{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8H7cEQXhAODl"
      },
      "source": [
        "# **PEMODELAN RANDOM FOREST DAN XGBOOST DALAM KLASIFIKASI KELIMPAHAN IKAN KEMBUNG DI SELAT SUNDA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZjB3gPAAkjD"
      },
      "source": [
        "Mengimport libraries yang diperlukan\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "FTUSExp__eQf"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbSqHuVbAqQW"
      },
      "source": [
        "Membaca dan Memahami Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eE3I9_nFAs7S",
        "outputId": "ead84a0c-8eff-42df-db7e-bc4f2aebf445"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  month  day    sst  salinity      chl     RR  predict\n",
              "0  2019      1    1  28.77     33.19  0.79000  13.07        0\n",
              "1  2019      1    2  28.75     33.19  0.79125  13.07        0\n",
              "2  2019      1    3  28.71     33.20  0.79250   1.98        0\n",
              "3  2019      1    4  28.81     33.22  0.79375   0.00        0\n",
              "4  2019      1    5  29.06     33.21  0.79500   1.34        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-48572607-c64a-41cf-98f7-d09996341c0b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>sst</th>\n",
              "      <th>salinity</th>\n",
              "      <th>chl</th>\n",
              "      <th>RR</th>\n",
              "      <th>predict</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>28.77</td>\n",
              "      <td>33.19</td>\n",
              "      <td>0.79000</td>\n",
              "      <td>13.07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>28.75</td>\n",
              "      <td>33.19</td>\n",
              "      <td>0.79125</td>\n",
              "      <td>13.07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>28.71</td>\n",
              "      <td>33.20</td>\n",
              "      <td>0.79250</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>28.81</td>\n",
              "      <td>33.22</td>\n",
              "      <td>0.79375</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.21</td>\n",
              "      <td>0.79500</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48572607-c64a-41cf-98f7-d09996341c0b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-48572607-c64a-41cf-98f7-d09996341c0b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-48572607-c64a-41cf-98f7-d09996341c0b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2557,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2019,\n        \"max\": 2025,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2019,\n          2020,\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          28,\n          16,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sst\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5811589385743201,\n        \"min\": 28.25,\n        \"max\": 31.24,\n        \"num_unique_values\": 280,\n        \"samples\": [\n          29.84,\n          29.75,\n          29.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salinity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5675401139647137,\n        \"min\": 30.84,\n        \"max\": 34.01,\n        \"num_unique_values\": 270,\n        \"samples\": [\n          32.39,\n          33.03,\n          31.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3320.6734197628584,\n        \"min\": -32767.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 959,\n        \"samples\": [\n          0.73875,\n          0.265,\n          0.62125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.897112145495726,\n        \"min\": 0.0,\n        \"max\": 74.93,\n        \"num_unique_values\": 1374,\n        \"samples\": [\n          7.46,\n          1.86,\n          1.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predict\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv('/content/DataseIkantKembungFix.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "9nMHHnY6N7mZ",
        "outputId": "b9dce1b6-89f7-4a53-b496-2ad695f382ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
        "axes = axes.flatten()\n",
        "\n",
        "variables = ['sst', 'chl', 'salinity', 'RR']\n",
        "labels = ['SPL (°C)', 'Klorofil-a (mg/m³)', 'Salinitas (psu)', 'Curah Hujan (mm)']\n",
        "colors = ['#e74c3c', '#27ae60', '#2980b9', '#8e44ad']\n",
        "bulan = ['Jan','Feb','Mar','Apr','Mei','Jun','Jul','Agu','Sep','Okt','Nov','Des']\n",
        "\n",
        "for i, (var, label, color) in enumerate(zip(variables, labels, colors)):\n",
        "    monthly_mean = df.groupby('month')[var].mean()\n",
        "    monthly_std  = df.groupby('month')[var].std()\n",
        "\n",
        "    axes[i].plot(monthly_mean.index, monthly_mean.values,\n",
        "                 marker='o', color=color, linewidth=2, label='Rata-rata')\n",
        "    axes[i].fill_between(monthly_mean.index,\n",
        "                         monthly_mean - monthly_std,\n",
        "                         monthly_mean + monthly_std,\n",
        "                         alpha=0.2, color=color, label='±1 Std Dev')\n",
        "    axes[i].set_title(label, fontsize=12, fontweight='bold')\n",
        "    axes[i].set_xlabel('Bulan')\n",
        "    axes[i].set_ylabel(label)\n",
        "    axes[i].set_xticks(range(1, 13))\n",
        "    axes[i].set_xticklabels(bulan, fontsize=9)\n",
        "    axes[i].legend(fontsize=8)\n",
        "    axes[i].grid(True, alpha=0.3)\n",
        "\n",
        "plt.suptitle('Rata-rata Bulanan Variabel Oseanografi\\nPerairan Selat Sunda 2019–2025',\n",
        "             fontsize=13, fontweight='bold', y=1.02)\n",
        "plt.tight_layout()\n",
        "plt.savefig('kondisi_oseanografi.png', dpi=300, bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIgXqqpvBCNW"
      },
      "source": [
        "**Transformasi Data**\n",
        "\n",
        "Transfrmasi data pada tahap ini yaitu pada fitur sst dan klorofil diberlakukan nilai kemarin atau lag effect dengan rumur t - 1. Kemudian siklus bulan dilakukan pendekatan sine-cosine Encoding agar model membaca bahwa bulan 12 (Desember) dan bulan 1 (Januari) memiliki waktu yang berdekatan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yEId52FA15c",
