very_bad_loss_aqi_pred.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLDKv4HFeNSZ",
        "outputId": "655abf00-8de6-488a-e227-24c9147f46f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0GhBYS7ySy6K"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"city_day.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "rWzoCrBeCRL8",
        "outputId": "7991c6e4-c6b1-40dd-9144-ce10b8ceb1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'city_day.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-26cefd310bd9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"city_day.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'city_day.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "# Read the original CSV file\n",
        "original_data = pd.read_csv('city_day.csv')\n",
        "\n",
        "# Get unique cities from the 'City' column\n",
        "unique_cities = original_data['City'].unique()\n",
        "\n",
        "# Create a folder to store the city-specific CSV files\n",
        "output_folder = '/content/drive/MyDrive/Colab Notebooks/city_data_files'\n",
        "os.makedirs(output_folder, exist_ok=True)\n",
        "\n",
        "# Loop through each city and save its data to a separate CSV file\n",
        "for city in unique_cities:\n",
        "    city_data = original_data[original_data['City'] == city]\n",
        "\n",
        "    # Create a folder with the same name as the main file (city_day.csv)\n",
        "    city_folder = os.path.join(output_folder, 'city_day')\n",
        "    os.makedirs(city_folder, exist_ok=True)\n",
        "\n",
        "    # Save the city-specific data to a CSV file within the city folder\n",
        "    city_data.to_csv(os.path.join(city_folder, f'{city}_data.csv'), index=False)\n",
        "\n",
        "print(\"CSV files created for each city in the 'main_file' folder.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "oVmFY-o_mGo7",
        "outputId": "513cfd3b-0e68-467e-907f-056a4afb3a41"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'city_day.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-96527fa3f8b1>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Read the original CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'city_day.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Get unique cities from the 'City' column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'city_day.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/city_data_files/city_day/Delhi_data.csv\")"
      ],
      "metadata": {
        "id": "2YvoiHXGZgdt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "c0bb4960-48ed-4157-9553-30e3c83469ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/drive/MyDrive/Colab Notebooks/city_data_files/city_day/Delhi_data.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-0b2525631446>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/MyDrive/Colab Notebooks/city_data_files/city_day/Delhi_data.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/Colab Notebooks/city_data_files/city_day/Delhi_data.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Delhi_data.csv\")\n",
        "nan_counts = df.isna().sum()\n",
        "\n",
        "# Display the result\n",
        "print(\"Total NaN values in each column:\")\n",
        "print(nan_counts)\n",
        "df[\"AQI\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWVaUArNcl09",
        "outputId": "6029c829-86ef-4dfb-82fe-2c4e40e4dc4f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total NaN values in each column:\n",
            "City            0\n",
            "Date            0\n",
            "PM2.5           2\n",
            "PM10           77\n",
            "NO              2\n",
            "NO2             2\n",
            "NOx             0\n",
            "NH3             9\n",
            "CO              0\n",
            "SO2           110\n",
            "O3             84\n",
            "Benzene         0\n",
            "Toluene         0\n",
            "Xylene        781\n",
            "AQI            10\n",
            "AQI_Bucket     10\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       472.0\n",
              "1       454.0\n",
              "2       143.0\n",
              "3       319.0\n",
              "4       325.0\n",
              "        ...  \n",
              "2004    112.0\n",
              "2005    196.0\n",
              "2006    233.0\n",
              "2007    114.0\n",
              "2008    101.0\n",
              "Name: AQI, Length: 2009, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta\n",
        "\n",
        "date_range = pd.date_range(start='2015-01-01', end='2020-07-01', freq='D')\n",
        "aqi_values = df[\"AQI\"]\n",
        "\n",
        "df = pd.DataFrame({'Date': date_range, 'AQI': aqi_values})\n",
        "df.set_index('Date', inplace=True)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6USYHJMfK1z",
        "outputId": "cb4ee212-d5b1-4ae1-a052-2f1f04e59a99"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              AQI\n",
            "Date             \n",
            "2015-01-01  472.0\n",
            "2015-01-02  454.0\n",
            "2015-01-03  143.0\n",
            "2015-01-04  319.0\n",
            "2015-01-05  325.0\n",
            "...           ...\n",
            "2020-06-27  112.0\n",
            "2020-06-28  196.0\n",
            "2020-06-29  233.0\n",
            "2020-06-30  114.0\n",
            "2020-07-01  101.0\n",
            "\n",
            "[2009 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot Date vs AQI\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.index, df['AQI'], linestyle='-', color='g')\n",
