6) Predict_the_future_download_rate_of_a_python_ML_module.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "In this task, I will use a publicly available dataset to predict the future download rate of a python ML module called pycaret"
      ],
      "metadata": {
        "id": "Uly7RNvYRQfB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#We first install  pycaret\n",
        "!pip install pycaret-ts-alpha"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hbd0dgbZSA7w",
        "outputId": "02eebbe7-14aa-48d9-d694-abb3160f0bf1"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "4pMRSPoKQp33"
      },
      "outputs": [],
      "source": [
        "#Import important modules\n",
        "import pandas as pd\n",
        "from pycaret.datasets import get_data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Load data and display first ten rows\n",
        "Caret_df = get_data('pycaret_downloads')\n",
        "Caret_df.head(5)"
      ],
      "metadata": {
        "colab": {
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            "INFO:logs:Preparing display monitor\n"
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              "        Date  Total\n",
              "0   7/8/2021   3773\n",
              "1   7/9/2021   3172\n",
              "2  7/10/2021   2079\n",
              "3  7/11/2021   2725\n",
              "4  7/12/2021   4542"
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              "      <td>3172</td>\n",
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              "            '<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",
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              "        Date  Total\n",
              "0   7/8/2021   3773\n",
              "1   7/9/2021   3172\n",
              "2  7/10/2021   2079\n",
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              "      <td>7/11/2021</td>\n",
              "      <td>2725</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summary of data info\n",
        "Caret_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6FCaiOJTROw",
        "outputId": "148c56e1-8e30-4bb4-b534-d57b841bb591"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 90 entries, 0 to 89\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   Date    90 non-null     object\n",
            " 1   Total   90 non-null     int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 1.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting the date to appropriate format \n",
        "Caret_df['Date'] = pd.to_datetime(Caret_df['Date'])\n",
        "Caret_df = Caret_df.groupby('Date').sum()"
      ],
      "metadata": {
        "id": "6Rxg4RVGTRUO"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Caret_df = Caret_df.asfreq('D')\n",
        "Caret_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Wkp4Bv1oTRZN",
        "outputId": "689219cd-8a9b-4a6e-8e68-b8c04dd2e045"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Total\n",
              "Date             \n",
              "2021-07-08   3773\n",
              "2021-07-09   3172\n",
              "2021-07-10   2079\n",
              "2021-07-11   2725\n",
              "2021-07-12   4542"
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              "      <th>2021-07-08</th>\n",
              "      <td>3773</td>\n",
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              "    <tr>\n",
              "      <th>2021-07-09</th>\n",
              "      <td>3172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-10</th>\n",
              "      <td>2079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-11</th>\n",
              "      <td>2725</td>\n",
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              "    <tr>\n",
              "      <th>2021-07-12</th>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-74f42905-1200-499f-893e-2fe02cbdc7c4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-74f42905-1200-499f-893e-2fe02cbdc7c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
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          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Display data shape\n",
        "Caret_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDddVbuSUsfe",
        "outputId": "ec7ecd95-23d4-4d55-f1c2-c0ad4563ff0e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the data\n",
        "Caret_df.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "ZQKSykbSTRkG",
        "outputId": "b0d8430e-e8ee-4b9b-86b7-e5817231958b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessing the data\n",
        "from pycaret.time_series import *\n",
        "#from pycaret.internal.pycaret_experiment import TimeSeriesExperiment\n",
        "#exp = TimeSeriesExperiment()\n",
        "exp = setup(Caret_df, fh = 7, fold = 3, session_id = 123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QsneBQ8eVDgd",
        "outputId": "71973166-cfbe-4f21-fa27-9f6b180c017e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:PyCaret TSForecastingExperiment\n",
            "INFO:logs:Logging name: ts-default-name\n",
            "INFO:logs:ML Usecase: MLUsecase.TIME_SERIES\n",
            "INFO:logs:version 3.0.0\n",
            "INFO:logs:Initializing setup()\n",
            "INFO:logs:self.USI: 282d\n",
            "INFO:logs:self.variable_keys: {'_all_metrics', 'exp_name_log', 'fold_generator', 'fh', 'n_jobs_param', 'seasonal_period', 'seasonality_present', 'variable_keys', 'pipeline', 'log_plots_param', 'experiment__', 'html_param', 'approach_type', '_all_models', 'exogenous_present', 'X_transformed', 'X_test_transformed', '_all_models_internal', 'y_test_transformed', 'X', 'exp_id', 'y_transformed', 'X_train', 'gpu_param', 'y', 'USI', 'display_container', 'y_test', 'y_train_transformed', 'X_test', 'all_sp_values', 'index_type', 'enforce_exogenous', 'logging_param', 'data', '_available_plots', 'strictly_positive', '_ml_usecase', '_gpu_n_jobs_param', 'idx', 'X_train_transformed', 'enforce_pi', 'fold_param', 'y_train', 'fold_groups_param_full', 'primary_sp_to_use', 'seed', 'master_model_container', 'memory'}\n",
            "INFO:logs:Checking environment\n",
            "INFO:logs:python_version: 3.7.14\n",
            "INFO:logs:python_build: ('default', 'Sep  8 2022 00:06:44')\n",
            "INFO:logs:machine: x86_64\n",
            "INFO:logs:platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\n",
            "INFO:logs:Memory: svmem(total=13616361472, available=12122587136, percent=11.0, used=1277419520, free=7923077120, active=562089984, inactive=4788858880, buffers=158593024, cached=4257271808, shared=1286144, slab=265199616)\n",
