{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMZJ2PMya3A0H3mMYWi7Mh5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lenasajj117/AI-for-Environmental-Decision-Intelligence---The-AI-Forecasting-Hackathon-/blob/main/AI_for_Environmental_Decision_Intelligence_The_AI_Forecasting_Hackathon_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random, numpy as np, tensorflow as tf, pandas as pd\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "print(\"numpy\", np.__version__, \"pandas\", pd.__version__, \"tensorflow\", tf.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrbeauHfS-M9",
        "outputId": "f781ebad-50f0-4f90-9d54-47215332ea40"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "numpy 2.0.2 pandas 2.2.2 tensorflow 2.19.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "9T20h-_ksUgF",
        "outputId": "bdc7459f-d287-4f6e-a5ee-99d4099a850f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.12/dist-packages (2.19.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (1.6.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (25.9.23)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from tensorflow) (25.0)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (5.29.5)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.32.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from tensorflow) (75.2.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.17.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.2.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (4.15.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.0.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (1.76.0)\n",
            "Requirement already satisfied: tensorboard~=2.19.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (2.19.0)\n",
            "Requirement already satisfied: keras>=3.5.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.10.0)\n",
            "Requirement already satisfied: h5py>=3.11.0 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (3.15.1)\n",
            "Requirement already satisfied: ml-dtypes<1.0.0,>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from tensorflow) (0.5.3)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.16.3)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (1.5.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn) (3.6.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n",
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            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.4.9)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.2.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from astunparse>=1.6.0->tensorflow) (0.45.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (13.9.4)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (0.1.0)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.12/dist-packages (from keras>=3.5.0->tensorflow) (0.17.0)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests<3,>=2.21.0->tensorflow) (2025.10.5)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.9)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from tensorboard~=2.19.0->tensorflow) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.12/dist-packages (from werkzeug>=1.0.1->tensorboard~=2.19.0->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (4.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich->keras>=3.5.0->tensorflow) (2.19.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.5.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow scikit-learn matplotlib pandas numpy\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 0. Import dependencies\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "print(\"Environment successfully initialized!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SrjKVQktXcj",
        "outputId": "8c31b54c-09dc-462a-a870-e3b9fa83ed2a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Environment successfully initialized!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.1 LOAD & CLEAN DATA FROM the UCI Repository\n",
        "\n",
        "df = pd.read_csv(\"/content/AirQualityUCI.csv\", sep=',', decimal=',')\n",
        "df = df.dropna(axis=1, how='all')  # Drop empty columns\n",
        "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]\n",
        "df = df.replace(-200, np.nan)\n",
        "df = df.dropna()\n",
        "\n",
        "# Combine Date + Time\n",
        "df['Datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'], errors='coerce')\n",
        "df = df.set_index('Datetime')\n",
        "df = df.sort_index()\n",
        "\n",
        "# Select relevant pollutants\n",
        "cols = ['CO(GT)', 'NO2(GT)', 'C6H6(GT)', 'T', 'RH', 'AH']\n",
        "data = df[cols]"
      ],
      "metadata": {
        "id": "lV4ZqRI6XNMV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d91bd177",
        "outputId": "a8768c4f-7369-49f1-f9fb-0e8ff5aaa59c"
      },
      "source": [
        "# 1.2 Inspect the first few lines of the CSV file\n",
        "with open(\"/content/AirQualityUCI.csv\", 'r') as f:\n",
        "    for i in range(5):\n",
        "        print(f.readline())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date,Time,CO(GT),PT08.S1(CO),NMHC(GT),C6H6(GT),PT08.S2(NMHC),NOx(GT),PT08.S3(NOx),NO2(GT),PT08.S4(NO2),PT08.S5(O3),T,RH,AH\n",
            "\n",
            "3/10/2004,18:00:00,2.6,1360,150,11.9,1046,166,1056,113,1692,1268,13.6,48.9,0.7578\n",
            "\n",
            "3/10/2004,19:00:00,2,1292,112,9.4,955,103,1174,92,1559,972,13.3,47.7,0.7255\n",
            "\n",
            "3/10/2004,20:00:00,2.2,1402,88,9,939,131,1140,114,1555,1074,11.9,54,0.7502\n",
            "\n",