        "plt.title('Date vs AQI')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('AQI')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "j3d_XedhfLPK",
        "outputId": "147c5e6e-ac31-426d-fec8-7d135dca84a3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# time series imputation\n"
      ],
      "metadata": {
        "id": "r5E_hTg5Qw_g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate dates\n",
        "duplicate_dates = df[df.index.duplicated(keep=False)]\n",
        "print(duplicate_dates)\n",
        "nan_rows = df[df.isna().any(axis=1)]\n",
        "print(nan_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45fy5yvZNSU5",
        "outputId": "f4c224d1-9425-4e41-e8ba-a9db55001fea"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [AQI]\n",
            "Index: []\n",
            "            AQI\n",
            "Date           \n",
            "2016-07-24  NaN\n",
            "2017-06-23  NaN\n",
            "2017-08-13  NaN\n",
            "2017-08-14  NaN\n",
            "2017-08-22  NaN\n",
            "2017-08-23  NaN\n",
            "2017-08-26  NaN\n",
            "2017-08-27  NaN\n",
            "2017-08-28  NaN\n",
            "2017-08-29  NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_t = df.interpolate(method='time')\n",
        "\n",
        "df_l = df.interpolate(method='linear')\n",
        "\n",
        "df_c = df.interpolate(method='spline', order = 3)\n",
        "print(\"interpolation using time method:\")\n",
        "print(df_t['2017-08-13':'2017-08-29'])\n",
        "print(\"interpolation using linear method:\")\n",
        "print(df_l['2017-08-13':'2017-08-29'])\n",
        "print(\"interpolation using cubic spline method:\")\n",
        "print(df_c['2017-08-13':'2017-08-29'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nw2GOTlfNSHf",
        "outputId": "a00f434f-ffa7-4fb4-c3d7-113a2c2c3fac"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "interpolation using time method:\n",
            "                   AQI\n",
            "Date                  \n",
            "2017-08-13   49.666667\n",
            "2017-08-14   54.333333\n",
            "2017-08-15   59.000000\n",
            "2017-08-16   53.000000\n",
            "2017-08-17   66.000000\n",
            "2017-08-18   70.000000\n",
            "2017-08-19   70.000000\n",
            "2017-08-20   63.000000\n",
            "2017-08-21   42.000000\n",
            "2017-08-22   47.333333\n",
            "2017-08-23   52.666667\n",
            "2017-08-24   58.000000\n",
            "2017-08-25  104.000000\n",
            "2017-08-26   97.800000\n",
            "2017-08-27   91.600000\n",
            "2017-08-28   85.400000\n",
            "2017-08-29   79.200000\n",
            "interpolation using linear method:\n",
            "                   AQI\n",
            "Date                  \n",
            "2017-08-13   49.666667\n",
            "2017-08-14   54.333333\n",
            "2017-08-15   59.000000\n",
            "2017-08-16   53.000000\n",
            "2017-08-17   66.000000\n",
            "2017-08-18   70.000000\n",
            "2017-08-19   70.000000\n",
            "2017-08-20   63.000000\n",
            "2017-08-21   42.000000\n",
            "2017-08-22   47.333333\n",
            "2017-08-23   52.666667\n",
            "2017-08-24   58.000000\n",
            "2017-08-25  104.000000\n",
            "2017-08-26   97.800000\n",
            "2017-08-27   91.600000\n",
            "2017-08-28   85.400000\n",
            "2017-08-29   79.200000\n",
            "interpolation using cubic spline method:\n",
            "                   AQI\n",
            "Date                  \n",
            "2017-08-13   45.705420\n",
            "2017-08-14   56.329514\n",
            "2017-08-15   59.000000\n",
            "2017-08-16   53.000000\n",
            "2017-08-17   66.000000\n",
            "2017-08-18   70.000000\n",
            "2017-08-19   70.000000\n",
            "2017-08-20   63.000000\n",
            "2017-08-21   42.000000\n",
            "2017-08-22   31.324602\n",
            "2017-08-23   32.075336\n",
            "2017-08-24   58.000000\n",
            "2017-08-25  104.000000\n",
            "2017-08-26  123.677237\n",
            "2017-08-27  115.456509\n",
            "2017-08-28   94.441187\n",
            "2017-08-29   75.571158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "dfs1 = df_t['2017-08-13':'2017-08-29']\n",
        "dfs2 = df_l['2017-08-13':'2017-08-29']\n",
        "dfs3 = df_c['2017-08-13':'2017-08-29']\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(dfs1.index, dfs1['AQI'], linestyle='-', color='b', marker='o', label='time')\n",
        "\n",
        "plt.plot(dfs2.index, dfs2['AQI'], linestyle='-', color='r', marker='o', label='linear')\n",
        "\n",
        "plt.plot(dfs3.index, dfs3['AQI'], linestyle='-', color='purple', marker='o', label='spline')\n",
        "\n",
        "plt.title('Date vs AQI and Features')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Values')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n",
        "df = df_t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "UXn_2pYdNSfa",
        "outputId": "4919e6f8-6485-4282-cf71-1656a4227c80"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# train val test split\n"
      ],
      "metadata": {
        "id": "gPYr3qu5XAtq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "7Yn_HPi8b_rt",
        "outputId": "8d484f5a-b35b-4c2b-bed3-3d6835b85891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              AQI\n",
              "Date             \n",
              "2015-01-01  472.0\n",
