TIF_E41212325/model gru dari google colab.ipynb

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            "/tmp/ipython-input-1727953062.py:18: FutureWarning: YF.download() has changed argument auto_adjust default to True\n",
            "  df = yf.download(ticker, start=start_date, end=end_date_adjusted)\n",
            "\r[*********************100%***********************]  1 of 1 completed"
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          "text": [
            "Sedang mengunduh data dari 2024-01-01 sampai 2025-08-31 (System adjusted to: 2025-09-01)...\n",
            "\n",
            "✅ Jumlah data yang berhasil diambil: 609 baris\n",
            "📈 Data harga Ethereum:\n",
            "\n"
          ]
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              "Price        Date        Close         High          Low         Open  \\\n",
              "Ticker                 ETH-USD      ETH-USD      ETH-USD      ETH-USD   \n",
              "0      2024-01-01  2352.327881  2352.327881  2267.018066  2282.870361   \n",
              "1      2024-01-02  2355.836426  2431.212402  2348.892334  2352.593506   \n",
              "2      2024-01-03  2210.761963  2385.117676  2113.925293  2355.981445   \n",
              "3      2024-01-04  2269.038086  2294.608154  2204.865723  2210.529053   \n",
              "4      2024-01-05  2268.647217  2276.764648  2209.537109  2269.409424   \n",
              "\n",
              "Price        Volume  \n",
              "Ticker      ETH-USD  \n",
              "0        6906765990  \n",
              "1       12910543630  \n",
              "2       19332933581  \n",
              "3       11044564896  \n",
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              "      <th>0</th>\n",
              "      <td>2024-01-01</td>\n",
              "      <td>2352.327881</td>\n",
              "      <td>2352.327881</td>\n",
              "      <td>2267.018066</td>\n",
              "      <td>2282.870361</td>\n",
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              "      <th>1</th>\n",
              "      <td>2024-01-02</td>\n",
              "      <td>2355.836426</td>\n",
              "      <td>2431.212402</td>\n",
              "      <td>2348.892334</td>\n",
              "      <td>2352.593506</td>\n",
              "      <td>12910543630</td>\n",
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              "      <th>2</th>\n",
              "      <td>2024-01-03</td>\n",
              "      <td>2210.761963</td>\n",
              "      <td>2385.117676</td>\n",
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              "      <td>2355.981445</td>\n",
              "      <td>19332933581</td>\n",
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              "      <th>3</th>\n",
              "      <td>2024-01-04</td>\n",
              "      <td>2269.038086</td>\n",
              "      <td>2294.608154</td>\n",
              "      <td>2204.865723</td>\n",
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              "      <th>4</th>\n",
              "      <td>2024-01-05</td>\n",
              "      <td>2268.647217</td>\n",
              "      <td>2276.764648</td>\n",
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              "Price        Date        Close         High          Low         Open  \\\n",
              "Ticker                 ETH-USD      ETH-USD      ETH-USD      ETH-USD   \n",
              "604    2025-08-27  4503.393066  4659.987305  4489.297852  4600.510254   \n",
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              "      <th>608</th>\n",
              "      <td>2025-08-31</td>\n",
              "      <td>4390.019043</td>\n",
              "      <td>4497.176758</td>\n",
              "      <td>4373.596680</td>\n",
              "      <td>4374.893555</td>\n",
              "      <td>26683044984</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "        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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Date\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2025-08-27 00:00:00\",\n        \"max\": \"2025-08-31 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2025-08-28 00:00:00\",\n          \"2025-08-31 00:00:00\",\n          \"2025-08-29 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Close\",\n        \"ETH-USD\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72.27252957292615,\n        \"min\": 4360.15283203125,\n        \"max\": 4507.177734375,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4507.177734375,\n          4390.01904296875,\n          4360.15283203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"High\",\n        \"ETH-USD\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 101.074717279445,\n        \"min\": 4413.27490234375,\n        \"max\": 4659.9873046875,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4629.03125,\n          4497.1767578125,\n          4513.859375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Low\",\n        \"ETH-USD\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 98.92656294783664,\n        \"min\": 4264.1953125,\n        \"max\": 4489.2978515625,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4435.109375,\n          4373.5966796875,\n          4272.45947265625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Open\",\n        \"ETH-USD\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100.87578968592224,\n        \"min\": 4360.0888671875,\n        \"max\": 4600.51025390625,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4503.63134765625,\n          4374.8935546875,\n          4507.63134765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Volume\",\n        \"ETH-USD\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9541698153,\n        \"min\": 25883112278,\n        \"max\": 46899991962,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          36045274078,\n          26683044984,\n          46899991962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# --- LANGKAH 1: PENGUMPULAN DATA (DATA COLLECTION) ---\n",
        "\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "\n",
        "# --- KONFIGURASI ---\n",
        "ticker = \"ETH-USD\"\n",
        "start_date = \"2024-01-01\"\n",
        "end_date = \"2025-08-31\"\n",
        "\n",
        "# --- LOGIKA PENYESUAIAN TANGGAL ---\n",
        "end_date_adjusted = (pd.to_datetime(end_date) + pd.Timedelta(days=1)).strftime(\"%Y-%m-%d\")\n",
        "\n",
        "print(f\"Sedang mengunduh data dari {start_date} sampai {end_date} (System adjusted to: {end_date_adjusted})...\")\n",
        "\n",
        "# --- DOWNLOAD DATA ---\n",
        "df = yf.download(ticker, start=start_date, end=end_date_adjusted)\n",
        "\n",
        "# Reset index agar 'Date' menjadi kolom biasa (lebih rapi saat ditampilkan)\n",
        "df = df.reset_index()\n",
        "\n",
        "# --- TAMPILKAN HASIL ---\n",
        "print(f\"\\n✅ Jumlah data yang berhasil diambil: {len(df)} baris\")\n",
        "print(\"📈 Data harga Ethereum:\\n\")\n",
        "\n",
        "# Menampilkan 5 baris pertama & terakhir\n",
        "try:\n",
        "    display(df.head())\n",
        "    display(df.tail())\n",
        "except:\n",
        "    print(df.head())\n",
        "    print(df.tail())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Pastikan df sudah ada (dari langkah download data sebelumnya)\n",
        "# Jika df belum reset index (Date masih jadi index), kita reset dulu agar kolom 'Date' muncul\n",
        "if 'Date' not in df.columns:\n",
        "    df = df.reset_index()\n",
        "\n",
        "# --- Buat plot harga historis (Close) ---\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "# Plotting\n",
        "plt.plot(df[\"Date\"], df[\"Close\"], color=\"steelblue\", linewidth=2)\n",
        "\n",
        "# Judul dan Label\n",
        "plt.title(\"Grafik Harga Historis Ethereum (ETH-USD)\\nPeriode 1 Januari 2024 - 31 Agustus 2025\",\n",
        "          fontsize=14, fontweight=\"bold\")\n",
        "plt.xlabel(\"Tanggal\", fontsize=12)\n",
        "plt.ylabel(\"Harga Penutupan (USD)\", fontsize=12)\n",
        "\n",
        "# Grid tipis agar mudah dibaca\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "# Simpan gambar agar bisa dimasukkan ke Word nanti\n",
        "plt.savefig('Gambar_4_1_Harga_Ethereum.png')\n",
        "\n",
        "# Tampilkan\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "dwYVKpHDAMrA",