            "3/10/2004,21:00:00,2.2,1376,80,9.2,948,172,1092,122,1584,1203,11,60,0.7867\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.1 EXPLORE DATA\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(data.corr(), annot=True, cmap='YlGnBu')\n",
        "plt.title(\"Correlation between Air Quality Metrics\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "EOUpR6FBZyYf",
        "outputId": "6d2c8641-8e23-41bd-bf5c-36099064c06e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.2 Plotting the trends\n",
        "data[['CO(GT)', 'NO2(GT)']].plot(figsize=(12,5), title=\"Pollution Levels Over Time\")\n",
        "plt.ylabel(\"Concentration (mg/m^3)\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "NKNLjnG6Z9RR",
        "outputId": "e1bddeef-e155-4c6b-a139-0648165ec8ac"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.1 FORECASTING MODEL (LSTM)\n",
        "\n",
        "# Normalize\n",
        "scaler = MinMaxScaler()\n",
        "scaled = scaler.fit_transform(data[['CO(GT)', 'NO2(GT)']])\n",
        "scaled_df = pd.DataFrame(scaled, index=data.index, columns=['CO', 'NO2'])\n",
        "\n"
      ],
      "metadata": {
        "id": "-sY5REYUaPrv"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.2 Create sequences for time series\n",
        "def create_sequences_multifeature(data, seq_length=24):\n",
        "    X, y = [], []\n",
        "    for i in range(len(data) - seq_length):\n",
        "        X.append(data[i:i + seq_length])\n",
        "        y.append(data[i + seq_length])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "seq_length = 24  # 24 hours\n",
        "X, y = create_sequences_multifeature(scaled_df.values, seq_length)\n",
        "\n",
        "split = int(0.8 * len(X))\n",
        "X_train, X_test = X[:split], X[split:]\n",
        "y_train, y_test = y[:split], y[split:]"
      ],
      "metadata": {
        "id": "Dhr7i0pJaegp"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.3 Build LSTM model\n",
        "\n",
        "model = Sequential([\n",
        "    LSTM(128, activation='tanh', return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])),\n",
        "    Dropout(0.2),\n",
        "    LSTM(64, activation='tanh'),\n",
        "    Dropout(0.1),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(2, activation='linear')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
        "\n",
        "early_stop = EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=10,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    epochs=100,\n",
        "    batch_size=32,\n",
        "    validation_data=(X_test, y_test),\n",
        "    callbacks=[early_stop],\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# -----------------------------"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6G25XLWalGe",
        "outputId": "bc868cfa-9900-4850-eb5f-9127aca857c2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 277ms/step - loss: 0.3180 - mae: 0.4610 - val_loss: 0.0457 - val_mae: 0.1784\n",
            "Epoch 2/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 160ms/step - loss: 0.0359 - mae: 0.1382 - val_loss: 0.0351 - val_mae: 0.1302\n",
            "Epoch 3/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 84ms/step - loss: 0.0315 - mae: 0.1197 - val_loss: 0.0343 - val_mae: 0.1219\n",
            "Epoch 4/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 66ms/step - loss: 0.0296 - mae: 0.1124 - val_loss: 0.0342 - val_mae: 0.1192\n",
            "Epoch 5/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 75ms/step - loss: 0.0308 - mae: 0.1132 - val_loss: 0.0336 - val_mae: 0.1133\n",
            "Epoch 6/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 86ms/step - loss: 0.0290 - mae: 0.1091 - val_loss: 0.0334 - val_mae: 0.1087\n",
            "Epoch 7/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.0286 - mae: 0.1080 - val_loss: 0.0332 - val_mae: 0.1050\n",
            "Epoch 8/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0277 - mae: 0.1051 - val_loss: 0.0328 - val_mae: 0.0999\n",
            "Epoch 9/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0274 - mae: 0.1031 - val_loss: 0.0326 - val_mae: 0.0943\n",
            "Epoch 10/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0276 - mae: 0.1057 - val_loss: 0.0321 - val_mae: 0.1015\n",
            "Epoch 11/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0264 - mae: 0.1013 - val_loss: 0.0318 - val_mae: 0.1024\n",
            "Epoch 12/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0266 - mae: 0.1026 - val_loss: 0.0317 - val_mae: 0.0974\n",
            "Epoch 13/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.0264 - mae: 0.1014 - val_loss: 0.0311 - val_mae: 0.1046\n",
            "Epoch 14/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 0.0251 - mae: 0.0988 - val_loss: 0.0311 - val_mae: 0.1019\n",
            "Epoch 15/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 63ms/step - loss: 0.0233 - mae: 0.0919 - val_loss: 0.0296 - val_mae: 0.0851\n",
            "Epoch 16/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 92ms/step - loss: 0.0238 - mae: 0.0947 - val_loss: 0.0279 - val_mae: 0.0966\n",
            "Epoch 17/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0237 - mae: 0.0957 - val_loss: 0.0313 - val_mae: 0.1002\n",
            "Epoch 18/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0214 - mae: 0.0886 - val_loss: 0.0295 - val_mae: 0.0919\n",
            "Epoch 19/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0214 - mae: 0.0887 - val_loss: 0.0279 - val_mae: 0.0934\n",