              "2015-01-02  454.0\n",
              "2015-01-03  143.0\n",
              "2015-01-04  319.0\n",
              "2015-01-05  325.0\n",
              "...           ...\n",
              "2020-06-27  112.0\n",
              "2020-06-28  196.0\n",
              "2020-06-29  233.0\n",
              "2020-06-30  114.0\n",
              "2020-07-01  101.0\n",
              "\n",
              "[2009 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-155c0488-0fbf-4473-86fc-c55f5ea55cf1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AQI</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>472.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>454.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-03</th>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-04</th>\n",
              "      <td>319.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>325.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-27</th>\n",
              "      <td>112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-28</th>\n",
              "      <td>196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-29</th>\n",
              "      <td>233.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-30</th>\n",
              "      <td>114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>101.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2009 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-155c0488-0fbf-4473-86fc-c55f5ea55cf1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-155c0488-0fbf-4473-86fc-c55f5ea55cf1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-155c0488-0fbf-4473-86fc-c55f5ea55cf1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5a2c21d8-6f56-477d-882c-82a879b7f671\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a2c21d8-6f56-477d-882c-82a879b7f671')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a2c21d8-6f56-477d-882c-82a879b7f671 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f163977c-722e-4aeb-b755-0fe073af7abe\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f163977c-722e-4aeb-b755-0fe073af7abe button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from matplotlib import pyplot\n",
        "series = df\n",
        "result = seasonal_decompose(series, model='multiplicative')\n",
        "result.plot()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "HCWMK0Kh8qbG",
        "outputId": "bff29859-df91-4a96-c072-79ddedb7b5db"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df['AQI_scaled'] = scaler.fit_transform(df[['AQI']])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "1kDm98IKHA2g",
        "outputId": "77b09156-9609-4fdf-8b31-ef16719c46fa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              AQI  AQI_scaled\n",
              "Date                         \n",
              "2015-01-01  472.0    0.644833\n",
              "2015-01-02  454.0    0.618632\n",
              "2015-01-03  143.0    0.165939\n",
              "2015-01-04  319.0    0.422125\n",
              "2015-01-05  325.0    0.430859\n",
              "...           ...         ...\n",
              "2020-06-27  112.0    0.120815\n",
              "2020-06-28  196.0    0.243086\n",
              "2020-06-29  233.0    0.296943\n",
              "2020-06-30  114.0    0.123726\n",
              "2020-07-01  101.0    0.104803\n",
              "\n",
              "[2009 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-addf3753-690e-421a-bdde-868342d2a1e3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AQI</th>\n",
              "      <th>AQI_scaled</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>472.0</td>\n",
              "      <td>0.644833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-02</th>\n",
              "      <td>454.0</td>\n",
              "      <td>0.618632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-03</th>\n",
              "      <td>143.0</td>\n",
              "      <td>0.165939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-04</th>\n",
              "      <td>319.0</td>\n",
              "      <td>0.422125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-05</th>\n",
              "      <td>325.0</td>\n",
              "      <td>0.430859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-27</th>\n",
              "      <td>112.0</td>\n",
              "      <td>0.120815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-28</th>\n",
              "      <td>196.0</td>\n",
              "      <td>0.243086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-29</th>\n",
              "      <td>233.0</td>\n",
              "      <td>0.296943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-30</th>\n",
              "      <td>114.0</td>\n",
              "      <td>0.123726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>101.0</td>\n",
              "      <td>0.104803</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2009 rows × 2 columns</p>\n",
              "</div>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-addf3753-690e-421a-bdde-868342d2a1e3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-addf3753-690e-421a-bdde-868342d2a1e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-addf3753-690e-421a-bdde-868342d2a1e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c6a59b29-2024-475c-8a8f-50244358f357\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6a59b29-2024-475c-8a8f-50244358f357')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c6a59b29-2024-475c-8a8f-50244358f357 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b9582294-99eb-4ad9-bc83-041404ee1d31\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b9582294-99eb-4ad9-bc83-041404ee1d31 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index = pd.to_datetime(df.index)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.index, df['AQI_scaled'], linestyle='-', color='g')\n",
