Pycaret_Petrol.ipynb

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            "Successfully built htmlmin databricks-cli pyLDAvis pyod umap-learn pynndescent\n",
            "Installing collected packages: jedi, numpy, urllib3, tangled-up-in-unicode, smmap, scipy, multimethod, llvmlite, websocket-client, visions, srsly, slicer, scikit-learn, requests, pyjwt, plac, numba, Mako, imagehash, gitdb, catalogue, typeguard, thinc, statsmodels, shap, querystring-parser, pyyaml, pynndescent, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, yellowbrick, umap-learn, spacy, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.6\n",
            "    Uninstalling numpy-1.21.6:\n",
            "      Successfully uninstalled numpy-1.21.6\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.7.3\n",
            "    Uninstalling scipy-1.7.3:\n",
            "      Successfully uninstalled scipy-1.7.3\n",
            "  Attempting uninstall: llvmlite\n",
            "    Found existing installation: llvmlite 0.39.1\n",
            "    Uninstalling llvmlite-0.39.1:\n",
            "      Successfully uninstalled llvmlite-0.39.1\n",
            "  Attempting uninstall: srsly\n",
            "    Found existing installation: srsly 2.4.5\n",
            "    Uninstalling srsly-2.4.5:\n",
            "      Successfully uninstalled srsly-2.4.5\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.56.4\n",
            "    Uninstalling numba-0.56.4:\n",
            "      Successfully uninstalled numba-0.56.4\n",
            "  Attempting uninstall: catalogue\n",
            "    Found existing installation: catalogue 2.0.8\n",
            "    Uninstalling catalogue-2.0.8:\n",
            "      Successfully uninstalled catalogue-2.0.8\n",
            "  Attempting uninstall: typeguard\n",
            "    Found existing installation: typeguard 2.7.1\n",
            "    Uninstalling typeguard-2.7.1:\n",
            "      Successfully uninstalled typeguard-2.7.1\n",
            "  Attempting uninstall: thinc\n",
            "    Found existing installation: thinc 8.1.5\n",
            "    Uninstalling thinc-8.1.5:\n",
            "      Successfully uninstalled thinc-8.1.5\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.12.2\n",
            "    Uninstalling statsmodels-0.12.2:\n",
            "      Successfully uninstalled statsmodels-0.12.2\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "  Attempting uninstall: yellowbrick\n",
            "    Found existing installation: yellowbrick 1.5\n",
            "    Uninstalling yellowbrick-1.5:\n",
            "      Successfully uninstalled yellowbrick-1.5\n",
            "  Attempting uninstall: spacy\n",
            "    Found existing installation: spacy 3.4.3\n",
            "    Uninstalling spacy-3.4.3:\n",
            "      Successfully uninstalled spacy-3.4.3\n",
            "  Attempting uninstall: pandas-profiling\n",
            "    Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Attempting uninstall: mlxtend\n",
            "    Found existing installation: mlxtend 0.14.0\n",
            "    Uninstalling mlxtend-0.14.0:\n",
            "      Successfully uninstalled mlxtend-0.14.0\n",
            "  Attempting uninstall: lightgbm\n",
            "    Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Attempting uninstall: imbalanced-learn\n",
            "    Found existing installation: imbalanced-learn 0.8.1\n",
            "    Uninstalling imbalanced-learn-0.8.1:\n",
            "      Successfully uninstalled imbalanced-learn-0.8.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "tensorflow 2.9.2 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "jaxlib 0.3.25+cuda11.cudnn805 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "jax 0.3.25 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "en-core-web-sm 3.4.1 requires spacy<3.5.0,>=3.4.0, but you have spacy 2.3.8 which is incompatible.\n",
            "confection 0.0.3 requires srsly<3.0.0,>=2.4.0, but you have srsly 1.0.6 which is incompatible.\n",
            "cmdstanpy 1.0.8 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\u001b[0m\n",
            "Successfully installed Boruta-0.3 Mako-1.2.4 alembic-1.8.1 catalogue-1.0.2 databricks-cli-0.17.3 docker-6.0.1 funcy-1.17 gitdb-4.0.10 gitpython-3.1.29 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.3.1 imbalanced-learn-0.7.0 jedi-0.18.2 kmodes-0.12.2 lightgbm-3.3.3 llvmlite-0.37.0 mlflow-2.0.1 mlxtend-0.19.0 multimethod-1.9 numba-0.54.1 numpy-1.19.5 pandas-profiling-3.5.0 phik-0.12.2 plac-1.1.3 pyLDAvis-3.2.2 pycaret-2.3.10 pyjwt-2.6.0 pynndescent-0.5.8 pyod-1.0.6 pyyaml-5.4.1 querystring-parser-1.2.4 requests-2.28.1 scikit-learn-0.23.2 scikit-plot-0.3.7 scipy-1.5.4 shap-0.41.0 slicer-0.0.7 smmap-5.0.0 spacy-2.3.8 srsly-1.0.6 statsmodels-0.13.5 tangled-up-in-unicode-0.2.0 thinc-7.4.6 typeguard-2.13.3 umap-learn-0.5.3 urllib3-1.26.13 visions-0.7.5 websocket-client-1.4.2 yellowbrick-1.3.post1\n"
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      "execution_count": null,
      "metadata": {
        "id": "-sLrthLvEinb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('/content/train_data.csv')"
      ],
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['Date'] =pd.to_datetime(df_train['Date'])"
      ],
      "metadata": {
        "id": "ko6JQxglRGK8"
      },
      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  Petrol (USD)\n",
              "0 2003-06-09         74.59\n",
              "1 2003-06-16         74.47\n",
              "2 2003-06-23         74.42\n",
              "3 2003-06-30         74.35\n",
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        "df_train.rename(columns={df_train.columns[1]:'USD'},inplace=True)"
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      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "import plotly.express as px\n",
        "fig = px.line(df_train,x='Date',y=['USD','12-month-avg'],template = 'plotly_dark')\n",
        "fig.show()"
      ],
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    {
      "cell_type": "code",
      "source": [
        "df_train['month'] = [i.month for i in df_train['Date']]\n",
        "df_train['year'] = [i.year for i in df_train['Date']]\n",
        "df_train['week'] = [i.week for i in df_train['Date']]\n",
        "df_train['Series'] = np.arange(1,len(df_train)+1)"
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      "metadata": {
        "id": "ahwzI-8US9Zo"
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      "execution_count": null,
      "outputs": []
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    {
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      "source": [
        "df_train.drop(['12-month-avg','Date'],axis=1,inplace=True)\n",
        "df_train = df_train[['Series','year','month','week','USD']]"
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "   Series  year  month  week    USD\n",
              "0       1  2003      6    24  74.59\n",
              "1       2  2003      6    25  74.47\n",
              "2       3  2003      6    26  74.42\n",
              "3       4  2003      6    27  74.35\n",
              "4       5  2003      7    28  74.28"
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              "\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = df_train[df_train['year']<2018]\n",
        "test = df_train[df_train['year']>=2018]"
      ],
      "metadata": {
