbRt3ry5AlGjhwpnr6Uy+U4evQoPDw8oKKighUrVmDMmDF4//49Ll26hOTkZF6fiL677J5DbW1tPHnyRNnlkIQwyBSAR48eQUVFReyWNzY2RpcuXQAACxYsgFwux4oVK7B48WJUrVoVEREReP36NWrXro2hQ4dCTU2NN4Ckb5J98bCaNWuK32KTk5Ohrq6OBg0aiOs1b94cO3fuRJs2bdCqVSu0aNECU6dORXx8PExMTJRVPv3Asj+v3t7eqFevnpKrISnhYN98tmfPHnTs2BHm5uZo0qQJatasidatW6NUqVIAgGXLlmH58uVo1KgRVq1a9dltcGAvfQtBEGBtbQ19fX1s3rwZzs7OkMlkuHv3LqpUqYLg4GA0a9YMWVlZUFFRQUpKCmrWrImBAwcqTL8mUiZOr6a84nmLfPbw4UPo6OigVKlSCA8Px549e1CpUiW4ublh0aJFsLW1hbe3N06ePIkJEyZ8dhsMMZRX2b/8Hz9+DC0tLXh5eeHKlSvitTi6du2KadOm4fLly1BVVRW78HV0dHj6kgoVhhjKK/bIFICAgAAcPXoULi4u8PHxwaNHj3D27FkEBgbCwMAAkZGR0NLSQnR0NNatW4e+ffsqu2QqAtLS0qCpqYl3797BwcEBpUqVQkBAAFxdXXHy5EksXLgQsbGxmDBhAkxNTbF//36sW7cOly9fRunSpZVdPhHRN2GQyUcfD86dOnUq9u/fj+bNm2P06NEwMTFBUlISXr58iZ07d+L27duIjo5GSEgIx8LQf5bdI7Nz506cPHkS9+/fx6lTp+Dg4IDff/8djo6OOHXqFDZu3IgtW7agTJkyUFFRwZYtW3ibCyKSNAaZfPZxmJkxYwb27NkDd3d3+Pj4wNra+rOv4cBeyg9nzpyBu7s7li1bhsqVKyMjIwP9+/eHqqqqQmB5/Pgx1NTUoKury4G9RCR5DDL/wZcG5X4cZqZPn459+/bB3d0dw4YNg4WFhcK6HNhG+WXRokXYtWsXTp8+DXV1dQBAYmIiXFxcoKenh99++03hBqREREUBR/l9o7S0NDHEvHnzRmGZiooK5HI5AGDy5Mlo3749jh8/jhkzZuRYlyGG/qvs7yIJCQmIj48XQ8z79+9hYGCApUuX4vr16/D29satW7eUWSoRUb5jkPkGx44dw4IFCwAAgwYNgoeHBzIyMhTW+TjMTJo0CQ0bNsT79+9hbGz83euloi07DHt6euL58+cICAgAAGhrawMANDQ00Lp1a2hqasLIyEhZZRIRFQj2MeeRXC7Hnj17cOnSJRw7dgy3b9/GuXPnxG/BH8sOMyoqKpg/f754Gomnk+i/yP783LhxA3fv3kX58uVha2uLSpUqwc/PD+vWrYNcLseECRPw7t07HD9+HHZ2dti9ezdPKxFRkcMxMt+odu3auHjxIoYNG4YlS5YA+PJ4l4/bGWIoP+zZswdeXl4oUaIE4uLi0K1bN4wYMQKmpqZYvnw5Zs+eDRMTE+jp6eHZs2c4ceIEZycRUZHEIJNLHw/gTUtLE2/u+PjxY7Rp0wZDhw6Fvr4+r8pLBSY7BD99+hQ+Pj5o3bo1unfvLk6pLl26NKZNmwZ7e3s8evQIBw4cgKGhIerXr48yZcoou3wiogLBIJMLH4eYzZs3o0qVKnBwcAAADB06FBcvXkSHDh0wZMgQ8WZ70dHRMDc3V1bJVERduXIFmzZtwvPnz7FmzRoUL14cALBp0yasWrUKdnZ28PPzQ9WqVZVcKRHR98HBvv9CEAQxxIwbNw4TJ07EgQMH8OrVKwDAr7/+iho1amDfvn1YuHAhoqKi0KRJEwwaNEiZZVMRdezYMezYsQMXL15EfHy82N6rVy8MHDgQz58/x8SJE3Hv3j3lFUlE9B2xRyaX5s6diwULFiA4OBhOTk6QyWRiT40gCPD398ehQ4cQFxcHCwsLnD17FhoaGsoum4qgFStWYNGiRXB3d4efnx9sbGzEZWvXrsWePXvw+++/w9LSUolVEhF9HwwyuZCYmIiePXuibdu26Nu3L548eYK7d+9i9erVKFeuHMaOHQtTU1NcuXIFcXFxaNKkCVRVVXnFXvpPssfEpKSkQC6XQ09PT1w2d+5c7NixAw0bNoSvr694d3Xgw/VkDA0NlVEyEdF3x7+yuaCvr4/Xr19jx44dsLKywtKlS5GYmAhra2usXr0aiYmJWLNmDVxcXMTXZGVlMcTQN8sOMX/99RfWrVuHO3fuoEOHDmjQoAF+/vln+Pn5QS6XY9euXVBTU8PgwYNha2sLAAwxRPRD4RiZT2RfxO7j5zKZDFOnTkVsbCw8PT3h6OiI2bNnIygoCJMmTUJsbCzS09MVXseZS/RfyGQyHDhwAJ6enqhcuTJGjx6NsLAwzJgxA0FBQQAAf39/dOnSBbt27cK6deuQmZmp5KqJiL4/nlr6yMezk9asWYOLFy8iJSUF9erVg4+PDzIzM/Hy5UuFmz82adIEFSpUwPLly5VVNhVBERER6NixI4YMGYIBAwbg/fv3sLGxgbGxMYyMjDBixAh07twZALB48WK0a9cOdnZ2Sq6aiOj7Y4/MR7JDjJ+fH6ZNmwYTExM4Oztj6NCh8PX1hUwmg7W1NZKTk3Hs2DG0aNECr169UrggHlFefOkzo62tjZYtW6JTp0549uwZKleujE6dOiEoKAixsbGYO3cufv/9dwDAiBEjGGKI6IfFIPOJs2fP4o8//sD27dsxf/58VK1aFerq6qhcubJ4uigsLAxBQUFQV1fHtWvXoKamhszMTF6xl/Ik+7TlmzdvcO/ePdy+fVtcZmVlhVGjRsHY2BgzZsxAzZo1MWfOHDg5OaFmzZp49eoVDhw4gISEBAZoIvqh/fCjUT8+nQQAb9++hZmZGerVq4e9e/eiV69eWLp0Kfr374+EhATcvXsX9erVg7m5Oezt7aGiosLZSZRn2Z+7O3fuoG/fvnj16hUEQUCzZs2wZs0aqKqqokSJEgA+nGaqXr26eLFFfX19jBo1Cl27duXAXiL64f3wPTLZIebXX3/FiRMnYGpqCjU1NaxYsQK9e/fG/PnzMWDAAADA5cuXMX/+fERFRaFs2bLiTSEZYigvskPMzZs3UbNmTdSvXx8bNmxAq1atEBgYiJUrVwL4MPMtJSUFpUqVQkREBNasWQM/Pz8cPHgQnTp1gpmZmZKPhIhI+X7YIPPx7KTVq1dj7ty50NfXR7FixSAIAkaNGoXRo0dj4MCBAIDU1FT8+uuv0NPTUxjs+3FvDlFuqKio4OHDh6hZsyZGjBiBBQsWoGHDhhg1ahQA4NGjRwA+zHzT0dFBjx49kJmZiXnz5uGvv/7CX3/9BSsrK2UeAhFRofHDdiVkB5CwsDDcuXMH8+bNE68D4+Pjg9GjR+PRo0fYsmULdHR0sHLlSsTExGDfvn0KV/Ulyiu5XI7169dDX18fJiYmYvv27duRkZGBBw8eYMmSJTA2NoanpyeaNWuGRo0a4e3bt1BVVRXvr0RERD9wkAGAM2fOwN3dHWpqanB2dhbbu3TpgrS0NOzfvx+DBg2Cs7MzTE1NcejQIaipqfEO1/SfqKioYMiQIUhJScH27duhqamJpKQkzJ8/HxMmTICDgwO2bt2Kp0+fYvz48ShXrhx8fX3RunVrZZdORFTo/PDXkVm0aBEmTZqEDh06YPbs2QqnjdLT0/H69WsYGhpCR0cHMpmMA3sp30RHR2PWrFk4duwYHj16hCNHjqBx48YAIH7Oli9fjrCwMIwePRoVK1ZUcsVERIXPDxNkvnYqKCAgAMuXL8egQYPQr18/WFhYAPjfZeKzffqc6L+KiYnB7NmzERoail69eonjZNLT08WbjjI8ExF92Q/x2/HjEHPw4EE8f/4cBgYGqFu3LkqVKgV/f39kZGRg9erVkMlk6NevH8zNzXOEFoYYym9mZmbw9/cX75uUmZkJPz8/aGhoiAGGIYaI6MuKfI/Mx70o48aNw8aNG1GxYkWEh4ejTp066NOnD1q1agUAmD59OtavX48uXbpgzJgxCgMxiQpS9mmm69evo0mTJpg2bZqySyIikoQi/1UvO8QsWbIEQUFBOHDgAGrUqIHly5fD19cXSUlJyMzMRLt27TB58mQkJiYiIiICxsbGSq6cfiTm5uaYMGEC/P39cf78ebx584ZBmogoF4p8jwwAJCQkYPz48ahatSoGDBiAPXv2oF+/fhg4cCD+/PNP6OvrY9y4cWjTpg2A//XicEwMfW8xMTEAwIvdERHl0g8RZLKysnDjxg1YW1sjNjYWbdu2xbBhwzB8+HBs27YN3t7eqFSpEmbPni3OGmGIISIiKvyK3KmlT2cnyeVyqKqqwsnJCTKZDDt37oSVlRW8vLwAfJgdUr9+fZQvXx4NGzYUX8cQQ0REVPgVqUvTfhxiVqxYgSFDhqBx48bYs2cPHj9+DODDrQaSk5Nx//59pKenY8+ePXB3d8fChQvFeycRERGRNBTJU0t+fn4IDAyEt7c33r17h40bN6Jr16749ddfce3aNXh5eSEzMxMZGRnQ09NDWFgY1NXVeTqJiIhIYopMkMkOISdPnkS/fv3wxx9/wMnJCVeuXIGrqyu2bNmCbt26AQBu3LiBGzduICUlBd7e3lBTU+NFx4iIiCRI8n+5k5OToaurK/akpKamwtraGk5OTti2bRsGDBiAFStWoFu3bkhMTER4eDhcXV3h4OAgbiMrK4shhoiISIIkPUZm37598PHxwcOHD8W2uLg4vH37FocOHcKgQYMwZ84cDBo0CAAQEhKCVatW4eXLlwrb4Q0giYiIpEnSQUZdXR1bt27FwoULxTDj4eEBPT09tGrVCjNnzsTgwYMBAGlpaVi/fj0yMjJgbm6uzLKJiIgon0hujIxcLhcvVqeiooKjR4+ic+fO6NixI/z8/FCmTBns378f06ZNg76+PmbMmIFnz55h8+bNePbsGa5fvw41NTUO7CUiIioCJBdk0tLSoKmpqdB2+PBhdOvWDR06dMC0adNgYWGBo0ePYv78+bh9+zbs7e1hZ2eHTZs2QV1dHVlZWTydREREVARIKshs3boVc+bMwYABA2BlZYX27dsjPT0dGhoaOHLkCLp06YK2bdti+vTpKFWqFADg8ePHMDMzg46ODmQyGWcnERERFSGSCTJxcXFwd3fH1atXUaNGDURHR6NYsWKwt7fHwIED4erqivDwcDRv3hw9e/bEgAEDULFiRYVt8HQSERFR0SKZwb6GhoZYunQp6tSpg3fv3iE0NBT9+/dHRkYGvLy8YG9vjz179qBq1apYv3495s+fj2fPnilsgyGGiIioaJFMjwzwoUfl2rVr6NSpE8qXL4/9+/dDQ0MDd+/exT///INNmzYhPj4ex44dQ506dXDq1CmF+y4RERFR0VKog0xqaiq0tLRytF+9ehWenp4wNzfH6dOnxTEvmZmZUFFRwfHjx9GkSROoqqrmuIkkERERFR2FNshs2bIFsbGxGDlyJLJLzJ5yDQDXr19Hx44dYWFhgZMnT0JdXV0c+JuNs5OIiIiKtkLXVSEIAjIzM7Fw4ULo6OiIbQCgoqKCY8eO4eDBg3B0dMTOnTsRExMDNzc3ZGRkKIQYgFfsJSIiKuoKXZDJniIdHx8vXi9GJpNBJpNh79698PDwQEpKCgDA2dkZ27dvR1hYGIYPH67MsomIiEgJCu0FVfT19WFmZgbgQ5A5evQounXrhiVLlqBz587ies7Ozrh69SrKlCmjrFKJiIhISQpNj0xISAhmzpwJ4MM9lJKTk6Gvry8uL168ODZs2IABAwaIbdmnnMqVKwdVVVVkZWV936KJiIhIqQpFj0xaWhp27tyJixcvQldXF/369UNaWhoyMjLEdZycnODk5KTwuk+vC8MxMURERD+WQjNr6cWLF5g3bx4uXbqE2rVr4+DBg+jevTv09fUhl8vF8TJpaWl4+fIlOnfujBo1aii5aiIiIlKmQhNkAODly5eYNWsWQkNDce/ePdjb20NfXx/JycniXa+1tLRgbGyM48eP855JREREP7hClQQsLCwwceJEqKqqwsjICA0bNhTHzaSnp0NNTQ0qKiriPZN4nRgiIqIfW6EZ7JvN3Nwc48aNg6OjI44ePYqAgAAAgIaGhjiYVyaTQRAEhhgiIqIfXKE6tfSx6OhozJ49G9euXUOjRo3EnhkiIiKibIWuRyabubk5xo8fD3t7e8TGxqKQ5i0iIiJSokLbI5Pt7du3MDIyUhgbQ0RERARIIMhk412siYiI6FOSCTJEREREn2IXBxEREUkWgwwRERFJFoMMERERSRaDDBEREUkWgwwRERFJFoMMERUpoaGhkMlkiI+Pz/VrbG1tsWTJkgKriYgKDoMMEX1Xffr0gUwmw8CBA3Ms8/HxgUwmQ58+fb5/YUQkSQwyRPTdWVtbY/v27Xj//r3YlpqaiqCgIJQqVUqJlRGR1DDIENF35+TkBGtra+zZs0ds27NnD0qVKgVHR0exLS0tDcOGDYOpqSm0tLRQt25dXLlyRWFbhw4dwk8//QRtbW00atQI//zzT479nT17FvXq1YO2tjasra0xbNgwJCcnF9jxEdH3wyBDRErRt29fbNiwQXy+fv16eHl5KawzduxY7N69G4GBgQgLC0OZMmXg7u6Ot2/fAgCePn2KDh06oHXr1rhx4wb69++PcePGKWzj0aNHaN68OTw8PHDr1i3s2LEDZ8+exZAhQwr+IImowDHIEJFS9OjRA2fPnkVkZCQiIyNx7tw59OjRQ1yenJyMlStXYv78+WjRogUqVqyItWvXQltbG7///jsAYOXKlbC3t8fChQtRrlw5dO/ePcf4moCAAHTv3h2+vr4oW7YsateujaVLl2LTpk1ITU39nodMRAVATdkFENGPqUSJEmjZsiU2btwIQRDQsmVLFC9eXFz+6NEjZGRkoE6dOmKburo6atSogfDwcABAeHg4XF1dFbZbq1Ythec3b97ErVu3sHXrVrFNEATI5XI8efIEFSpUKIjDI6LvhEGGiJSmb9++4imeFStWFMg+3r17hwEDBmDYsGE5lnFgMZH0McgQkdI0b94c6enpkMlkcHd3V1hmb28PDQ0NnDt3DjY2NgCAjIwMXLlyBb6+vgCAChUq4MCBAwqvu3jxosJzJycn3Lt3D2XKlCm4AyEipeEYGSJSGlVVVYSHh+PevXtQVVVVWKarq4tBgwZhzJgxCA4Oxr179/DLL78gJSUF/fr1AwAMHDgQDx48wJgxYxAREYGgoCBs3LhRYTt+fn44f/48hgwZghs3buDBgwfYv38/B/sSFREMMkSkVAYGBjAwMPjssjlz5sDDwwM9e/aEk5MTHj58iCNHjqBYsWIAPpwa2r17N/bt24dq1aph1apVmD17tsI2qlatilOnTuHvv/9GvXr14OjoiMmTJ8PS0rLAj42ICp5MEARB2UUQERERfQv2yBAREZFkMcgQERGRZDHIEBERkWQxyBAREZFkMcgQERGRZDHIEBERkWQxyBAREZFkMcgQERGRZDHIEBERkWQxyBAREZFkMcgQERGRZDHIEBERkWT9H5Ek2ezPXtBpAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Membuat bar plot dengan keterangan jumlah\n",