        "plt.title('AQI vs Date')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('AQI (Scaled)')\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "f4a1991d-a395-48a4-ea89-00365e78d3f2",
        "id": "YhsBiXgwHg5g"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "    # Build and compile the model\n",
        "model = Sequential([\n",
        "        layers.Input(shape=(1, 1)),\n",
        "        layers.LSTM(64, activation='relu', return_sequences=True),\n",
        "        layers.LSTM(32, activation='relu'),\n",
        "        layers.Dense(32, activation=\"relu\"),\n",
        "        layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=Adam(learning_rate=0.001), metrics=[\"mean_absolute_error\"])\n",
        "model.summary()\n",
        "df\n"
      ],
      "metadata": {
        "id": "3yyjHJl08-UC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "outputId": "36ce8b22-7268-4093-f3d1-90b0bfefc3c0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 1, 64)             16896     \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 32)                12416     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 30401 (118.75 KB)\n",
            "Trainable params: 30401 (118.75 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              AQI  AQI_scaled\n",
              "Date                         \n",
              "2015-01-01  472.0    0.644833\n",
              "2015-01-02  454.0    0.618632\n",
              "2015-01-03  143.0    0.165939\n",
              "2015-01-04  319.0    0.422125\n",
              "2015-01-05  325.0    0.430859\n",
              "...           ...         ...\n",
              "2020-06-27  112.0    0.120815\n",
              "2020-06-28  196.0    0.243086\n",
              "2020-06-29  233.0    0.296943\n",
              "2020-06-30  114.0    0.123726\n",
              "2020-07-01  101.0    0.104803\n",
              "\n",
              "[2009 rows x 2 columns]"
            ],
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              "      <th>2015-01-01</th>\n",
              "      <td>472.0</td>\n",
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              "    <tr>\n",
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              "      <td>454.0</td>\n",
              "      <td>0.618632</td>\n",
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              "      <th>2015-01-03</th>\n",
              "      <td>143.0</td>\n",
              "      <td>0.165939</td>\n",
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              "      <td>319.0</td>\n",
              "      <td>0.422125</td>\n",
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              "      <th>2020-06-29</th>\n",
              "      <td>233.0</td>\n",
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              "      <td>0.123726</td>\n",
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              "      <th>2020-07-01</th>\n",
              "      <td>101.0</td>\n",
              "      <td>0.104803</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-d3e92f4c-c692-491b-ae95-90e6dda41ee0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d3e92f4c-c692-491b-ae95-90e6dda41ee0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    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",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-73a64f5e-bd65-444c-8325-d3a0fb75837d button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "      .colab-df-generate:hover {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
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              "        document.querySelector('#id_65d54fbe-8532-4bf0-a8c4-a6e1c326a5ec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "n_splits = 5\n",
        "\n",
        "tscv = TimeSeriesSplit(n_splits=n_splits)\n",
        "\n",
        "# Iterate through splits to get indices for train, validation, and test sets\n",
        "for train_index, test_index in tscv.split(df):\n",
        "    train_data = df.iloc[train_index]\n",
        "    test_data = df.iloc[test_index]\n",
        "    #print(\"shape:\", train_data.shape)\n",
        "\n",
        "    # Further split the training set into training and validation\n",
        "    split_tscv = TimeSeriesSplit(n_splits=2)\n",
        "    for train_idx, val_idx in split_tscv.split(train_data):\n",
        "        train_set = train_data.iloc[train_idx]\n",
        "        val_set = train_data.iloc[val_idx]\n",
        "        #print(\"shape:\", train_set.shape)\n",
        "\n",
        "        # Use train_set, val_set, and test_data for your model training and testing\n",
        "        print(f\"Training set size: {len(train_set)}, Validation set size: {len(val_set)}, Test set size: {len(test_data)}\")\n",
        "        X_train = train_set.index.to_series().astype(np.int64) // 10**9\n",
        "        y_train = train_set['AQI_scaled']\n",
        "        X_val = val_set.index.to_series().astype(np.int64) // 10**9\n",
        "        y_val  = val_set['AQI_scaled']\n",
        "        X_test = test_data.index.to_series().astype(np.int64) // 10**9\n",
        "        y_test = test_data['AQI_scaled']\n",
        "\n",
        "        X_train = np.reshape(X_train.values , (X_train.shape[0], 1, 1))\n",
        "        X_val = np.reshape(X_val.values , (X_val.shape[0], 1, 1))\n",