        "outputId": "de2eb930-f4b0-46ef-fad4-37f63910120b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data setelah Lag SPL & Chl:\n",
            "   year  month    sst  sst_lag1   chl  chl_lag1\n",
            "0  2019      1  28.75     28.77  0.11      0.10\n",
            "1  2019      1  28.71     28.75  0.11      0.11\n",
            "2  2019      1  28.81     28.71  0.11      0.11\n",
            "3  2019      1  29.06     28.81  0.10      0.11\n",
            "4  2019      1  29.47     29.06  0.10      0.10\n"
          ]
        }
      ],
      "source": [
        "#Lag untuk Klorofil (chl)\n",
        "df['chl_lag1'] = df['chl'].shift(1)\n",
        "\n",
        "#Lag untuk SPL (sst)\n",
        "df['sst_lag1'] = df['sst'].shift(1)\n",
        "\n",
        "#Siklus untuk bulan\n",
        "df['month_sin'] = np.sin(2 * np.pi * df['month']/12)\n",
        "df['month_cos'] = np.cos(2 * np.pi * df['month']/12)\n",
        "\n",
        "#Menghapus baris NaN (baris pertama tiap periode akan berisi NaN karena shift)\n",
        "df_clean = df.dropna(subset=['chl_lag1', 'sst_lag1']).reset_index(drop=True)\n",
        "\n",
        "\n",
        "print(\"Data setelah Lag SPL & Chl:\")\n",
        "print(df_clean[['year', 'month', 'sst', 'sst_lag1', 'chl', 'chl_lag1']].head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "Nvh0b7WEBsML",
        "outputId": "a6403bc4-9436-43f6-c108-92837e4edf37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      year  month  day    sst  Salinitas   chl  Curah hujan  Potensi  \\\n",
              "0     2019      1    2  28.75      33.19  0.11        13.07        0   \n",
              "1     2019      1    3  28.71      33.20  0.11         1.98        0   \n",
              "2     2019      1    4  28.81      33.22  0.11         0.00        0   \n",
              "3     2019      1    5  29.06      33.21  0.10         1.34        0   \n",
              "4     2019      1    6  29.47      33.16  0.10         0.01        0   \n",
              "...    ...    ...  ...    ...        ...   ...          ...      ...   \n",
              "2551  2025     12   27  29.12      33.28  0.17         5.85        0   \n",
              "2552  2025     12   28  29.12      33.28  0.17         9.38        0   \n",
              "2553  2025     12   29  29.11      33.26  0.17        17.47        0   \n",
              "2554  2025     12   30  29.21      33.26  0.17        22.77        0   \n",
              "2555  2025     12   31  29.06      33.28  0.17        35.52        0   \n",
              "\n",
              "      Klorofil    SPL     Bulan_sin  Bulan_cos  \n",
              "0         0.10  28.77  5.000000e-01   0.866025  \n",
              "1         0.11  28.75  5.000000e-01   0.866025  \n",
              "2         0.11  28.71  5.000000e-01   0.866025  \n",
              "3         0.11  28.81  5.000000e-01   0.866025  \n",
              "4         0.10  29.06  5.000000e-01   0.866025  \n",
              "...        ...    ...           ...        ...  \n",
              "2551      0.17  29.19 -2.449294e-16   1.000000  \n",
              "2552      0.17  29.12 -2.449294e-16   1.000000  \n",
              "2553      0.17  29.12 -2.449294e-16   1.000000  \n",
              "2554      0.17  29.11 -2.449294e-16   1.000000  \n",
              "2555      0.17  29.21 -2.449294e-16   1.000000  \n",
              "\n",
              "[2556 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf0d1d53-61da-4284-a5ac-40c89c6521f2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>sst</th>\n",
              "      <th>Salinitas</th>\n",
              "      <th>chl</th>\n",
              "      <th>Curah hujan</th>\n",
              "      <th>Potensi</th>\n",
              "      <th>Klorofil</th>\n",
              "      <th>SPL</th>\n",
              "      <th>Bulan_sin</th>\n",
              "      <th>Bulan_cos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>28.75</td>\n",
              "      <td>33.19</td>\n",
              "      <td>0.11</td>\n",
              "      <td>13.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>28.77</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>28.71</td>\n",
              "      <td>33.20</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>28.75</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>28.81</td>\n",
              "      <td>33.22</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>28.71</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.21</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>28.81</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>29.47</td>\n",
              "      <td>33.16</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>29.06</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2551</th>\n",
              "      <td>2025</td>\n",
              "      <td>12</td>\n",
              "      <td>27</td>\n",
              "      <td>29.12</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>5.85</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>29.19</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2552</th>\n",
              "      <td>2025</td>\n",
              "      <td>12</td>\n",
              "      <td>28</td>\n",
              "      <td>29.12</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>9.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>29.12</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2553</th>\n",
              "      <td>2025</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "      <td>29.11</td>\n",
              "      <td>33.26</td>\n",
              "      <td>0.17</td>\n",
              "      <td>17.47</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>29.12</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2554</th>\n",
              "      <td>2025</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>29.21</td>\n",
              "      <td>33.26</td>\n",
              "      <td>0.17</td>\n",
              "      <td>22.77</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>29.11</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2555</th>\n",
