Untitled7.ipynb

{
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/yankit247/Different-Machine-Learning-Models/blob/main/Untitled7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "c25777ad",
      "metadata": {
        "id": "c25777ad"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "pd.options.display.float_format = '{:.5f}'.format\n",
        "pd.options.display.max_rows = None\n",
        "\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "6002dfbb",
      "metadata": {
        "id": "6002dfbb"
      },
      "outputs": [],
      "source": [
        "train=pd.read_csv('train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6509d854",
      "metadata": {
        "id": "6509d854"
      },
      "outputs": [],
      "source": [
        "test=pd.read_csv('test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "12d211e7",
      "metadata": {
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          "height": 1000
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        "id": "12d211e7",
        "outputId": "ffbab44c-e523-46e3-c1cb-2971e421a409"
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            "text/plain": [
              "       ID_LAT_LON_YEAR_WEEK  latitude  longitude  year  week_no  \\\n",
              "0  ID_-0.510_29.290_2019_00  -0.51000   29.29000  2019        0   \n",
              "1  ID_-0.510_29.290_2019_01  -0.51000   29.29000  2019        1   \n",
              "2  ID_-0.510_29.290_2019_02  -0.51000   29.29000  2019        2   \n",
              "3  ID_-0.510_29.290_2019_03  -0.51000   29.29000  2019        3   \n",
              "4  ID_-0.510_29.290_2019_04  -0.51000   29.29000  2019        4   \n",
              "\n",
              "   SulphurDioxide_SO2_column_number_density  \\\n",
              "0                                  -0.00011   \n",
              "1                                   0.00002   \n",
              "2                                   0.00051   \n",
              "3                                   0.00000   \n",
              "4                                  -0.00008   \n",
              "\n",
              "   SulphurDioxide_SO2_column_number_density_amf  \\\n",
              "0                                       0.60302   \n",
              "1                                       0.72821   \n",
              "2                                       0.74820   \n",
              "3                                       0.00000   \n",
              "4                                       0.67630   \n",
              "\n",
              "   SulphurDioxide_SO2_slant_column_number_density  \\\n",
              "0                                        -0.00007   \n",
              "1                                         0.00001   \n",
              "2                                         0.00038   \n",
              "3                                         0.00000   \n",
              "4                                        -0.00005   \n",
              "\n",
              "   SulphurDioxide_cloud_fraction  SulphurDioxide_sensor_azimuth_angle  ...  \\\n",
              "0                        0.25567                            -98.59389  ...   \n",
              "1                        0.13099                             16.59286  ...   \n",
              "2                        0.11002                             72.79584  ...   \n",
              "3                        0.00000                              0.00000  ...   \n",
              "4                        0.12116                              4.12127  ...   \n",
              "\n",
              "   Cloud_cloud_top_height  Cloud_cloud_base_pressure  Cloud_cloud_base_height  \\\n",
              "0              3664.43622                61085.80957               2615.12048   \n",
              "1              3651.19031                66969.47873               3174.57242   \n",
              "2              4216.98649                60068.89445               3516.28267   \n",
              "3              5228.50774                51064.54734               4180.97332   \n",
              "4              3980.59812                63751.12578               3355.71011   \n",
              "\n",
              "   Cloud_cloud_optical_depth  Cloud_surface_albedo  \\\n",
              "0                   15.56853               0.27229   \n",
              "1                    8.69060               0.25683   \n",
              "2                   21.10341               0.25110   \n",
              "3                   15.38690               0.26204   \n",
              "4                    8.11469               0.23585   \n",
              "\n",
              "   Cloud_sensor_azimuth_angle  Cloud_sensor_zenith_angle  \\\n",
              "0                   -12.62899                   35.63242   \n",
              "1                    30.35938                   39.55763   \n",
              "2                    15.37788                   30.40182   \n",
              "3                   -11.29340                   24.38036   \n",
              "4                    38.53226                   37.39298   \n",
              "\n",
              "   Cloud_solar_azimuth_angle  Cloud_solar_zenith_angle  emission  \n",
              "0                 -138.78642                  30.75214   3.75099  \n",
              "1                 -145.18393                  27.25178   4.02518  \n",