            "INFO:logs:Physical Core: 1\n",
            "INFO:logs:Logical Core: 2\n",
            "INFO:logs:Checking libraries\n",
            "INFO:logs:pd==1.3.5\n",
            "INFO:logs:numpy==1.21.6\n",
            "INFO:logs:sklearn==1.0.2\n",
            "INFO:logs:lightgbm==3.3.2\n",
            "INFO:logs:xgboost==0.90\n",
            "WARNING:logs:catboost not found\n",
            "WARNING:logs:mlflow not found\n",
            "INFO:logs:Set Forecast Horizon.\n",
            "INFO:logs:Set up Train-Test Splits.\n",
            "INFO:logs:Preparing preprocessing pipeline...\n",
            "INFO:logs:Finished creating preprocessing pipeline.\n",
            "INFO:logs:Pipeline: PyCaretForecastingPipeline(steps=[('forecaster',\n",
            "                                   TransformedTargetForecaster(steps=[('model',\n",
            "                                                                       DummyForecaster())]))])\n",
            "INFO:logs:Set up Seasonal Period.\n",
            "INFO:logs:Set up whether Multiplicative components allowed.\n",
            "INFO:logs:Creating final display dataframe.\n",
            "INFO:logs:Setup Display Container:                     Description                    Value\n",
            "0                    session_id                      123\n",
            "1                        Target                    Total\n",
            "2                      Approach               Univariate\n",
            "3           Exogenous Variables              Not Present\n",
            "4                    Data shape                  (90, 1)\n",
            "5              Train data shape                  (83, 1)\n",
            "6               Test data shape                   (7, 1)\n",
            "7                Fold Generator  ExpandingWindowSplitter\n",
            "8                   Fold Number                        3\n",
            "9   Enforce Prediction Interval                    False\n",
            "10    Seasonal Period(s) Tested                        7\n",
            "11          Seasonality Present                     True\n",
            "12       Seasonalities Detected                      [7]\n",
            "13          Primary Seasonality                        7\n",
            "14     Target Strictly Positive                     True\n",
            "15           Target White Noise                       No\n",
            "16                Recommended d                        1\n",
            "17       Recommended Seasonal D                        1\n",
            "18               Missing Values                        0\n",
            "19                   Preprocess                     True\n",
            "20                     CPU Jobs                       -1\n",
            "21                      Use GPU                    False\n",
            "22               Log Experiment                    False\n",
            "23              Experiment Name          ts-default-name\n",
            "24                          USI                     282d\n",
            "INFO:logs:Preparing display monitor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    Description                    Value\n",
              "0                    session_id                      123\n",
              "1                        Target                    Total\n",
              "2                      Approach               Univariate\n",
              "3           Exogenous Variables              Not Present\n",
              "4                    Data shape                  (90, 1)\n",
              "5              Train data shape                  (83, 1)\n",
              "6               Test data shape                   (7, 1)\n",
              "7                Fold Generator  ExpandingWindowSplitter\n",
              "8                   Fold Number                        3\n",
              "9   Enforce Prediction Interval                    False\n",
              "10    Seasonal Period(s) Tested                        7\n",
              "11          Seasonality Present                     True\n",
              "12       Seasonalities Detected                      [7]\n",
              "13          Primary Seasonality                        7\n",
              "14     Target Strictly Positive                     True\n",
              "15           Target White Noise                       No\n",
              "16                Recommended d                        1\n",
              "17       Recommended Seasonal D                        1\n",
              "18               Missing Values                        0\n",
              "19                   Preprocess                     True\n",
              "20                     CPU Jobs                       -1\n",
              "21                      Use GPU                    False\n",
              "22               Log Experiment                    False\n",
              "23              Experiment Name          ts-default-name\n",
              "24                          USI                     282d"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efac6357-b191-45b3-9642-8ebf59c91c43\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Total</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Approach</td>\n",
              "      <td>Univariate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Exogenous Variables</td>\n",
              "      <td>Not Present</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Data shape</td>\n",
              "      <td>(90, 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Train data shape</td>\n",
              "      <td>(83, 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Test data shape</td>\n",
              "      <td>(7, 1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>ExpandingWindowSplitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Enforce Prediction Interval</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Seasonal Period(s) Tested</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Seasonality Present</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Seasonalities Detected</td>\n",