              "      <td>2025</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>35.52</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>29.21</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_laporan",
              "summary": "{\n  \"name\": \"df_laporan\",\n  \"rows\": 2556,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2025,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2019,\n          2020,\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          29,\n          17,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sst\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5809154992368496,\n        \"min\": 28.25,\n        \"max\": 31.24,\n        \"num_unique_values\": 280,\n        \"samples\": [\n          29.85,\n          29.66,\n          28.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salinitas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5674428672772488,\n        \"min\": 30.84,\n        \"max\": 34.01,\n        \"num_unique_values\": 270,\n        \"samples\": [\n          32.39,\n          33.03,\n          31.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26783081599553354,\n        \"min\": 0.09,\n        \"max\": 2.73,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          0.78,\n          0.44,\n          0.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Curah hujan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.898311830502708,\n        \"min\": 0.0,\n        \"max\": 74.93,\n        \"num_unique_values\": 1374,\n        \"samples\": [\n          7.46,\n          1.86,\n          1.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Potensi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klorofil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2678456486931925,\n        \"min\": 0.09,\n        \"max\": 2.73,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          0.78,\n          0.44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.581088384661337,\n        \"min\": 28.25,\n        \"max\": 31.24,\n        \"num_unique_values\": 280,\n        \"samples\": [\n          29.84,\n          29.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan_sin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7059819066728217,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.4999999999999997,\n          0.49999999999999994\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan_cos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7084854026535019,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -1.0,\n          0.8660254037844387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df_laporan = df_clean.rename(columns={\n",
        "    'month_sin': 'Bulan_sin',\n",
        "    'month_cos': 'Bulan_cos',\n",
        "    'sst_lag1': 'SPL',\n",
        "    'salinity': 'Salinitas',\n",
        "    'chl_lag1': 'Klorofil',\n",
        "    'RR': 'Curah hujan',\n",
        "    'predict': 'Potensi'\n",
        "})\n",
        "\n",
        "df_laporan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "leLb8k9IBvnB",
        "outputId": "bc66ede0-6022-4e8c-f0d6-dd3ad87b9f38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Bulan_sin  Bulan_cos    SPL  Salinitas  Klorofil  Curah hujan  \\\n",
              "0     5.000000e-01   0.866025  28.77      33.19      0.10        13.07   \n",
              "1     5.000000e-01   0.866025  28.75      33.20      0.11         1.98   \n",
              "2     5.000000e-01   0.866025  28.71      33.22      0.11         0.00   \n",
              "3     5.000000e-01   0.866025  28.81      33.21      0.11         1.34   \n",
              "4     5.000000e-01   0.866025  29.06      33.16      0.10         0.01   \n",
              "...            ...        ...    ...        ...       ...          ...   \n",
              "2551 -2.449294e-16   1.000000  29.19      33.28      0.17         5.85   \n",
              "2552 -2.449294e-16   1.000000  29.12      33.28      0.17         9.38   \n",
              "2553 -2.449294e-16   1.000000  29.12      33.26      0.17        17.47   \n",
              "2554 -2.449294e-16   1.000000  29.11      33.26      0.17        22.77   \n",
              "2555 -2.449294e-16   1.000000  29.21      33.28      0.17        35.52   \n",
              "\n",
              "      Potensi  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  \n",
              "...       ...  \n",
              "2551        0  \n",
              "2552        0  \n",
              "2553        0  \n",
              "2554        0  \n",
              "2555        0  \n",
              "\n",
              "[2556 rows x 7 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Bulan_sin</th>\n",
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              "      <td>1.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>28.71</td>\n",
              "      <td>33.22</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>28.81</td>\n",
              "      <td>33.21</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.16</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2551</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.19</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>5.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2552</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.12</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>9.38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2553</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.12</td>\n",
              "      <td>33.26</td>\n",
              "      <td>0.17</td>\n",
              "      <td>17.47</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2554</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.11</td>\n",
              "      <td>33.26</td>\n",
              "      <td>0.17</td>\n",
              "      <td>22.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2555</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.21</td>\n",
              "      <td>33.28</td>\n",
              "      <td>0.17</td>\n",
              "      <td>35.52</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2556 rows × 7 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cdf",
