syrup_production_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "syrup_production_prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMXvJlpEBCNx62GpLU3tFud",
      "include_colab_link": true
    },
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      "name": "python3",
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    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Krystalz2018/SVM_prediction/blob/main/syrup_production_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "resources": {
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              "ok": true,
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              "status": 200,
              "status_text": ""
            }
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          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "G40Xf_mSxFwg",
        "outputId": "ff5e9240-bf66-4892-ecd9-56929991ebdf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d4b97fe9-6166-487d-8c80-4fd13700c935\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d4b97fe9-6166-487d-8c80-4fd13700c935\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Daily Precipitation.csv to Daily Precipitation.csv\n",
            "Saving Daily Soil Mositure.csv to Daily Soil Mositure.csv\n",
            "Saving Daily Temperature.csv to Daily Temperature.csv\n",
            "Saving Eight Day NDVI.csv to Eight Day NDVI.csv\n",
            "Saving Production Quantity.csv to Production Quantity.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "percipitation = pd.read_csv(io.BytesIO(uploaded['Daily Precipitation.csv']))\n",
        "soil = pd.read_csv(io.BytesIO(uploaded['Daily Soil Mositure.csv']))\n",
        "temperature = pd.read_csv(io.BytesIO(uploaded['Daily Temperature.csv']))\n",
        "NDVI = pd.read_csv(io.BytesIO(uploaded['Eight Day NDVI.csv']))\n",
        "production_quantity = pd.read_csv(io.BytesIO(uploaded['Production Quantity.csv']))"
      ],
      "metadata": {
        "id": "AYomRsL5x8-V"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "First I will groupby the percipitation, soil, temperature and NDVI data using year and month. and then I will combine them with the production in respective date for my model building."
      ],
      "metadata": {
        "id": "4M8QQITH0AiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percipitation.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FR-tYs9UyQuJ",
        "outputId": "74d488f1-5500-4d68-d486-4dd988b8c1d9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 start_date                  end_date     precip  region_id\n",
              "0  2014-01-01T00:00:00.000Z  2014-01-01T00:00:00.000Z   1.392393         93\n",
              "1  2014-01-02T00:00:00.000Z  2014-01-02T00:00:00.000Z   0.315380         93\n",
              "2  2014-01-03T00:00:00.000Z  2014-01-03T00:00:00.000Z   2.347846         93\n",
              "3  2014-01-04T00:00:00.000Z  2014-01-04T00:00:00.000Z  21.466357         93\n",
              "4  2014-01-05T00:00:00.000Z  2014-01-05T00:00:00.000Z  32.823651         93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b4282f9-1f6e-4a4f-8f4a-69cadc83c088\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>precip</th>\n",
              "      <th>region_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-01T00:00:00.000Z</td>\n",
              "      <td>2014-01-01T00:00:00.000Z</td>\n",
              "      <td>1.392393</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-02T00:00:00.000Z</td>\n",
              "      <td>2014-01-02T00:00:00.000Z</td>\n",
              "      <td>0.315380</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-03T00:00:00.000Z</td>\n",
              "      <td>2014-01-03T00:00:00.000Z</td>\n",
              "      <td>2.347846</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-04T00:00:00.000Z</td>\n",
              "      <td>2014-01-04T00:00:00.000Z</td>\n",
              "      <td>21.466357</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-05T00:00:00.000Z</td>\n",
              "      <td>2014-01-05T00:00:00.000Z</td>\n",
              "      <td>32.823651</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b4282f9-1f6e-4a4f-8f4a-69cadc83c088')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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    {
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        "percipitation['year'] = pd.to_datetime(percipitation.start_date).dt.year\n",
        "percipitation['month'] = pd.to_datetime(percipitation.start_date).dt.month\n",
        "group1 = percipitation.groupby(['year', 'month']).sum().reset_index()\n",
        "group1"
      ],
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        "colab": {
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          "height": 424
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        "id": "wekNK6mQy_Ju",
        "outputId": "392d0206-819e-4f94-d028-ff801ca5f1e7"
      },
      "execution_count": 4,
      "outputs": [
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              "    year  month       precip  region_id\n",
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              "..   ...    ...          ...        ...\n",
              "94  2021     11  3059.851405      29700\n",
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              "97  2022      2  3393.950845      27720\n",
              "98  2022      3   937.437454      12870\n",