              "      <td>[7]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Primary Seasonality</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Target Strictly Positive</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Target White Noise</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Recommended d</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Recommended Seasonal D</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Preprocess</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>ts-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>USI</td>\n",
              "      <td>282d</td>\n",
              "    </tr>\n",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:logs:Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost==(0, 90)\n",
            "WARNING:logs:Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost==(0, 90)\n",
            "WARNING:logs:Couldn't import catboost.CatBoostRegressor\n",
            "WARNING:logs:Couldn't import catboost.CatBoostRegressor\n",
            "WARNING:logs:Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost==(0, 90)\n",
            "WARNING:logs:Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost==(0, 90)\n",
            "WARNING:logs:Couldn't import catboost.CatBoostRegressor\n",
            "WARNING:logs:Couldn't import catboost.CatBoostRegressor\n",
            "INFO:logs:setup() successfully completed in 0.62s...............\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploratory Data Analysis\n",
        "exp.plot_model(plot = 'ts')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "gFShXktsVQks",
        "outputId": "eec06d63-ec62-4fd5-c27f-e0ac6bf56dba"
      },
      "execution_count": 18,
      "outputs": [
        {
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              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Visual Rendered Successfully\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cross-validation plot\n",
        "exp.plot_model(plot = 'cv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "4QoWmXpEVQub",
        "outputId": "1ac49058-d5a5-41a8-a43c-df14460012ae"
      },
      "execution_count": 19,
      "outputs": [
        {
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          "text": [
            "INFO:logs:Visual Rendered Successfully\n"
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    {
      "cell_type": "code",
      "source": [
        "# Diagnostics plot\n",
        "exp.plot_model(plot = 'diagnostics')"
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          "height": 559
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        "id": "qA2XrulJX-EL",
        "outputId": "04654778-e1b7-4f96-ff7c-b1dfba621fb4"
      },
      "execution_count": 20,
      "outputs": [
        {
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          "text": [
            "INFO:logs:Visual Rendered Successfully\n"
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    {
      "cell_type": "code",
      "source": [
        "# Decomposition plot\n",
        "exp.plot_model(plot = 'decomp_stl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "HDHNB3w2YGky",
        "outputId": "7b8f0277-5162-45de-f133-d96d5c440860"
      },
      "execution_count": 21,
      "outputs": [
        {
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            "INFO:logs:Visual Rendered Successfully\n"
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      "source": [
        "#Training the model\n",
        "best = exp.compare_models()"
      ],
      "metadata": {
        "colab": {
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          "height": 965,
          "referenced_widgets": [
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      "execution_count": 22,
      "outputs": [
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              "                                                             Model        MAE  \\\n",
              "auto_arima                                              Auto ARIMA   531.9167   \n",
              "arima                                                        ARIMA   568.4974   \n",
              "lightgbm_cds_dt  Light Gradient Boosting w/ Cond. Deseasonalize...   571.1749   \n",
              "et_cds_dt          Extra Trees w/ Cond. Deseasonalize & Detrending    583.112   \n",
              "theta                                             Theta Forecaster   543.8821   \n",
              "gbr_cds_dt       Gradient Boosting w/ Cond. Deseasonalize & Det...   604.1178   \n",
              "rf_cds_dt        Random Forest w/ Cond. Deseasonalize & Detrending   614.7098   \n",
              "exp_smooth                                   Exponential Smoothing   542.7593   \n",
              "lr_cds_dt               Linear w/ Cond. Deseasonalize & Detrending   632.7132   \n",
              "lar_cds_dt       Least Angular Regressor w/ Cond. Deseasonalize...   632.7132   \n",
              "en_cds_dt          Elastic Net w/ Cond. Deseasonalize & Detrending   632.7142   \n",
              "lasso_cds_dt             Lasso w/ Cond. Deseasonalize & Detrending   632.7151   \n",
              "ridge_cds_dt             Ridge w/ Cond. Deseasonalize & Detrending   632.7132   \n",
              "br_cds_dt        Bayesian Ridge w/ Cond. Deseasonalize & Detren...   643.3474   \n",
              "llar_cds_dt      Lasso Least Angular Regressor w/ Cond. Deseaso...   643.3241   \n",
              "huber_cds_dt             Huber w/ Cond. Deseasonalize & Detrending   640.1743   \n",
              "omp_cds_dt       Orthogonal Matching Pursuit w/ Cond. Deseasona...   651.9323   \n",
              "knn_cds_dt         K Neighbors w/ Cond. Deseasonalize & Detrending   673.6021   \n",
              "dt_cds_dt        Decision Tree w/ Cond. Deseasonalize & Detrending   613.1794   \n",
              "ada_cds_dt            AdaBoost w/ Cond. Deseasonalize & Detrending   657.4745   \n",
              "ets                                                            ETS   611.4513   \n",
              "snaive                                   Seasonal Naive Forecaster    758.619   \n",
              "par_cds_dt       Passive Aggressive w/ Cond. Deseasonalize & De...  1112.9499   \n",
              "naive                                             Naive Forecaster   1340.619   \n",
              "polytrend                              Polynomial Trend Forecaster  1636.9624   \n",
              "croston                                                    Croston  1824.9388   \n",
              "grand_means                                 Grand Means Forecaster  2193.9728   \n",
              "\n",
              "                      RMSE    MAPE   SMAPE    MASE   RMSSE      R2  TT (Sec)  \n",
              "auto_arima        626.2099  0.0911  0.0916   0.849  0.6902  0.8632    2.4133  \n",