        "id": "nu5BlNZgbtwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape,test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAOuK2tlcf12",
        "outputId": "33bc1b64-b7a8-402e-f8e9-bba0172f94e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((760, 5), (53, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.regression import *"
      ],
      "metadata": {
        "id": "witupaZzcsEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s = setup(data =train,test_data =test,target='USD',fold_strategy = 'timeseries',numeric_features = ['year', 'Series'],fold = 3, transform_target = True, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "5c1800868b78447d832af1d9497fee3b",
            "bd09aad84fea4dcda20015426c4b1129",
            "2f3789f466c94e01a636fc46f37eefc6",
            "96744eee64fd429a9a107634a8f8dbc0",
            "4329588ce0194fcb9aa8dae998b0285f",
            "610fae28ff9d41cb99d30c2e0b0c2474"
          ]
        },
        "id": "lym04ryqgQBP",
        "outputId": "c7e919e4-aa9d-44d3-ff37-02c9aa5a48a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id               123\n",
              "1                                   Target               USD\n",
              "2                            Original Data          (760, 5)\n",
              "3                           Missing Values              True\n",
              "4                         Numeric Features                 3\n",
              "5                     Categorical Features                 1\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set         (758, 14)\n",
              "10                    Transformed Test Set          (53, 14)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator   TimeSeriesSplit\n",
              "14                             Fold Number                 3\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU             False\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              39c8\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize             False\n",
              "28                        Normalize Method              None\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels             False\n",
              "36                    Rare Level Threshold              None\n",
              "37                         Numeric Binning             False\n",
              "38                         Remove Outliers             False\n",
              "39                      Outliers Threshold              None\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42             Remove Perfect Collinearity              True\n",
              "43                              Clustering             False\n",
              "44                    Clustering Iteration              None\n",
              "45                     Polynomial Features             False\n",
              "46                       Polynomial Degree              None\n",
              "47                    Trignometry Features             False\n",
              "48                    Polynomial Threshold              None\n",
              "49                          Group Features             False\n",
              "50                       Feature Selection             False\n",
              "51                Feature Selection Method           classic\n",
              "52            Features Selection Threshold              None\n",
              "53                     Feature Interaction             False\n",
              "54                           Feature Ratio             False\n",
              "55                   Interaction Threshold              None\n",
              "56                        Transform Target              True\n",
              "57                 Transform Target Method           box-cox"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13db4e9c-888d-4928-a45f-66047bc0ea3d\">\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
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              "      <th>2</th>\n",
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              "      <td>(760, 5)</td>\n",
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              "      <th>3</th>\n",
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              "      <th>7</th>\n",
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              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(758, 14)</td>\n",
              "    </tr>\n",
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              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
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              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
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              "      <td>Fold Generator</td>\n",
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              "      <th>14</th>\n",
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              "      <td>CPU Jobs</td>\n",
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              "      <td>Use GPU</td>\n",
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              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>39c8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
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              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
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              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
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              "    </tr>\n",
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              "      <th>27</th>\n",
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              "      <th>28</th>\n",
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              "      <th>29</th>\n",
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              "      <th>30</th>\n",
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              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
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              "      <th>33</th>\n",
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              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
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              "    </tr>\n",
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              "      <th>35</th>\n",
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              "      <th>36</th>\n",
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              "      <td>Clustering Iteration</td>\n",
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              "      <th>45</th>\n",
              "      <td>Polynomial Features</td>\n",
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              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Polynomial Degree</td>\n",
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              "      <th>47</th>\n",
              "      <td>Trignometry Features</td>\n",
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              "      <th>48</th>\n",
              "      <td>Polynomial Threshold</td>\n",
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              "      <td>Feature Interaction</td>\n",
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              "      <th>54</th>\n",
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              "    <tr>\n",