              "2                 -142.51954                  26.19330   4.23138  \n",
              "3                 -132.66583                  28.82915   4.30529  \n",
              "4                 -141.50981                  22.20461   4.34732  \n",
              "\n",
              "[5 rows x 76 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_LAT_LON_YEAR_WEEK</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>year</th>\n",
              "      <th>week_no</th>\n",
              "      <th>SulphurDioxide_SO2_column_number_density</th>\n",
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              "      <th>SulphurDioxide_SO2_slant_column_number_density</th>\n",
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              "      <th>SulphurDioxide_sensor_azimuth_angle</th>\n",
              "      <th>...</th>\n",
              "      <th>Cloud_cloud_top_height</th>\n",
              "      <th>Cloud_cloud_base_pressure</th>\n",
              "      <th>Cloud_cloud_base_height</th>\n",
              "      <th>Cloud_cloud_optical_depth</th>\n",
              "      <th>Cloud_surface_albedo</th>\n",
              "      <th>Cloud_sensor_azimuth_angle</th>\n",
              "      <th>Cloud_sensor_zenith_angle</th>\n",
              "      <th>Cloud_solar_azimuth_angle</th>\n",
              "      <th>Cloud_solar_zenith_angle</th>\n",
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              "      <th>0</th>\n",
              "      <td>ID_-0.510_29.290_2019_00</td>\n",
              "      <td>-0.51000</td>\n",
              "      <td>29.29000</td>\n",
              "      <td>2019</td>\n",
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              "      <td>-0.00007</td>\n",
              "      <td>0.25567</td>\n",
              "      <td>-98.59389</td>\n",
              "      <td>...</td>\n",
              "      <td>3664.43622</td>\n",
              "      <td>61085.80957</td>\n",
              "      <td>2615.12048</td>\n",
              "      <td>15.56853</td>\n",
              "      <td>0.27229</td>\n",
              "      <td>-12.62899</td>\n",
              "      <td>35.63242</td>\n",
              "      <td>-138.78642</td>\n",
              "      <td>30.75214</td>\n",
              "      <td>3.75099</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ID_-0.510_29.290_2019_01</td>\n",
              "      <td>-0.51000</td>\n",
              "      <td>29.29000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00002</td>\n",
              "      <td>0.72821</td>\n",
              "      <td>0.00001</td>\n",
              "      <td>0.13099</td>\n",
              "      <td>16.59286</td>\n",
              "      <td>...</td>\n",
              "      <td>3651.19031</td>\n",
              "      <td>66969.47873</td>\n",
              "      <td>3174.57242</td>\n",
              "      <td>8.69060</td>\n",
              "      <td>0.25683</td>\n",
              "      <td>30.35938</td>\n",
              "      <td>39.55763</td>\n",
              "      <td>-145.18393</td>\n",
              "      <td>27.25178</td>\n",
              "      <td>4.02518</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ID_-0.510_29.290_2019_02</td>\n",
              "      <td>-0.51000</td>\n",
              "      <td>29.29000</td>\n",
              "      <td>2019</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00051</td>\n",
              "      <td>0.74820</td>\n",
              "      <td>0.00038</td>\n",
              "      <td>0.11002</td>\n",
              "      <td>72.79584</td>\n",
              "      <td>...</td>\n",
              "      <td>4216.98649</td>\n",
              "      <td>60068.89445</td>\n",
              "      <td>3516.28267</td>\n",
              "      <td>21.10341</td>\n",
              "      <td>0.25110</td>\n",
              "      <td>15.37788</td>\n",
              "      <td>30.40182</td>\n",
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              "      <td>26.19330</td>\n",
              "      <td>4.23138</td>\n",
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              "      <th>3</th>\n",
              "      <td>ID_-0.510_29.290_2019_03</td>\n",
              "      <td>-0.51000</td>\n",
              "      <td>29.29000</td>\n",
              "      <td>2019</td>\n",
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              "      <td>0.00000</td>\n",
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              "      <td>0.00000</td>\n",
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              "      <td>5228.50774</td>\n",
              "      <td>51064.54734</td>\n",
              "      <td>4180.97332</td>\n",
              "      <td>15.38690</td>\n",
              "      <td>0.26204</td>\n",
              "      <td>-11.29340</td>\n",
              "      <td>24.38036</td>\n",
              "      <td>-132.66583</td>\n",
              "      <td>28.82915</td>\n",
              "      <td>4.30529</td>\n",
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              "      <th>4</th>\n",
              "      <td>ID_-0.510_29.290_2019_04</td>\n",
              "      <td>-0.51000</td>\n",
              "      <td>29.29000</td>\n",
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            "text/plain": [
              "       ID_LAT_LON_YEAR_WEEK  latitude  longitude  year  week_no  \\\n",