        "outputId": "5c77e252-c17c-4f00-9dde-e4a82329e456"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan informasi umum tentang dataset\n",
        "print(\"📋 Informasi Dataset:\")\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6impT73ODl-B",
        "outputId": "adebeace-773a-498f-d3f6-842f5b97d54b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📋 Informasi Dataset:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 609 entries, 0 to 608\n",
            "Data columns (total 6 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   (Date, )           609 non-null    datetime64[ns]\n",
            " 1   (Close, ETH-USD)   609 non-null    float64       \n",
            " 2   (High, ETH-USD)    609 non-null    float64       \n",
            " 3   (Low, ETH-USD)     609 non-null    float64       \n",
            " 4   (Open, ETH-USD)    609 non-null    float64       \n",
            " 5   (Volume, ETH-USD)  609 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(1)\n",
            "memory usage: 28.7 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- LANGKAH 2.1: CEK & TANGANI MISSING VALUE ---\n",
        "\n",
        "print(\"🔍 MEMERIKSA MISSING VALUES...\")\n",
        "\n",
        "# 1. Cek jumlah null\n",
        "missing_count = df[['Date', 'Close']].isnull().sum()\n",
        "print(f\"\\nJumlah Data Kosong:\\n{missing_count}\")\n",
        "\n",
        "# 2. Cek baris spesifik (jika ada)\n",
        "missing_rows = df[df[['Date', 'Close']].isnull().any(axis=1)]\n",
        "\n",
        "if len(missing_rows) > 0:\n",
        "    print(f\"\\n⚠️ PERINGATAN: Ditemukan {len(missing_rows)} baris data kosong!\")\n",
        "    display(missing_rows)\n",
        "\n",
        "    print(\"\\n🛠️ Melakukan penanganan missing value (Interpolasi)...\")\n",
        "    df['Close'] = df['Close'].interpolate(method='linear')\n",
        "\n",
        "    # Cek ulang\n",
        "    print(f\"Status setelah perbaikan: {df['Close'].isnull().sum()} missing value.\")\n",
        "else:\n",
        "    print(\"\\n✅ STATUS: Data Bersih. Tidak ditemukan missing value pada kolom Date dan Close.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTVYHAsLLJWv",
        "outputId": "267baccc-8a7a-4709-f8e4-c387d7d74ba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔍 MEMERIKSA MISSING VALUES...\n",
            "\n",
            "Jumlah Data Kosong:\n",
            "Price  Ticker \n",
            "Date              0\n",
            "Close  ETH-USD    0\n",
            "dtype: int64\n",
            "\n",
            "✅ STATUS: Data Bersih. Tidak ditemukan missing value pada kolom Date dan Close.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- LANGKAH 2.2: NORMALISASI DATA (MINMAX SCALER) ---\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# 1. Ambil kolom 'Close' saja\n",
        "# Kita ubah ke bentuk array 2D karena MinMaxScaler membutuhkan input (n_samples, n_features)\n",
        "data_close = df[['Close']].values\n",
        "\n",
        "# 2. Inisialisasi Scaler (Range 0 sampai 1)\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "# 3. Lakukan Normalisasi (Fit & Transform)\n",
        "scaled_data = scaler.fit_transform(data_close)\n",
        "\n",
        "# --- TAMPILKAN HASIL UNTUK VALIDASI ---\n",
        "# Kita buat DataFrame sementara untuk melihat perbandingan Harga Asli vs Normalisasi\n",
        "df_comparison = pd.DataFrame(data_close, columns=['Harga Asli (USD)'])\n",
        "df_comparison['Harga Normalisasi'] = scaled_data\n",
        "\n",
        "print(\"✅ Normalisasi Selesai.\")\n",
        "print(f\"Nilai Min (X_min): {scaler.data_min_[0]}\")\n",
        "print(f\"Nilai Max (X_max): {scaler.data_max_[0]}\")\n",
        "print(\"\\n🔍 5 Data Pertama Setelah Normalisasi:\")\n",
        "display(df_comparison.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "roLsFKchN3zz",
        "outputId": "0fd89fbb-736c-4210-dfeb-51bba3c636bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Normalisasi Selesai.\n",
            "Nilai Min (X_min): 1472.5531005859375\n",
            "Nilai Max (X_max): 4831.3486328125\n",
            "\n",
            "🔍 5 Data Pertama Setelah Normalisasi:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Harga Asli (USD)  Harga Normalisasi\n",
              "0       2352.327881           0.261932\n",
              "1       2355.836426           0.262976\n",
              "2       2210.761963           0.219784\n",
              "3       2269.038086           0.237134\n",
              "4       2268.647217           0.237018"
            ],
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              "      <th>Harga Asli (USD)</th>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_comparison\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Harga Asli (USD)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.01706787477087,\n        \"min\": 2210.761962890625,\n        \"max\": 2355.83642578125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2355.83642578125,\n          2268.647216796875,\n          2210.761962890625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Harga Normalisasi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018464079542722143,\n        \"min\": 0.219783805004446,\n        \"max\": 0.2629762117760647,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2629762117760647,\n          0.23701773703480028,\n          0.219783805004446\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# --- LANGKAH 2.3: SLIDING WINDOW ---\n",
        "\n",
        "# Mendefinisikan fungsi create_dataset\n",
        "def create_dataset(data, time_step):\n",
        "    X, y = [], []\n",
        "    # Menggunakan logika loop (len(data) - time_step - 1)\n",
        "    for i in range(len(data) - time_step - 1):\n",
        "        X.append(data[i:(i + time_step), 0])\n",
        "        y.append(data[i + time_step, 0])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "# --- IMPLEMENTASI ---\n",
        "# Mengatur time_step menjadi 30\n",
        "time_step = 30\n",
        "\n",
        "# Memanggil fungsi\n",
        "X, y = create_dataset(scaled_data, time_step)\n",
        "\n",
        "# --- [PENTING!] RESHAPE KE 3D (YANG HILANG TADI) ---\n",
        "# Mengubah dimensi dari (Samples, 30) menjadi (Samples, 30, 1)\n",
        "X = X.reshape(X.shape[0], X.shape[1], 1)\n",
        "\n",
        "# --- CEK HASIL ---\n",
        "print(f\"✅ Sliding Window Selesai dengan Time Step: {time_step}\")\n",
        "print(f\"Dimensi X (Input) : {X.shape}\") # Sekarang harusnya (xxx, 30, 1)\n",
        "print(f\"Dimensi y (Target): {y.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjWCnXz-W_9U",
        "outputId": "5ec19bfd-9e75-487a-93bc-5243bf38df60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Sliding Window Selesai dengan Time Step: 30\n",
            "Dimensi X (Input) : (578, 30, 1)\n",
            "Dimensi y (Target): (578,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- CEK PEMBAGIAN DATA 80:20 ---\n",
        "\n",
        "# Hitung jumlah sampel total setelah Sliding Window\n",
        "total_samples = len(X)\n",
        "\n",
        "# Hitung batas training (80%)\n",
        "train_limit = int(total_samples * 0.8)\n",
        "\n",
        "# Lakukan Splitting data\n",
        "X_train, X_test = X[:train_limit], X[train_limit:]\n",
        "y_train, y_test = y[:train_limit], y[train_limit:]\n",
        "\n",
        "print(\"\\n📊 VALIDASI PEMBAGIAN DATA (80:20)\")\n",
        "print(\"====================================\")\n",
        "print(f\"1. Total Data Mentah (Baris Excel) : {len(scaled_data)}\")\n",
        "print(f\"2. Dikurangi Time Step (30 hari)   : -30\")\n",
        "print(f\"3. Total Sampel Siap Pakai (X)     : {total_samples}\")\n",
        "print(\"------------------------------------\")\n",
        "print(f\"✅ Data Latih (X_train) - 80%      : {len(X_train)} sampel\")\n",
        "print(f\"✅ Data Uji   (X_test)  - 20%      : {len(X_test)} sampel\")\n",
        "print(\"====================================\")\n",
        "\n",
        "# Cek Dimensi Akhir (Harus 3D untuk GRU)\n",
        "print(f\"Dimensi X_train Final: {X_train.shape}\")\n",
        "print(f\"Dimensi X_test Final : {X_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbD0kiXXagMI",
        "outputId": "f2feee1d-416d-43ab-fd15-72564ff29c44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📊 VALIDASI PEMBAGIAN DATA (80:20)\n",
            "====================================\n",