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              "      <td>Transform Target Method</td>\n",
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            "INFO:logs:create_model_container: 0\n",
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            "INFO:logs:Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
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            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
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            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:setup() succesfully completed......................................\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models(sort = 'MAE')"
      ],
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            "text/plain": [
              "                                    Model      MAE       MSE     RMSE  \\\n",
              "rf                Random Forest Regressor  14.9150  278.6949  16.6745   \n",
              "dt                Decision Tree Regressor  15.4646  335.8461  18.2370   \n",
              "gbr           Gradient Boosting Regressor  15.7710  305.8409  17.4707   \n",
              "lightgbm  Light Gradient Boosting Machine  15.9754  313.2480  17.6150   \n",
              "et                  Extra Trees Regressor  16.1120  317.5053  17.7652   \n",
              "knn                 K Neighbors Regressor  17.7311  382.7877  19.3428   \n",
              "par          Passive Aggressive Regressor  17.8231  513.2194  19.3619   \n",
              "ada                    AdaBoost Regressor  18.5254  442.7487  20.1369   \n",
              "huber                     Huber Regressor  23.4541  764.2233  24.9623   \n",
              "llar         Lasso Least Angle Regression  23.7609  777.3559  25.2580   \n",
              "dummy                     Dummy Regressor  23.7609  777.3559  25.2580   \n",
              "lasso                    Lasso Regression  29.0255  954.3090  30.3507   \n",
              "\n",
              "               R2   RMSLE    MAPE  TT (Sec)  \n",
              "rf        -5.7470  0.1447  0.1262    0.2200  \n",
              "dt        -6.3477  0.1532  0.1318    0.0233  \n",
              "gbr       -6.1030  0.1528  0.1339    0.0567  \n",
              "lightgbm  -6.3339  0.1511  0.1355    0.2800  \n",
              "et        -6.1380  0.1565  0.1373    0.1833  \n",
              "knn       -9.2365  0.1671  0.1487    0.0300  \n",
              "par      -17.0905  0.1720  0.1429    1.0100  \n",
              "ada      -12.5471  0.1751  0.1531    0.0633  \n",
              "huber    -23.8155  0.2306  0.1907    0.0367  \n",
              "llar     -24.0994  0.2336  0.1933    0.9967  \n",
              "dummy    -24.0994  0.2336  0.1933    0.0233  \n",
              "lasso    -17.5978  0.2894  0.2533    1.0433  "
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
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              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>14.9150</td>\n",
              "      <td>278.6949</td>\n",
              "      <td>16.6745</td>\n",
              "      <td>-5.7470</td>\n",
              "      <td>0.1447</td>\n",
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              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
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              "      <td>-6.3477</td>\n",
              "      <td>0.1532</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>15.7710</td>\n",
              "      <td>305.8409</td>\n",
              "      <td>17.4707</td>\n",
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              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>15.9754</td>\n",
              "      <td>313.2480</td>\n",
              "      <td>17.6150</td>\n",
              "      <td>-6.3339</td>\n",
              "      <td>0.1511</td>\n",
              "      <td>0.1355</td>\n",
              "      <td>0.2800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>16.1120</td>\n",
              "      <td>317.5053</td>\n",
              "      <td>17.7652</td>\n",
              "      <td>-6.1380</td>\n",
              "      <td>0.1565</td>\n",
              "      <td>0.1373</td>\n",
              "      <td>0.1833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>17.7311</td>\n",
              "      <td>382.7877</td>\n",
              "      <td>19.3428</td>\n",
              "      <td>-9.2365</td>\n",
              "      <td>0.1671</td>\n",
              "      <td>0.1487</td>\n",
              "      <td>0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>17.8231</td>\n",
              "      <td>513.2194</td>\n",
              "      <td>19.3619</td>\n",
              "      <td>-17.0905</td>\n",
              "      <td>0.1720</td>\n",
              "      <td>0.1429</td>\n",
              "      <td>1.0100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>18.5254</td>\n",
              "      <td>442.7487</td>\n",
              "      <td>20.1369</td>\n",
              "      <td>-12.5471</td>\n",
              "      <td>0.1751</td>\n",
              "      <td>0.1531</td>\n",
              "      <td>0.0633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>23.4541</td>\n",
              "      <td>764.2233</td>\n",
              "      <td>24.9623</td>\n",
              "      <td>-23.8155</td>\n",
              "      <td>0.2306</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.0367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>23.7609</td>\n",
              "      <td>777.3559</td>\n",
              "      <td>25.2580</td>\n",
              "      <td>-24.0994</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.1933</td>\n",
              "      <td>0.9967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dummy</th>\n",
              "      <td>Dummy Regressor</td>\n",
              "      <td>23.7609</td>\n",
              "      <td>777.3559</td>\n",
              "      <td>25.2580</td>\n",
              "      <td>-24.0994</td>\n",
              "      <td>0.2336</td>\n",
              "      <td>0.1933</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>29.0255</td>\n",
              "      <td>954.3090</td>\n",
              "      <td>30.3507</td>\n",
              "      <td>-17.5978</td>\n",
              "      <td>0.2894</td>\n",
              "      <td>0.2533</td>\n",
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            "INFO:logs:create_model_container: 12\n",
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            "INFO:logs:PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
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            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False)\n",
            "INFO:logs:compare_models() succesfully completed......................................\n"
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        "prediction_holdout = predict_model(best);"
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            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
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            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
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            "INFO:logs:Checking exceptions\n",
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              "                     Model     MAE      MSE    RMSE      R2   RMSLE    MAPE\n",