            "Epoch 20/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0217 - mae: 0.0909 - val_loss: 0.0265 - val_mae: 0.0855\n",
            "Epoch 21/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0209 - mae: 0.0875 - val_loss: 0.0263 - val_mae: 0.0917\n",
            "Epoch 22/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0189 - mae: 0.0821 - val_loss: 0.0258 - val_mae: 0.0817\n",
            "Epoch 23/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0199 - mae: 0.0849 - val_loss: 0.0257 - val_mae: 0.0862\n",
            "Epoch 24/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 57ms/step - loss: 0.0201 - mae: 0.0852 - val_loss: 0.0258 - val_mae: 0.0876\n",
            "Epoch 25/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.0192 - mae: 0.0838 - val_loss: 0.0251 - val_mae: 0.0826\n",
            "Epoch 26/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 92ms/step - loss: 0.0181 - mae: 0.0800 - val_loss: 0.0251 - val_mae: 0.0788\n",
            "Epoch 27/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - loss: 0.0183 - mae: 0.0781 - val_loss: 0.0247 - val_mae: 0.0819\n",
            "Epoch 28/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0187 - mae: 0.0796 - val_loss: 0.0248 - val_mae: 0.0771\n",
            "Epoch 29/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0190 - mae: 0.0810 - val_loss: 0.0248 - val_mae: 0.0820\n",
            "Epoch 30/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0184 - mae: 0.0805 - val_loss: 0.0244 - val_mae: 0.0805\n",
            "Epoch 31/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0175 - mae: 0.0779 - val_loss: 0.0242 - val_mae: 0.0846\n",
            "Epoch 32/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0171 - mae: 0.0744 - val_loss: 0.0235 - val_mae: 0.0775\n",
            "Epoch 33/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0172 - mae: 0.0766 - val_loss: 0.0235 - val_mae: 0.0785\n",
            "Epoch 34/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0163 - mae: 0.0740 - val_loss: 0.0235 - val_mae: 0.0766\n",
            "Epoch 35/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 85ms/step - loss: 0.0174 - mae: 0.0746 - val_loss: 0.0235 - val_mae: 0.0833\n",
            "Epoch 36/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 88ms/step - loss: 0.0166 - mae: 0.0736 - val_loss: 0.0229 - val_mae: 0.0771\n",
            "Epoch 37/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0174 - mae: 0.0760 - val_loss: 0.0228 - val_mae: 0.0803\n",
            "Epoch 38/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0163 - mae: 0.0728 - val_loss: 0.0231 - val_mae: 0.0761\n",
            "Epoch 39/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0164 - mae: 0.0730 - val_loss: 0.0230 - val_mae: 0.0817\n",
            "Epoch 40/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 0.0162 - mae: 0.0714 - val_loss: 0.0221 - val_mae: 0.0744\n",
            "Epoch 41/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 0.0147 - mae: 0.0669 - val_loss: 0.0221 - val_mae: 0.0743\n",
            "Epoch 42/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 0.0148 - mae: 0.0677 - val_loss: 0.0217 - val_mae: 0.0685\n",
            "Epoch 43/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0150 - mae: 0.0692 - val_loss: 0.0216 - val_mae: 0.0692\n",
            "Epoch 44/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0161 - mae: 0.0717 - val_loss: 0.0218 - val_mae: 0.0781\n",
            "Epoch 45/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 86ms/step - loss: 0.0156 - mae: 0.0686 - val_loss: 0.0220 - val_mae: 0.0792\n",
            "Epoch 46/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 80ms/step - loss: 0.0149 - mae: 0.0680 - val_loss: 0.0216 - val_mae: 0.0776\n",
            "Epoch 47/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0149 - mae: 0.0682 - val_loss: 0.0215 - val_mae: 0.0722\n",
            "Epoch 48/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 74ms/step - loss: 0.0144 - mae: 0.0649 - val_loss: 0.0213 - val_mae: 0.0721\n",
            "Epoch 49/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 0.0147 - mae: 0.0669 - val_loss: 0.0214 - val_mae: 0.0755\n",
            "Epoch 50/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 0.0146 - mae: 0.0642 - val_loss: 0.0216 - val_mae: 0.0753\n",
            "Epoch 51/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 65ms/step - loss: 0.0153 - mae: 0.0688 - val_loss: 0.0214 - val_mae: 0.0720\n",
            "Epoch 52/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 97ms/step - loss: 0.0145 - mae: 0.0658 - val_loss: 0.0214 - val_mae: 0.0751\n",
            "Epoch 53/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 87ms/step - loss: 0.0143 - mae: 0.0647 - val_loss: 0.0212 - val_mae: 0.0671\n",
            "Epoch 54/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 0.0145 - mae: 0.0658 - val_loss: 0.0211 - val_mae: 0.0755\n",
            "Epoch 55/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step - loss: 0.0147 - mae: 0.0674 - val_loss: 0.0211 - val_mae: 0.0792\n",
            "Epoch 56/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 59ms/step - loss: 0.0141 - mae: 0.0627 - val_loss: 0.0213 - val_mae: 0.0775\n",
            "Epoch 57/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0143 - mae: 0.0635 - val_loss: 0.0209 - val_mae: 0.0710\n",
            "Epoch 58/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0142 - mae: 0.0642 - val_loss: 0.0211 - val_mae: 0.0704\n",
            "Epoch 59/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 66ms/step - loss: 0.0145 - mae: 0.0662 - val_loss: 0.0219 - val_mae: 0.0738\n",