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        "group1.iloc[:,0:3]"
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        "id": "Zx8a1_ufE51e",
        "outputId": "9e4413ba-f1fe-43e4-df65-768988f57228"
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      "execution_count": 5,
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              "    year  month       precip\n",
              "0   2014      1  1176.375213\n",
              "1   2014      2   398.785653\n",
              "2   2014      3  2008.844979\n",
              "3   2014      4  2941.900539\n",
              "4   2014      5  3064.141433\n",
              "..   ...    ...          ...\n",
              "94  2021     11  3059.851405\n",
              "95  2021     12  2872.624468\n",
              "96  2022      1  1268.163614\n",
              "97  2022      2  3393.950845\n",
              "98  2022      3   937.437454\n",
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              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
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        "outputId": "9224c52e-bf21-49a1-b650-f235f1d98c42"
      },
      "execution_count": 7,
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              "                 start_date                  end_date      smos  region_id  \\\n",
              "0  2014-01-01T00:00:00.000Z  2014-01-01T00:00:00.000Z  0.310787         93   \n",
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              "\n",
              "   year  month  \n",
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      "cell_type": "code",
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        "soil['year'] = pd.to_datetime(soil.start_date).dt.year\n",
        "soil['month'] = pd.to_datetime(soil.start_date).dt.month\n",
        "group2 = soil.groupby(['year', 'month']).sum().reset_index()\n",
        "group2"
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      "execution_count": 6,
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              "    year  month        smos  region_id\n",
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              "..   ...    ...         ...        ...\n",
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              "98  2022      3   22.294479       5846\n",
              "\n",
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              "    year  month        smos\n",
              "0   2014      1   86.768681\n",
              "1   2014      2   69.821505\n",
              "2   2014      3   74.370433\n",
              "3   2014      4   70.900122\n",
              "4   2014      5   75.706701\n",
              "..   ...    ...         ...\n",
              "94  2021     11  102.989309\n",
              "95  2021     12  103.003425\n",
              "96  2022      1  112.405853\n",
              "97  2022      2  104.197857\n",
              "98  2022      3   22.294479\n",
              "\n",
              "[99 rows x 3 columns]"
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              "    <tr>\n",
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              "      <td>2014</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>2022</td>\n",
              "      <td>3</td>\n",
              "      <td>22.294479</td>\n",
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      "execution_count": 9,
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              "                 start_date                  end_date       temp  region_id\n",
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        "temperature['year'] = pd.to_datetime(temperature.start_date).dt.year\n",
        "temperature['month'] = pd.to_datetime(temperature.start_date).dt.month\n",
        "group3 = temperature.groupby(['year', 'month']).mean().reset_index()\n",
        "group3"
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      "execution_count": 10,
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              "    year  month       temp  region_id\n",
              "0   2014      1  24.329243  98.587838\n",
              "1   2014      2  26.372440  98.906103\n",
              "2   2014      3  27.840244  99.060748\n",
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              "4   2014      5  25.311771  99.455090\n",
              "..   ...    ...        ...        ...\n",
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              "95  2021     12  25.857554  98.802198\n",
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              "97  2022      2  27.613655  99.294737\n",
              "98  2022      3  27.440000  98.486111\n",
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              "    year  month       temp\n",
              "0   2014      1  24.329243\n",
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              "..   ...    ...        ...\n",
              "94  2021     11  25.779901\n",
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              "96  2022      1  27.134638\n",
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              "98  2022      3  27.440000\n",
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        "NDVI.head()"
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      "execution_count": 12,
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            "text/plain": [
              "                 start_date                  end_date      ndvi  region_id\n",