              "summary": "{\n  \"name\": \"cdf\",\n  \"rows\": 2556,\n  \"fields\": [\n    {\n      \"column\": \"Bulan_sin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7059819066728217,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.4999999999999997,\n          0.49999999999999994,\n          -0.5000000000000004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan_cos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7084854026535019,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -1.0,\n          0.8660254037844387,\n          0.8660254037844384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.581088384661337,\n        \"min\": 28.25,\n        \"max\": 31.24,\n        \"num_unique_values\": 280,\n        \"samples\": [\n          29.84,\n          29.75,\n          29.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salinitas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5674428672772488,\n        \"min\": 30.84,\n        \"max\": 34.01,\n        \"num_unique_values\": 270,\n        \"samples\": [\n          32.39,\n          33.03,\n          31.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klorofil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2678456486931925,\n        \"min\": 0.09,\n        \"max\": 2.73,\n        \"num_unique_values\": 130,\n        \"samples\": [\n          0.78,\n          0.44,\n          0.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Curah hujan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.898311830502708,\n        \"min\": 0.0,\n        \"max\": 74.93,\n        \"num_unique_values\": 1374,\n        \"samples\": [\n          7.46,\n          1.86,\n          1.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Potensi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "cdf = df_laporan[['Bulan_sin', 'Bulan_cos', 'SPL', 'Salinitas', 'Klorofil', 'Curah hujan', 'Potensi']]\n",
        "cdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48bTVEh1B3GP"
      },
      "source": [
        "**Deskripsi Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yFXhHKv4By3r",
        "outputId": "5ee4f08d-a0dd-42ec-b1db-aaf258e2f588"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Bulan_sin     Bulan_cos          SPL    Salinitas     Klorofil  \\\n",
              "count  2.556000e+03  2.556000e+03  2556.000000  2556.000000  2556.000000   \n",
              "mean  -4.896972e-03 -2.319328e-03    29.799914    32.412606     0.279992   \n",
              "std    7.059819e-01  7.084854e-01     0.581088     0.567443     0.267846   \n",
              "min   -1.000000e+00 -1.000000e+00    28.250000    30.840000     0.090000   \n",
              "25%   -8.660254e-01 -8.660254e-01    29.420000    31.950000     0.130000   \n",
              "50%   -2.449294e-16 -1.836970e-16    29.780000    32.440000     0.210000   \n",
              "75%    5.000000e-01  8.660254e-01    30.240000    32.830000     0.330000   \n",
              "max    1.000000e+00  1.000000e+00    31.240000    34.010000     2.730000   \n",
              "\n",
              "       Curah hujan      Potensi  \n",
              "count  2556.000000  2556.000000  \n",
              "mean      8.107966     0.439358  \n",
              "std       8.898312     0.496406  \n",
              "min       0.000000     0.000000  \n",
              "25%       1.117500     0.000000  \n",
              "50%       5.430000     0.000000  \n",
              "75%      11.967500     1.000000  \n",
              "max      74.930000     1.000000  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Bulan_sin</th>\n",
              "      <th>Bulan_cos</th>\n",
              "      <th>SPL</th>\n",
              "      <th>Salinitas</th>\n",
              "      <th>Klorofil</th>\n",
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              "      <th>Potensi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.556000e+03</td>\n",
              "      <td>2.556000e+03</td>\n",
              "      <td>2556.000000</td>\n",
              "      <td>2556.000000</td>\n",
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              "      <td>2556.000000</td>\n",
              "      <td>2556.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-4.896972e-03</td>\n",
              "      <td>-2.319328e-03</td>\n",
              "      <td>29.799914</td>\n",
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              "      <td>7.084854e-01</td>\n",
              "      <td>0.581088</td>\n",
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              "      <th>min</th>\n",
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              "      <td>30.840000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.660254e-01</td>\n",
              "      <td>-8.660254e-01</td>\n",
              "      <td>29.420000</td>\n",
              "      <td>31.950000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>1.117500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>-1.836970e-16</td>\n",
              "      <td>29.780000</td>\n",
              "      <td>32.440000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>5.430000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>8.660254e-01</td>\n",
              "      <td>30.240000</td>\n",
              "      <td>32.830000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>11.967500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>31.240000</td>\n",
              "      <td>34.010000</td>\n",
              "      <td>2.730000</td>\n",
              "      <td>74.930000</td>\n",