            "Epoch 60/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step - loss: 0.0138 - mae: 0.0640 - val_loss: 0.0213 - val_mae: 0.0756\n",
            "Epoch 61/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 0.0140 - mae: 0.0631 - val_loss: 0.0214 - val_mae: 0.0694\n",
            "Epoch 62/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 93ms/step - loss: 0.0141 - mae: 0.0627 - val_loss: 0.0214 - val_mae: 0.0728\n",
            "Epoch 63/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0140 - mae: 0.0649 - val_loss: 0.0217 - val_mae: 0.0796\n",
            "Epoch 64/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0139 - mae: 0.0619 - val_loss: 0.0209 - val_mae: 0.0786\n",
            "Epoch 65/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0132 - mae: 0.0613 - val_loss: 0.0210 - val_mae: 0.0776\n",
            "Epoch 66/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0136 - mae: 0.0599 - val_loss: 0.0206 - val_mae: 0.0695\n",
            "Epoch 67/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 0.0139 - mae: 0.0627 - val_loss: 0.0209 - val_mae: 0.0750\n",
            "Epoch 68/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 101ms/step - loss: 0.0139 - mae: 0.0630 - val_loss: 0.0217 - val_mae: 0.0770\n",
            "Epoch 69/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - loss: 0.0139 - mae: 0.0627 - val_loss: 0.0217 - val_mae: 0.0784\n",
            "Epoch 70/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 81ms/step - loss: 0.0140 - mae: 0.0646 - val_loss: 0.0209 - val_mae: 0.0772\n",
            "Epoch 71/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - loss: 0.0137 - mae: 0.0607 - val_loss: 0.0220 - val_mae: 0.0805\n",
            "Epoch 72/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0142 - mae: 0.0650 - val_loss: 0.0218 - val_mae: 0.0777\n",
            "Epoch 73/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0135 - mae: 0.0620 - val_loss: 0.0208 - val_mae: 0.0728\n",
            "Epoch 74/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 73ms/step - loss: 0.0134 - mae: 0.0631 - val_loss: 0.0217 - val_mae: 0.0721\n",
            "Epoch 75/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 55ms/step - loss: 0.0133 - mae: 0.0616 - val_loss: 0.0208 - val_mae: 0.0703\n",
            "Epoch 76/100\n",
            "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 0.0135 - mae: 0.0602 - val_loss: 0.0213 - val_mae: 0.0831\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "import numpy as np\n",
        "\n",
        "# Generate predictions\n",
        "pred = model.predict(X_test)\n",
        "\n",
        "# Rescale values back to original scale\n",
        "pred_rescaled = scaler.inverse_transform(pred)\n",
        "actual_rescaled = scaler.inverse_transform(y_test)\n",
        "\n",
        "# Compute metrics\n",
        "mse = mean_squared_error(actual_rescaled, pred_rescaled)\n",
        "mae = mean_absolute_error(actual_rescaled, pred_rescaled)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "# Display evaluation summary\n",
        "print(\"\\n===============================\")\n",
        "print(\"MODEL PERFORMANCE SUMMARY\")\n",
        "print(\"===============================\")\n",
        "print(f\"Mean Absolute Error (MAE): {mae:.3f}\")\n",
        "print(f\"Root Mean Squared Error (RMSE): {rmse:.3f}\")\n",
        "print(f\"Mean Squared Error (MSE): {mse:.3f}\")\n",
        "print(\"===============================\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elxGNy-2gVco",
        "outputId": "9d788a56-cfc9-4b4b-cb2d-fd3da6941992"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 77ms/step\n",
            "\n",
            "===============================\n",
            "MODEL PERFORMANCE SUMMARY\n",
            "===============================\n",
            "Mean Absolute Error (MAE): 13.493\n",
            "Root Mean Squared Error (RMSE): 29.241\n",
            "Mean Squared Error (MSE): 855.044\n",
            "===============================\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "import numpy as np\n",
        "\n",
        "# Compute metrics for CO\n",
        "mse_co = mean_squared_error(actual_rescaled[:,0], pred_rescaled[:,0])\n",
        "rmse_co = np.sqrt(mse_co)\n",
        "mae_co = mean_absolute_error(actual_rescaled[:,0], pred_rescaled[:,0])\n",
        "print(f\"CO RMSE={rmse_co:.3f}, MAE={mae_co:.3f}\")\n",
        "\n",
        "# Compute metrics for NO2\n",
        "mse_no2 = mean_squared_error(actual_rescaled[:,1], pred_rescaled[:,1])\n",
        "rmse_no2 = np.sqrt(mse_no2)\n",
        "mae_no2 = mean_absolute_error(actual_rescaled[:,1], pred_rescaled[:,1])\n",
        "print(f\"NO2 RMSE={rmse_no2:.3f}, MAE={mae_no2:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SK4EafZaCdr",
        "outputId": "22faf08e-f38a-4699-c231-3101089b18b8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CO RMSE=38.602, MAE=15.101\n",
            "NO2 RMSE=14.832, MAE=11.885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.1 EVALUATE & FORECAST\n",
        "\n",
        "pred = model.predict(X_test)\n",
        "pred_rescaled = scaler.inverse_transform(pred)\n",
        "actual_rescaled = scaler.inverse_transform(y_test)\n",
        "\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(actual_rescaled[:,0], label='Actual CO(GT)')\n",
        "plt.plot(pred_rescaled[:,0], label='Predicted CO(GT)')\n",
        "plt.title(\"CO(GT) Forecast vs Actual\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "pCPoib1wa54N",
        "outputId": "ab488e30-f78f-4c18-e350-43fe3b313de0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.1 POLICY DECISION LOGIC\n",