            "1. Total Data Mentah (Baris Excel) : 609\n",
            "2. Dikurangi Time Step (30 hari)   : -30\n",
            "3. Total Sampel Siap Pakai (X)     : 578\n",
            "------------------------------------\n",
            "✅ Data Latih (X_train) - 80%      : 462 sampel\n",
            "✅ Data Uji   (X_test)  - 20%      : 116 sampel\n",
            "====================================\n",
            "Dimensi X_train Final: (462, 30, 1)\n",
            "Dimensi X_test Final : (116, 30, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- CEK NILAI UNTUK PERHITUNGAN MANUAL ---\n",
        "sample_real = X_train[0][-1][0]\n",
        "\n",
        "print(f\"\\n✅ Nilai x_t asli dari dataset: {sample_real}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPV_U5e8alQr",
        "outputId": "845fdd75-4e92-4ccf-fcd8-3a7d999a210f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "✅ Nilai x_t asli dari dataset: 0.2595991757735246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Input, GRU, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# ================================\n",
        "# 1. KONFIGURASI GRID SEARCH\n",
        "# ================================\n",
        "param_grid = {\n",
        "    \"GRU_unit\":   [16, 32, 64, 128],\n",
        "    \"dropout\":    [0.1, 0.2],\n",
        "    \"batch_size\": [32],\n",
        "    \"epochs\":     [50],\n",
        "}\n",
        "\n",
        "es = EarlyStopping(\n",
        "    monitor='val_loss', patience=5, restore_best_weights=True, verbose=0\n",
        ")\n",
        "\n",
        "best_rmse = float('inf')\n",
        "best_model = None\n",
        "best_history = None\n",
        "best_params = {}\n",
        "best_y_pred = None\n",
        "\n",
        "results = []\n",
        "\n",
        "print(f\"🚀 MEMULAI TRAINING (8 KOMBINASI)...\\n\")\n",
        "\n",
        "# ================================\n",
        "# 2. PROSES TRAINING (LOOPING)\n",
        "# ================================\n",
        "total_iter = len(param_grid['GRU_unit']) * len(param_grid['dropout'])\n",
        "current_iter = 0\n",
        "\n",
        "for units in param_grid[\"GRU_unit\"]:\n",
        "    for drop in param_grid[\"dropout\"]:\n",
        "        for batch in param_grid[\"batch_size\"]:\n",
        "            for epoch in param_grid[\"epochs\"]:\n",
        "\n",
        "                current_iter += 1\n",
        "                print(f\"🔄 [{current_iter}/{total_iter}] Training: Units={units}, Drop={drop}...\", end=\" \")\n",
        "\n",
        "                # --- Bangun Model ---\n",
        "                model = Sequential([\n",
        "                    Input(shape=(X_train.shape[1], 1)),\n",
        "                    GRU(units, activation='tanh', return_sequences=False),\n",
        "                    Dropout(drop),\n",
        "                    Dense(1, activation='linear')\n",
        "                ])\n",
        "                model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n",
        "\n",
        "                # --- Latih Model ---\n",
        "                history = model.fit(\n",
        "                    X_train, y_train,\n",
        "                    validation_data=(X_test, y_test),\n",
        "                    epochs=epoch,\n",
        "                    batch_size=batch,\n",
        "                    callbacks=[es],\n",
        "                    verbose=1\n",
        "                )\n",
        "\n",
        "                # --- Evaluasi ---\n",
        "                y_pred = model.predict(X_test, verbose=0)\n",
        "\n",
        "                mse = mean_squared_error(y_test, y_pred)\n",
        "                rmse = np.sqrt(mse)\n",
        "                mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "                print(f\"✅ RMSE: {rmse:.5f}\")\n",
        "\n",
        "                # --- Simpan ke Tabel (TANPA KOLOM SKENARIO) ---\n",
        "                results.append({\n",
        "                    'GRU Units': units,\n",
        "                    'Dropout': drop,\n",
        "                    'RMSE': rmse,\n",
        "                    'MSE': mse,\n",
        "                    'MAE': mae\n",
        "                })\n",
        "\n",
        "                # --- Cek Terbaik ---\n",
        "                if rmse < best_rmse:\n",
        "                    best_rmse = rmse\n",
        "                    best_model = model\n",
        "                    best_history = history\n",
        "                    best_params = {'Unit': units, 'Drop': drop}\n",
        "                    best_y_pred = y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mN3JtG8WdzYU",
        "outputId": "c80b1647-9e9c-4689-81da-5c0b76cc0920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🚀 MEMULAI TRAINING (8 KOMBINASI)...\n",
            "\n",
            "🔄 [1/8] Training: Units=16, Drop=0.1... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - loss: 0.4583 - mae: 0.6148 - val_loss: 0.3786 - val_mae: 0.5325\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.2172 - mae: 0.4041 - val_loss: 0.1756 - val_mae: 0.3263\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0840 - mae: 0.2451 - val_loss: 0.0726 - val_mae: 0.1902\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0350 - mae: 0.1558 - val_loss: 0.0427 - val_mae: 0.1778\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0321 - mae: 0.1455 - val_loss: 0.0392 - val_mae: 0.1698\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0284 - mae: 0.1367 - val_loss: 0.0394 - val_mae: 0.1628\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0235 - mae: 0.1242 - val_loss: 0.0355 - val_mae: 0.1543\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0210 - mae: 0.1203 - val_loss: 0.0312 - val_mae: 0.1452\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0220 - mae: 0.1200 - val_loss: 0.0277 - val_mae: 0.1358\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0193 - mae: 0.1138 - val_loss: 0.0234 - val_mae: 0.1255\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0163 - mae: 0.1034 - val_loss: 0.0206 - val_mae: 0.1157\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0148 - mae: 0.0988 - val_loss: 0.0177 - val_mae: 0.1053\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0134 - mae: 0.0941 - val_loss: 0.0137 - val_mae: 0.0931\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0115 - mae: 0.0850 - val_loss: 0.0116 - val_mae: 0.0833\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0101 - mae: 0.0774 - val_loss: 0.0082 - val_mae: 0.0705\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0080 - mae: 0.0680 - val_loss: 0.0069 - val_mae: 0.0626\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0074 - mae: 0.0660 - val_loss: 0.0056 - val_mae: 0.0547\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0072 - mae: 0.0648 - val_loss: 0.0057 - val_mae: 0.0539\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0068 - mae: 0.0623 - val_loss: 0.0044 - val_mae: 0.0476\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0056 - mae: 0.0579 - val_loss: 0.0054 - val_mae: 0.0535\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0067 - mae: 0.0629 - val_loss: 0.0048 - val_mae: 0.0503\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0060 - mae: 0.0578 - val_loss: 0.0042 - val_mae: 0.0463\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0066 - mae: 0.0612 - val_loss: 0.0059 - val_mae: 0.0569\n",
            "Epoch 24/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0050 - mae: 0.0538 - val_loss: 0.0038 - val_mae: 0.0441\n",
            "Epoch 25/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0050 - mae: 0.0522 - val_loss: 0.0042 - val_mae: 0.0464\n",
            "Epoch 26/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0046 - mae: 0.0519 - val_loss: 0.0043 - val_mae: 0.0471\n",
            "Epoch 27/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0059 - mae: 0.0549 - val_loss: 0.0046 - val_mae: 0.0490\n",
            "Epoch 28/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0052 - mae: 0.0536 - val_loss: 0.0036 - val_mae: 0.0427\n",
            "Epoch 29/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0050 - mae: 0.0541 - val_loss: 0.0039 - val_mae: 0.0445\n",
            "Epoch 30/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0054 - mae: 0.0544 - val_loss: 0.0038 - val_mae: 0.0442\n",
            "Epoch 31/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - loss: 0.0046 - mae: 0.0519 - val_loss: 0.0039 - val_mae: 0.0450\n",