              "0  Random Forest Regressor  7.5794  84.5679  9.1961 -4.0275  0.0746  0.0594"
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    {
      "cell_type": "code",
      "source": [
        "# generate predictions on the original dataset**\n",
        "predictions = predict_model(best, data=df_train)\n",
        "\n",
        "# add a date column in the dataset**\n",
        "predictions['Date'] = pd.date_range(start='2003-06-09', end = '2018-12-31', freq = 'W-MON')\n",
        "\n",
        "# line plot**\n",
        "fig = px.line(predictions, x='Date', y=[\"USD\", \"Label\"], template = 'plotly_dark')\n",
        "\n",
        "# add a vertical rectange for test-set separation**\n",
        "fig.add_vrect(x0=\"2018-01-01\", x1=\"2018-12-31\", fillcolor=\"grey\", opacity=0.25, line_width=0)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "V_cJkEXyhn3X",
        "outputId": "d8018c59-4700-47f1-b075-fe2beb32acf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False), probability_threshold=None, encoded_labels=True, drift_report=False, raw_score=False, round=4, verbose=True, ml_usecase=MLUsecase.REGRESSION, display=None, drift_kwargs=None)\n",
            "INFO:logs:Checking exceptions\n",
            "INFO:logs:Preloading libraries\n",
            "INFO:logs:Preparing display monitor\n"
          ]
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        {
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          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "final_best = finalize_model(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePqnbZBtmaA1",
        "outputId": "4b34adc2-f52b-4c5f-c301-229b6d847cae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing finalize_model()\n",
            "INFO:logs:finalize_model(estimator=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False), fit_kwargs=None, groups=None, model_only=True, display=None, experiment_custom_tags=None, return_train_score=False)\n",
            "INFO:logs:Finalizing PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False)\n",
            "INFO:logs:Initializing create_model()\n",
            "INFO:logs:create_model(estimator=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, verbose=False, system=False, metrics=None, experiment_custom_tags=None, add_to_model_list=False, probability_threshold=None, display=None, return_train_score=False, kwargs={})\n",
            "INFO:logs:Checking exceptions\n",
            "INFO:logs:Importing libraries\n",
            "INFO:logs:Copying training dataset\n",
            "INFO:logs:Defining folds\n",
            "INFO:logs:Declaring metric variables\n",
            "INFO:logs:Importing untrained model\n",
            "INFO:logs:Declaring custom model\n",
            "INFO:logs:Random Forest Regressor Imported succesfully\n",
            "INFO:logs:Starting cross validation\n",
            "INFO:logs:Cross validating with TimeSeriesSplit(max_train_size=None, n_splits=3), n_jobs=-1\n",
            "INFO:logs:Calculating mean and std\n",
            "INFO:logs:Creating metrics dataframe\n",
            "INFO:logs:Finalizing model\n",
            "INFO:logs:create_model_container: 12\n",
            "INFO:logs:master_model_container: 12\n",
            "INFO:logs:display_container: 5\n",
            "INFO:logs:PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False)\n",
            "INFO:logs:create_model() succesfully completed......................................\n",
            "INFO:logs:create_model_container: 12\n",
            "INFO:logs:master_model_container: 12\n",
            "INFO:logs:display_container: 4\n",
            "INFO:logs:PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False)\n",
            "INFO:logs:finalize_model() succesfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future_dates = pd.date_range(start = '2019-01-01', end = '2020-04-30', freq = 'W-MON')\n",
        "#1st Jan 2019 to April 2020\n",
        "   \n"
      ],
      "metadata": {
        "id": "7NPBGWEimnE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future_dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lsj0JHABzy-q",
        "outputId": "2691be3c-5fdc-4431-dcff-239dccca7529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2019-01-07', '2019-01-14', '2019-01-21', '2019-01-28',\n",
              "               '2019-02-04', '2019-02-11', '2019-02-18', '2019-02-25',\n",
              "               '2019-03-04', '2019-03-11', '2019-03-18', '2019-03-25',\n",
              "               '2019-04-01', '2019-04-08', '2019-04-15', '2019-04-22',\n",
              "               '2019-04-29', '2019-05-06', '2019-05-13', '2019-05-20',\n",
              "               '2019-05-27', '2019-06-03', '2019-06-10', '2019-06-17',\n",
              "               '2019-06-24', '2019-07-01', '2019-07-08', '2019-07-15',\n",
              "               '2019-07-22', '2019-07-29', '2019-08-05', '2019-08-12',\n",
              "               '2019-08-19', '2019-08-26', '2019-09-02', '2019-09-09',\n",
              "               '2019-09-16', '2019-09-23', '2019-09-30', '2019-10-07',\n",
              "               '2019-10-14', '2019-10-21', '2019-10-28', '2019-11-04',\n",
              "               '2019-11-11', '2019-11-18', '2019-11-25', '2019-12-02',\n",
              "               '2019-12-09', '2019-12-16', '2019-12-23', '2019-12-30',\n",
              "               '2020-01-06', '2020-01-13', '2020-01-20', '2020-01-27',\n",
              "               '2020-02-03', '2020-02-10', '2020-02-17', '2020-02-24',\n",
              "               '2020-03-02', '2020-03-09', '2020-03-16', '2020-03-23',\n",
              "               '2020-03-30', '2020-04-06', '2020-04-13', '2020-04-20',\n",
              "               '2020-04-27'],\n",
              "              dtype='datetime64[ns]', freq='W-MON')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future_df = pd.DataFrame()\n",
        "\n"
      ],
      "metadata": {
        "id": "vCgHAKq2zfIS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future_df['month'] = [i.month for i in future_dates]\n",
        "future_df['year'] = [i.year for i in future_dates] \n",
        "future_df['week'] = [i.week for i in future_dates] "
      ],
      "metadata": {
        "id": "eazWF9vC1Quh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "df_train['Series'].tail()"
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        "id": "Eh1_N2atdjLT",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "808    809\n",
              "809    810\n",
              "810    811\n",
              "811    812\n",
              "812    813\n",
              "Name: Series, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(future_dates)"
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      "metadata": {
        "colab": {
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        "id": "4h6EBojCdp5y",
        "outputId": "d31c5b51-48af-4305-e73b-f2bc054213c6"
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      "outputs": [
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              "69"
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          "metadata": {},
          "execution_count": 27
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    {
      "cell_type": "code",
      "source": [
        "future_df['Series'] = list(range(814,883))"
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      "metadata": {
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              "    month  year  week  Series\n",