        "        X_test = np.reshape(X_test.values ,(X_test.shape[0], 1, 1))\n",
        "        #print(\"shape:\", X_train.shape)\n",
        "\n",
        "        model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))\n",
        "\n",
        "        predictions = model.predict(X_test)\n",
        "        #print(f\"predition for this part is {predictions}\")\n",
        "        mse = mean_squared_error(y_test, predictions)\n",
        "        print(f\"Mean Squared Error on Test Set: {mse}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "875cd113-6689-4bff-a140-5c977c3a5cd4",
        "id": "cuNE2pB2K__s"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 113, Validation set size: 113, Test set size: 334\n",
            "Epoch 1/10\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 38837.5977 - mean_absolute_error: 189.6123 - val_loss: 11.2674 - val_mean_absolute_error: 3.1889\n",
            "Epoch 2/10\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 10999.6807 - mean_absolute_error: 86.6693 - val_loss: 23943.4512 - val_mean_absolute_error: 154.7332\n",
            "Epoch 3/10\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 12325.3828 - mean_absolute_error: 100.0275 - val_loss: 1772.6292 - val_mean_absolute_error: 42.0898\n",
            "Epoch 4/10\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 7682.6929 - mean_absolute_error: 83.0185 - val_loss: 9625.5469 - val_mean_absolute_error: 98.1031\n",
            "Epoch 5/10\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 4460.0190 - mean_absolute_error: 58.1986 - val_loss: 1625.0303 - val_mean_absolute_error: 40.2951\n",
            "Epoch 6/10\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 3731.3826 - mean_absolute_error: 59.6794 - val_loss: 3037.1169 - val_mean_absolute_error: 55.0960\n",
            "Epoch 7/10\n",
            "4/4 [==============================] - 0s 21ms/step - loss: 1214.0719 - mean_absolute_error: 29.3898 - val_loss: 960.3301 - val_mean_absolute_error: 30.9659\n",
            "Epoch 8/10\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 1607.2679 - mean_absolute_error: 39.6137 - val_loss: 1047.8103 - val_mean_absolute_error: 32.3465\n",
            "Epoch 9/10\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 439.9892 - mean_absolute_error: 18.1172 - val_loss: 419.8989 - val_mean_absolute_error: 20.4588\n",
            "Epoch 10/10\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 760.0572 - mean_absolute_error: 27.1986 - val_loss: 591.2200 - val_mean_absolute_error: 24.2907\n",
            "11/11 [==============================] - 0s 4ms/step\n",
            "Mean Squared Error on Test Set: 617.3935743617217\n",
            "r2_score on Test Set: -23374.47745104112\n",
            "Training set size: 226, Validation set size: 113, Test set size: 334\n",
            "Epoch 1/10\n",
            "8/8 [==============================] - 0s 26ms/step - loss: 167.4015 - mean_absolute_error: 10.4361 - val_loss: 31.4188 - val_mean_absolute_error: 5.4771\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 58.2827 - mean_absolute_error: 6.9042 - val_loss: 99.1577 - val_mean_absolute_error: 9.8814\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 72.1165 - mean_absolute_error: 8.4012 - val_loss: 89.2512 - val_mean_absolute_error: 9.3902\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 97.1445 - mean_absolute_error: 9.6934 - val_loss: 84.3781 - val_mean_absolute_error: 9.1070\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 0s 15ms/step - loss: 92.6732 - mean_absolute_error: 9.5090 - val_loss: 94.9356 - val_mean_absolute_error: 9.6778\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 0s 18ms/step - loss: 84.1114 - mean_absolute_error: 9.0444 - val_loss: 13.2791 - val_mean_absolute_error: 3.3858\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 468.7384 - mean_absolute_error: 17.7470 - val_loss: 3146.8894 - val_mean_absolute_error: 56.0877\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 8178.3975 - mean_absolute_error: 87.7977 - val_loss: 23368.1777 - val_mean_absolute_error: 152.8620\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 57520.9531 - mean_absolute_error: 231.6999 - val_loss: 208034.1250 - val_mean_absolute_error: 456.1054\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 0s 13ms/step - loss: 717827.0000 - mean_absolute_error: 799.7145 - val_loss: 3575440.2500 - val_mean_absolute_error: 1890.8799\n",
            "11/11 [==============================] - 0s 3ms/step\n",
            "Mean Squared Error on Test Set: 3672385.927035657\n",
            "r2_score on Test Set: -139042221.00188974\n",
            "Training set size: 225, Validation set size: 224, Test set size: 334\n",
            "Epoch 1/10\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 16971888.0000 - mean_absolute_error: 3805.0603 - val_loss: 104872744.0000 - val_mean_absolute_error: 10240.6641\n",
            "Epoch 2/10\n",
            "8/8 [==============================] - 0s 16ms/step - loss: 605423552.0000 - mean_absolute_error: 22309.3496 - val_loss: 4454600704.0000 - val_mean_absolute_error: 66742.3047\n",
            "Epoch 3/10\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 17891420160.0000 - mean_absolute_error: 127043.7578 - val_loss: 20615770112.0000 - val_mean_absolute_error: 143580.8594\n",
            "Epoch 4/10\n",
            "8/8 [==============================] - 0s 14ms/step - loss: 9117022208.0000 - mean_absolute_error: 86940.0234 - val_loss: 7631852.0000 - val_mean_absolute_error: 2762.5601\n",
            "Epoch 5/10\n",
            "8/8 [==============================] - 0s 20ms/step - loss: 2873810688.0000 - mean_absolute_error: 45262.0508 - val_loss: 3969342976.0000 - val_mean_absolute_error: 63002.2539\n",