            "Epoch 32/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0051 - mae: 0.0539 - val_loss: 0.0038 - val_mae: 0.0440\n",
            "Epoch 33/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 26ms/step - loss: 0.0046 - mae: 0.0529 - val_loss: 0.0035 - val_mae: 0.0424\n",
            "Epoch 34/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0041 - mae: 0.0507 - val_loss: 0.0038 - val_mae: 0.0443\n",
            "Epoch 35/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 0.0039 - mae: 0.0467 - val_loss: 0.0033 - val_mae: 0.0409\n",
            "Epoch 36/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0037 - mae: 0.0477 - val_loss: 0.0032 - val_mae: 0.0404\n",
            "Epoch 37/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0039 - mae: 0.0483 - val_loss: 0.0039 - val_mae: 0.0452\n",
            "Epoch 38/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0040 - mae: 0.0488 - val_loss: 0.0035 - val_mae: 0.0423\n",
            "Epoch 39/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0040 - mae: 0.0470 - val_loss: 0.0034 - val_mae: 0.0413\n",
            "Epoch 40/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0046 - mae: 0.0507 - val_loss: 0.0043 - val_mae: 0.0477\n",
            "Epoch 41/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0039 - mae: 0.0468 - val_loss: 0.0029 - val_mae: 0.0390\n",
            "Epoch 42/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0038 - mae: 0.0465 - val_loss: 0.0037 - val_mae: 0.0439\n",
            "Epoch 43/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0040 - mae: 0.0461 - val_loss: 0.0032 - val_mae: 0.0404\n",
            "Epoch 44/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0041 - mae: 0.0482 - val_loss: 0.0031 - val_mae: 0.0399\n",
            "Epoch 45/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0047 - mae: 0.0516 - val_loss: 0.0031 - val_mae: 0.0396\n",
            "Epoch 46/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0033 - mae: 0.0447 - val_loss: 0.0032 - val_mae: 0.0405\n",
            "✅ RMSE: 0.05400\n",
            "🔄 [2/8] Training: Units=16, Drop=0.2... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - loss: 0.1869 - mae: 0.3899 - val_loss: 0.1227 - val_mae: 0.2886\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0612 - mae: 0.2011 - val_loss: 0.0309 - val_mae: 0.1339\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0222 - mae: 0.1229 - val_loss: 0.0197 - val_mae: 0.1257\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0235 - mae: 0.1235 - val_loss: 0.0216 - val_mae: 0.1190\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0167 - mae: 0.1020 - val_loss: 0.0209 - val_mae: 0.1133\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0156 - mae: 0.0997 - val_loss: 0.0172 - val_mae: 0.1046\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0149 - mae: 0.0973 - val_loss: 0.0142 - val_mae: 0.0958\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0129 - mae: 0.0858 - val_loss: 0.0125 - val_mae: 0.0879\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0111 - mae: 0.0846 - val_loss: 0.0111 - val_mae: 0.0805\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0124 - mae: 0.0850 - val_loss: 0.0073 - val_mae: 0.0683\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0093 - mae: 0.0763 - val_loss: 0.0065 - val_mae: 0.0617\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0084 - mae: 0.0737 - val_loss: 0.0046 - val_mae: 0.0523\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0087 - mae: 0.0688 - val_loss: 0.0048 - val_mae: 0.0504\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0059 - mae: 0.0593 - val_loss: 0.0034 - val_mae: 0.0423\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0048 - mae: 0.0517 - val_loss: 0.0036 - val_mae: 0.0428\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0066 - mae: 0.0614 - val_loss: 0.0034 - val_mae: 0.0418\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0051 - mae: 0.0527 - val_loss: 0.0033 - val_mae: 0.0407\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.0054 - mae: 0.0550 - val_loss: 0.0028 - val_mae: 0.0382\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0046 - mae: 0.0534 - val_loss: 0.0041 - val_mae: 0.0475\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0044 - mae: 0.0499 - val_loss: 0.0026 - val_mae: 0.0370\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0052 - mae: 0.0558 - val_loss: 0.0029 - val_mae: 0.0388\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0047 - mae: 0.0525 - val_loss: 0.0032 - val_mae: 0.0408\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0044 - mae: 0.0497 - val_loss: 0.0029 - val_mae: 0.0389\n",
            "Epoch 24/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0048 - mae: 0.0517 - val_loss: 0.0032 - val_mae: 0.0406\n",
            "Epoch 25/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0039 - mae: 0.0482 - val_loss: 0.0024 - val_mae: 0.0354\n",
            "Epoch 26/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0054 - mae: 0.0553 - val_loss: 0.0030 - val_mae: 0.0393\n",
            "Epoch 27/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0047 - mae: 0.0504 - val_loss: 0.0033 - val_mae: 0.0416\n",
            "Epoch 28/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0061 - mae: 0.0569 - val_loss: 0.0024 - val_mae: 0.0353\n",
            "Epoch 29/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0045 - mae: 0.0511 - val_loss: 0.0037 - val_mae: 0.0453\n",
            "Epoch 30/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0055 - mae: 0.0561 - val_loss: 0.0022 - val_mae: 0.0344\n",
            "Epoch 31/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0049 - mae: 0.0528 - val_loss: 0.0027 - val_mae: 0.0375\n",
            "Epoch 32/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0041 - mae: 0.0474 - val_loss: 0.0029 - val_mae: 0.0394\n",
            "Epoch 33/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0046 - mae: 0.0518 - val_loss: 0.0022 - val_mae: 0.0339\n",
            "Epoch 34/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0038 - mae: 0.0456 - val_loss: 0.0026 - val_mae: 0.0364\n",
            "Epoch 35/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0047 - mae: 0.0519 - val_loss: 0.0023 - val_mae: 0.0345\n",
            "✅ RMSE: 0.04690\n",
            "🔄 [3/8] Training: Units=32, Drop=0.1... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 41ms/step - loss: 0.1660 - mae: 0.3598 - val_loss: 0.0587 - val_mae: 0.1811\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0205 - mae: 0.1153 - val_loss: 0.0148 - val_mae: 0.1083\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0149 - mae: 0.0996 - val_loss: 0.0164 - val_mae: 0.1014\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0116 - mae: 0.0871 - val_loss: 0.0145 - val_mae: 0.0935\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0097 - mae: 0.0816 - val_loss: 0.0097 - val_mae: 0.0809\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0076 - mae: 0.0702 - val_loss: 0.0093 - val_mae: 0.0749\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0064 - mae: 0.0639 - val_loss: 0.0071 - val_mae: 0.0654\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 0.0056 - mae: 0.0580 - val_loss: 0.0054 - val_mae: 0.0568\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 0.0043 - mae: 0.0528 - val_loss: 0.0043 - val_mae: 0.0495\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0037 - mae: 0.0474 - val_loss: 0.0039 - val_mae: 0.0450\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0030 - mae: 0.0405 - val_loss: 0.0035 - val_mae: 0.0417\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - loss: 0.0033 - mae: 0.0434 - val_loss: 0.0035 - val_mae: 0.0424\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0027 - mae: 0.0394 - val_loss: 0.0031 - val_mae: 0.0400\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0034 - mae: 0.0432 - val_loss: 0.0033 - val_mae: 0.0413\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0033 - mae: 0.0427 - val_loss: 0.0028 - val_mae: 0.0385\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0032 - mae: 0.0421 - val_loss: 0.0035 - val_mae: 0.0429\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0028 - mae: 0.0406 - val_loss: 0.0030 - val_mae: 0.0393\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0030 - mae: 0.0425 - val_loss: 0.0026 - val_mae: 0.0371\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0036 - mae: 0.0423 - val_loss: 0.0028 - val_mae: 0.0378\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0028 - mae: 0.0404 - val_loss: 0.0027 - val_mae: 0.0374\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0032 - mae: 0.0431 - val_loss: 0.0028 - val_mae: 0.0380\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0026 - mae: 0.0375 - val_loss: 0.0027 - val_mae: 0.0375\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0027 - mae: 0.0386 - val_loss: 0.0026 - val_mae: 0.0368\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x78255096ade0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:6 out of the last 12 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x78255096ade0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ RMSE: 0.05103\n",