        "\n",
        "forecast_mean = np.mean(pred_rescaled, axis=0)\n",
        "co_level, no2_level = forecast_mean[0], forecast_mean[1]\n",
        "\n",
        "def policy_recommendation(co, no2):\n",
        "    recs = []\n",
        "    if co > 3 or no2 > 80:\n",
        "        recs.append(\"High Pollution: Enforce emission control & vehicle restriction\")\n",
        "        recs.append(\"Urgent reforestation & carbon offset initiatives\")\n",
        "    elif co > 1.5 or no2 > 40:\n",
        "        recs.append(\"Moderate: Increase green zone monitoring & public advisories\")\n",
        "    else:\n",
        "        recs.append(\"Air quality stable: Maintain sustainable transport incentives\")\n",
        "    return recs\n",
        "\n",
        "policy_actions = policy_recommendation(co_level, no2_level)"
      ],
      "metadata": {
        "id": "ksB7qHNKa8nP"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.2 Display policy suggestions\n",
        "\n",
        "print(\"\\n Forecast Summary:\")\n",
        "print(f\"Avg CO(GT): {co_level:.2f} mg/m³ | Avg NO₂(GT): {no2_level:.2f} µg/m³\\n\")\n",
        "print(\"Recommended Policy Actions:\")\n",
        "for r in policy_actions:\n",
        "    print(\"-\", r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8TYb5UwhBJ1",
        "outputId": "1f85f5b9-cefe-4834-842b-83fd6588f4c7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Forecast Summary:\n",
            "Avg CO(GT): -5.87 mg/m³ | Avg NO₂(GT): 92.59 µg/m³\n",
            "\n",
            "Recommended Policy Actions:\n",
            "- High Pollution: Enforce emission control & vehicle restriction\n",
            "- Urgent reforestation & carbon offset initiatives\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. SAVE THE MODEL\n",
        "\n",
        "model.save(\"Env_Decision_Model.h5\")\n",
        "import joblib\n",
        "joblib.dump(scaler, \"scaler.save\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHJ9dLE8d070",
        "outputId": "71d42ec2-02cb-4d0b-f7a2-defc61b87eac"
      },
      "execution_count": 16,
      "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": "execute_result",
          "data": {
            "text/plain": [
              "['scaler.save']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LIVE DATA SECTION\n"
      ],
      "metadata": {
        "id": "TBMbtcjmiXxw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================================================\n",
        "#   FIXED + ENHANCED: Monte Carlo Dropout Forecasting\n",
        "#   (with proper uncertainty, clipping, and Epoch merge)\n",
        "# ==========================================================\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# ----- Monte Carlo Dropout Wrapper -----\n",
        "class MCDropoutModel(nn.Module):\n",
        "    def __init__(self, base_model):\n",
        "        super().__init__()\n",
        "        self.base = base_model\n",
        "        # Note: Dropout layers for TF models are handled by model(x, training=True)\n",
        "        # These are added here for potential PyTorch compatibility but not strictly necessary\n",
        "        # for the TF model wrapping shown later.\n",
        "\n",
        "    def forward(self, x):\n",
        "        # This forward is for a potential PyTorch base model.\n",
        "        # For the wrapped TF model, we use model(x, training=True) directly.\n",
        "        pass\n",
        "\n",
        "\n",
        "# The following code is designed to wrap a TensorFlow Keras model\n",
        "# Assuming 'model' is your trained Keras model\n",
        "\n",
        "# ----- Monte Carlo Simulation for Keras Model -----\n",
        "def monte_carlo_predict_keras(model, X_input, n_samples=100):\n",
        "    \"\"\"\n",
        "    Run Keras model multiple times with training=True to enable Dropout.\n",
        "    X_input: numpy array or TensorFlow tensor (batch, seq_len, features)\n",
        "    Returns: mean_preds, std_preds, all_preds (n_samples, batch, targets)\n",
        "    \"\"\"\n",
        "    preds = []\n",
        "    # Ensure X_input is a TensorFlow tensor\n",
        "    X_tensor = tf.convert_to_tensor(X_input, dtype=tf.float32)\n",
        "\n",
        "    for _ in range(n_samples):\n",
        "        # Pass training=True to enable dropout during inference\n",
        "        out = model(X_tensor, training=True).numpy()\n",
        "        preds.append(out)\n",
        "    preds = np.stack(preds, axis=0)  # shape (n_samples, batch, targets)\n",
        "    mean_preds = preds.mean(axis=0)\n",
        "    std_preds = preds.std(axis=0)\n",
        "    return mean_preds, std_preds, preds\n",
        "\n",
        "\n",
        "# ----- Perform MC prediction -----\n",
        "# Convert X_test to a TensorFlow tensor\n",
        "X_test_tensor = tf.convert_to_tensor(X_test, dtype=tf.float32)\n",
        "mean_pred, std_pred, all_preds = monte_carlo_predict_keras(model, X_test_tensor, n_samples=100)\n",
        "\n",
        "# Clip and invert-scale properly\n",
        "# Ensure clipping is applied to scaled values before inverse transform\n",
        "mean_pred_clipped = np.clip(mean_pred, 0, 1)\n",
        "std_pred_clipped = np.clip(std_pred, 0, 1) # Clipping std might not be appropriate depending on interpretation\n",
        "\n",
        "mean_rescaled = scaler.inverse_transform(mean_pred_clipped)\n",
        "\n",
        "# A more accurate way to get std in original scale:\n",
        "# Calculate std in original scale from all_preds\n",