              "0  2013-12-27T00:00:00.000Z  2014-01-03T00:00:00.000Z  0.679106         93\n",
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              "                                                     [key], {});\n",
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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",
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    {
      "cell_type": "code",
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        "NDVI['year'] = pd.to_datetime(NDVI.start_date).dt.year\n",
        "NDVI['month'] = pd.to_datetime(NDVI.start_date).dt.month\n",
        "group4 = NDVI.groupby(['year', 'month']).mean().reset_index()\n",
        "group4"
      ],
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        "id": "HORLchbWxgty",
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      },
      "execution_count": 13,
      "outputs": [
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          "data": {
            "text/plain": [
              "    year  month      ndvi  region_id\n",
              "0   2013     12  0.744614  98.857143\n",
              "1   2014      1  0.767099  98.580645\n",
              "2   2014      2  0.764008  98.923077\n",
              "3   2014      3  0.755824  99.242424\n",
              "4   2014      4  0.782554  99.000000\n",
              "..   ...    ...       ...        ...\n",
              "95  2021     11  0.815173  98.870968\n",
              "96  2021     12  0.809898  98.470588\n",
              "97  2022      1  0.804939  99.026316\n",
              "98  2022      2  0.804086  98.727273\n",
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              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>0.764008</td>\n",
              "      <td>98.923077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>3</td>\n",
              "      <td>0.755824</td>\n",
              "      <td>99.242424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014</td>\n",
              "      <td>4</td>\n",
              "      <td>0.782554</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2021</td>\n",
              "      <td>11</td>\n",
              "      <td>0.815173</td>\n",
              "      <td>98.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "      <td>0.809898</td>\n",
              "      <td>98.470588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>0.804939</td>\n",
              "      <td>99.026316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>2022</td>\n",
              "      <td>2</td>\n",
              "      <td>0.804086</td>\n",
              "      <td>98.727273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>2022</td>\n",
              "      <td>3</td>\n",
              "      <td>0.807689</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>100 rows × 4 columns</p>\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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      ]
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    {
      "cell_type": "code",
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        "group4.iloc[1:100,0:3]"
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          "height": 424
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        "id": "jZ0HPcdszIeQ",
        "outputId": "71351a28-9956-4792-9224-b78426e8855a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    year  month      ndvi\n",
              "1   2014      1  0.767099\n",
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              "4   2014      4  0.782554\n",
              "5   2014      5  0.803742\n",
              "..   ...    ...       ...\n",
              "95  2021     11  0.815173\n",
              "96  2021     12  0.809898\n",
              "97  2022      1  0.804939\n",
              "98  2022      2  0.804086\n",
              "99  2022      3  0.807689\n",
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              "[99 rows x 3 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "production_quantity.head()"
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      "metadata": {
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          "height": 206
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        "id": "QWdiZ-YbyQ-7",
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      "execution_count": 15,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 start_date                  end_date    prod  region_id\n",
              "0  2015-01-01T00:00:00.000Z  2015-01-31T00:00:00.000Z  171725         93\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "production_quantity['year'] = pd.to_datetime(production_quantity.start_date).dt.year\n",
        "production_quantity['month'] = pd.to_datetime(production_quantity.start_date).dt.month\n",
        "group5 = production_quantity.groupby(['year', 'month']).sum().reset_index()\n",
        "group5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2hlPdVZhxmfy",
        "outputId": "591830dd-747d-4ae2-cc11-5dfafa4c4481"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month     prod  region_id\n",
              "0   2015      1  1149403        990\n",
              "1   2015      2  1109353        990\n",
              "2   2015      3  1476830        990\n",
              "3   2015      4  1675049        990\n",
              "4   2015      5  1790152        990\n",
              "..   ...    ...      ...        ...\n",
              "67  2020      8  1846230        990\n",