            "Epoch 6/10\n",
            "8/8 [==============================] - 0s 9ms/step - loss: 1669978112.0000 - mean_absolute_error: 37804.6328 - val_loss: 185627440.0000 - val_mean_absolute_error: 13624.4141\n",
            "Epoch 7/10\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 603236224.0000 - mean_absolute_error: 22334.3164 - val_loss: 499683936.0000 - val_mean_absolute_error: 22353.4434\n",
            "Epoch 8/10\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 248872800.0000 - mean_absolute_error: 14375.5791 - val_loss: 202481040.0000 - val_mean_absolute_error: 14229.4785\n",
            "Epoch 9/10\n",
            "8/8 [==============================] - 0s 10ms/step - loss: 133364664.0000 - mean_absolute_error: 10713.0332 - val_loss: 21486834.0000 - val_mean_absolute_error: 4635.3545\n",
            "Epoch 10/10\n",
            "8/8 [==============================] - 0s 11ms/step - loss: 35127260.0000 - mean_absolute_error: 5160.7510 - val_loss: 54708232.0000 - val_mean_absolute_error: 7396.4468\n",
            "11/11 [==============================] - 0s 2ms/step\n",
            "Mean Squared Error on Test Set: 58042092.35833321\n",
            "r2_score on Test Set: -1203554308.0359943\n",
            "Training set size: 449, Validation set size: 224, Test set size: 334\n",
            "Epoch 1/10\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 20029474.0000 - mean_absolute_error: 4013.7998 - val_loss: 15949.0029 - val_mean_absolute_error: 126.2824\n",
            "Epoch 2/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 3670149.7500 - mean_absolute_error: 1621.5544 - val_loss: 3483428.0000 - val_mean_absolute_error: 1866.3806\n",
            "Epoch 3/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 1039561.0625 - mean_absolute_error: 904.3375 - val_loss: 159713.7500 - val_mean_absolute_error: 399.6373\n",
            "Epoch 4/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 182355.9688 - mean_absolute_error: 381.4447 - val_loss: 2461.4883 - val_mean_absolute_error: 49.5993\n",
            "Epoch 5/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 42152.4062 - mean_absolute_error: 182.9047 - val_loss: 1342.1302 - val_mean_absolute_error: 36.6149\n",
            "Epoch 6/10\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 10728.6191 - mean_absolute_error: 94.3967 - val_loss: 303.9671 - val_mean_absolute_error: 17.3962\n",
            "Epoch 7/10\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 2827.1694 - mean_absolute_error: 46.4722 - val_loss: 2796.2634 - val_mean_absolute_error: 52.8650\n",
            "Epoch 8/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 1079.8625 - mean_absolute_error: 29.8940 - val_loss: 25.3155 - val_mean_absolute_error: 4.8694\n",
            "Epoch 9/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 305.8880 - mean_absolute_error: 15.4958 - val_loss: 555.0934 - val_mean_absolute_error: 23.5323\n",
            "Epoch 10/10\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 96.5229 - mean_absolute_error: 7.2355 - val_loss: 392.6649 - val_mean_absolute_error: 19.7868\n",
            "11/11 [==============================] - 0s 3ms/step\n",
            "Mean Squared Error on Test Set: 412.17231904868163\n",
            "r2_score on Test Set: -8545.758920988097\n",
            "Training set size: 337, Validation set size: 335, Test set size: 334\n",
            "Epoch 1/10\n",
            "11/11 [==============================] - 0s 14ms/step - loss: 1122.2559 - mean_absolute_error: 33.0427 - val_loss: 1546.8009 - val_mean_absolute_error: 39.3135\n",
            "Epoch 2/10\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 722.6365 - mean_absolute_error: 25.2354 - val_loss: 33.1609 - val_mean_absolute_error: 5.6313\n",
            "Epoch 3/10\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 68.5830 - mean_absolute_error: 7.9737 - val_loss: 87.8139 - val_mean_absolute_error: 9.2956\n",
            "Epoch 4/10\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 82.4153 - mean_absolute_error: 8.9212 - val_loss: 119.5945 - val_mean_absolute_error: 10.8791\n",
            "Epoch 5/10\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 995.5649 - mean_absolute_error: 28.2679 - val_loss: 4611.8076 - val_mean_absolute_error: 67.9001\n",
            "Epoch 6/10\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 52983.7578 - mean_absolute_error: 195.4448 - val_loss: 398309.6250 - val_mean_absolute_error: 631.1060\n",
            "Epoch 7/10\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 10836872.0000 - mean_absolute_error: 2630.1353 - val_loss: 107955512.0000 - val_mean_absolute_error: 10389.9951\n",
            "Epoch 8/10\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 4724556288.0000 - mean_absolute_error: 53801.8086 - val_loss: 40062722048.0000 - val_mean_absolute_error: 200153.5000\n",
            "Epoch 9/10\n",
            "11/11 [==============================] - 0s 13ms/step - loss: 22523740160.0000 - mean_absolute_error: 133463.3750 - val_loss: 3402827776.0000 - val_mean_absolute_error: 58332.8125\n",
            "Epoch 10/10\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 4405216768.0000 - mean_absolute_error: 59290.0430 - val_loss: 3513988352.0000 - val_mean_absolute_error: 59277.9336\n",
            "11/11 [==============================] - 0s 3ms/step\n",
            "Mean Squared Error on Test Set: 3797221845.399265\n",
            "r2_score on Test Set: -139035730674.97284\n",
            "Training set size: 672, Validation set size: 335, Test set size: 334\n",