            "🔄 [4/8] Training: Units=32, Drop=0.2... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - loss: 0.1502 - mae: 0.3375 - val_loss: 0.0274 - val_mae: 0.1191\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0186 - mae: 0.1130 - val_loss: 0.0126 - val_mae: 0.0995\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0156 - mae: 0.1049 - val_loss: 0.0190 - val_mae: 0.1009\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0131 - mae: 0.0903 - val_loss: 0.0116 - val_mae: 0.0851\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0100 - mae: 0.0821 - val_loss: 0.0089 - val_mae: 0.0755\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0107 - mae: 0.0822 - val_loss: 0.0080 - val_mae: 0.0692\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0085 - mae: 0.0733 - val_loss: 0.0060 - val_mae: 0.0602\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0062 - mae: 0.0626 - val_loss: 0.0064 - val_mae: 0.0586\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0054 - mae: 0.0571 - val_loss: 0.0042 - val_mae: 0.0485\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0067 - mae: 0.0645 - val_loss: 0.0038 - val_mae: 0.0450\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0052 - mae: 0.0556 - val_loss: 0.0036 - val_mae: 0.0429\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0054 - mae: 0.0554 - val_loss: 0.0030 - val_mae: 0.0402\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 0.0053 - mae: 0.0559 - val_loss: 0.0038 - val_mae: 0.0443\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0043 - mae: 0.0489 - val_loss: 0.0031 - val_mae: 0.0401\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 0.0045 - mae: 0.0523 - val_loss: 0.0030 - val_mae: 0.0399\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0049 - mae: 0.0541 - val_loss: 0.0035 - val_mae: 0.0424\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0036 - mae: 0.0448 - val_loss: 0.0030 - val_mae: 0.0393\n",
            "✅ RMSE: 0.05440\n",
            "🔄 [5/8] Training: Units=64, Drop=0.1... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 42ms/step - loss: 0.0972 - mae: 0.2590 - val_loss: 0.0122 - val_mae: 0.0951\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0164 - mae: 0.1107 - val_loss: 0.0123 - val_mae: 0.0820\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0068 - mae: 0.0654 - val_loss: 0.0075 - val_mae: 0.0680\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0048 - mae: 0.0566 - val_loss: 0.0056 - val_mae: 0.0587\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0039 - mae: 0.0508 - val_loss: 0.0049 - val_mae: 0.0530\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0035 - mae: 0.0468 - val_loss: 0.0036 - val_mae: 0.0451\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0030 - mae: 0.0415 - val_loss: 0.0033 - val_mae: 0.0417\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0029 - mae: 0.0411 - val_loss: 0.0031 - val_mae: 0.0398\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0026 - mae: 0.0389 - val_loss: 0.0030 - val_mae: 0.0392\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0032 - mae: 0.0419 - val_loss: 0.0029 - val_mae: 0.0382\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0025 - mae: 0.0378 - val_loss: 0.0028 - val_mae: 0.0376\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0023 - mae: 0.0355 - val_loss: 0.0028 - val_mae: 0.0378\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0021 - mae: 0.0362 - val_loss: 0.0028 - val_mae: 0.0380\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0023 - mae: 0.0353 - val_loss: 0.0030 - val_mae: 0.0391\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 0.0024 - mae: 0.0359 - val_loss: 0.0028 - val_mae: 0.0377\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0023 - mae: 0.0364 - val_loss: 0.0024 - val_mae: 0.0347\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0022 - mae: 0.0352 - val_loss: 0.0026 - val_mae: 0.0366\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0024 - mae: 0.0362 - val_loss: 0.0024 - val_mae: 0.0343\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0024 - mae: 0.0356 - val_loss: 0.0026 - val_mae: 0.0365\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0016 - mae: 0.0303 - val_loss: 0.0023 - val_mae: 0.0335\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0347 - val_loss: 0.0023 - val_mae: 0.0340\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0019 - mae: 0.0330 - val_loss: 0.0022 - val_mae: 0.0332\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0020 - mae: 0.0335 - val_loss: 0.0022 - val_mae: 0.0327\n",
            "Epoch 24/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 0.0021 - mae: 0.0335 - val_loss: 0.0021 - val_mae: 0.0326\n",
            "Epoch 25/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0019 - mae: 0.0337 - val_loss: 0.0022 - val_mae: 0.0328\n",
            "Epoch 26/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0017 - mae: 0.0317 - val_loss: 0.0023 - val_mae: 0.0340\n",
            "Epoch 27/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0017 - mae: 0.0301 - val_loss: 0.0025 - val_mae: 0.0361\n",
            "Epoch 28/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0019 - mae: 0.0330 - val_loss: 0.0023 - val_mae: 0.0338\n",
            "Epoch 29/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0020 - val_mae: 0.0319\n",
            "Epoch 30/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0021 - mae: 0.0344 - val_loss: 0.0023 - val_mae: 0.0338\n",
            "Epoch 31/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0018 - mae: 0.0325 - val_loss: 0.0025 - val_mae: 0.0358\n",
            "Epoch 32/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0016 - mae: 0.0309 - val_loss: 0.0020 - val_mae: 0.0314\n",
            "Epoch 33/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0015 - mae: 0.0284 - val_loss: 0.0021 - val_mae: 0.0325\n",
            "Epoch 34/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - loss: 0.0020 - mae: 0.0338 - val_loss: 0.0020 - val_mae: 0.0311\n",
            "Epoch 35/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0016 - mae: 0.0298 - val_loss: 0.0021 - val_mae: 0.0319\n",
            "Epoch 36/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0016 - mae: 0.0302 - val_loss: 0.0021 - val_mae: 0.0326\n",
            "Epoch 37/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0020 - mae: 0.0335 - val_loss: 0.0021 - val_mae: 0.0320\n",
            "Epoch 38/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0016 - mae: 0.0294 - val_loss: 0.0020 - val_mae: 0.0312\n",
            "Epoch 39/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0018 - mae: 0.0313 - val_loss: 0.0019 - val_mae: 0.0305\n",
            "Epoch 40/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0018 - mae: 0.0318 - val_loss: 0.0019 - val_mae: 0.0313\n",
            "Epoch 41/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - loss: 0.0015 - mae: 0.0301 - val_loss: 0.0020 - val_mae: 0.0314\n",
            "Epoch 42/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0015 - mae: 0.0299 - val_loss: 0.0022 - val_mae: 0.0332\n",
            "Epoch 43/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0017 - mae: 0.0306 - val_loss: 0.0018 - val_mae: 0.0303\n",
            "Epoch 44/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0017 - mae: 0.0309 - val_loss: 0.0018 - val_mae: 0.0311\n",
            "Epoch 45/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0016 - mae: 0.0314 - val_loss: 0.0019 - val_mae: 0.0304\n",
            "Epoch 46/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0015 - mae: 0.0295 - val_loss: 0.0019 - val_mae: 0.0302\n",
            "Epoch 47/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0015 - mae: 0.0302 - val_loss: 0.0020 - val_mae: 0.0313\n",
            "Epoch 48/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0016 - mae: 0.0297 - val_loss: 0.0019 - val_mae: 0.0301\n",