        "fig, ax = plt.subplots()\n",
        "bars = plt.bar(model_comp2['Model'], model_comp2['Accuracy'])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Normalization + Oversampling')\n",
        "plt.xticks(rotation=45, ha='right') # Untuk memutar label sumbu x agar lebih mudah dibaca\n",
        "# Menambahkan keterangan jumlah di atas setiap bar\n",
        "for bar in bars:\n",
        " yval = bar.get_height()\n",
        " plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCCedhMlAcrM"
      },
      "source": [
        "## Oversample + Normalization + Tunning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "9sH0cms49kZi",
        "outputId": "439ad5d1-172e-469b-eb80-ccc4d6f7dcc4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbour</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>92.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Model  Accuracy\n",
              "0  K-Nearest Neighbour      92.0\n",
              "1        Random Forest      92.5\n",
              "2              XGBoost      92.0"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_comp3 = pd.DataFrame({\n",
        "    'Model': ['K-Nearest Neighbour','Random Forest','XGBoost'],\n",
        "    'Accuracy': [accuracy_knn_smote_normal_Tun*100,accuracy_rf_smote_normal_Tun*100,accuracy_xgb_smote_normal_Tun*100]\n",
        "    })\n",
        "model_comp3.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "MqO9tvYl9mEr",
        "outputId": "867a6278-b764-46b2-b70c-091851aa2b76"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Membuat bar plot dengan keterangan jumlah\n",
        "fig, ax = plt.subplots()\n",
        "bars = plt.bar(model_comp3['Model'], model_comp3['Accuracy'])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Normalization + Oversampling + Tunning')\n",
        "plt.xticks(rotation=45, ha='right') # Untuk memutar label sumbu x agar lebih mudah dibaca\n",
        "# Menambahkan keterangan jumlah di atas setiap bar\n",
        "for bar in bars:\n",
        " yval = bar.get_height()\n",
        " plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqABfzuZAnkZ"
      },
      "source": [
        "## Best Model Comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "kp9Xn2ry9nLa",
        "outputId": "2e0e4cb0-c6ce-4831-b6b3-06f1c026df2a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Data frame\n",
        "model_compBest = pd.DataFrame({\n",