              "68  2020      9  1852739        990\n",
              "69  2020     10  1711262        990\n",
              "70  2020     11  1480695        990\n",
              "71  2020     12  1323012        990\n",
              "\n",
              "[72 rows x 4 columns]"
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              "      <td>1675049</td>\n",
              "      <td>990</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>1790152</td>\n",
              "      <td>990</td>\n",
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              "      <th>67</th>\n",
              "      <td>2020</td>\n",
              "      <td>8</td>\n",
              "      <td>1846230</td>\n",
              "      <td>990</td>\n",
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              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>2020</td>\n",
              "      <td>9</td>\n",
              "      <td>1852739</td>\n",
              "      <td>990</td>\n",
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              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>2020</td>\n",
              "      <td>10</td>\n",
              "      <td>1711262</td>\n",
              "      <td>990</td>\n",
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              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2020</td>\n",
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              "      <td>1323012</td>\n",
              "      <td>990</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "merge1 = pd.merge(group5, group4, how='left', on=['year', 'month'])\n",
        "merge1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ugjlfghJyRBn",
        "outputId": "c1cc9146-31bc-4b16-e3c7-f46511946d5d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month     prod  region_id_x      ndvi  region_id_y\n",
              "0   2015      1  1149403          990  0.765153    98.648649\n",
              "1   2015      2  1109353          990  0.757016    98.947368\n",
              "2   2015      3  1476830          990  0.757462    99.000000\n",
              "3   2015      4  1675049          990  0.777145    99.148148\n",
              "4   2015      5  1790152          990  0.802823    98.972973\n",
              "..   ...    ...      ...          ...       ...          ...\n",
              "67  2020      8  1846230          990  0.817843    99.000000\n",
              "68  2020      9  1852739          990  0.813437    99.081081\n",
              "69  2020     10  1711262          990  0.820761    99.071429\n",
              "70  2020     11  1480695          990  0.812049    99.125000\n",
              "71  2020     12  1323012          990  0.811183    98.681818\n",
              "\n",
              "[72 rows x 6 columns]"
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              "<p>72 rows × 6 columns</p>\n",
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              "          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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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
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        "merge2 = pd.merge(group5, group3, how='left', on=['year', 'month'])\n",
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              "    year  month     prod  region_id_x       temp  region_id_y\n",
              "0   2015      1  1149403          990  24.878109    98.660714\n",
              "1   2015      2  1109353          990  26.269102    98.718919\n",
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              "4   2015      5  1790152          990  25.330221    99.388571\n",
              "..   ...    ...      ...          ...        ...          ...\n",
              "67  2020      8  1846230          990  27.060771    99.534722\n",
              "68  2020      9  1852739          990  27.215247    99.526316\n",
              "69  2020     10  1711262          990  27.167178    98.788235\n",
              "70  2020     11  1480695          990  25.622195    99.732394\n",
              "71  2020     12  1323012          990  24.825073    99.349398\n",
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              "                                                     [key], {});\n",
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    {
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        "merge3 = pd.merge(group5, group2, how='left', on=['year', 'month'])\n",
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            "text/plain": [
              "    year  month     prod  region_id_x        smos  region_id_y\n",
              "0   2015      1  1149403          990   90.331394        29306\n",
              "1   2015      2  1109353          990   72.392165        27720\n",
              "2   2015      3  1476830          990   68.611206        29700\n",
              "3   2015      4  1675049          990   70.428552        29700\n",
              "4   2015      5  1790152          990   70.971349        30690\n",
              "..   ...    ...      ...          ...         ...          ...\n",
              "67  2020      8  1846230          990   81.261205        30070\n",
              "68  2020      9  1852739          990   84.132815        29490\n",
              "69  2020     10  1711262          990   87.229262        29275\n",
              "70  2020     11  1480695          990   96.860011        29490\n",