            "Epoch 49/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0016 - mae: 0.0300 - val_loss: 0.0018 - val_mae: 0.0306\n",
            "Epoch 50/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0018 - mae: 0.0321 - val_loss: 0.0019 - val_mae: 0.0328\n",
            "✅ RMSE: 0.04241\n",
            "🔄 [6/8] Training: Units=64, Drop=0.2... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 44ms/step - loss: 0.1033 - mae: 0.2655 - val_loss: 0.0105 - val_mae: 0.0883\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0168 - mae: 0.1113 - val_loss: 0.0130 - val_mae: 0.0827\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0084 - mae: 0.0717 - val_loss: 0.0066 - val_mae: 0.0644\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0072 - mae: 0.0665 - val_loss: 0.0056 - val_mae: 0.0576\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0049 - mae: 0.0552 - val_loss: 0.0044 - val_mae: 0.0500\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0047 - mae: 0.0551 - val_loss: 0.0037 - val_mae: 0.0445\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0047 - mae: 0.0521 - val_loss: 0.0037 - val_mae: 0.0432\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 0.0038 - mae: 0.0477 - val_loss: 0.0031 - val_mae: 0.0398\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0028 - mae: 0.0399 - val_loss: 0.0027 - val_mae: 0.0383\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.0038 - mae: 0.0475 - val_loss: 0.0037 - val_mae: 0.0440\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0038 - mae: 0.0439 - val_loss: 0.0030 - val_mae: 0.0395\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0033 - mae: 0.0426 - val_loss: 0.0027 - val_mae: 0.0374\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 0.0035 - mae: 0.0441 - val_loss: 0.0031 - val_mae: 0.0403\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 0.0025 - mae: 0.0374 - val_loss: 0.0032 - val_mae: 0.0406\n",
            "✅ RMSE: 0.05193\n",
            "🔄 [7/8] Training: Units=128, Drop=0.1... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 56ms/step - loss: 0.0742 - mae: 0.2229 - val_loss: 0.0058 - val_mae: 0.0632\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0069 - mae: 0.0668 - val_loss: 0.0063 - val_mae: 0.0610\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0044 - mae: 0.0531 - val_loss: 0.0057 - val_mae: 0.0556\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0034 - mae: 0.0462 - val_loss: 0.0033 - val_mae: 0.0432\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0025 - mae: 0.0393 - val_loss: 0.0035 - val_mae: 0.0422\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0024 - mae: 0.0368 - val_loss: 0.0029 - val_mae: 0.0383\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0336 - val_loss: 0.0028 - val_mae: 0.0381\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0019 - mae: 0.0318 - val_loss: 0.0032 - val_mae: 0.0409\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0020 - mae: 0.0340 - val_loss: 0.0025 - val_mae: 0.0360\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0019 - mae: 0.0317 - val_loss: 0.0026 - val_mae: 0.0365\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0021 - mae: 0.0334 - val_loss: 0.0027 - val_mae: 0.0374\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.0018 - mae: 0.0309 - val_loss: 0.0027 - val_mae: 0.0368\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0018 - mae: 0.0319 - val_loss: 0.0026 - val_mae: 0.0360\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0015 - mae: 0.0293 - val_loss: 0.0024 - val_mae: 0.0347\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0018 - mae: 0.0324 - val_loss: 0.0025 - val_mae: 0.0357\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0020 - mae: 0.0334 - val_loss: 0.0024 - val_mae: 0.0346\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0015 - mae: 0.0297 - val_loss: 0.0024 - val_mae: 0.0345\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0016 - mae: 0.0287 - val_loss: 0.0024 - val_mae: 0.0350\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0018 - mae: 0.0314 - val_loss: 0.0024 - val_mae: 0.0350\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0015 - mae: 0.0288 - val_loss: 0.0020 - val_mae: 0.0327\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0016 - mae: 0.0301 - val_loss: 0.0021 - val_mae: 0.0320\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0020 - mae: 0.0323 - val_loss: 0.0020 - val_mae: 0.0316\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0020 - val_mae: 0.0314\n",
            "Epoch 24/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0016 - mae: 0.0292 - val_loss: 0.0022 - val_mae: 0.0331\n",
            "Epoch 25/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 88ms/step - loss: 0.0014 - mae: 0.0275 - val_loss: 0.0020 - val_mae: 0.0312\n",
            "Epoch 26/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0015 - mae: 0.0302 - val_loss: 0.0020 - val_mae: 0.0311\n",
            "Epoch 27/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0013 - mae: 0.0266 - val_loss: 0.0025 - val_mae: 0.0362\n",
            "Epoch 28/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0012 - mae: 0.0260 - val_loss: 0.0021 - val_mae: 0.0318\n",
            "Epoch 29/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0015 - mae: 0.0294 - val_loss: 0.0023 - val_mae: 0.0348\n",
            "Epoch 30/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0015 - mae: 0.0286 - val_loss: 0.0022 - val_mae: 0.0330\n",
            "Epoch 31/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0012 - mae: 0.0259 - val_loss: 0.0019 - val_mae: 0.0304\n",
            "Epoch 32/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0014 - mae: 0.0286 - val_loss: 0.0019 - val_mae: 0.0305\n",
            "Epoch 33/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0015 - mae: 0.0278 - val_loss: 0.0019 - val_mae: 0.0309\n",
            "Epoch 34/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0014 - mae: 0.0265 - val_loss: 0.0022 - val_mae: 0.0334\n",
            "Epoch 35/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0015 - mae: 0.0281 - val_loss: 0.0018 - val_mae: 0.0302\n",
            "Epoch 36/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0015 - mae: 0.0283 - val_loss: 0.0018 - val_mae: 0.0302\n",
            "Epoch 37/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0013 - mae: 0.0266 - val_loss: 0.0018 - val_mae: 0.0312\n",
            "Epoch 38/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0013 - mae: 0.0278 - val_loss: 0.0019 - val_mae: 0.0306\n",
            "Epoch 39/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0014 - mae: 0.0275 - val_loss: 0.0021 - val_mae: 0.0326\n",
            "Epoch 40/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0015 - mae: 0.0285 - val_loss: 0.0018 - val_mae: 0.0300\n",
            "Epoch 41/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0013 - mae: 0.0267 - val_loss: 0.0020 - val_mae: 0.0309\n",
            "Epoch 42/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0016 - mae: 0.0287 - val_loss: 0.0021 - val_mae: 0.0327\n",
            "Epoch 43/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0014 - mae: 0.0279 - val_loss: 0.0018 - val_mae: 0.0293\n",
            "Epoch 44/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0012 - mae: 0.0252 - val_loss: 0.0018 - val_mae: 0.0294\n",
            "Epoch 45/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0012 - mae: 0.0243 - val_loss: 0.0019 - val_mae: 0.0299\n",
            "Epoch 46/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0013 - mae: 0.0265 - val_loss: 0.0018 - val_mae: 0.0294\n",
            "Epoch 47/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0016 - mae: 0.0301 - val_loss: 0.0018 - val_mae: 0.0292\n",
            "Epoch 48/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0013 - mae: 0.0263 - val_loss: 0.0017 - val_mae: 0.0291\n",
            "Epoch 49/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0013 - mae: 0.0265 - val_loss: 0.0017 - val_mae: 0.0297\n",
            "Epoch 50/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0015 - mae: 0.0284 - val_loss: 0.0019 - val_mae: 0.0303\n",
            "✅ RMSE: 0.04158\n",
            "🔄 [8/8] Training: Units=128, Drop=0.2... Epoch 1/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 56ms/step - loss: 0.0969 - mae: 0.2581 - val_loss: 0.0085 - val_mae: 0.0783\n",
            "Epoch 2/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0103 - mae: 0.0839 - val_loss: 0.0116 - val_mae: 0.0787\n",
            "Epoch 3/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0061 - mae: 0.0641 - val_loss: 0.0057 - val_mae: 0.0594\n",