              "0       1  2019     2     814\n",
              "1       1  2019     3     815\n",
              "2       1  2019     4     816\n",
              "3       1  2019     5     817\n",
              "4       2  2019     6     818\n",
              "..    ...   ...   ...     ...\n",
              "64      3  2020    14     878\n",
              "65      4  2020    15     879\n",
              "66      4  2020    16     880\n",
              "67      4  2020    17     881\n",
              "68      4  2020    18     882\n",
              "\n",
              "[69 rows x 4 columns]"
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      "execution_count": null,
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        {
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          "name": "stderr",
          "text": [
            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
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              "   month  year  week  Series       Label\n",
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              "2      1  2019     4     816  119.160391\n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "2019-04-28"
      ],
      "metadata": {
        "id": "ZUocxtX624Y5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "concat_df = pd.concat([df_train,predictions_future], axis=0)\n",
        "concat_df_i = pd.date_range(start='2003-06-09', end = '2020-04-30', freq = 'W-MON')\n",
        "concat_df.set_index(concat_df_i, inplace=True)\n",
        "fig = px.line(concat_df, x=concat_df.index, y=[\"USD\", \"Label\"], template = 'plotly_dark')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "yn9TndMA1sYu",
        "outputId": "47c4e8a3-0349-4ccd-af99-8e4fb444864b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
      "cell_type": "code",
      "source": [
        "save_model(final_best, 'model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOjfeley4RN-",
        "outputId": "9ec860c8-2b92-4484-8ad6-f7dc941d96a7"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing save_model()\n",
            "INFO:logs:save_model(model=PowerTransformedTargetRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
            "                                max_depth=None, max_features='auto',\n",
            "                                max_leaf_nodes=None, max_samples=None,\n",
            "                                min_impurity_decrease=0.0,\n",
            "                                min_impurity_split=None, min_samples_leaf=1,\n",
            "                                min_samples_split=2,\n",
            "                                min_weight_fraction_leaf=0.0, n_estimators=100,\n",
            "                                n_jobs=-1, oob_score=False,\n",
            "                                power_transformer_method='box-cox',\n",
            "                                power...\n",
            "                                regressor=RandomForestRegressor(bootstrap=True,\n",
            "                                                                ccp_alpha=0.0,\n",
            "                                                                criterion='mse',\n",
            "                                                                max_depth=None,\n",
            "                                                                max_features='auto',\n",
            "                                                                max_leaf_nodes=None,\n",
            "                                                                max_samples=None,\n",
            "                                                                min_impurity_decrease=0.0,\n",
            "                                                                min_impurity_split=None,\n",
            "                                                                min_samples_leaf=1,\n",
            "                                                                min_samples_split=2,\n",
            "                                                                min_weight_fraction_leaf=0.0,\n",
            "                                                                n_estimators=100,\n",
            "                                                                n_jobs=-1,\n",
            "                                                                oob_score=False,\n",
            "                                                                random_state=123,\n",
            "                                                                verbose=0,\n",
            "                                                                warm_start=False),\n",
            "                                verbose=0, warm_start=False), model_name=model, prep_pipe_=Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
            "                ('cluster_all', 'passthrough'),\n",
            "                ('dummy', Dummify(target='USD')),\n",
            "                ('fix_perfect', Remove_100(target='USD')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False), verbose=True, kwargs={})\n",
            "INFO:logs:Adding model into prep_pipe\n",
            "INFO:logs:model.pkl saved in current working directory\n",
            "INFO:logs:Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                                                                                 ccp_alpha=0.0,\n",
            "                                                                                 criterion='mse',\n",
            "                                                                                 max_depth=None,\n",
            "                                                                                 max_features='auto',\n",
            "                                                                                 max_leaf_nodes=None,\n",
            "                                                                                 max_samples=None,\n",
            "                                                                                 min_impurity_decrease=0.0,\n",
            "                                                                                 min_impurity_split=None,\n",
            "                                                                                 min_samples_leaf=1,\n",
            "                                                                                 min_samples_split=2,\n",
            "                                                                                 min_weight_fraction_leaf=0.0,\n",
            "                                                                                 n_estimators=100,\n",
            "                                                                                 n_jobs=-1,\n",
            "                                                                                 oob_score=False,\n",
            "                                                                                 random_state=123,\n",
            "                                                                                 verbose=0,\n",
            "                                                                                 warm_start=False),\n",
            "                                                 verbose=0,\n",