            "Epoch 1/10\n",
            "21/21 [==============================] - 0s 12ms/step - loss: 821657920.0000 - mean_absolute_error: 24416.2031 - val_loss: 225674064.0000 - val_mean_absolute_error: 15022.2148\n",
            "Epoch 2/10\n",
            "21/21 [==============================] - 0s 6ms/step - loss: 84605760.0000 - mean_absolute_error: 7887.3789 - val_loss: 7983500.0000 - val_mean_absolute_error: 2825.4651\n",
            "Epoch 3/10\n",
            "21/21 [==============================] - 0s 7ms/step - loss: 7597647.0000 - mean_absolute_error: 2364.5234 - val_loss: 2187597.5000 - val_mean_absolute_error: 1479.0304\n",
            "Epoch 4/10\n",
            "21/21 [==============================] - 0s 8ms/step - loss: 1101430.2500 - mean_absolute_error: 902.9250 - val_loss: 604724.6250 - val_mean_absolute_error: 777.6263\n",
            "Epoch 5/10\n",
            "21/21 [==============================] - 0s 6ms/step - loss: 141317.4844 - mean_absolute_error: 313.7204 - val_loss: 56229.4805 - val_mean_absolute_error: 237.1230\n",
            "Epoch 6/10\n",
            "21/21 [==============================] - 0s 7ms/step - loss: 13958.5703 - mean_absolute_error: 99.1920 - val_loss: 468.3605 - val_mean_absolute_error: 21.6083\n",
            "Epoch 7/10\n",
            "21/21 [==============================] - 0s 8ms/step - loss: 1223.5908 - mean_absolute_error: 28.5515 - val_loss: 106.3947 - val_mean_absolute_error: 10.2486\n",
            "Epoch 8/10\n",
            "21/21 [==============================] - 0s 6ms/step - loss: 335.6941 - mean_absolute_error: 15.7129 - val_loss: 1154.5892 - val_mean_absolute_error: 33.9591\n",
            "Epoch 9/10\n",
            "21/21 [==============================] - 0s 6ms/step - loss: 254.2747 - mean_absolute_error: 14.1171 - val_loss: 280.3617 - val_mean_absolute_error: 16.7066\n",
            "Epoch 10/10\n",
            "21/21 [==============================] - 0s 6ms/step - loss: 225.4230 - mean_absolute_error: 14.1469 - val_loss: 28.8420 - val_mean_absolute_error: 5.2514\n",
            "11/11 [==============================] - 0s 2ms/step\n",
            "Mean Squared Error on Test Set: 30.96371869337143\n",
            "r2_score on Test Set: -1132.740252283183\n",
            "Training set size: 447, Validation set size: 447, Test set size: 334\n",
            "Epoch 1/10\n",
            "14/14 [==============================] - 0s 13ms/step - loss: 133.3512 - mean_absolute_error: 10.0293 - val_loss: 263.2971 - val_mean_absolute_error: 16.1862\n",
            "Epoch 2/10\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 285.8924 - mean_absolute_error: 14.7629 - val_loss: 316.2846 - val_mean_absolute_error: 17.7534\n",
            "Epoch 3/10\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 429.5408 - mean_absolute_error: 19.3655 - val_loss: 26.2523 - val_mean_absolute_error: 5.0062\n",
            "Epoch 4/10\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 575.7919 - mean_absolute_error: 20.6695 - val_loss: 227.6258 - val_mean_absolute_error: 15.0464\n",
            "Epoch 5/10\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 944.8605 - mean_absolute_error: 30.2283 - val_loss: 674.6558 - val_mean_absolute_error: 25.9491\n",
            "Epoch 6/10\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 120.7077 - mean_absolute_error: 7.9277 - val_loss: 109.3540 - val_mean_absolute_error: 10.3965\n",
            "Epoch 7/10\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 106.5752 - mean_absolute_error: 10.0819 - val_loss: 148.3030 - val_mean_absolute_error: 12.1236\n",
            "Epoch 8/10\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 83.6651 - mean_absolute_error: 8.9162 - val_loss: 113.9337 - val_mean_absolute_error: 10.6158\n",
            "Epoch 9/10\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 374.5850 - mean_absolute_error: 16.7079 - val_loss: 2750.3904 - val_mean_absolute_error: 52.4290\n",
            "Epoch 10/10\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 218358.5312 - mean_absolute_error: 346.7642 - val_loss: 2240085.2500 - val_mean_absolute_error: 1496.6479\n",
            "11/11 [==============================] - 0s 3ms/step\n",
            "Mean Squared Error on Test Set: 2464454.537653607\n",
            "r2_score on Test Set: -92959029.38370611\n",
            "Training set size: 894, Validation set size: 447, Test set size: 334\n",
            "Epoch 1/10\n",
            "28/28 [==============================] - 0s 9ms/step - loss: 25845469184.0000 - mean_absolute_error: 116862.6094 - val_loss: 7598797824.0000 - val_mean_absolute_error: 87168.7344\n",
            "Epoch 2/10\n",
            "28/28 [==============================] - 0s 7ms/step - loss: 3803971072.0000 - mean_absolute_error: 52506.7773 - val_loss: 3738066.0000 - val_mean_absolute_error: 1933.3555\n",
            "Epoch 3/10\n",
            "28/28 [==============================] - 0s 6ms/step - loss: 185432096.0000 - mean_absolute_error: 11312.6123 - val_loss: 127799.1250 - val_mean_absolute_error: 357.4785\n",
            "Epoch 4/10\n",
            "28/28 [==============================] - 0s 6ms/step - loss: 10820823.0000 - mean_absolute_error: 2739.3176 - val_loss: 1034396.8750 - val_mean_absolute_error: 1017.0255\n",
            "Epoch 5/10\n",
            "28/28 [==============================] - 0s 7ms/step - loss: 596921.1875 - mean_absolute_error: 644.7524 - val_loss: 210100.0938 - val_mean_absolute_error: 458.3533\n",
            "Epoch 6/10\n",
            "28/28 [==============================] - 0s 7ms/step - loss: 36686.7461 - mean_absolute_error: 162.4797 - val_loss: 333.9961 - val_mean_absolute_error: 18.2441\n",