              "      <td>1.000000</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7f25b1f7-fd7e-47d9-95f8-6f1cd7f5a12d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7f25b1f7-fd7e-47d9-95f8-6f1cd7f5a12d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"cdf\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Bulan_sin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 903.6658185540795,\n        \"min\": -1.0,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0048969720259763065,\n          -2.4492935982947064e-16,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan_cos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 903.6471102863143,\n        \"min\": -1.0,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.002319328337353543,\n          -1.8369701987210297e-16,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 894.6846555480741,\n        \"min\": 0.581088384661337,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.79991392801252,\n          29.78,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salinitas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 893.9008496892111,\n        \"min\": 0.5674428672772488,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.41260563380281,\n          32.44,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klorofil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 903.4789557712539,\n        \"min\": 0.09,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.27999217527386544,\n          0.21,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Curah hujan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 898.4370617019144,\n        \"min\": 0.0,\n        \"max\": 2556.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.107965571205007,\n          5.43,\n          2556.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Potensi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 903.5342833741798,\n        \"min\": 0.0,\n        \"max\": 2556.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.439358372456964,\n          1.0,\n          0.496406084842657\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "cdf.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZunLP7kOB_GM"
      },
      "source": [
        "**Data understanding**\n",
        "\n",
        "variabel/fitur yang ada dapat diplot sebagai berikut"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "BTnGefq2B8iU",
        "outputId": "4d8b04d4-83a9-4392-9882-e6090faccc77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cdf[['Bulan_sin', 'Bulan_cos','SPL','Salinitas', 'Curah hujan']].hist(figsize=(8,6))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1elL0BHCNuy"
      },
      "source": [
        "**BOXPLOT**\n",
        "\n",
        "Distribusi antar kelas untuk melihat apakah fitur/variabel mempengaruhi kelas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SPiJwJ1NCM6O",
        "outputId": "98cf9abf-2f0b-4ea1-eb37-b35764b04808"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "features = ['SPL', 'Salinitas', 'Klorofil', 'Curah hujan']\n",
        "\n",
        "for f in features:\n",
        "    sns.boxplot(x='Potensi', y=f, data=cdf)\n",
        "    plt.title(f\"{f} vs Kelas\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKuUdXP1Cgqp"
      },
      "source": [
        "**Pelabelan data dan pembagian Data**\n",
        "\n",
        "Data X (independen) yaitu Bulan, SPL, Salinitas, Klorofil, dan Curah Hujan.\n",
        "Data Y (dependent) yaitu kelas target 'potensi'\n",
        "\n",
        "pembagian data berdasarkan pendekatan time series yaitu antara tahun 2019-2023 sebagai data training dan data tahun 2024-2025 sebagai data testing. Perkiraan pembagian ini yaitu sebesar 70:30 dari seluruh data yang ada."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "jPbZh4cYCb_z"
      },
      "outputs": [],
      "source": [
        "X = ['Bulan_sin', 'Bulan_cos', 'SPL', 'Salinitas', 'Klorofil', 'Curah hujan']\n",
        "Y = 'Potensi'\n",
        "\n",
        "# Split berdasarkan tahun\n",
        "train_df = df_laporan[df_laporan['year'] < 2024]\n",
        "test_df = df_laporan[(df_laporan['year'] >= 2024) & (df_laporan['year'] <= 2025)]\n",
        "\n",
        "#Split data latih dan data uji\n",
        "X_train = train_df[X]\n",
        "y_train = train_df[Y]\n",
        "\n",
        "X_test = test_df[X]\n",
        "y_test = test_df[Y]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qso0OYzeDNDg"
      },
      "source": [
        "**RANDOM FOREST DAN XGBOOST TRAINING MODEL**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwB8QycEDe7H"
      },
      "source": [
        "Random Forest pemodelan dan hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "uEG0P_6YDbFL",
        "outputId": "8d55ee09-c70b-49a4-b492-acc3a6fea22b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced_subsample', max_depth=10,\n",
              "                       min_samples_leaf=4, min_samples_split=5,\n",
              "                       n_estimators=200, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, max_depth=10,\n",
              "                       min_samples_leaf=4, min_samples_split=5,\n",
              "                       n_estimators=200, 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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, max_depth=10,\n",
              "                       min_samples_leaf=4, min_samples_split=5,\n",
              "                       n_estimators=200, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import TimeSeriesSplit, RandomizedSearchCV\n",
        "\n",
        "param_dist = {\n",
        "    'n_estimators': [100, 200, 300, 500],\n",
        "    'max_depth': [5, 10, 15, 20, None],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'max_features': ['sqrt', 'log2'],\n",
        "    'bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "model = RandomForestClassifier(class_weight='balanced_subsample', random_state=42)\n",
        "tscv = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "random_search = RandomizedSearchCV(\n",
        "    estimator=model,\n",
        "    param_distributions=param_dist,\n",
        "    n_iter=20,              # jumlah kombinasi yang dicoba, 20 kombinasi secara acak untuk dites\n",
        "    cv=tscv,                   # 7 Fold Cross Validation, model dolatih 5x, setaip bagian pernah menjadi data uji.\n",
        "    verbose=2,\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "best_model = random_search.best_estimator_\n",
        "\n",
        "best_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "PXImiwgHGST3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3ab87cd-4fc5-4a89-d824-c048ff03c4c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 200,\n",
              " 'min_samples_split': 5,\n",