            "                                                 warm_start=False)]],\n",
            "         verbose=False)\n",
            "INFO:logs:save_model() successfully completed......................................\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Saved\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=None,\n",
              "          steps=[('dtypes',\n",
              "                  DataTypes_Auto_infer(categorical_features=[],\n",
              "                                       display_types=True, features_todrop=[],\n",
              "                                       id_columns=[], ml_usecase='regression',\n",
              "                                       numerical_features=['year', 'Series'],\n",
              "                                       target='USD', time_features=[])),\n",
              "                 ('imputer',\n",
              "                  Simple_Imputer(categorical_strategy='not_available',\n",
              "                                 fill_value_categorical=None,\n",
              "                                 fill_value_numerical=None,\n",
              "                                 numer...\n",
              "                                                                                  ccp_alpha=0.0,\n",
              "                                                                                  criterion='mse',\n",
              "                                                                                  max_depth=None,\n",
              "                                                                                  max_features='auto',\n",
              "                                                                                  max_leaf_nodes=None,\n",
              "                                                                                  max_samples=None,\n",
              "                                                                                  min_impurity_decrease=0.0,\n",
              "                                                                                  min_impurity_split=None,\n",
              "                                                                                  min_samples_leaf=1,\n",
              "                                                                                  min_samples_split=2,\n",
              "                                                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                                                  n_estimators=100,\n",
              "                                                                                  n_jobs=-1,\n",
              "                                                                                  oob_score=False,\n",
              "                                                                                  random_state=123,\n",
              "                                                                                  verbose=0,\n",
              "                                                                                  warm_start=False),\n",
              "                                                  verbose=0,\n",
              "                                                  warm_start=False)]],\n",
              "          verbose=False), 'model.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_model = load_model('/content/model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cNqk0O3wmdA",
        "outputId": "45ac86dd-f821-4e23-ead4-e134f38d91a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing load_model()\n",
            "INFO:logs:load_model(model_name=/content/model, platform=None, authentication=None, verbose=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_data = pd.read_csv('/content/sample_submission.csv')"
      ],
      "metadata": {
        "id": "lKYYCMmJyANq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_data['Date'] =pd.to_datetime(unseen_data['Date'])"
      ],
      "metadata": {
        "id": "GvuTifka4BqU"
      },
      "execution_count": null,
      "outputs": []
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        "unseen_data.dropna(axis=1,inplace=True)"
      ],
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            "text/plain": [
              "        Date  Prediction\n",
              "0 2019-01-01         100\n",
              "1 2019-01-02         100\n",
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              "3 2019-01-04         100\n",
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    {
      "cell_type": "code",
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        "unseen_data['month'] = [i.month for i in unseen_data['Date']]\n",
        "unseen_data['year'] = [i.year for i in unseen_data['Date']]\n",
        "unseen_data['week'] = [i.week for i in unseen_data['Date']]\n",
        "unseen_data['Series'] = np.arange(1,len(unseen_data)+1)"
      ],
      "metadata": {
        "id": "r8r_t0C00Q-7"
      },
      "execution_count": null,
      "outputs": []
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        "unseen_data.head()"
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              "        Date  Prediction  month  year  week  Series\n",
              "0 2019-01-01         100      1  2019     1       1\n",
              "1 2019-01-02         100      1  2019     1       2\n",
              "2 2019-01-03         100      1  2019     1       3\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b981ef0d-d8af-4484-803a-e3c15d828d0f');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXKRNwq0xja3",
        "outputId": "fd20d364-f464-4cc4-f2ed-18bdee1c37b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.23.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecasting = predict_model(my_model,unseen_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5_dkvpkyaWF",
        "outputId": "26b91f69-0924-40cc-a788-0815629c3eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing predict_model()\n",
            "INFO:logs:predict_model(estimator=Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                                                                                 ccp_alpha=0.0,\n",
            "                                                                                 criterion='mse',\n",
            "                                                                                 max_depth=None,\n",
            "                                                                                 max_features='auto',\n",
            "                                                                                 max_leaf_nodes=None,\n",
            "                                                                                 max_samples=None,\n",
            "                                                                                 min_impurity_decrease=0.0,\n",
            "                                                                                 min_impurity_split=None,\n",
            "                                                                                 min_samples_leaf=1,\n",
            "                                                                                 min_samples_split=2,\n",
            "                                                                                 min_weight_fraction_leaf=0.0,\n",