              "0  ID_-0.510_29.290_2022_00  -0.51000   29.29000  2022        0   \n",
              "1  ID_-0.510_29.290_2022_01  -0.51000   29.29000  2022        1   \n",
              "2  ID_-0.510_29.290_2022_02  -0.51000   29.29000  2022        2   \n",
              "3  ID_-0.510_29.290_2022_03  -0.51000   29.29000  2022        3   \n",
              "4  ID_-0.510_29.290_2022_04  -0.51000   29.29000  2022        4   \n",
              "\n",
              "   SulphurDioxide_SO2_column_number_density  \\\n",
              "0                                       NaN   \n",
              "1                                   0.00046   \n",
              "2                                   0.00016   \n",
              "3                                   0.00035   \n",
              "4                                  -0.00032   \n",
              "\n",
              "   SulphurDioxide_SO2_column_number_density_amf  \\\n",
              "0                                           NaN   \n",
              "1                                       0.69116   \n",
              "2                                       0.60511   \n",
              "3                                       0.69692   \n",
              "4                                       0.58053   \n",
              "\n",
              "   SulphurDioxide_SO2_slant_column_number_density  \\\n",
              "0                                             NaN   \n",
              "1                                         0.00032   \n",
              "2                                         0.00011   \n",
              "3                                         0.00024   \n",
              "4                                        -0.00018   \n",
              "\n",
              "   SulphurDioxide_cloud_fraction  SulphurDioxide_sensor_azimuth_angle  ...  \\\n",
              "0                            NaN                                  NaN  ...   \n",
              "1                        0.00000                             76.23920  ...   \n",
              "2                        0.07987                            -42.05534  ...   \n",
              "3                        0.20103                             72.16957  ...   \n",
              "4                        0.20435                             76.19086  ...   \n",
              "\n",
              "   Cloud_cloud_top_pressure  Cloud_cloud_top_height  \\\n",
              "0               36022.02734              8472.31348   \n",
              "1               48539.73724              6476.14732   \n",
              "2               34133.08047              8984.79570   \n",
              "3               50854.99108              6014.72406   \n",
              "4               46594.68514              6849.28048   \n",
              "\n",
              "   Cloud_cloud_base_pressure  Cloud_cloud_base_height  \\\n",
              "0                41047.93750               7472.31348   \n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 79023 entries, 0 to 79022\n",
            "Data columns (total 76 columns):\n",
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            "---  ------                                                    --------------  -----  \n",
            " 0   ID_LAT_LON_YEAR_WEEK                                      79023 non-null  object \n",
            " 1   latitude                                                  79023 non-null  float64\n",
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            " 8   SulphurDioxide_cloud_fraction                             79023 non-null  float64\n",
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            " 11  SulphurDioxide_solar_azimuth_angle                        79023 non-null  float64\n",
            " 12  SulphurDioxide_solar_zenith_angle                         79023 non-null  float64\n",
            " 13  SulphurDioxide_SO2_column_number_density_15km             79023 non-null  float64\n",
            " 14  CarbonMonoxide_CO_column_number_density                   79023 non-null  float64\n",
            " 15  CarbonMonoxide_H2O_column_number_density                  79023 non-null  float64\n",
            " 16  CarbonMonoxide_cloud_height                               79023 non-null  float64\n",
            " 17  CarbonMonoxide_sensor_altitude                            79023 non-null  float64\n",
            " 18  CarbonMonoxide_sensor_azimuth_angle                       79023 non-null  float64\n",
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            " 20  CarbonMonoxide_solar_azimuth_angle                        79023 non-null  float64\n",
            " 21  CarbonMonoxide_solar_zenith_angle                         79023 non-null  float64\n",
            " 22  NitrogenDioxide_NO2_column_number_density                 79023 non-null  float64\n",
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            " 25  NitrogenDioxide_NO2_slant_column_number_density           79023 non-null  float64\n",
            " 26  NitrogenDioxide_tropopause_pressure                       79023 non-null  float64\n",
            " 27  NitrogenDioxide_absorbing_aerosol_index                   79023 non-null  float64\n",
            " 28  NitrogenDioxide_cloud_fraction                            79023 non-null  float64\n",
            " 29  NitrogenDioxide_sensor_altitude                           79023 non-null  float64\n",