              "arima             687.2509  0.0957  0.0975  0.9085   0.758  0.8316    0.1933  \n",
              "lightgbm_cds_dt   672.9608  0.1022   0.099  0.9132  0.7415  0.8368    0.2533  \n",
              "et_cds_dt         715.5584  0.1014  0.0996  0.9318   0.789  0.8204    0.6200  \n",
              "theta             691.2579  0.0947  0.1017  0.8724  0.7628   0.814    0.0533  \n",
              "gbr_cds_dt        706.2955  0.1052  0.1041  0.9669  0.7788  0.8196    0.0700  \n",
              "rf_cds_dt         728.1451  0.1055  0.1044  0.9818  0.8031  0.8137    0.6800  \n",
              "exp_smooth        635.8877  0.0994  0.1062  0.8697  0.7016  0.8487    0.0867  \n",
              "lr_cds_dt         746.7205  0.1081  0.1075  1.0093  0.8238  0.8079    0.0967  \n",
              "lar_cds_dt        746.7205  0.1081  0.1075  1.0093  0.8238  0.8079    0.0433  \n",
              "en_cds_dt         746.7212  0.1081  0.1075  1.0093  0.8238  0.8079    0.0433  \n",
              "lasso_cds_dt      746.7218  0.1081  0.1075  1.0093  0.8238  0.8079    0.0433  \n",
              "ridge_cds_dt      746.7205  0.1081  0.1075  1.0093  0.8238  0.8079    0.0433  \n",
              "br_cds_dt         757.0428  0.1098  0.1094   1.026  0.8352   0.803    0.0433  \n",
              "llar_cds_dt       753.9132    0.11  0.1095  1.0262  0.8318   0.804    0.0567  \n",
              "huber_cds_dt      770.1119  0.1107  0.1095  1.0212  0.8492  0.7953    0.0700  \n",
              "omp_cds_dt        774.8189  0.1137  0.1129  1.0398  0.8547  0.7928    0.0467  \n",
              "knn_cds_dt        808.2099  0.1122  0.1133  1.0758  0.8918   0.774    0.5200  \n",
              "dt_cds_dt          797.467  0.1147  0.1134  0.9835  0.8794  0.7701    0.0433  \n",
              "ada_cds_dt        754.4896  0.1137  0.1135  1.0496  0.8323  0.8027    0.1133  \n",
              "ets               741.2183  0.1061  0.1149  0.9795  0.8183  0.7908    0.1033  \n",
              "snaive            868.8086  0.1273  0.1342  1.2136  0.9589  0.7328    0.0200  \n",
              "par_cds_dt       1277.1558  0.1995  0.2208  1.7956  1.4039  0.1633    0.0433  \n",
              "naive            1921.1123  0.3362  0.2386  2.1383  2.1183 -0.2648    1.0533  \n",
              "polytrend        1793.4953  0.3259  0.2898   2.609  1.9779 -0.0999    0.0267  \n",
              "croston          1911.9352  0.3321  0.3244  2.9103  2.1094 -0.2519    0.0167  \n",
              "grand_means      2395.2384  0.3474  0.3964  3.4979  2.6432  -0.962    0.0233  "
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>MASE</th>\n",
              "      <th>RMSSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>auto_arima</th>\n",
              "      <td>Auto ARIMA</td>\n",
              "      <td>531.9167</td>\n",
              "      <td>626.2099</td>\n",
              "      <td>0.0911</td>\n",
              "      <td>0.0916</td>\n",
              "      <td>0.849</td>\n",
              "      <td>0.6902</td>\n",
              "      <td>0.8632</td>\n",
              "      <td>2.4133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arima</th>\n",
              "      <td>ARIMA</td>\n",
              "      <td>568.4974</td>\n",
              "      <td>687.2509</td>\n",
              "      <td>0.0957</td>\n",
              "      <td>0.0975</td>\n",
              "      <td>0.9085</td>\n",
              "      <td>0.758</td>\n",
              "      <td>0.8316</td>\n",
              "      <td>0.1933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm_cds_dt</th>\n",
              "      <td>Light Gradient Boosting w/ Cond. Deseasonalize...</td>\n",
              "      <td>571.1749</td>\n",
              "      <td>672.9608</td>\n",
              "      <td>0.1022</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.9132</td>\n",
              "      <td>0.7415</td>\n",
              "      <td>0.8368</td>\n",
              "      <td>0.2533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et_cds_dt</th>\n",
              "      <td>Extra Trees w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>583.112</td>\n",
              "      <td>715.5584</td>\n",
              "      <td>0.1014</td>\n",
              "      <td>0.0996</td>\n",
              "      <td>0.9318</td>\n",
              "      <td>0.789</td>\n",
              "      <td>0.8204</td>\n",
              "      <td>0.6200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>theta</th>\n",
              "      <td>Theta Forecaster</td>\n",
              "      <td>543.8821</td>\n",
              "      <td>691.2579</td>\n",
              "      <td>0.0947</td>\n",
              "      <td>0.1017</td>\n",
              "      <td>0.8724</td>\n",
              "      <td>0.7628</td>\n",
              "      <td>0.814</td>\n",
              "      <td>0.0533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr_cds_dt</th>\n",
              "      <td>Gradient Boosting w/ Cond. Deseasonalize &amp; Det...</td>\n",
              "      <td>604.1178</td>\n",
              "      <td>706.2955</td>\n",
              "      <td>0.1052</td>\n",
              "      <td>0.1041</td>\n",
              "      <td>0.9669</td>\n",
              "      <td>0.7788</td>\n",
              "      <td>0.8196</td>\n",
              "      <td>0.0700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf_cds_dt</th>\n",
              "      <td>Random Forest w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>614.7098</td>\n",
              "      <td>728.1451</td>\n",
              "      <td>0.1055</td>\n",
              "      <td>0.1044</td>\n",
              "      <td>0.9818</td>\n",
              "      <td>0.8031</td>\n",
              "      <td>0.8137</td>\n",
              "      <td>0.6800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exp_smooth</th>\n",
              "      <td>Exponential Smoothing</td>\n",
              "      <td>542.7593</td>\n",
              "      <td>635.8877</td>\n",
              "      <td>0.0994</td>\n",
              "      <td>0.1062</td>\n",
              "      <td>0.8697</td>\n",
              "      <td>0.7016</td>\n",
              "      <td>0.8487</td>\n",
              "      <td>0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr_cds_dt</th>\n",
              "      <td>Linear w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>632.7132</td>\n",
              "      <td>746.7205</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>1.0093</td>\n",
              "      <td>0.8238</td>\n",
              "      <td>0.8079</td>\n",