            "                                                                                 n_estimators=100,\n",
            "                                                                                 n_jobs=-1,\n",
            "                                                                                 oob_score=False,\n",
            "                                                                                 random_state=123,\n",
            "                                                                                 verbose=0,\n",
            "                                                                                 warm_start=False),\n",
            "                                                 verbose=0,\n",
            "                                                 warm_start=False)]],\n",
            "         verbose=False), probability_threshold=None, encoded_labels=True, drift_report=False, raw_score=False, round=4, verbose=True, ml_usecase=MLUsecase.REGRESSION, display=None, drift_kwargs=None)\n",
            "INFO:logs:Checking exceptions\n",
            "INFO:logs:Preloading libraries\n",
            "INFO:logs:Preparing display monitor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecasting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "_TfZLko06ACx",
        "outputId": "0db55fe9-7efd-4e7f-e319-d1084ba7c876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date  Prediction  month  year  week  Series       Label\n",
              "0  2019-01-01         100      1  2019     1       1  117.396901\n",
              "1  2019-01-02         100      1  2019     1       2  117.396901\n",
              "2  2019-01-03         100      1  2019     1       3  117.396901\n",
              "3  2019-01-04         100      1  2019     1       4  117.396901\n",
              "4  2019-01-05         100      1  2019     1       5  117.396901\n",
              "5  2019-01-06         100      1  2019     1       6  117.396901\n",
              "6  2019-01-07         100      1  2019     2       7  117.847794\n",
              "7  2019-01-08         100      1  2019     2       8  117.847794\n",
              "8  2019-01-09         100      1  2019     2       9  117.847794\n",
              "9  2019-01-10         100      1  2019     2      10  117.847794\n",
              "10 2019-01-11         100      1  2019     2      11  117.847794\n",
              "11 2019-01-12         100      1  2019     2      12  117.847794\n",
              "12 2020-01-01         100      1  2020     1      13  117.396901\n",
              "13 2020-01-02         100      1  2020     1      14  117.396901\n",
              "14 2020-01-03         100      1  2020     1      15  117.396901\n",
              "15 2020-01-04         100      1  2020     1      16  117.396901"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install fastapi"
      ],
      "metadata": {
        "id": "X_lB-QF2pXq7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install uvicorn"
      ],
      "metadata": {
        "id": "0PIy2xPnpe_F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_api(my_model,'petrol-forecast')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-MsJ_9EpDcZ",
        "outputId": "9bb19ca5-d087-4032-dd13-a71fea65a31e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing get_config()\n",
            "INFO:logs:get_config(variable=prep_pipe)\n",
            "INFO:logs:Global variable: prep_pipe returned as Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
            "                ('cluster_all', 'passthrough'),\n",
            "                ('dummy', Dummify(target='USD')),\n",
            "                ('fix_perfect', Remove_100(target='USD')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:get_config() succesfully completed......................................\n",
            "INFO:logs:Initializing get_config()\n",
            "INFO:logs:get_config(variable=data_before_preprocess)\n",
            "INFO:logs:Global variable: data_before_preprocess returned as      Series  year  month  week     USD\n",
            "0         1  2003      6    24   74.59\n",
            "1         2  2003      6    25   74.47\n",
            "2         3  2003      6    26   74.42\n",
            "3         4  2003      6    27   74.35\n",
            "4         5  2003      7    28   74.28\n",
            "..      ...   ...    ...   ...     ...\n",
            "755     756  2017     11    48  120.13\n",
            "756     757  2017     12    49  119.75\n",
            "757     758  2017     12    50  119.91\n",
            "758     759  2017     12    51  119.98\n",
            "759     760  2017     12    52  120.00\n",
            "\n",
            "[760 rows x 5 columns]\n",
            "INFO:logs:get_config() succesfully completed......................................\n",
            "INFO:logs:Initializing get_config()\n",
            "INFO:logs:get_config(variable=prep_pipe)\n",
            "INFO:logs:Global variable: prep_pipe returned as Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
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            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
            "                ('cluster_all', 'passthrough'),\n",
            "                ('dummy', Dummify(target='USD')),\n",
            "                ('fix_perfect', Remove_100(target='USD')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:get_config() succesfully completed......................................\n",
            "INFO:logs:Initializing get_config()\n",
            "INFO:logs:get_config(variable=prep_pipe)\n",
            "INFO:logs:Global variable: prep_pipe returned as Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
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            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
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            "                                numer...\n",
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            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:get_config() succesfully completed......................................\n",
            "INFO:logs:Initializing get_config()\n",
            "INFO:logs:get_config(variable=prep_pipe)\n",
            "INFO:logs:Global variable: prep_pipe returned as Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
            "                ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
            "                ('cluster_all', 'passthrough'),\n",
            "                ('dummy', Dummify(target='USD')),\n",
            "                ('fix_perfect', Remove_100(target='USD')),\n",
            "                ('clean_names', Clean_Colum_Names()),\n",
            "                ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False)\n",
            "INFO:logs:get_config() succesfully completed......................................\n",
            "INFO:logs:Initializing save_model()\n",
            "INFO:logs:save_model(model=Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                                                                                 ccp_alpha=0.0,\n",
            "                                                                                 criterion='mse',\n",
            "                                                                                 max_depth=None,\n",
            "                                                                                 max_features='auto',\n",