        "all_preds_clipped = np.clip(all_preds, 0, 1) # Clip before inverse transform\n",
        "all_preds_rescaled = scaler.inverse_transform(all_preds_clipped.reshape(-1, all_preds_clipped.shape[-1])).reshape(all_preds_clipped.shape)\n",
        "std_rescaled = all_preds_rescaled.std(axis=0)\n",
        "\n",
        "\n",
        "print(f\"\\n Monte Carlo inference done: {len(mean_pred)} samples\")\n",
        "print(f\"Example CO mean (rescaled): {mean_rescaled.mean():.3f} ± {std_rescaled.mean():.3f}\")\n",
        "\n",
        "# ----- Merge Epoch dataset (summarized) -----\n",
        "# Assuming the epoch_df is already created and merged in the previous steps within ingest_live_indicators\n",
        "# If not, you might need to call ingest_live_indicators again or ensure enriched_df is available\n",
        "if 'enriched_df' not in locals():\n",
        "    print(\"Warning: 'enriched_df' not found. Skipping Epoch merge summary.\")\n",
        "else:\n",
        "    try:\n",
        "        # Assuming enriched_df already has the merged epoch data from ingest_live_indicators\n",
        "        epoch_cols_merged = [col for col in enriched_df.columns if col not in data.columns and not col.startswith('metaculus_q_')]\n",
        "        if len(epoch_cols_merged) > 0:\n",
        "             print(f\"Epoch dataset merged successfully with time alignment ({len(epoch_cols_merged)} columns).\")\n",
        "        else:\n",
        "             print(\"Epoch dataset was not merged with time alignment (no new epoch columns found).\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(\"Epoch merge summary skipped:\", e)\n",
        "\n",
        "\n",
        "# ----- Policy / Decision Layer -----\n",
        "# Using the std_rescaled calculated from inverse transformed predictions\n",
        "uncertainty_threshold = np.percentile(std_rescaled, 90)\n",
        "mean_CO = mean_rescaled[:, 0].mean() # Get mean of CO predictions across samples\n",
        "mean_NO2 = mean_rescaled[:, 1].mean() # Get mean of NO2 predictions across samples\n",
        "\n",
        "# Simple policy based on mean prediction and a general uncertainty threshold\n",
        "policy = (\n",
        "    \"High Uncertainty — Recommend Additional Monitoring\"\n",
        "    if std_rescaled.mean() > uncertainty_threshold # Using mean of std across targets and samples\n",
        "    else (\"Air Quality Improving\" if mean_CO < 4 and mean_NO2 < 80 else \"Policy Action Required\") # Example thresholds\n",
        ")\n",
        "\n",
        "print(f\"\\n Final Decision: {policy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2vQfEwUibVK",
        "outputId": "04494b99-c4fa-485c-c520-37158d1edc63"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Monte Carlo inference done: 166 samples\n",
            "Example CO mean (rescaled): 48.373 ± 6.190\n",
            "Warning: 'enriched_df' not found. Skipping Epoch merge summary.\n",
            "\n",
            " Final Decision: Policy Action Required\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Percentile-based conservative decision (use MC samples all_preds):\n",
        "\n",
        "# all_preds shape (mc_iters, n_samples, n_targets)\n",
        "# use 95th percentile across MC samples for conservatism\n",
        "upper95 = np.percentile(all_preds, 95, axis=0)  # shape (n_samples, targets)\n",
        "co_95 = upper95[0,0]  # next step\n",
        "no2_95 = upper95[0,1]\n",
        "# decision\n",
        "if co_95 > 3 or no2_95 > 80:\n",
        "    rec = \"Emergency: immediate emission control & traffic restriction\"\n",
        "elif co_95 > 1.5 or no2_95 > 40:\n",
        "    rec = \"Moderate: increased monitoring & advisories\"\n",
        "else:\n",
        "    rec = \"Stable\"\n",
        "print(\"Decision (95th percentile):\", rec)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2ZqwDSebt1L",
        "outputId": "cceac298-c3f7-45de-be43-15d860a9ca20"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision (95th percentile): Stable\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import json # Import the json library\n",
        "\n",
        "# ---------- CONFIG ----------\n",
        "N = 50  # number of past timesteps to plot\n",
        "policy_thresholds = {'CO': 3, 'NO2': 80}  # conservative thresholds\n",
        "\n",
        "# ---------- 1) Time-series forecast + uncertainty ----------\n",
        "# Ensure enriched_df is available and has the necessary columns\n",
        "if 'enriched_df' not in locals() or enriched_df.empty:\n",
        "    print(\"Error: enriched_df not found or is empty. Please run the previous cells.\")\n",
        "else:\n",
        "    y_co = enriched_df['CO(GT)'][-N:].values\n",
        "    y_no2 = enriched_df['NO2(GT)'][-N:].values\n",
        "\n",
        "    # Ensure mean_rescaled and std_rescaled are available\n",
        "    if 'mean_rescaled' not in locals() or 'std_rescaled' not in locals():\n",
        "         print(\"Error: mean_rescaled or std_rescaled not found. Please run the model prediction cell.\")\n",
        "    else:\n",
        "        mean_co, mean_no2 = mean_rescaled[0,0], mean_rescaled[0,1]\n",
        "        std_co, std_no2 = std_rescaled[0,0], std_rescaled[0,1]\n",
        "\n",
        "        plt.figure(figsize=(14,6))\n",
        "        plt.plot(range(N), y_co, label='CO observed', color='green')\n",
        "        plt.plot(range(N, N+1), mean_co, 'o', label='CO forecast', color='darkgreen')\n",
        "        plt.fill_between([N, N+1], mean_co-std_co, mean_co+std_co, color='green', alpha=0.2)\n",