              "71  2020     12  1323012          990  105.480416        30690\n",
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              "            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",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "merge4 = pd.merge(group5, group1, how='left', on=['year', 'month'])\n",
        "merge4"
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      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    year  month     prod  region_id_x       precip  region_id_y\n",
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              "..   ...    ...      ...          ...          ...          ...\n",
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              "69  2020     10  1711262          990  2307.890441        30690\n",
              "70  2020     11  1480695          990  3148.844076        29700\n",
              "71  2020     12  1323012          990  2459.007805        30690\n",
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              "                                                     [key], {});\n",
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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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complete = merge4.iloc[:,0:3]\n",
        "complete['precip'] = merge4.precip\n",
        "complete['soil'] = merge3.smos\n",
        "complete['temp'] = merge2.temp\n",
        "complete['ndvi'] = merge1.ndvi\n",
        "complete"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0flgOItRxv5G",
        "outputId": "23106d34-43f0-4be3-f6dc-3813bc4368e6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month     prod       precip        soil       temp      ndvi\n",
              "0   2015      1  1149403  1332.910649   90.331394  24.878109  0.765153\n",
              "1   2015      2  1109353   609.399687   72.392165  26.269102  0.757016\n",
              "2   2015      3  1476830  1204.618027   68.611206  26.962245  0.757462\n",
              "3   2015      4  1675049  2480.186327   70.428552  26.244691  0.777145\n",
              "4   2015      5  1790152  2826.495489   70.971349  25.330221  0.802823\n",
              "..   ...    ...      ...          ...         ...        ...       ...\n",
              "67  2020      8  1846230  1836.043880   81.261205  27.060771  0.817843\n",
              "68  2020      9  1852739  2585.076965   84.132815  27.215247  0.813437\n",
              "69  2020     10  1711262  2307.890441   87.229262  27.167178  0.820761\n",
              "70  2020     11  1480695  3148.844076   96.860011  25.622195  0.812049\n",
              "71  2020     12  1323012  2459.007805  105.480416  24.825073  0.811183\n",
              "\n",
              "[72 rows x 7 columns]"
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84013958-4fb7-43e2-9f2a-d9db7a517d8b')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84013958-4fb7-43e2-9f2a-d9db7a517d8b 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-84013958-4fb7-43e2-9f2a-d9db7a517d8b');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = complete.iloc[:,3:7]\n",
        "y = complete.iloc[:,2]"
      ],
      "metadata": {
        "id": "qsKwNXZox1EP"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After having a complete set of my data, I will use the precip, soil, temp, ndvi to predict the production accordingly and use the model with best score for prediction in the prediction dataset."
      ],
      "metadata": {
        "id": "GvAlDWaj0P54"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.linear_model\n",
        "reg = sklearn.linear_model.LinearRegression().fit(x,y)\n",
        "reg.score(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nK73sltx1G4",
        "outputId": "db68f398-aa23-4704-fc14-20340b119ea2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.26865750470115346"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "re = sklearn.linear_model.Ridge(alpha=.8).fit(x,y)\n",
        "re.score(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvbTJvGYxv7s",
        "outputId": "b84cbc73-022b-47ca-fdb5-c45b6eb6afb0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.245563378851127"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
        "clf.fit(x, y)\n",
        "clf.score(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxbWx00Rx7_7",
        "outputId": "c483b189-31cf-4363-8f22-bd3d3d6702b2"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks that the model is best fit using SVM, so I will choose SVM in the following prediction."
      ],
      "metadata": {
        "id": "v6MaHEBiyB78"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "B8v5EDC6x-AZ",
        "outputId": "bea067e1-175d-406d-db89-009839d119e5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3ea8ab9a-b99e-4e6a-b46a-453039d96d58\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3ea8ab9a-b99e-4e6a-b46a-453039d96d58\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving predicted_production_qty.csv to predicted_production_qty.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = pd.read_csv(io.BytesIO(uploaded['predicted_production_qty.csv']))\n",
        "predicted.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k2a3jNa-x8EH",
        "outputId": "abead195-33aa-47ba-e550-17fcb198c0f7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 start_date                  end_date  prod  region_id\n",
              "0  2021-01-01T00:00:00.000Z  2021-01-31T00:00:00.000Z   NaN         93\n",