              "      <td>0.0967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar_cds_dt</th>\n",
              "      <td>Least Angular Regressor w/ Cond. Deseasonalize...</td>\n",
              "      <td>632.7132</td>\n",
              "      <td>746.7205</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>1.0093</td>\n",
              "      <td>0.8238</td>\n",
              "      <td>0.8079</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en_cds_dt</th>\n",
              "      <td>Elastic Net w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>632.7142</td>\n",
              "      <td>746.7212</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>1.0093</td>\n",
              "      <td>0.8238</td>\n",
              "      <td>0.8079</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso_cds_dt</th>\n",
              "      <td>Lasso w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>632.7151</td>\n",
              "      <td>746.7218</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>1.0093</td>\n",
              "      <td>0.8238</td>\n",
              "      <td>0.8079</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge_cds_dt</th>\n",
              "      <td>Ridge w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>632.7132</td>\n",
              "      <td>746.7205</td>\n",
              "      <td>0.1081</td>\n",
              "      <td>0.1075</td>\n",
              "      <td>1.0093</td>\n",
              "      <td>0.8238</td>\n",
              "      <td>0.8079</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br_cds_dt</th>\n",
              "      <td>Bayesian Ridge w/ Cond. Deseasonalize &amp; Detren...</td>\n",
              "      <td>643.3474</td>\n",
              "      <td>757.0428</td>\n",
              "      <td>0.1098</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.8352</td>\n",
              "      <td>0.803</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar_cds_dt</th>\n",
              "      <td>Lasso Least Angular Regressor w/ Cond. Deseaso...</td>\n",
              "      <td>643.3241</td>\n",
              "      <td>753.9132</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.1095</td>\n",
              "      <td>1.0262</td>\n",
              "      <td>0.8318</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.0567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber_cds_dt</th>\n",
              "      <td>Huber w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>640.1743</td>\n",
              "      <td>770.1119</td>\n",
              "      <td>0.1107</td>\n",
              "      <td>0.1095</td>\n",
              "      <td>1.0212</td>\n",
              "      <td>0.8492</td>\n",
              "      <td>0.7953</td>\n",
              "      <td>0.0700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>omp_cds_dt</th>\n",
              "      <td>Orthogonal Matching Pursuit w/ Cond. Deseasona...</td>\n",
              "      <td>651.9323</td>\n",
              "      <td>774.8189</td>\n",
              "      <td>0.1137</td>\n",
              "      <td>0.1129</td>\n",
              "      <td>1.0398</td>\n",
              "      <td>0.8547</td>\n",
              "      <td>0.7928</td>\n",
              "      <td>0.0467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn_cds_dt</th>\n",
              "      <td>K Neighbors w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>673.6021</td>\n",
              "      <td>808.2099</td>\n",
              "      <td>0.1122</td>\n",
              "      <td>0.1133</td>\n",
              "      <td>1.0758</td>\n",
              "      <td>0.8918</td>\n",
              "      <td>0.774</td>\n",
              "      <td>0.5200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt_cds_dt</th>\n",
              "      <td>Decision Tree w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>613.1794</td>\n",
              "      <td>797.467</td>\n",
              "      <td>0.1147</td>\n",
              "      <td>0.1134</td>\n",
              "      <td>0.9835</td>\n",
              "      <td>0.8794</td>\n",
              "      <td>0.7701</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada_cds_dt</th>\n",
              "      <td>AdaBoost w/ Cond. Deseasonalize &amp; Detrending</td>\n",
              "      <td>657.4745</td>\n",
              "      <td>754.4896</td>\n",
              "      <td>0.1137</td>\n",
              "      <td>0.1135</td>\n",
              "      <td>1.0496</td>\n",
              "      <td>0.8323</td>\n",
              "      <td>0.8027</td>\n",
              "      <td>0.1133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ets</th>\n",
              "      <td>ETS</td>\n",
              "      <td>611.4513</td>\n",
              "      <td>741.2183</td>\n",
              "      <td>0.1061</td>\n",
              "      <td>0.1149</td>\n",
              "      <td>0.9795</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>0.7908</td>\n",
              "      <td>0.1033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>snaive</th>\n",
              "      <td>Seasonal Naive Forecaster</td>\n",
              "      <td>758.619</td>\n",
              "      <td>868.8086</td>\n",
              "      <td>0.1273</td>\n",
              "      <td>0.1342</td>\n",
              "      <td>1.2136</td>\n",
              "      <td>0.9589</td>\n",
              "      <td>0.7328</td>\n",
              "      <td>0.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par_cds_dt</th>\n",
              "      <td>Passive Aggressive w/ Cond. Deseasonalize &amp; De...</td>\n",
              "      <td>1112.9499</td>\n",
              "      <td>1277.1558</td>\n",
              "      <td>0.1995</td>\n",
              "      <td>0.2208</td>\n",
              "      <td>1.7956</td>\n",
              "      <td>1.4039</td>\n",
              "      <td>0.1633</td>\n",
              "      <td>0.0433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>naive</th>\n",
              "      <td>Naive Forecaster</td>\n",
              "      <td>1340.619</td>\n",
              "      <td>1921.1123</td>\n",
              "      <td>0.3362</td>\n",
              "      <td>0.2386</td>\n",
              "      <td>2.1383</td>\n",
              "      <td>2.1183</td>\n",
              "      <td>-0.2648</td>\n",
              "      <td>1.0533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>polytrend</th>\n",
              "      <td>Polynomial Trend Forecaster</td>\n",
              "      <td>1636.9624</td>\n",