              " 'min_samples_leaf': 4,\n",
              " 'max_features': 'sqrt',\n",
              " 'max_depth': 10,\n",
              " 'bootstrap': True}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "random_search.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "MVnlU2t5WU_L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20dd3dfb-0061-4647-da32-98b0e0d1b6c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean CV Score: 0.8284848484848484\n"
          ]
        }
      ],
      "source": [
        "# Melihat rata-rata akurasi dari 10 lipatan (fold) TimeSeriesSplit\n",
        "print(f\"Mean CV Score: {random_search.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qW2OP-5FGU-p"
      },
      "source": [
        "XGboost Training model hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "tPtnjGuQGZ5y",
        "outputId": "07f11e29-c75c-4723-9fc8-f99be94ae999"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric='logloss',\n",
              "              feature_types=None, feature_weights=None, gamma=0,\n",
              "              grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" 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=1.0, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, feature_weights=None, gamma=0,\n",
              "              grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, ...)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.2.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, feature_weights=None, gamma=0,\n",
              "              grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=200, n_jobs=None,\n",
              "              num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import TimeSeriesSplit, RandomizedSearchCV\n",
        "\n",
        "#Penyesuaian Parameter untuk XGBoost\n",
        "param_dist_xgb = {\n",
        "    'n_estimators': [100, 200, 300, 500],\n",
        "    'max_depth': [3, 5, 10, 15],\n",
        "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
        "    'subsample': [0.7, 0.8, 0.9, 1.0],\n",
        "    'colsample_bytree': [0.7, 0.8, 0.9, 1.0],\n",
        "    'gamma': [0, 0.1, 0.2, 0.3]\n",
        "}\n",
        "\n",
        "model_xgb = XGBClassifier(\n",
        "    eval_metric='logloss',\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "tscv = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "random_search_xgb = RandomizedSearchCV(\n",
        "    estimator=model_xgb,\n",
        "    param_distributions=param_dist_xgb,\n",
        "    n_iter=20,\n",
        "    cv=tscv,\n",
        "    verbose=2,\n",
        "    random_state=42,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "Training\n",
        "random_search_xgb.fit(X_train, y_train)\n",
        "\n",
        " #Ambil Model Terbaik\n",
        "best_model_xgb = random_search_xgb.best_estimator_\n",
        "\n",
        "best_model_xgb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTzhtHHhXxTw",
        "outputId": "5cc0be6c-8923-483c-d404-dae7e402627d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'subsample': 0.7,\n",
              " 'n_estimators': 200,\n",
              " 'max_depth': 5,\n",
              " 'learning_rate': 0.01,\n",
              " 'gamma': 0,\n",
              " 'colsample_bytree': 1.0}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "random_search_xgb.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2OTYcQkX2p7",
        "outputId": "f8cfaee7-e3e3-4524-f62d-f95f050412f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean CV Score: 0.8612121212121213\n"
          ]
        }
      ],
      "source": [
        "print(f\"Mean CV Score: {random_search_xgb.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDTnq3ptX90_"
      },
      "source": [
        "**Fine Tuning Random Forest dan XGBOOST**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwd_OklSel_9"
      },
      "source": [
        "Random Forest\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "0uwi0Xv9YCeG",
        "outputId": "a515f4ca-1e57-4f1c-a8c0-a0547539d719"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 81 candidates, totalling 810 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(class_weight='balanced_subsample', max_depth=9,\n",
              "                       min_samples_leaf=5, min_samples_split=4,\n",
              "                       n_estimators=250, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, max_depth=9,\n",
              "                       min_samples_leaf=5, min_samples_split=4,\n",
              "                       n_estimators=250, 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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, max_depth=9,\n",
              "                       min_samples_leaf=5, min_samples_split=4,\n",
              "                       n_estimators=250, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid_fine = {\n",
        "    'n_estimators': [\n",
        "        random_search.best_params_['n_estimators'] - 50,\n",
        "        random_search.best_params_['n_estimators'],\n",
        "        random_search.best_params_['n_estimators'] + 50\n",
        "    ],\n",
        "    'max_depth': [\n",
        "        random_search.best_params_['max_depth'] - 1 if random_search.best_params_['max_depth'] is not None else 3,\n",
        "        random_search.best_params_['max_depth'],\n",
        "        random_search.best_params_['max_depth'] + 1 if random_search.best_params_['max_depth'] is not None else 10\n",
        "    ],\n",
        "\n",
        "    'min_samples_split': [\n",
        "        random_search.best_params_['min_samples_split'] - 1 if random_search.best_params_['min_samples_split'] > 2 else 2,\n",
        "        random_search.best_params_['min_samples_split'],\n",
        "        random_search.best_params_['min_samples_split'] + 1\n",
        "    ],\n",
        "    'min_samples_leaf': [\n",
        "        random_search.best_params_['min_samples_leaf'] - 1 if random_search.best_params_['min_samples_leaf'] > 1 else 1,\n",
        "        random_search.best_params_['min_samples_leaf'],\n",
        "        random_search.best_params_['min_samples_leaf'] + 1\n",