              "1  2021-02-01T00:00:00.000Z  2021-02-28T00:00:00.000Z   NaN         93\n",
              "2  2021-03-01T00:00:00.000Z  2021-03-31T00:00:00.000Z   NaN         93\n",
              "3  2021-04-01T00:00:00.000Z  2021-04-30T00:00:00.000Z   NaN         93\n",
              "4  2021-05-01T00:00:00.000Z  2021-05-31T00:00:00.000Z   NaN         93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e095f0a-52eb-47b9-94ee-e8579655eff7\">\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>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>prod</th>\n",
              "      <th>region_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-01-01T00:00:00.000Z</td>\n",
              "      <td>2021-01-31T00:00:00.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-02-01T00:00:00.000Z</td>\n",
              "      <td>2021-02-28T00:00:00.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-03-01T00:00:00.000Z</td>\n",
              "      <td>2021-03-31T00:00:00.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-04-01T00:00:00.000Z</td>\n",
              "      <td>2021-04-30T00:00:00.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-05-01T00:00:00.000Z</td>\n",
              "      <td>2021-05-31T00:00:00.000Z</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e095f0a-52eb-47b9-94ee-e8579655eff7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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        "predicted['year'] = pd.to_datetime(predicted.start_date).dt.year\n",
        "predicted['month'] = pd.to_datetime(predicted.start_date).dt.month\n",
        "group6 = predicted.groupby(['year', 'month']).sum().reset_index()\n",
        "group6"
      ],
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          "height": 426
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      },
      "execution_count": 28,
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              "    year  month  prod  region_id\n",
              "0   2021      1   0.0        990\n",
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        "merge5 = pd.merge(group6, group1, how='left', on=['year', 'month'])\n",
        "merge5"
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              "    year  month  prod  region_id_x       precip  region_id_y\n",
              "0   2021      1   0.0          990  2810.815175        30690\n",
              "1   2021      2   0.0          990   466.812833        27720\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-f25cae15-3b0c-4765-8211-3b0d3efead9e');\n",
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              "            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": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merge6 = pd.merge(group6, group2, how='left', on=['year', 'month'])\n",
        "merge6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "VISKntT2yJFF",
        "outputId": "cf564f69-945e-4f45-b976-ccd25e6323b1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month  prod  region_id_x        smos  region_id_y\n",
              "0   2021      1   0.0          990  104.801214        30690\n",
              "1   2021      2   0.0          990   88.159716        27720\n",
              "2   2021      3   0.0          990   88.701442        30690\n",
              "3   2021      4   0.0          990   83.650384        29700\n",
              "4   2021      5   0.0          990   79.926523        29224\n",
              "5   2021      6   0.0          990   84.031279        29495\n",
              "6   2021      7   0.0          990   75.940355        30394\n",
              "7   2021      8   0.0          990   82.399781        30480\n",
              "8   2021      9   0.0          990   87.566793        29700\n",
              "9   2021     10   0.0          990   85.844143        29905\n",
              "10  2021     11   0.0          990  102.989309        28915\n",
              "11  2021     12   0.0          990  103.003425        28896"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0cdcea2-be76-43c2-929d-1a63c930c039');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merge7 = pd.merge(group6, group3, how='left', on=['year', 'month'])\n",
        "merge7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
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        "id": "3V0SqfSMx8Kr",
        "outputId": "6a6aacce-05ce-4d81-9a26-2a515acd1d10"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month  prod  region_id_x       temp  region_id_y\n",
              "0   2021      1   0.0          990  24.985233    99.000000\n",
              "1   2021      2   0.0          990  26.977112    98.655172\n",
              "2   2021      3   0.0          990  27.726707    98.923664\n",
              "3   2021      4   0.0          990  27.098498    99.058824\n",
              "4   2021      5   0.0          990  26.668545    99.357143\n",
              "5   2021      6   0.0          990  25.954743    99.060976\n",
              "6   2021      7   0.0          990  26.159738    99.717172\n",
              "7   2021      8   0.0          990  26.292082    99.145631\n",
              "8   2021      9   0.0          990  26.455555    99.009901\n",
              "9   2021     10   0.0          990  27.191448    99.088235\n",
              "10  2021     11   0.0          990  25.779901    99.455882\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merge8 = pd.merge(group6, group4, how='left', on=['year', 'month'])\n",