            "Epoch 7/10\n",
            "28/28 [==============================] - 0s 5ms/step - loss: 1971.7958 - mean_absolute_error: 36.7945 - val_loss: 96.4927 - val_mean_absolute_error: 9.7676\n",
            "Epoch 8/10\n",
            "28/28 [==============================] - 0s 7ms/step - loss: 76.9525 - mean_absolute_error: 6.4261 - val_loss: 5.9677 - val_mean_absolute_error: 2.2057\n",
            "Epoch 9/10\n",
            "28/28 [==============================] - 0s 8ms/step - loss: 6.7193 - mean_absolute_error: 1.7610 - val_loss: 7.4735 - val_mean_absolute_error: 2.5306\n",
            "Epoch 10/10\n",
            "28/28 [==============================] - 0s 8ms/step - loss: 41.1053 - mean_absolute_error: 5.3163 - val_loss: 2.6739 - val_mean_absolute_error: 1.3837\n",
            "11/11 [==============================] - 0s 4ms/step\n",
            "Mean Squared Error on Test Set: 3.1197353466311126\n",
            "r2_score on Test Set: -116.67617070863807\n",
            "Training set size: 559, Validation set size: 558, Test set size: 334\n",
            "Epoch 1/10\n",
            "18/18 [==============================] - 0s 16ms/step - loss: 14.1340 - mean_absolute_error: 2.8901 - val_loss: 1.9108 - val_mean_absolute_error: 1.1226\n",
            "Epoch 2/10\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 1.5117 - mean_absolute_error: 0.9917 - val_loss: 1.5995 - val_mean_absolute_error: 1.0390\n",
            "Epoch 3/10\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 1.2500 - mean_absolute_error: 0.9002 - val_loss: 1.1212 - val_mean_absolute_error: 0.8328\n",
            "Epoch 4/10\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 1.2551 - mean_absolute_error: 0.8936 - val_loss: 1.3363 - val_mean_absolute_error: 0.9197\n",
            "Epoch 5/10\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 1.5504 - mean_absolute_error: 1.0045 - val_loss: 1.9917 - val_mean_absolute_error: 1.1369\n",
            "Epoch 6/10\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 1.7169 - mean_absolute_error: 1.0628 - val_loss: 1.2701 - val_mean_absolute_error: 0.9026\n",
            "Epoch 7/10\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 1.5042 - mean_absolute_error: 0.9761 - val_loss: 1.1464 - val_mean_absolute_error: 0.8511\n",
            "Epoch 8/10\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 1.1184 - mean_absolute_error: 0.8470 - val_loss: 1.1541 - val_mean_absolute_error: 0.8504\n",
            "Epoch 9/10\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 1.3643 - mean_absolute_error: 0.9428 - val_loss: 1.7133 - val_mean_absolute_error: 1.0541\n",
            "Epoch 10/10\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 2.2391 - mean_absolute_error: 1.1935 - val_loss: 3.2708 - val_mean_absolute_error: 1.5340\n",
            "11/11 [==============================] - 0s 2ms/step\n",
            "Mean Squared Error on Test Set: 3.4349792279507843\n",
            "r2_score on Test Set: -110.81355427959522\n",
            "Training set size: 1117, Validation set size: 558, Test set size: 334\n",
            "Epoch 1/10\n",
            "35/35 [==============================] - 0s 8ms/step - loss: 15.8428 - mean_absolute_error: 3.2371 - val_loss: 40.8239 - val_mean_absolute_error: 6.2853\n",
            "Epoch 2/10\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 10.4505 - mean_absolute_error: 2.5085 - val_loss: 2.6090 - val_mean_absolute_error: 1.3141\n",
            "Epoch 3/10\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 3.3365 - mean_absolute_error: 1.4408 - val_loss: 1.3848 - val_mean_absolute_error: 0.9312\n",
            "Epoch 4/10\n",
            "35/35 [==============================] - 0s 6ms/step - loss: 1.6242 - mean_absolute_error: 1.0121 - val_loss: 10.8731 - val_mean_absolute_error: 3.1042\n",
            "Epoch 5/10\n",
            "35/35 [==============================] - 0s 6ms/step - loss: 4.4764 - mean_absolute_error: 1.7551 - val_loss: 2.1206 - val_mean_absolute_error: 1.1708\n",
            "Epoch 6/10\n",
            "35/35 [==============================] - 0s 6ms/step - loss: 1.9729 - mean_absolute_error: 1.1091 - val_loss: 3.2851 - val_mean_absolute_error: 1.5241\n",
            "Epoch 7/10\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 2.5358 - mean_absolute_error: 1.2759 - val_loss: 1.8090 - val_mean_absolute_error: 1.0851\n",
            "Epoch 8/10\n",
            "35/35 [==============================] - 0s 6ms/step - loss: 1.6173 - mean_absolute_error: 1.0030 - val_loss: 1.4489 - val_mean_absolute_error: 0.9526\n",
            "Epoch 9/10\n",
            "35/35 [==============================] - 0s 7ms/step - loss: 75.6592 - mean_absolute_error: 7.6609 - val_loss: 104.8748 - val_mean_absolute_error: 10.1816\n",
            "Epoch 10/10\n",
            "35/35 [==============================] - 0s 6ms/step - loss: 8622589952.0000 - mean_absolute_error: 44236.9062 - val_loss: 7481807872.0000 - val_mean_absolute_error: 86493.9219\n",
            "11/11 [==============================] - 0s 2ms/step\n",
            "Mean Squared Error on Test Set: 7860341460.228496\n",
            "r2_score on Test Set: -255865511314.98407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[1] , y_train[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLrrTu3I1C3O",
        "outputId": "0af7cbf4-b1f2-4ec0-9880-4c0826b193cd"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[1420156800]]), 0.6186317321688501)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hNbenoBU-4_y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}