        "    ],\n",
        "    'max_features': [random_search.best_params_['max_features']],\n",
        "    'bootstrap': [random_search.best_params_['bootstrap']]\n",
        "}\n",
        "grid_search = GridSearchCV(\n",
        "    estimator=model,\n",
        "    param_grid=param_grid_fine,\n",
        "    cv=tscv,\n",
        "    verbose=2,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "rf_final_model = grid_search.best_estimator_\n",
        "\n",
        "rf_final_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ymH_bWaBeoxX"
      },
      "source": [
        "XGBOOST"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "xlk6q6lbb0aO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba3ee35d-64b4-4514-d4f8-21e1366ce1d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Memulai Fine-Tuning berbasis hasil Tuning Awal...\n",
            "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n",
            "\n",
            "--- Fine-Tuning Selesai ---\n",
            "Best Params Baru: {'colsample_bytree': 0.8, 'learning_rate': 0.005, 'max_depth': 4, 'n_estimators': 250, 'subsample': 0.7}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "best_initial = random_search_xgb.best_params_\n",
        "\n",
        "param_grid_fine_xgb = {\n",
        "\n",
        "    'n_estimators': [best_initial['n_estimators'], best_initial['n_estimators'] + 50],\n",
        "\n",
        "    'max_depth': [best_initial['max_depth'] - 1, best_initial['max_depth'], best_initial['max_depth'] + 1],\n",
        "\n",
        "    'learning_rate': [best_initial['learning_rate'] * 0.5, best_initial['learning_rate'], best_initial['learning_rate'] * 1.5],\n",
        "\n",
        "    'subsample': [0.7, 0.8],\n",
        "    'colsample_bytree': [0.8, 1.0]\n",
        "}\n",
        "\n",
        "param_grid_fine_xgb['max_depth'] = [d for d in param_grid_fine_xgb['max_depth'] if d > 0]\n",
        "\n",
        "model_xgb_fine = XGBClassifier(\n",
        "    eval_metric='logloss',\n",
        "    random_state=42,\n",
        "    tree_method='hist'\n",
        ")\n",
        "\n",
        "\n",
        "tscv = TimeSeriesSplit(n_splits=10)\n",
        "grid_search_xgb = GridSearchCV(\n",
        "    estimator=model_xgb_fine,\n",
        "    param_grid=param_grid_fine_xgb,\n",
        "    cv=tscv,\n",
        "    scoring='accuracy',\n",
        "    verbose=1,\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "print(\"Memulai Fine-Tuning berbasis hasil Tuning Awal...\")\n",
        "grid_search_xgb.fit(X_train, y_train)\n",
        "\n",
        "final_best_xgb = grid_search_xgb.best_estimator_\n",
        "\n",
        "print(\"\\n--- Fine-Tuning Selesai ---\")\n",
        "print(f\"Best Params Baru: {grid_search_xgb.best_params_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "pKkshMchc9vX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf0a9afd-85c7-486a-a8dd-6b97ef53510f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean CV RF fine tuning: 0.833939393939394\n",
            "Mean CV XGB fine tuning: 0.8624242424242425\n"
          ]
        }
      ],
      "source": [
        "print(f\"Mean CV RF fine tuning: {grid_search.best_score_}\")\n",
        "print(f\"Mean CV XGB fine tuning: {grid_search_xgb.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmqaYobjeZeQ"
      },
      "source": [
        "**PREDIKSI DAN EVALUASI MODEL MENGGUNAKAN DATA TEST**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "mO0VqSSrejnJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b07529cc-d7f4-4b96-c04c-ee368eb2de63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ],
      "source": [
        "#RANDOM FOREST\n",
        "#Prediksi data dengan nilai baru\n",
        "\n",
        "predict = pd.DataFrame([[2, 2, 29.04, 32.27, 0.31, 10.15]],\n",
        "                       columns=X_train.columns)\n",
        "\n",
        "y_new = rf_final_model.predict(predict)\n",
        "\n",
        "print(y_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "HaOcYpTQe4xB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3caac665-56a8-4123-9eca-439b009c6892"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ],
      "source": [
        "#XGBOOST\n",
        "#Prediksi data dengan nilai baru\n",
        "\n",
        "predict = pd.DataFrame([[2, 2, 29.04, 32.27, 0.31, 10.15]],\n",
        "                       columns=X_train.columns)\n",
        "\n",
        "y_new = final_best_xgb.predict(predict)\n",
        "\n",
        "print(y_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "jeXG-ARqfAbC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac62c730-2d76-4df1-b6ff-4fce4a1b1e5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy RF: 0.948016415868673\n",
            "Accuracy XGB: 0.9384404924760602\n"
          ]
        }
      ],
      "source": [
        "#PREDIKSI dan Akurasi Model\n",
        "\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "\n",
        "#RANDOM FOREST\n",
        "y_pred_rf = rf_final_model.predict(X_test)\n",
        "print(\"Accuracy RF:\", accuracy_score(y_test, y_pred_rf))\n",
        "\n",
        "#XGBOOST\n",
        "y_pred_xgb= final_best_xgb.predict(X_test)\n",
        "print(\"Accuracy XGB:\", accuracy_score(y_test, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "5JjkeqLpfVEo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3abfa5e-899a-4fef-98d6-af872976c040"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[313  23]\n",
            " [ 15 380]]\n"
          ]
        }
      ],
      "source": [
        "#Confution Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_rf)\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ibMEItymf4AB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "693b59a1-b8bf-4df8-9ee8-9131f7340d72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[306  30]\n",
            " [ 15 380]]\n"
          ]
        }
      ],
      "source": [
        "#Confution Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_xgb)\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "eyX-ddrrf7uM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c44a89dc-d48e-479e-c42e-9eafb815a28a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report RANDOM FOREST:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.93      0.94       336\n",