        "merge8"
      ],
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        "colab": {
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          "height": 426
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        "id": "AjEbxtHlxv-M",
        "outputId": "9d88a211-ff82-4c37-9823-5e6ad0adc237"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month  prod  region_id_x      ndvi  region_id_y\n",
              "0   2021      1   0.0          990  0.804684    99.320000\n",
              "1   2021      2   0.0          990  0.777552    98.736842\n",
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              "4   2021      5   0.0          990  0.822307    98.777778\n",
              "5   2021      6   0.0          990  0.826986    99.000000\n",
              "6   2021      7   0.0          990  0.820485    99.000000\n",
              "7   2021      8   0.0          990  0.822101    99.419355\n",
              "8   2021      9   0.0          990  0.809393    99.128205\n",
              "9   2021     10   0.0          990  0.808639    98.923077\n",
              "10  2021     11   0.0          990  0.815173    98.870968\n",
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              "                                                     [key], {});\n",
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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",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complete_new = merge6.iloc[:,0:3]\n",
        "complete_new['precip'] = merge5.precip\n",
        "complete_new['soil'] = merge6.smos\n",
        "complete_new['temp'] = merge7.temp\n",
        "complete_new['ndvi'] = merge8.ndvi\n",
        "complete_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "kKa5X1ogy8ln",
        "outputId": "4ed427d3-c99c-46b5-9083-50c735872d23"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month  prod       precip        soil       temp      ndvi\n",
              "0   2021      1   0.0  2810.815175  104.801214  24.985233  0.804684\n",
              "1   2021      2   0.0   466.812833   88.159716  26.977112  0.777552\n",
              "2   2021      3   0.0  2595.858572   88.701442  27.726707  0.790195\n",
              "3   2021      4   0.0  2516.465034   83.650384  27.098498  0.810823\n",
              "4   2021      5   0.0  3116.795361   79.926523  26.668545  0.822307\n",
              "5   2021      6   0.0  1719.712429   84.031279  25.954743  0.826986\n",
              "6   2021      7   0.0  1703.708847   75.940355  26.159738  0.820485\n",
              "7   2021      8   0.0  2395.107301   82.399781  26.292082  0.822101\n",
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              "9   2021     10   0.0  2175.680056   85.844143  27.191448  0.808639\n",
              "10  2021     11   0.0  3059.851405  102.989309  25.779901  0.815173\n",
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              "      <td>25.779901</td>\n",
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              "      <td>12</td>\n",
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              "      <td>2872.624468</td>\n",
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              "            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",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 34
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_new = complete_new.iloc[:,3:7]\n",
        "x_new"
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          "height": 426
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      },
      "execution_count": 36,
      "outputs": [
        {
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              "         precip        soil       temp      ndvi\n",
              "0   2810.815175  104.801214  24.985233  0.804684\n",
              "1    466.812833   88.159716  26.977112  0.777552\n",
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              "5   1719.712429   84.031279  25.954743  0.826986\n",
              "6   1703.708847   75.940355  26.159738  0.820485\n",
              "7   2395.107301   82.399781  26.292082  0.822101\n",
              "8   2573.153220   87.566793  26.455555  0.809393\n",
              "9   2175.680056   85.844143  27.191448  0.808639\n",
              "10  3059.851405  102.989309  25.779901  0.815173\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "clf.predict(x_new)"
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        "outputId": "9097c467-36fb-4c81-c689-855a5bf3cd49"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1323012, 1272850, 1636201, 1852739, 1789153, 1814304, 1744617,\n",
              "       1789153, 1852739, 1852739, 1480695, 1321968])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
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    {
      "cell_type": "code",
      "source": [
        "complete_new['prod'] = clf.predict(x_new)\n",
        "complete_new"
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          "height": 426
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        "id": "oKwFozkDy8uW",
        "outputId": "4d4ee509-034e-44f5-db8e-3b5c81829a0d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month     prod       precip        soil       temp      ndvi\n",
              "0   2021      1  1323012  2810.815175  104.801214  24.985233  0.804684\n",
              "1   2021      2  1272850   466.812833   88.159716  26.977112  0.777552\n",