            "                                                                                 max_leaf_nodes=None,\n",
            "                                                                                 max_samples=None,\n",
            "                                                                                 min_impurity_decrease=0.0,\n",
            "                                                                                 min_impurity_split=None,\n",
            "                                                                                 min_samples_leaf=1,\n",
            "                                                                                 min_samples_split=2,\n",
            "                                                                                 min_weight_fraction_leaf=0.0,\n",
            "                                                                                 n_estimators=100,\n",
            "                                                                                 n_jobs=-1,\n",
            "                                                                                 oob_score=False,\n",
            "                                                                                 random_state=123,\n",
            "                                                                                 verbose=0,\n",
            "                                                                                 warm_start=False),\n",
            "                                                 verbose=0,\n",
            "                                                 warm_start=False)]],\n",
            "         verbose=False), model_name=petrol-forecast, prep_pipe_=Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
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            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
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            "                ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
            "         verbose=False), verbose=False, kwargs={})\n",
            "INFO:logs:Adding model into prep_pipe\n",
            "WARNING:logs:Only Model saved as it was a pipeline.\n",
            "INFO:logs:petrol-forecast.pkl saved in current working directory\n",
            "INFO:logs:Pipeline(memory=None,\n",
            "         steps=[('dtypes',\n",
            "                 DataTypes_Auto_infer(categorical_features=[],\n",
            "                                      display_types=True, features_todrop=[],\n",
            "                                      id_columns=[], ml_usecase='regression',\n",
            "                                      numerical_features=['year', 'Series'],\n",
            "                                      target='USD', time_features=[])),\n",
            "                ('imputer',\n",
            "                 Simple_Imputer(categorical_strategy='not_available',\n",
            "                                fill_value_categorical=None,\n",
            "                                fill_value_numerical=None,\n",
            "                                numer...\n",
            "                                                                                 ccp_alpha=0.0,\n",
            "                                                                                 criterion='mse',\n",
            "                                                                                 max_depth=None,\n",
            "                                                                                 max_features='auto',\n",
            "                                                                                 max_leaf_nodes=None,\n",
            "                                                                                 max_samples=None,\n",
            "                                                                                 min_impurity_decrease=0.0,\n",
            "                                                                                 min_impurity_split=None,\n",
            "                                                                                 min_samples_leaf=1,\n",
            "                                                                                 min_samples_split=2,\n",
            "                                                                                 min_weight_fraction_leaf=0.0,\n",
            "                                                                                 n_estimators=100,\n",
            "                                                                                 n_jobs=-1,\n",
            "                                                                                 oob_score=False,\n",
            "                                                                                 random_state=123,\n",
            "                                                                                 verbose=0,\n",
            "                                                                                 warm_start=False),\n",
            "                                                 verbose=0,\n",
            "                                                 warm_start=False)]],\n",
            "         verbose=False)\n",
            "INFO:logs:save_model() successfully completed......................................\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "API sucessfully created. This function only creates a POST API, it doesn't run it automatically.\n",
            "\n",
            "To run your API, please run this command --> !python petrol-forecast.py\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python petrol-forecast.py"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iSw584dptND",
        "outputId": "fafe6a0f-cb48-476b-f713-bae28dd107bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n",
            "\u001b[32mINFO\u001b[0m:     Started server process [\u001b[36m555\u001b[0m]\n",
            "\u001b[32mINFO\u001b[0m:     Waiting for application startup.\n",
            "\u001b[32mINFO\u001b[0m:     Application startup complete.\n",
            "\u001b[32mINFO\u001b[0m:     Uvicorn running on \u001b[1mhttp://127.0.0.1:8000\u001b[0m (Press CTRL+C to quit)\n",
            "\u001b[32mINFO\u001b[0m:     Shutting down\n",
            "\u001b[32mINFO\u001b[0m:     Finished server process [\u001b[36m555\u001b[0m]\n",
            "\u001b[31mERROR\u001b[0m:    Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/starlette/routing.py\", line 674, in lifespan\n",
            "    await receive()\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/uvicorn/lifespan/on.py\", line 137, in receive\n",
            "    return await self.receive_queue.get()\n",
            "  File \"/usr/lib/python3.8/asyncio/queues.py\", line 163, in get\n",
            "    await getter\n",
            "asyncio.exceptions.CancelledError\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(forecasting)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Fks327nrEPu",
        "outputId": "090f2028-3e99-46a7-da28-fcd3f87ef626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fo1wyrCnzxNj",
        "outputId": "078d67f9-4834-4b9d-b7ac-6d55afc9d416"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.8.15\n"
          ]
        }
      ]
    }
  ]
}