            "           1       0.94      0.96      0.95       395\n",
            "\n",
            "    accuracy                           0.95       731\n",
            "   macro avg       0.95      0.95      0.95       731\n",
            "weighted avg       0.95      0.95      0.95       731\n",
            "\n",
            "\n",
            "==============================\n",
            "==============================\n",
            "Classification Report XGBOOST:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.91      0.93       336\n",
            "           1       0.93      0.96      0.94       395\n",
            "\n",
            "    accuracy                           0.94       731\n",
            "   macro avg       0.94      0.94      0.94       731\n",
            "weighted avg       0.94      0.94      0.94       731\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Classification Report RANDOM FOREST:\\n\", classification_report(y_test, y_pred_rf))\n",
        "print(\"\\n\" + \"=\"*30)\n",
        "print(\"=\"*30)\n",
        "print(\"Classification Report XGBOOST:\\n\", classification_report(y_test, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "fizzyC9fx5Hn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "3c88053b-d561-4916-93a2-ba8bfaca685a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_rf)\n",
        "plt.figure(figsize=(4, 3))\n",
        "\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Potensi Rendah', 'Potensi Tinggi'],\n",
        "            yticklabels=['Potensi Rendah', 'Potensi Tinggi'])\n",
        "\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Prediksi Model')\n",
        "plt.title('Confusion Matrix: Random Forest')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Q4rYuMmlx9TS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "12fcd8c6-164f-443a-84c3-986194f21821"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred_xgb)\n",
        "plt.figure(figsize=(6, 5))\n",
        "\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Potensi Rendah', 'Potensi Tinggi'],\n",
        "            yticklabels=['Potensi Rendah', 'Potensi Tinggi'])\n",
        "\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Prediksi Model')\n",
        "plt.title('Confusion Matrix: XGBOOST')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "L6azVF3Sg1d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81e3decc-dd18-4612-e6a5-6261e9319dbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tipe shap_values : <class 'numpy.ndarray'>\n",
            "Shape shap_values: (731, 6, 2)\n",
            "Shape X_test     : (731, 6)\n"
          ]
        }
      ],
      "source": [
        "import shap\n",
        "import numpy as np\n",
        "\n",
        "# Reset index X_test\n",
        "X_test_reset = X_test.reset_index(drop=True)\n",
        "\n",
        "explainer = shap.TreeExplainer(rf_final_model)\n",
        "shap_values = explainer.shap_values(X_test_reset)\n",
        "\n",
        "print(\"Tipe shap_values :\", type(shap_values))\n",
        "print(\"Shape shap_values:\", np.array(shap_values).shape)\n",
        "print(\"Shape X_test     :\", X_test_reset.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "77k-Ak_2hzjG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "90a7ee05-5798-4136-9ee6-ecc0347941ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shap_vals_class1 shape: (731, 6)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x390 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "feature_names = X_test_reset.columns.tolist()\n",
        "shap_vals_class1 = shap_values[:, :, 1]\n",
        "\n",
        "print(\"shap_vals_class1 shape:\", shap_vals_class1.shape)\n",
        "\n",
        "# Summary plot\n",
        "shap.summary_plot(shap_vals_class1, X_test_reset, feature_names=feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "HQ4sStBwcMGx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "fea42295-32c5-4219-a130-6ba0e1cfc71c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_df = df_laporan[(df_laporan['year'] >= 2024) & (df_laporan['year'] <= 2025)].copy()\n",
        "test_df['y_pred_rf'] = y_pred_rf\n",
        "seasonal_pattern = test_df.groupby('month')['y_pred_rf'].mean() * 100\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(seasonal_pattern.index, seasonal_pattern.values, marker='o', linewidth=2, color='teal')\n",
        "\n",
        "plt.title('Pola Musiman Potensi Ikan Kembung (Hasil Prediksi 2024-2025)', fontsize=14)\n",
        "plt.xlabel('Bulan', fontsize=12)\n",
        "plt.ylabel('Persentase Titik Potensial (%)', fontsize=12)\n",
        "plt.xticks(range(1, 13))\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "LomY30xS6FsD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7abbf50e-97cc-4a35-9940-4f74dd1c7c66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.1030303  0.76969697 0.6969697  1.         1.         0.99393939\n",
            " 1.         0.93333333 0.97575758 0.86666667]\n",
            "0.833939393939394\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "\n",
        "tscv = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "scores = cross_val_score(rf_final_model, X_train, y_train, cv=tscv)\n",
        "print(scores)\n",
        "print(scores.mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cross Validation**"
      ],
      "metadata": {
        "id": "TQR0xebFNSXp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "_S7Vdzlc6aMR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f70c0c33-bb6f-4ad9-c4f8-7bba3857bdfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.92727273 0.76969697 0.59393939 1.         1.         0.73333333\n",
            " 0.85454545 0.93333333 0.97575758 0.83636364]\n",
            "0.8624242424242425\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "\n",
        "tscv = TimeSeriesSplit(n_splits=10)\n",
        "\n",
        "scores = cross_val_score(final_best_xgb, X_train, y_train, cv=tscv)\n",
        "print(scores)\n",
        "print(scores.mean())"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}