            " 30  NitrogenDioxide_sensor_azimuth_angle                      79023 non-null  float64\n",
            " 31  NitrogenDioxide_sensor_zenith_angle                       79023 non-null  float64\n",
            " 32  NitrogenDioxide_solar_azimuth_angle                       79023 non-null  float64\n",
            " 33  NitrogenDioxide_solar_zenith_angle                        79023 non-null  float64\n",
            " 34  Formaldehyde_tropospheric_HCHO_column_number_density      79023 non-null  float64\n",
            " 35  Formaldehyde_tropospheric_HCHO_column_number_density_amf  79023 non-null  float64\n",
            " 36  Formaldehyde_HCHO_slant_column_number_density             79023 non-null  float64\n",
            " 37  Formaldehyde_cloud_fraction                               79023 non-null  float64\n",
            " 38  Formaldehyde_solar_zenith_angle                           79023 non-null  float64\n",
            " 39  Formaldehyde_solar_azimuth_angle                          79023 non-null  float64\n",
            " 40  Formaldehyde_sensor_zenith_angle                          79023 non-null  float64\n",
            " 41  Formaldehyde_sensor_azimuth_angle                         79023 non-null  float64\n",
            " 42  UvAerosolIndex_absorbing_aerosol_index                    79023 non-null  float64\n",
            " 43  UvAerosolIndex_sensor_altitude                            79023 non-null  float64\n",
            " 44  UvAerosolIndex_sensor_azimuth_angle                       79023 non-null  float64\n",
            " 45  UvAerosolIndex_sensor_zenith_angle                        79023 non-null  float64\n",
            " 46  UvAerosolIndex_solar_azimuth_angle                        79023 non-null  float64\n",
            " 47  UvAerosolIndex_solar_zenith_angle                         79023 non-null  float64\n",
            " 48  Ozone_O3_column_number_density                            79023 non-null  float64\n",
            " 49  Ozone_O3_column_number_density_amf                        79023 non-null  float64\n",
            " 50  Ozone_O3_slant_column_number_density                      79023 non-null  float64\n",
            " 51  Ozone_O3_effective_temperature                            79023 non-null  float64\n",
            " 52  Ozone_cloud_fraction                                      79023 non-null  float64\n",
            " 53  Ozone_sensor_azimuth_angle                                79023 non-null  float64\n",
            " 54  Ozone_sensor_zenith_angle                                 79023 non-null  float64\n",
            " 55  Ozone_solar_azimuth_angle                                 79023 non-null  float64\n",
            " 56  Ozone_solar_zenith_angle                                  79023 non-null  float64\n",
            " 57  UvAerosolLayerHeight_aerosol_height                       79023 non-null  float64\n",
            " 58  UvAerosolLayerHeight_aerosol_pressure                     79023 non-null  float64\n",
            " 59  UvAerosolLayerHeight_aerosol_optical_depth                79023 non-null  float64\n",
            " 60  UvAerosolLayerHeight_sensor_zenith_angle                  79023 non-null  float64\n",
            " 61  UvAerosolLayerHeight_sensor_azimuth_angle                 79023 non-null  float64\n",
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            " 63  UvAerosolLayerHeight_solar_zenith_angle                   79023 non-null  float64\n",
            " 64  Cloud_cloud_fraction                                      79023 non-null  float64\n",
            " 65  Cloud_cloud_top_pressure                                  79023 non-null  float64\n",
            " 66  Cloud_cloud_top_height                                    79023 non-null  float64\n",
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            " 68  Cloud_cloud_base_height                                   79023 non-null  float64\n",
            " 69  Cloud_cloud_optical_depth                                 79023 non-null  float64\n",
            " 70  Cloud_surface_albedo                                      79023 non-null  float64\n",
            " 71  Cloud_sensor_azimuth_angle                                79023 non-null  float64\n",
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            " 74  Cloud_solar_zenith_angle                                  79023 non-null  float64\n",
            " 75  emission                                                  79023 non-null  float64\n",
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        }
      ],
      "source": [
        "train=train.fillna(0)\n",
        "display(train.head())\n",
        "display(test.head())\n",
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "8d57e12f",
      "metadata": {
        "id": "8d57e12f"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.svm import SVR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "47c4e0e6",
      "metadata": {
        "id": "47c4e0e6"
      },
      "outputs": [],
      "source": [
        "# Create a LabelEncoder object\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Applying level encoding on each column with categorical data\n",
        "train_ds = train.apply(lambda col: label_encoder.fit_transform(col) if col.dtype == 'object' else col)\n",