            "Epoch 4/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0042 - mae: 0.0508 - val_loss: 0.0047 - val_mae: 0.0516\n",
            "Epoch 5/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0034 - mae: 0.0476 - val_loss: 0.0040 - val_mae: 0.0455\n",
            "Epoch 6/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 0.0030 - mae: 0.0441 - val_loss: 0.0033 - val_mae: 0.0415\n",
            "Epoch 7/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0025 - mae: 0.0379 - val_loss: 0.0032 - val_mae: 0.0405\n",
            "Epoch 8/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0026 - mae: 0.0389 - val_loss: 0.0035 - val_mae: 0.0423\n",
            "Epoch 9/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0024 - mae: 0.0372 - val_loss: 0.0034 - val_mae: 0.0424\n",
            "Epoch 10/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0024 - mae: 0.0364 - val_loss: 0.0029 - val_mae: 0.0388\n",
            "Epoch 11/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0024 - mae: 0.0361 - val_loss: 0.0028 - val_mae: 0.0380\n",
            "Epoch 12/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0347 - val_loss: 0.0030 - val_mae: 0.0393\n",
            "Epoch 13/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0025 - mae: 0.0383 - val_loss: 0.0027 - val_mae: 0.0370\n",
            "Epoch 14/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0025 - mae: 0.0378 - val_loss: 0.0026 - val_mae: 0.0363\n",
            "Epoch 15/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0021 - mae: 0.0333 - val_loss: 0.0028 - val_mae: 0.0376\n",
            "Epoch 16/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0020 - mae: 0.0327 - val_loss: 0.0029 - val_mae: 0.0387\n",
            "Epoch 17/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0354 - val_loss: 0.0026 - val_mae: 0.0358\n",
            "Epoch 18/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0020 - mae: 0.0342 - val_loss: 0.0031 - val_mae: 0.0408\n",
            "Epoch 19/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0021 - mae: 0.0346 - val_loss: 0.0025 - val_mae: 0.0351\n",
            "Epoch 20/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0020 - mae: 0.0330 - val_loss: 0.0023 - val_mae: 0.0357\n",
            "Epoch 21/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0347 - val_loss: 0.0023 - val_mae: 0.0340\n",
            "Epoch 22/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0022 - mae: 0.0345 - val_loss: 0.0028 - val_mae: 0.0379\n",
            "Epoch 23/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0023 - mae: 0.0333 - val_loss: 0.0024 - val_mae: 0.0348\n",
            "Epoch 24/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 0.0021 - mae: 0.0350 - val_loss: 0.0022 - val_mae: 0.0333\n",
            "Epoch 25/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0017 - mae: 0.0306 - val_loss: 0.0023 - val_mae: 0.0334\n",
            "Epoch 26/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0018 - mae: 0.0311 - val_loss: 0.0021 - val_mae: 0.0337\n",
            "Epoch 27/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0021 - mae: 0.0326 - val_loss: 0.0025 - val_mae: 0.0358\n",
            "Epoch 28/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0021 - mae: 0.0346 - val_loss: 0.0021 - val_mae: 0.0328\n",
            "Epoch 29/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0019 - mae: 0.0331 - val_loss: 0.0022 - val_mae: 0.0332\n",
            "Epoch 30/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 0.0017 - mae: 0.0327 - val_loss: 0.0027 - val_mae: 0.0376\n",
            "Epoch 31/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0021 - mae: 0.0346 - val_loss: 0.0029 - val_mae: 0.0400\n",
            "Epoch 32/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0021 - mae: 0.0343 - val_loss: 0.0031 - val_mae: 0.0412\n",
            "Epoch 33/50\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0020 - mae: 0.0339 - val_loss: 0.0030 - val_mae: 0.0404\n",
            "✅ RMSE: 0.04599\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ================================\n",
        "# 3. HASIL AKHIR\n",
        "# ================================\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"📊 HASIL EVALUASI MODEL (Diurutkan dari Error Terkecil)\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Buat DataFrame & Urutkan\n",
        "df_results = pd.DataFrame(results)\n",
        "df_results_sorted = df_results.sort_values(by='RMSE', ascending=True).reset_index(drop=True)\n",
        "\n",
        "# Tampilkan Tabel Bersih\n",
        "print(df_results_sorted.to_string(index=False))\n",
        "\n",
        "print(\"\\n\" + \"=\"*60)\n",
        "print(f\"🏆 MODEL JUARA:\")\n",
        "print(f\"   - Units   : {best_params['Unit']}\")\n",
        "print(f\"   - Dropout : {best_params['Drop']}\")\n",
        "print(f\"   - RMSE    : {best_rmse:.6f}\")\n",
        "print(\"=\"*60)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DIvJdh_NB4y",
        "outputId": "8f8996ea-8880-4dc0-d85c-37b75fd98b73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            "📊 HASIL EVALUASI MODEL (Diurutkan dari Error Terkecil)\n",
            "============================================================\n",
            " GRU Units  Dropout     RMSE      MSE      MAE\n",
            "       128      0.1 0.041577 0.001729 0.029717\n",
            "        64      0.1 0.042406 0.001798 0.030633\n",
            "       128      0.2 0.045987 0.002115 0.032829\n",
            "        16      0.2 0.046897 0.002199 0.034406\n",
            "        32      0.1 0.051025 0.002604 0.037115\n",
            "        64      0.2 0.051934 0.002697 0.038260\n",
            "        16      0.1 0.054004 0.002916 0.038986\n",
            "        32      0.2 0.054403 0.002960 0.040201\n",
            "\n",
            "============================================================\n",
            "🏆 MODEL JUARA:\n",
            "   - Units   : 128\n",
            "   - Dropout : 0.1\n",
            "   - RMSE    : 0.041577\n",
            "============================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- EKSPLORASI BOBOT GRU UNTUK SKRIPSI (FINAL) ---\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# 1. Pastikan model sudah ada\n",
        "if 'best_model' not in locals():\n",
        "    print(\"⚠️ Harap jalankan proses Training Model terlebih dahulu!\")\n",
        "else:\n",
        "    print(\"🔍 ANALISIS BOBOT MODEL GRU TERBAIK\")\n",
        "    print(\"===================================\")\n",
        "\n",
        "    # Ambil layer GRU\n",
        "    gru_layer = best_model.layers[0]\n",
        "    weights = gru_layer.get_weights()\n",
        "\n",
        "    # Ambil bobot mentah\n",
        "    W_all = weights[0] # Input Kernel\n",
        "    U_all = weights[1] # Recurrent Kernel\n",
        "    b_all = weights[2] # Bias (biasanya ada 2 array, dijumlahkan oleh Keras)\n",
        "\n",
        "    # Tentukan jumlah unit (neuron)\n",
        "    units = best_params['Unit']\n",
        "    print(f\"✅ Model Terbaik Menggunakan {units} Unit GRU\")\n",
        "\n",
        "    # --- MEMECAH MATRIKS MENJADI 3 GERBANG ---\n",
        "    # Urutan Keras: [z, r, h] (Update, Reset, New/Candidate)\n",
        "\n",
        "    # 1. Bobot Input (W)\n",
        "    W_z = W_all[:, :units]           # Update Gate\n",
        "    W_r = W_all[:, units:units*2]    # Reset Gate\n",
        "    W_h = W_all[:, units*2:]         # New Gate\n",
        "\n",
        "    # 2. Bobot Recurrent (U)\n",
        "    U_z = U_all[:, :units]\n",
        "    U_r = U_all[:, units:units*2]\n",
        "    U_h = U_all[:, units*2:]\n",
        "\n",
        "    # 3. Bias (b) - Dijumlahkan dulu (b0 + b1)\n",
        "    bias_total = b_all[0] + b_all[1]\n",
        "    b_z = bias_total[:units]\n",
        "    b_r = bias_total[units:units*2]\n",
        "    b_h = bias_total[units*2:]\n",
        "\n",
        "    # --- TAMPILKAN HASIL UNTUK BAB 4 ---\n",
        "    print(\"\\n[1] STRUKTUR MATRIKS (Dimensi)\")\n",
        "    print(f\"   - Input Kernel (W)     : {W_all.shape} -> Dipecah jadi 3 x {W_z.shape}\")\n",
        "    print(f\"   - Recurrent Kernel (U) : {U_all.shape} -> Dipecah jadi 3 x {U_z.shape}\")\n",
        "    print(f\"   - Bias (b)             : {b_all.shape}    -> Dipecah jadi 3 x {b_z.shape}\")\n",
        "\n",