              "      <td>1793.4953</td>\n",
              "      <td>0.3259</td>\n",
              "      <td>0.2898</td>\n",
              "      <td>2.609</td>\n",
              "      <td>1.9779</td>\n",
              "      <td>-0.0999</td>\n",
              "      <td>0.0267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>croston</th>\n",
              "      <td>Croston</td>\n",
              "      <td>1824.9388</td>\n",
              "      <td>1911.9352</td>\n",
              "      <td>0.3321</td>\n",
              "      <td>0.3244</td>\n",
              "      <td>2.9103</td>\n",
              "      <td>2.1094</td>\n",
              "      <td>-0.2519</td>\n",
              "      <td>0.0167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>grand_means</th>\n",
              "      <td>Grand Means Forecaster</td>\n",
              "      <td>2193.9728</td>\n",
              "      <td>2395.2384</td>\n",
              "      <td>0.3474</td>\n",
              "      <td>0.3964</td>\n",
              "      <td>3.4979</td>\n",
              "      <td>2.6432</td>\n",
              "      <td>-0.962</td>\n",
              "      <td>0.0233</td>\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:master_model_container: 27\n",
            "INFO:logs:display_container: 2\n",
            "INFO:logs:AutoARIMA(random_state=123, sp=7, suppress_warnings=True)\n",
            "INFO:logs:compare_models() successfully completed......................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Examine individual model performance\n",
        "prophet = exp.create_model('prophet')\n",
        "print(prophet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293,
          "referenced_widgets": [
            "5c15ef19038449c287e09b5eb1d71823",
            "887f2f5efc50496b85ec45755dd5847d",
            "9d7758830156434288931b08ebce84ea"
          ]
        },
        "id": "iHKmdyKTYgQB",
        "outputId": "90a233b8-e015-4f87-abc3-9575b254a299"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          cutoff       MAE      RMSE    MAPE   SMAPE    MASE   RMSSE      R2\n",
              "0     2021-09-07  579.0230  655.8323  0.1347  0.1232  0.9371  0.7150  0.8212\n",
              "1     2021-09-14  641.2997  816.9058  0.1024  0.1088  1.0449  0.9101  0.7413\n",
              "2     2021-09-21  870.3595  987.0928  0.1183  0.1250  1.3490  1.0889  0.7477\n",
              "Mean         NaT  696.8941  819.9436  0.1185  0.1190  1.1104  0.9047  0.7701\n",
              "SD           NaT  125.2658  135.2536  0.0132  0.0073  0.1744  0.1527  0.0363"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23dd84c2-7f15-40df-a36b-30a93166d18a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>MASE</th>\n",
              "      <th>RMSSE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-09-07</td>\n",
              "      <td>579.0230</td>\n",
              "      <td>655.8323</td>\n",
              "      <td>0.1347</td>\n",
              "      <td>0.1232</td>\n",
              "      <td>0.9371</td>\n",
              "      <td>0.7150</td>\n",
              "      <td>0.8212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-09-14</td>\n",
              "      <td>641.2997</td>\n",
              "      <td>816.9058</td>\n",
              "      <td>0.1024</td>\n",
              "      <td>0.1088</td>\n",
              "      <td>1.0449</td>\n",
              "      <td>0.9101</td>\n",
              "      <td>0.7413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-09-21</td>\n",
              "      <td>870.3595</td>\n",
              "      <td>987.0928</td>\n",
              "      <td>0.1183</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>1.3490</td>\n",
              "      <td>1.0889</td>\n",
              "      <td>0.7477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaT</td>\n",
              "      <td>696.8941</td>\n",
              "      <td>819.9436</td>\n",
              "      <td>0.1185</td>\n",
              "      <td>0.1190</td>\n",
              "      <td>1.1104</td>\n",
              "      <td>0.9047</td>\n",
              "      <td>0.7701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaT</td>\n",
              "      <td>125.2658</td>\n",
              "      <td>135.2536</td>\n",
              "      <td>0.0132</td>\n",
              "      <td>0.0073</td>\n",
              "      <td>0.1744</td>\n",
              "      <td>0.1527</td>\n",
              "      <td>0.0363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23dd84c2-7f15-40df-a36b-30a93166d18a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-23dd84c2-7f15-40df-a36b-30a93166d18a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-23dd84c2-7f15-40df-a36b-30a93166d18a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:master_model_container: 28\n",
            "INFO:logs:display_container: 3\n",
            "INFO:logs:ProphetPeriodPatched()\n",
            "INFO:logs:create_model() successfully completed......................................\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ProphetPeriodPatched()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Perform model tuning process on the trained prophet model\n",
        "tuned_prophet = exp.tune_model(prophet)\n",
        "print(tuned_prophet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397,
          "referenced_widgets": [
            "f717e1b82cdc47b09a5697edb6177d54",
            "495cf5a23e424ce299a000fd7e7f32f4",
            "fcbe15f1b8ee4653b4fa01a079885608"
          ]
        },
        "id": "BbGu2juMYvIZ",
        "outputId": "372e166f-55ee-4d12-bc6f-a3a51390d0e1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          cutoff       MAE      RMSE    MAPE   SMAPE    MASE   RMSSE      R2\n",
              "0     2021-09-07  649.0133  711.2920  0.1351  0.1270  1.0504  0.7755  0.7897\n",
              "1     2021-09-14  585.7227  718.3329  0.0986  0.1071  0.9544  0.8003  0.7999\n",
              "2     2021-09-21  654.0601  707.8418  0.0942  0.0991  1.0138  0.7809  0.8703\n",
              "Mean         NaT  629.5987  712.4889  0.1093  0.1111  1.0062  0.7855  0.8200\n",
              "SD           NaT   31.0934    4.3658  0.0183  0.0117  0.0396  0.0107  0.0358"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-951cf438-848f-4dcb-88b7-99942981fc6e\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cutoff</th>\n",
              "      <th>MAE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>SMAPE</th>\n",
              "      <th>MASE</th>\n",