        "\n",
        "        plt.plot(range(N), y_no2, label='NO2 observed', color='orange')\n",
        "        plt.plot(range(N, N+1), mean_no2, 'o', label='NO2 forecast', color='darkorange')\n",
        "        plt.fill_between([N, N+1], mean_no2-std_no2, mean_no2+std_no2, color='orange', alpha=0.2)\n",
        "\n",
        "        # Highlight policy escalation\n",
        "        if (mean_co + 1.5*std_co) > policy_thresholds['CO'] or (mean_no2 + 1.5*std_no2) > policy_thresholds['NO2']:\n",
        "            plt.axvline(N, color='red', linestyle='--', label='Policy escalation point')\n",
        "\n",
        "        plt.title(\"Pollutant Forecast with MC Dropout Uncertainty\")\n",
        "        plt.xlabel(\"Time steps\")\n",
        "        plt.ylabel(\"Concentration\")\n",
        "        plt.legend()\n",
        "        plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkVkzeXk17o_",
        "outputId": "ff13d9a5-afad-4513-d466-5ce93b884863"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: enriched_df not found or is empty. Please run the previous cells.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------- 2) Policy comparison bar ----------\n",
        "# Ensure co_mean and no2_mean are available\n",
        "if 'co_mean' not in locals() or 'no2_mean' not in locals():\n",
        "     print(\"Error: co_mean or no2_mean not found. Please run the policy decision logic cell.\")\n",
        "else:\n",
        "    labels = ['CO', 'NO2']\n",
        "    observed = [co_mean, no2_mean]\n",
        "    thresholds = [policy_thresholds['CO'], policy_thresholds['NO2']]\n",
        "\n",
        "    x = np.arange(len(labels))\n",
        "    width = 0.35\n",
        "\n",
        "    plt.figure(figsize=(6,5))\n",
        "    plt.bar(x - width/2, observed, width, label='Predicted', color=['green','orange'])\n",
        "    plt.bar(x + width/2, thresholds, width, label='Threshold', color='red', alpha=0.5)\n",
        "    plt.xticks(x, labels)\n",
        "    plt.ylabel(\"Concentration\")\n",
        "    plt.title(\"Predicted vs Policy Thresholds\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aLz3M1N4tpB",
        "outputId": "7f0ea03e-1d6c-494d-c035-b72547564dbe"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: co_mean or no2_mean not found. Please run the policy decision logic cell.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import json\n",
        "\n",
        "# ---------- CONFIG ----------\n",
        "N = 50  # number of past timesteps to visualize\n",
        "policy_thresholds = {'CO': 3, 'NO2': 80}  # conservative thresholds\n",
        "\n",
        "# ---------- SAFETY CHECKS ----------\n",
        "if 'enriched_df' not in locals() or enriched_df.empty:\n",
        "    print(\"Error: enriched_df not found or empty. Please run the previous data cell.\")\n",
        "    fig, ax = plt.subplots(figsize=(8, 4))\n",
        "    ax.text(0.5, 0.5, \"Data not available\", ha='center', va='center')\n",
        "    plt.show()\n",
        "elif 'mean_rescaled' not in locals() or 'std_rescaled' not in locals():\n",
        "    print(\"Error: mean_rescaled or std_rescaled missing. Please run the model cell.\")\n",
        "    fig, ax = plt.subplots(figsize=(8, 4))\n",
        "    ax.text(0.5, 0.5, \"Model prediction data not available\", ha='center', va='center')\n",
        "    plt.show()\n",
        "else:\n",
        "    # ---------- Extract main signals ----------\n",
        "    y_co = enriched_df['CO(GT)'][-N:].values\n",
        "    y_no2 = enriched_df['NO2(GT)'][-N:].values\n",
        "    mean_co, mean_no2 = mean_rescaled[0, 0], mean_rescaled[0, 1]\n",
        "    std_co, std_no2 = std_rescaled[0, 0], std_rescaled[0, 1]\n",
        "\n",
        "    # Identify metaculus and epoch columns\n",
        "    metaculus_cols = [c for c in enriched_df.columns if c.startswith('metaculus_q_')]\n",
        "    epoch_cols = [c for c in enriched_df.columns if c not in ['CO(GT)', 'NO2(GT)'] + metaculus_cols]\n",
        "\n",
        "    # ---------- Get timestamps (fix missing x-axis labels) ----------\n",
        "    if 'Timestamp' in enriched_df.columns:\n",
        "        timestamps = pd.to_datetime(enriched_df['Timestamp'].iloc[-N:])\n",
        "    elif 'Date' in enriched_df.columns:\n",
        "        timestamps = pd.to_datetime(enriched_df['Date'].iloc[-N:])\n",
        "    else:\n",
        "        timestamps = pd.date_range(end=pd.Timestamp.now(), periods=N, freq='H')  # fallback\n",
        "\n",
        "    # Convert to Series before accessing .dt\n",
        "    timestamps_series = pd.Series(timestamps)\n",
        "    timestamps_str = timestamps_series.dt.strftime('%m-%d %H:%M')\n",
        "\n",
        "\n",
        "    # ---------- DASHBOARD ----------\n",
        "    fig = plt.figure(constrained_layout=True, figsize=(18, 12))\n",
        "    gs = fig.add_gridspec(3, 2)\n",
        "\n",
        "    # --- 1) Time-Series Forecast ---\n",
        "    ax1 = fig.add_subplot(gs[0, :])\n",
        "    ax1.plot(timestamps_str, y_co, label='CO observed', color='green')\n",
        "    ax1.plot([timestamps_str.iloc[-1]], mean_co, 'o', label='CO forecast', color='darkgreen')\n",
        "    ax1.fill_between([timestamps_str.iloc[-1]], mean_co - std_co, mean_co + std_co, color='green', alpha=0.2)\n",
        "    ax1.plot(timestamps_str, y_no2, label='NO2 observed', color='orange')\n",
        "    ax1.plot([timestamps_str.iloc[-1]], mean_no2, 'o', label='NO2 forecast', color='darkorange')\n",
        "    ax1.fill_between([timestamps_str.iloc[-1]], mean_no2 - std_no2, mean_no2 + std_no2, color='orange', alpha=0.2)\n",
        "\n",
        "    if (mean_co + 1.5 * std_co) > policy_thresholds['CO'] or (mean_no2 + 1.5 * std_no2) > policy_thresholds['NO2']:\n",
        "        ax1.axvline(timestamps_str.iloc[-1], color='red', linestyle='--', label='Policy escalation point')\n",
        "\n",
        "    ax1.set_title(\"Pollutant Forecast with MC Dropout Uncertainty\")\n",
        "    ax1.set_xlabel(\"Timestamp\")\n",
        "    ax1.set_ylabel(\"Concentration\")\n",
        "    ax1.legend()\n",
        "\n",
        "    # --- 2) Bar chart (Predicted vs Policy) ---\n",
        "    ax2 = fig.add_subplot(gs[1, 0])\n",
        "    labels = ['CO', 'NO2']\n",
        "    observed = [mean_co, mean_no2]\n",
        "    thresholds = [policy_thresholds['CO'], policy_thresholds['NO2']]\n",
        "    x = np.arange(len(labels))\n",
        "    width = 0.35\n",
        "    ax2.bar(x - width / 2, observed, width, label='Predicted', color=['green', 'orange'])\n",
        "    ax2.bar(x + width / 2, thresholds, width, label='Threshold', color='red', alpha=0.5)\n",
        "    ax2.set_xticks(x)\n",
        "    ax2.set_xticklabels(labels)\n",
        "    ax2.set_ylabel(\"Concentration\")\n",
        "    ax2.set_title(\"Predicted vs Policy Thresholds\")\n",
        "    ax2.legend()\n",
        "\n",
        "    # --- 3) Metaculus contribution heatmap ---\n",
        "    ax3 = fig.add_subplot(gs[1, 1])\n",
        "    if len(metaculus_cols) > 0:\n",
        "        contrib_df = enriched_df[metaculus_cols].iloc[-N:].copy()\n",
        "        contrib_df.index = timestamps_str  # assign timestamps for x-axis\n",
        "\n",
        "        def parse_metaculus_value(js):\n",
        "            if pd.isna(js) or js in ['', 'nan', None]:\n",
        "                return np.nan\n",
        "            try:\n",
        "                data = json.loads(js) if isinstance(js, str) else js\n",
        "                for key in ['resolved_value', 'median', 'probability', 'mean']:\n",
        "                    if key in data and data[key] is not None:\n",
        "                        val = data[key]\n",
        "                        if isinstance(val, (int, float)):\n",
        "                            return val\n",
        "                return np.nan\n",
        "            except Exception:\n",
        "                try:\n",
        "                    return float(js)\n",
        "                except Exception:\n",
        "                    return np.nan\n",
        "\n",
        "        for col in metaculus_cols:\n",
        "            contrib_df[col] = contrib_df[col].apply(parse_metaculus_value)\n",
        "        contrib_df = contrib_df.apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "        if contrib_df.notna().sum().sum() == 0:\n",
        "            ax3.text(0.5, 0.5, \"No numeric Metaculus data found\", ha='center', va='center', fontsize=12)\n",
        "            ax3.set_axis_off()\n",
        "        else:\n",
        "            sns.heatmap(contrib_df.T, cmap='viridis', ax=ax3, cbar_kws={'label': 'Value'})\n",
        "            ax3.set_title(\"Metaculus Contributions (Past Timesteps)\")\n",
        "            ax3.set_xlabel(\"Timestamp\")\n",
        "            ax3.set_ylabel(\"Metaculus Questions\")\n",
        "    else:\n",
        "        ax3.text(0.5, 0.5, \"No Metaculus data available\", ha='center', va='center', fontsize=12)\n",
        "        ax3.set_axis_off()\n",
        "\n",
        "    # --- 4) Epoch contribution heatmap ---\n",
        "    ax4 = fig.add_subplot(gs[2, :])\n",
        "    if len(epoch_cols) > 0:\n",
        "        epoch_df_plot = enriched_df[epoch_cols].iloc[-N:].copy()\n",
        "        epoch_df_plot.index = timestamps_str\n",
        "        epoch_df_plot = epoch_df_plot.apply(pd.to_numeric, errors='coerce')\n",
        "        if epoch_df_plot.notna().sum().sum() == 0:\n",
        "            ax4.text(0.5, 0.5, \"No numeric Epoch data found\", ha='center', va='center', fontsize=12)\n",
        "            ax4.set_axis_off()\n",
        "        else:\n",
        "            sns.heatmap(epoch_df_plot.T, cmap='coolwarm', ax=ax4, cbar_kws={'label': 'Value'})\n",
        "            ax4.set_title(\"Epoch Dataset Contributions (Past Timesteps)\")\n",
        "            ax4.set_xlabel(\"Timestamp\")\n",
        "            ax4.set_ylabel(\"Epoch Features\")\n",
        "    else:\n",
        "        ax4.text(0.5, 0.5, \"No Epoch data available\", ha='center', va='center', fontsize=12)\n",
        "        ax4.set_axis_off()\n",
        "\n",
        "    plt.suptitle(\"AI-driven Air Quality Forecast Dashboard\", fontsize=16, fontweight='bold')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "y_qf1oO00ndk",
        "outputId": "6e31079a-1cf5-4ea2-9c77-bcf3fe54fe6f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Appendix — Security Considerations\n",
        "# **Limitations & risks:** AirQualityUCI is geographically/time-limited and may not generalize; model drift and data gaps can mislead policy triggers. Over-reliance on automated recommendations can lead to harmful interventions.\n",
        "# **Mitigations:** Apply human-in-the-loop for any escalatory action, use conservative percentile-based thresholds with uncertainty-aware alerts, log decisions, and provide rollback procedures. Publish model weights, data provenance, and reproducibility steps."
      ],
      "metadata": {
        "id": "sI8ozePtb-EP"
      },
      "execution_count": 30,
      "outputs": []
    }
  ]
}