        " 'Model': ['Random Forest OverSample', 'Random Forest OverSample Normalization', 'Random Forest OverSample Normalization Tunning'],\n",
        " 'Accuracy': [accuracy_rf_smote*100, accuracy_rf_smote_normal*100, accuracy_rf_smote_normal_Tun*100]\n",
        "})\n",
        "# Membuat bar plot dengan keterangan jumlah\n",
        "fig, ax = plt.subplots()\n",
        "bars = plt.bar(model_compBest['Model'], model_compBest['Accuracy'])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.title('Best Model Comparison')\n",
        "plt.xticks(rotation=45, ha='right') # Untuk memutar label sumbu x agar lebih mudah dibaca\n",
        "# Menambahkan keterangan jumlah di atas setiap bar\n",
        "for bar in bars:\n",
        " yval = bar.get_height()\n",
        " plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 9) Kesimpulan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Tahap akhir dari penelitian ini mencapai kesimpulan yang signifikan terkait dengan pembangunan model prediksi penyakit jantung menggunakan metode machine learning. Berdasarkan eksperimen yang dilakukan, dapat disimpulkan bahwa model Random Forest dengan penerapan oversampling, normalisasi, dan tuning parameter menunjukkan kinerja yang paling optimal dengan akurasi mencapai 92.5%. Hasil ini menandai kesuksesan metode tersebut dalam memprediksi keberadaan penyakit jantung berdasarkan sejumlah atribut pasien.\n",
        "Selain itu, implementasi model pada aplikasi Streamlit memberikan keunggulan dalam interaktivitas, memungkinkan pengguna untuk melakukan prediksi secara individu atau sekaligus untuk beberapa data. Fitur ini menjadikan aplikasi lebih user-friendly dan dapat diakses oleh berbagai pengguna tanpa pengetahuan teknis yang mendalam.\n",
        "Secara keseluruhan, penelitian ini menegaskan pentingnya pemilihan model yang sesuai dan strategi preprocessing data yang cermat dalam menghasilkan prediksi penyakit jantung yang akurat. Dengan demikian, hasil penelitian ini dapat memberikan kontribusi pada pengembangan alat diagnostik yang lebih efektif serta mendukung langkah-langkah pencegahan dan manajemen penyakit jantung secara lebih baik di masa depan."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 10) Streamlit"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Link Code   : https://github.com/bimarakajati/Heart-Failure-Prediction/blob/main/app.py\n",
        "\n",
        "Link Apps   : https://heart-failure.streamlit.app/ "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}