              "      <th>RMSSE</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-09-07</td>\n",
              "      <td>649.0133</td>\n",
              "      <td>711.2920</td>\n",
              "      <td>0.1351</td>\n",
              "      <td>0.1270</td>\n",
              "      <td>1.0504</td>\n",
              "      <td>0.7755</td>\n",
              "      <td>0.7897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-09-14</td>\n",
              "      <td>585.7227</td>\n",
              "      <td>718.3329</td>\n",
              "      <td>0.0986</td>\n",
              "      <td>0.1071</td>\n",
              "      <td>0.9544</td>\n",
              "      <td>0.8003</td>\n",
              "      <td>0.7999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-09-21</td>\n",
              "      <td>654.0601</td>\n",
              "      <td>707.8418</td>\n",
              "      <td>0.0942</td>\n",
              "      <td>0.0991</td>\n",
              "      <td>1.0138</td>\n",
              "      <td>0.7809</td>\n",
              "      <td>0.8703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>NaT</td>\n",
              "      <td>629.5987</td>\n",
              "      <td>712.4889</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>0.1111</td>\n",
              "      <td>1.0062</td>\n",
              "      <td>0.7855</td>\n",
              "      <td>0.8200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>NaT</td>\n",
              "      <td>31.0934</td>\n",
              "      <td>4.3658</td>\n",
              "      <td>0.0183</td>\n",
              "      <td>0.0117</td>\n",
              "      <td>0.0396</td>\n",
              "      <td>0.0107</td>\n",
              "      <td>0.0358</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-951cf438-848f-4dcb-88b7-99942981fc6e button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-951cf438-848f-4dcb-88b7-99942981fc6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:master_model_container: 31\n",
            "INFO:logs:display_container: 4\n",
            "INFO:logs:ProphetPeriodPatched(changepoint_prior_scale=0.07052367291265604,\n",
            "                     holidays_prior_scale=0.2772015819815348,\n",
            "                     seasonality_mode='multiplicative',\n",
            "                     seasonality_prior_scale=0.15960727025988383)\n",
            "INFO:logs:tune_model() succesfully completed......................................\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ProphetPeriodPatched(changepoint_prior_scale=0.07052367291265604,\n",
            "                     holidays_prior_scale=0.2772015819815348,\n",
            "                     seasonality_mode='multiplicative',\n",
            "                     seasonality_prior_scale=0.15960727025988383)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Plotting\n",
        "exp.plot_model(tuned_prophet, plot = 'forecast')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "wi6q_XFOZjTB",
        "outputId": "31215c77-aa92-44ad-ca45-99f593172c9d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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            "INFO:logs:Visual Rendered Successfully\n"
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    {
      "cell_type": "code",
      "source": [
        "# Best Model Plotting\n",
        "exp.plot_model(best, plot = 'forecast')"
      ],
      "metadata": {
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          "height": 559
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        "id": "udLGlu_BZtFJ",
        "outputId": "774d852d-8d33-457d-8c75-f6a5dc815827"
      },
      "execution_count": 26,
      "outputs": [
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            "INFO:logs:Visual Rendered Successfully\n"
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      "source": [
        "# forecast in unknown future with the best model\n",
        "exp.plot_model(best, plot = 'forecast', data_kwargs = {'fh' : 30})"
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        "id": "nAO-gzVrZ1cg",
        "outputId": "0e7fc796-4d02-49e5-d6ca-1772dc08dd86"
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      "execution_count": 27,
      "outputs": [
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          "text": [
            "WARNING:logs:predict_model >> Prediction Indices do not match test indices. Metrics will not be displayed.\n"
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      "source": [
        "#Best Model in-sample plot\n",
        "exp.plot_model(best, plot = 'insample')"
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        "id": "hJ9skgldaA7C",
        "outputId": "319dabc0-9eec-418f-9f03-2ae114a93f8c"
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      "execution_count": 28,
      "outputs": [
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          "text": [
            "WARNING:logs:predict_model >> Prediction Indices do not match test indices. Metrics will not be displayed.\n"
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        },
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      "source": [
        "#Best Model residuals plot\n",
        "exp.plot_model(best, plot = 'residuals')"
      ],
      "metadata": {
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          "height": 577
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        "id": "IvLTo6nYaDTx",
        "outputId": "e1341898-d3c9-409d-9463-9478a64c391f"
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      "execution_count": 29,
      "outputs": [
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          "text": [
            "WARNING:logs:predict_model >> Prediction Indices do not match test indices. Metrics will not be displayed.\n"
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        },
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            "INFO:logs:Visual Rendered Successfully\n"
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      "source": [
        "#Best Model diagnostics plot\n",
        "exp.plot_model(best, plot = 'diagnostics')"
      ],
      "metadata": {
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          "height": 577
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        "outputId": "465acf82-4453-404a-b0e8-5c19347bfe3f"
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      "execution_count": 30,
      "outputs": [
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          "text": [