        "    print(\"\\n[2] SAMPEL NILAI UNTUK PERHITUNGAN MANUAL (Neuron Pertama)\")\n",
        "\n",
        "    print(\"\\n   A. UPDATE GATE (z)\")\n",
        "    print(f\"      - Bobot Input (W_z)    : {W_z[0][0]:.4f}\")\n",
        "    print(f\"      - Bobot Recurrent (U_z): {U_z[0][0]:.4f}\")\n",
        "    print(f\"      - Bias (b_z)           : {b_z[0]:.4f}\")\n",
        "\n",
        "    print(\"\\n   B. RESET GATE (r)\")\n",
        "    print(f\"      - Bobot Input (W_r)    : {W_r[0][0]:.4f}\")\n",
        "    print(f\"      - Bobot Recurrent (U_r): {U_r[0][0]:.4f}\")\n",
        "    print(f\"      - Bias (b_r)           : {b_r[0]:.4f}\")\n",
        "\n",
        "    print(\"\\n   C. NEW/CANDIDATE GATE (h)\")\n",
        "    print(f\"      - Bobot Input (W_h)    : {W_h[0][0]:.4f}\")\n",
        "    print(f\"      - Bobot Recurrent (U_h): {U_h[0][0]:.4f}\")\n",
        "    print(f\"      - Bias (b_h)           : {b_h[0]:.4f}\")\n",
        "\n",
        "    print(\"\\n✅ Selesai. 'Perhitungan Manual'.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_k3RL2fUfAT",
        "outputId": "d5f00543-df49-4be7-ea7e-8a66e975fa5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔍 ANALISIS BOBOT MODEL GRU TERBAIK\n",
            "===================================\n",
            "✅ Model Terbaik Menggunakan 128 Unit GRU\n",
            "\n",
            "[1] STRUKTUR MATRIKS (Dimensi)\n",
            "   - Input Kernel (W)     : (1, 384) -> Dipecah jadi 3 x (1, 128)\n",
            "   - Recurrent Kernel (U) : (128, 384) -> Dipecah jadi 3 x (128, 128)\n",
            "   - Bias (b)             : (2, 384)    -> Dipecah jadi 3 x (128,)\n",
            "\n",
            "[2] SAMPEL NILAI UNTUK PERHITUNGAN MANUAL (Neuron Pertama)\n",
            "\n",
            "   A. UPDATE GATE (z)\n",
            "      - Bobot Input (W_z)    : -0.2888\n",
            "      - Bobot Recurrent (U_z): 0.1198\n",
            "      - Bias (b_z)           : -0.5776\n",
            "\n",
            "   B. RESET GATE (r)\n",
            "      - Bobot Input (W_r)    : 0.0001\n",
            "      - Bobot Recurrent (U_r): -0.0350\n",
            "      - Bias (b_r)           : -0.0524\n",
            "\n",
            "   C. NEW/CANDIDATE GATE (h)\n",
            "      - Bobot Input (W_h)    : -0.1294\n",
            "      - Bobot Recurrent (U_h): -0.0560\n",
            "      - Bias (b_h)           : -0.0059\n",
            "\n",
            "✅ Selesai. 'Perhitungan Manual'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Pastikan best_history tersedia dari proses training sebelumnya\n",
        "if best_history is None:\n",
        "    print(\"⚠️ Harap jalankan proses Grid Search (Langkah 3) terlebih dahulu!\")\n",
        "else:\n",
        "    # Ambil data history\n",
        "    hist = best_history.history\n",
        "    loss_train = hist['loss']\n",
        "    loss_val = hist['val_loss']\n",
        "    mae_train = hist['mae']\n",
        "    mae_val = hist['val_mae']\n",
        "\n",
        "    epochs_range = range(1, len(loss_train) + 1)\n",
        "\n",
        "    # Buat Canvas Gambar (2 Kolom: Kiri Loss, Kanan MAE)\n",
        "    plt.figure(figsize=(15, 6))\n",
        "\n",
        "    # --- GRAFIK 1: LOSS (MSE) ---\n",
        "    plt.subplot(1, 2, 1) # (Baris 1, Kolom 2, Gambar ke-1)\n",
        "    plt.plot(epochs_range, loss_train, label='Training Loss', color='blue', linewidth=2)\n",
        "    plt.plot(epochs_range, loss_val, label='Validation Loss', color='orange', linewidth=2)\n",
        "    plt.title(f\"Grafik Model Loss (MSE)\\nUnit={best_params['Unit']}, Dropout={best_params['Drop']}\", fontsize=12, fontweight='bold')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss (Mean Squared Error)')\n",
        "    plt.legend()\n",
        "    plt.grid(True, alpha=0.3)\n",
        "\n",
        "    # --- GRAFIK 2: MAE ---\n",
        "    plt.subplot(1, 2, 2) # (Baris 1, Kolom 2, Gambar ke-2)\n",
        "    plt.plot(epochs_range, mae_train, label='Training MAE', color='green', linewidth=2)\n",
        "    plt.plot(epochs_range, mae_val, label='Validation MAE', color='red', linewidth=2)\n",
        "    plt.title(f\"Grafik Model MAE (Mean Absolute Error)\\nUnit={best_params['Unit']}, Dropout={best_params['Drop']}\", fontsize=12, fontweight='bold')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('MAE')\n",
        "    plt.legend()\n",
        "    plt.grid(True, alpha=0.3)\n",
        "\n",
        "    # Simpan dan Tampilkan\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('Grafik_Evaluasi_Lengkap_Loss_MAE.png')\n",
        "    plt.show()\n",
        "\n",
        "    print(\"✅ Grafik Loss dan MAE berhasil dibuat dan disimpan.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "AQU9kmAHKQ72",
        "outputId": "cf74e982-f9f8-4b04-a863-458bbed06f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Grafik Loss dan MAE berhasil dibuat dan disimpan.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# --- PERSIAPAN DATA VISUALISASI (HARGA ASLI) ---\n",
        "\n",
        "# 1. Pastikan data testing dan prediksi tersedia\n",
        "if best_y_pred is None:\n",
        "    print(\"⚠️ Harap jalankan proses Grid Search terlebih dahulu!\")\n",
        "else:\n",
        "    # 2. Lakukan INVERSE TRANSFORM (PENTING!)\n",
        "    # Mengembalikan data 0-1 menjadi harga USD asli\n",
        "    y_test_real = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
        "    y_pred_real = scaler.inverse_transform(best_y_pred.reshape(-1, 1))\n",
        "\n",
        "    # 3. Siapkan Tanggal (Sumbu X)\n",
        "    total_samples = len(X)\n",
        "    train_size = int(total_samples * 0.8)\n",
        "\n",
        "    test_start_index = time_step + train_size\n",
        "    test_dates = df['Date'].iloc[test_start_index:].reset_index(drop=True)\n",
        "\n",
        "    # Potong panjang array agar sama persis\n",
        "    min_len = min(len(test_dates), len(y_test_real))\n",
        "    test_dates = test_dates[:min_len]\n",
        "    y_test_plot = y_test_real[:min_len]\n",
        "    y_pred_plot = y_pred_real[:min_len]\n",
        "\n",
        "    # --- MEMBUAT GRAFIK PREDIKSI VS AKTUAL (HARGA ASLI) ---\n",
        "    plt.figure(figsize=(14, 7))\n",
        "\n",
        "    # Plot Data Aktual (Biru)\n",
        "    plt.plot(test_dates, y_test_plot, label='Harga Aktual (Real Price)', color='#1f77b4', linewidth=2)\n",
        "\n",
        "    # Plot Data Prediksi (Merah Putus-putus)\n",
        "    plt.plot(test_dates, y_pred_plot, label='Harga Prediksi (Predicted)', color='#d62728', linestyle='--', linewidth=2)\n",
        "\n",
        "    # --- FORMATING GRAFIK ---\n",
        "    plt.title(f\"Perbandingan Harga Aktual vs Prediksi Ethereum (Data Testing 20%)\",\n",
        "              fontsize=14, fontweight='bold')\n",
        "    plt.xlabel(\"Tanggal\", fontsize=12)\n",
        "    plt.ylabel(\"Harga Penutupan (USD)\", fontsize=12) # Label sumbu Y berubah jadi USD\n",
        "\n",
        "    plt.legend(loc='upper left', fontsize=11)\n",
        "    plt.grid(True, linestyle='--', alpha=0.5)\n",
        "\n",
        "    # Format tanggal di sumbu X agar rapi\n",
        "    plt.gcf().autofmt_xdate()\n",
        "\n",
        "    # Simpan Gambar\n",
        "    plt.savefig('Gambar_4_Hasil_Prediksi_Ethereum_USD.png', dpi=300)\n",
        "    plt.show()\n",
        "\n",
        "    print(\"✅ Grafik Prediksi dalam satuan USD berhasil dibuat.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "6Mk_s7yZObBh",
        "outputId": "ac826180-3d96-4c57-e2eb-be9480ac9562"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Grafik Prediksi dalam satuan USD berhasil dibuat.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "# ===============================\n",
        "# 5. Simpan Model & Scaler\n",
        "# ===============================\n",
        "\n",
        "# Simpan scaler\n",
        "joblib.dump(scaler, \"scaler_gru.pkl\")\n",
        "print(\"📁 Saved scaler as scaler_gru.pkl\")\n",
        "\n",
        "# Simpan model GRU terbaik\n",
        "best_model.save(\"gru_model.h5\")\n",
        "print(\"📁 Saved GRU model as gru_model.h5\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7StkDF74Lik",
        "outputId": "8bd794ce-e32c-4d1b-c96a-798649a88cab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📁 Saved scaler as scaler_gru.pkl\n",
            "📁 Saved GRU model as gru_model.h5\n"
          ]
        }
      ]
    }
  ]
}