        "test_ds = test.apply(lambda col: label_encoder.fit_transform(col) if col.dtype == 'object' else col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "620c6ad4",
      "metadata": {
        "id": "620c6ad4"
      },
      "outputs": [],
      "source": [
        "target = train['emission']\n",
        "X = train_ds.drop(['ID_LAT_LON_YEAR_WEEK','emission'],axis=1)\n",
        "y = target.values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "70d2cf08",
      "metadata": {
        "id": "70d2cf08"
      },
      "outputs": [],
      "source": [
        "# Assuming X contains the features and y contains the labels\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "7b662ba8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b662ba8",
        "outputId": "2ce1bd7e-0643-4bad-f21d-51e345abf3eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 15902.678533477021\n",
            "R-squared Score: 0.033435204945940766\n"
          ]
        }
      ],
      "source": [
        "# Create and train the linear regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model's performance\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Mean Squared Error: {mse}\")\n",
        "print(f\"R-squared Score: {r2}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "bd828d04",
      "metadata": {
        "id": "bd828d04"
      },
      "outputs": [],
      "source": [
        "test=test.fillna(0)\n",
        "test1=test.drop('ID_LAT_LON_YEAR_WEEK',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "6abf65d0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6abf65d0",
        "outputId": "e1633c9b-e7ac-4a6d-b05b-7684889aa511"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 67.81035356,  83.59601545, 112.92190693, ..., 108.21599209,\n",
              "       118.16048442,  58.31823172])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y_pred=model.predict(X_test)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "2762ae70",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2762ae70",
        "outputId": "705625fc-3dd7-49b1-f6db-d33a0808968d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 66894976731.53202\n"
          ]
        }
      ],
      "source": [
        "model = SVR(kernel='linear')  # You can choose different kernel functions (linear, polynomial, etc.)\n",
        "\n",
        "# Train the model on the training data\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test data\n",
        "predicted_values = model.predict(X_test)\n",
        "\n",
        "# Calculate Mean Squared Error\n",
        "mse = mean_squared_error(y_test, predicted_values)\n",
        "print(f\"Mean Squared Error: {mse}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "ea7363f0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ea7363f0",
        "outputId": "fc1f6188-8b2a-496a-92a4-a1b45bfc3a2f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 288059.97045474, -136335.70203061,  183132.33800845, ...,\n",
              "       -110156.70007749, -259993.59875913, -339897.59607358])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "y_pred=model.predict(test1)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8193785d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8193785d",
        "outputId": "f4e4c7b3-8215-48e8-89c4-420d663fe369"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ID_LAT_LON_YEAR_WEEK': array(['ID_-0.510_29.290_2022_00', 'ID_-0.510_29.290_2022_01',\n",
              "        'ID_-0.510_29.290_2022_02', ..., 'ID_-3.299_30.301_2022_46',\n",
              "        'ID_-3.299_30.301_2022_47', 'ID_-3.299_30.301_2022_48'],\n",
              "       dtype=object),\n",
              " 'emission': array([ 288059.97045474, -136335.70203061,  183132.33800845, ...,\n",
              "        -110156.70007749, -259993.59875913, -339897.59607358])}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "submission = {\n",
        "    \"ID_LAT_LON_YEAR_WEEK\": test['ID_LAT_LON_YEAR_WEEK'].values,\n",
        "    \"emission\": y_pred\n",
        "}\n",
        "submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "f89548e3",
      "metadata": {
        "id": "f89548e3"
      },
      "outputs": [],
      "source": [
        "# dic to df convert\n",
        "\n",
        "submission_df = pd.DataFrame(submission)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "39590a47",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "39590a47",
        "outputId": "de7fec7d-799f-47f4-a435-552d48e923e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID_LAT_LON_YEAR_WEEK      emission\n",
              "0  ID_-0.510_29.290_2022_00  288059.97045\n",
              "1  ID_-0.510_29.290_2022_01 -136335.70203\n",
              "2  ID_-0.510_29.290_2022_02  183132.33801\n",
              "3  ID_-0.510_29.290_2022_03   37223.81066\n",
              "4  ID_-0.510_29.290_2022_04  -10868.93348"
            ],
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "submission_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "7c30f017",
      "metadata": {
        "id": "7c30f017"
      },
      "outputs": [],
      "source": [
        "submission_df.to_csv(\"submission.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "244e2b63",
      "metadata": {
        "id": "244e2b63"
      },
      "outputs": [],
      "source": [
        "sub=pd.read_csv('submission.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "209992f7",
      "metadata": {
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