            "WARNING:logs:predict_model >> Prediction Indices do not match test indices. Metrics will not be displayed.\n"
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            "INFO:logs:Visual Rendered Successfully\n"
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      "source": [
        "## Deployment of final model\n",
        "final_best = exp.finalize_model(best)# generate predictions\n",
        "exp.predict_model(final_best, fh = 90)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
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        "id": "AdDhjLEfafqB",
        "outputId": "1b7df912-214d-4305-c9d6-4d906a2c9123"
      },
      "execution_count": 31,
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          "name": "stderr",
          "text": [
            "INFO:logs:Initializing finalize_model()\n",
            "INFO:logs:finalize_model(self=<pycaret.time_series.forecasting.oop.TSForecastingExperiment object at 0x7f9c07d14410>, estimator=AutoARIMA(random_state=123, sp=7, suppress_warnings=True), fit_kwargs=None, groups=None, model_only=True, experiment_custom_tags=None, display=None)\n",
            "INFO:logs:Finalizing AutoARIMA(random_state=123, sp=7, suppress_warnings=True)\n",
            "INFO:logs:Initializing create_model()\n",
            "INFO:logs:create_model(self=<pycaret.time_series.forecasting.oop.TSForecastingExperiment object at 0x7f9c07d14410>, estimator=AutoARIMA(random_state=123, sp=7, suppress_warnings=True), fold=None, round=4, cross_validation=True, predict=True, fit_kwargs={}, groups=None, refit=True, probability_threshold=None, experiment_custom_tags=None, verbose=False, system=False, add_to_model_list=False, metrics=None, display=None, 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:Auto ARIMA Imported successfully\n",
            "INFO:logs:Starting cross validation\n",
            "INFO:logs:Cross validating with ExpandingWindowSplitter(fh=ForecastingHorizon([1, 2, 3, 4, 5, 6, 7], dtype='int64', is_relative=True),\n",
            "            initial_window=None, step_length=7), n_jobs=-1\n",
            "INFO:logs:Calculating mean and std\n",
            "INFO:logs:Creating metrics dataframe\n",
            "INFO:logs:Finalizing model\n",
            "INFO:logs:Uploading results into container\n",
            "INFO:logs:master_model_container: 31\n",
            "INFO:logs:display_container: 11\n",
            "INFO:logs:AutoARIMA(random_state=123, sp=7, suppress_warnings=True)\n",
            "INFO:logs:create_model() successfully completed......................................\n",
            "INFO:logs:master_model_container: 31\n",
            "INFO:logs:display_container: 10\n",
            "INFO:logs:AutoARIMA(random_state=123, sp=7, suppress_warnings=True)\n",
            "INFO:logs:finalize_model() succesfully completed......................................\n",
            "WARNING:logs:predict_model >> Prediction Indices do not match test indices. Metrics will not be displayed.\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
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              "                y_pred\n",
              "2021-10-06   8392.9108\n",
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              "2021-10-09   4390.7424\n",
              "2021-10-10   4972.7521\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the best model\n",
        "exp.save_model(final_best, 'my_best_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56rq5aOgap7A",
        "outputId": "14ba3f03-357d-45ad-fd06-48418c727a61"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing save_model()\n",
            "INFO:logs:save_model(model=AutoARIMA(random_state=123, sp=7, suppress_warnings=True), model_name=my_best_model, prep_pipe_=PyCaretForecastingPipeline(steps=[('forecaster',\n",
            "                                   TransformedTargetForecaster(steps=[('model',\n",
            "                                                                       DummyForecaster())]))]), verbose=True, use_case=MLUsecase.TIME_SERIES, kwargs={})\n",
            "INFO:logs:Adding model into prep_pipe\n",
            "INFO:logs:my_best_model.pkl saved in current working directory\n",
            "INFO:logs:PyCaretForecastingPipeline(steps=[('forecaster',\n",
            "                                   TransformedTargetForecaster(steps=[('model',\n",
            "                                                                       AutoARIMA(random_state=123,\n",
            "                                                                                 sp=7,\n",
            "                                                                                 suppress_warnings=True))]))])\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": [
              "(PyCaretForecastingPipeline(steps=[('forecaster',\n",
              "                                    TransformedTargetForecaster(steps=[('model',\n",
              "                                                                        AutoARIMA(random_state=123,\n",
              "                                                                                  sp=7,\n",
              "                                                                                  suppress_warnings=True))]))]),\n",
              " 'my_best_model.pkl')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the saved model\n",
        "autoARIMA_saved = exp.load_model('my_best_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjL42LFkay4z",
        "outputId": "f649b893-c502-48a4-9913-424faee18add"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:logs:Initializing load_model()\n",
            "INFO:logs:load_model(model_name=my_best_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": [
        "#Eplore the best model parameters\n",
        "print(autoARIMA_saved)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmy_Hw4ya3xf",
        "outputId": "f9c41241-8a20-4f1b-f162-ff0a44ceca95"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyCaretForecastingPipeline(steps=[('forecaster',\n",
            "                                   TransformedTargetForecaster(steps=[('model',\n",
            "                                                                       AutoARIMA(random_state=123,\n",
            "                                                                                 sp=7,\n",
            "                                                                                 suppress_warnings=True))]))])\n"
          ]
        }
      ]
    }
  ]
}