              "2   2021      3  1636201  2595.858572   88.701442  27.726707  0.790195\n",
              "3   2021      4  1852739  2516.465034   83.650384  27.098498  0.810823\n",
              "4   2021      5  1789153  3116.795361   79.926523  26.668545  0.822307\n",
              "5   2021      6  1814304  1719.712429   84.031279  25.954743  0.826986\n",
              "6   2021      7  1744617  1703.708847   75.940355  26.159738  0.820485\n",
              "7   2021      8  1789153  2395.107301   82.399781  26.292082  0.822101\n",
              "8   2021      9  1852739  2573.153220   87.566793  26.455555  0.809393\n",
              "9   2021     10  1852739  2175.680056   85.844143  27.191448  0.808639\n",
              "10  2021     11  1480695  3059.851405  102.989309  25.779901  0.815173\n",
              "11  2021     12  1321968  2872.624468  103.003425  25.857554  0.809898"
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              "      <td>2021</td>\n",
              "      <td>6</td>\n",
              "      <td>1814304</td>\n",
              "      <td>1719.712429</td>\n",
              "      <td>84.031279</td>\n",
              "      <td>25.954743</td>\n",
              "      <td>0.826986</td>\n",
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              "      <th>6</th>\n",
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              "      <td>7</td>\n",
              "      <td>1744617</td>\n",
              "      <td>1703.708847</td>\n",
              "      <td>75.940355</td>\n",
              "      <td>26.159738</td>\n",
              "      <td>0.820485</td>\n",
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              "      <td>2395.107301</td>\n",
              "      <td>82.399781</td>\n",
              "      <td>26.292082</td>\n",
              "      <td>0.822101</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2021</td>\n",
              "      <td>9</td>\n",
              "      <td>1852739</td>\n",
              "      <td>2573.153220</td>\n",
              "      <td>87.566793</td>\n",
              "      <td>26.455555</td>\n",
              "      <td>0.809393</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>1852739</td>\n",
              "      <td>2175.680056</td>\n",
              "      <td>85.844143</td>\n",
              "      <td>27.191448</td>\n",
              "      <td>0.808639</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2021</td>\n",
              "      <td>11</td>\n",
              "      <td>1480695</td>\n",
              "      <td>3059.851405</td>\n",
              "      <td>102.989309</td>\n",
              "      <td>25.779901</td>\n",
              "      <td>0.815173</td>\n",
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              "      <th>11</th>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "      <td>1321968</td>\n",
              "      <td>2872.624468</td>\n",
              "      <td>103.003425</td>\n",
              "      <td>25.857554</td>\n",
              "      <td>0.809898</td>\n",
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          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "complete_new['region_id'] = predicted.region_id\n",
        "complete_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "ozGFQ5j20z45",
        "outputId": "4393fa50-14ad-472f-f62f-8487683f4d7a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month     prod       precip        soil       temp      ndvi  \\\n",
              "0   2021      1  1323012  2810.815175  104.801214  24.985233  0.804684   \n",
              "1   2021      2  1272850   466.812833   88.159716  26.977112  0.777552   \n",
              "2   2021      3  1636201  2595.858572   88.701442  27.726707  0.790195   \n",
              "3   2021      4  1852739  2516.465034   83.650384  27.098498  0.810823   \n",
              "4   2021      5  1789153  3116.795361   79.926523  26.668545  0.822307   \n",
              "5   2021      6  1814304  1719.712429   84.031279  25.954743  0.826986   \n",
              "6   2021      7  1744617  1703.708847   75.940355  26.159738  0.820485   \n",
              "7   2021      8  1789153  2395.107301   82.399781  26.292082  0.822101   \n",
              "8   2021      9  1852739  2573.153220   87.566793  26.455555  0.809393   \n",
              "9   2021     10  1852739  2175.680056   85.844143  27.191448  0.808639   \n",
              "10  2021     11  1480695  3059.851405  102.989309  25.779901  0.815173   \n",
              "11  2021     12  1321968  2872.624468  103.003425  25.857554  0.809898   \n",
              "\n",
              "    region_id  \n",
              "0          93  \n",
              "1          93  \n",
              "2          93  \n",
              "3          93  \n",
              "4          93  \n",
              "5          93  \n",
              "6          93  \n",
              "7          93  \n",
              "8          93  \n",
              "9          93  \n",
              "10         93  \n",
              "11         93  "
            ],
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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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally I insert the clf predicted value into the prediction dataset and have a complete value for all the columns."
      ],
      "metadata": {
        "id": "GP4xCC9F0gyS"
      }
    }
  ]
}