Walmart_Sales_Forecast.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/ManuSinghYadav/Walmart-Sales-Forecast/blob/main/Walmart_Sales_Forecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woy2VyPYmTgR"
      },
      "source": [
        "##Importing the Data\n",
        "It can be downloaded from [here](https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pmdarima\n",
        "from pmdarima import auto_arima"
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      "execution_count": 6,
      "outputs": [
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      "execution_count": 97,
      "metadata": {
        "id": "YLBt2F6axzKD"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from sklearn.metrics import mean_absolute_error as MAE\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "from statsmodels.tsa.stattools import grangercausalitytests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
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        "id": "ICzkcrdJx2nL",
        "outputId": "001cb0f0-c47f-4e6c-85d6-b397bdee0395"
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          "text": [
            "<ipython-input-133-ad108aaf380d>:4: DtypeWarning: Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  train = pd.read_csv('/content/Walmart-Sales-Forecast/train.csv')\n"
          ]
        }
      ],
      "source": [
        "features = pd.read_csv('/content/Walmart-Sales-Forecast/features.csv')\n",
        "stores = pd.read_csv('/content/Walmart-Sales-Forecast/stores.csv')\n",
        "test = pd.read_csv('/content/Walmart-Sales-Forecast/test.csv')\n",
        "train = pd.read_csv('/content/Walmart-Sales-Forecast/train.csv')"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "##Basic Exploration"
      ]
    },
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      "execution_count": 9,
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              "   Store  Dept        Date  Weekly_Sales IsHoliday\n",
              "0      1     1  2010-02-05      24924.50     False\n",
              "1      1     1  2010-02-12      46039.49      True\n",
              "2      1     1  2010-02-19      41595.55     False\n",
              "3      1     1  2010-02-26      19403.54     False\n",
              "4      1     1  2010-03-05      21827.90     False"
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          "metadata": {},
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              "   Store  Dept        Date  IsHoliday\n",
              "0      1     1  2012-11-02      False\n",
              "1      1     1  2012-11-09      False\n",
              "2      1     1  2012-11-16      False\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce83ce85-c6bb-44bd-95c2-6cc2553ae088 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-ce83ce85-c6bb-44bd-95c2-6cc2553ae088');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e7349268-9eac-4d75-94dc-596b54b5d397\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7349268-9eac-4d75-94dc-596b54b5d397')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7349268-9eac-4d75-94dc-596b54b5d397 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZntX9lhCtfCP",
        "outputId": "4eb8c9a3-8625-43ce-a73d-0d720cbb7b3c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Store Type    Size\n",
              "0      1    A  151315\n",
              "1      2    A  202307\n",
              "2      3    B   37392\n",
              "3      4    A  205863\n",
              "4      5    B   34875"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f85a137-4012-4abd-beab-9f66985694a9\" 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>Store</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>A</td>\n",
              "      <td>202307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B</td>\n",
              "      <td>37392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>A</td>\n",
              "      <td>205863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B</td>\n",
              "      <td>34875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f85a137-4012-4abd-beab-9f66985694a9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8f85a137-4012-4abd-beab-9f66985694a9 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-8f85a137-4012-4abd-beab-9f66985694a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-426dd6b9-e0ed-4482-bd95-e272b19303fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-426dd6b9-e0ed-4482-bd95-e272b19303fc')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-426dd6b9-e0ed-4482-bd95-e272b19303fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "stores.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "HkrhUt26tglX",
        "outputId": "b81b043d-f5e0-418f-a709-3fe8b46c832e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
              "0      1  2010-02-05        42.31       2.572        NaN        NaN   \n",
              "1      1  2010-02-12        38.51       2.548        NaN        NaN   \n",
              "2      1  2010-02-19        39.93       2.514        NaN        NaN   \n",
              "3      1  2010-02-26        46.63       2.561        NaN        NaN   \n",
              "4      1  2010-03-05        46.50       2.625        NaN        NaN   \n",
              "\n",
              "   MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
              "0        NaN        NaN        NaN  211.096358         8.106      False  \n",
              "1        NaN        NaN        NaN  211.242170         8.106       True  \n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0b730ac0-9206-4424-bfce-f7cdbdd3ee84');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "features.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCXwDdO6ti_K",
        "outputId": "c3e5a9af-96d9-493c-be26-84e06fe8be93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 309679 entries, 0 to 309678\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   Store         309679 non-null  int64  \n",
            " 1   Dept          309679 non-null  int64  \n",
            " 2   Date          309679 non-null  object \n",
            " 3   Weekly_Sales  309679 non-null  float64\n",
            " 4   IsHoliday     309678 non-null  object \n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 11.8+ MB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ejlfckrctv9C",
        "outputId": "4d983092-5387-4b8f-ddba-4b3a8b6957b8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Store           Dept   Weekly_Sales\n",
              "count  309679.000000  309679.000000  309679.000000\n",
              "mean       16.219340      43.414981   17140.600746\n",
              "std         9.071128      29.839534   23941.832113\n",
              "min         1.000000       1.000000   -4988.940000\n",
              "25%         8.000000      19.000000    2661.720000\n",
              "50%        16.000000      36.000000    8337.490000\n",
              "75%        24.000000      71.000000   21601.720000\n",
              "max        32.000000      99.000000  693099.360000"
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>309679.000000</td>\n",
              "      <td>309679.000000</td>\n",
              "      <td>309679.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.219340</td>\n",
              "      <td>43.414981</td>\n",
              "      <td>17140.600746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.071128</td>\n",
              "      <td>29.839534</td>\n",
              "      <td>23941.832113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-4988.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>2661.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>8337.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>21601.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>693099.360000</td>\n",
              "    </tr>\n",
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              "        const element = document.querySelector('#df-e44f5e18-3e59-4863-bea3-7b109568d7dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ef16ca99-da65-4b4b-9dcc-cff7c08eea39\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef16ca99-da65-4b4b-9dcc-cff7c08eea39')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef16ca99-da65-4b4b-9dcc-cff7c08eea39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "uUXrutWbt3Uq",
        "outputId": "2f847736-123a-4eb9-d16b-69df933b33a2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2012-10-26'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "max(train['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "5JCwGhL6t2bJ",
        "outputId": "f0deb7ef-0578-4c86-97a2-cfbdbacc5819"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2012-11-02'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "min(test['Date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTV41n7-vI7G",
        "outputId": "a241223e-03a8-4ee2-fc96-237384f82e73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stores: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
            " 25 26 27 28 29 30 31 32]\n",
            "\n",
            "Total Stores: 32\n"
          ]
        }
      ],
      "source": [
        "print(\"Stores:\", np.sort(train['Store'].unique()))\n",
        "\n",
        "print(\"\\nTotal Stores:\", len(train['Store'].unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLDnLshouK2F",
        "outputId": "107878bc-c123-4c07-ff19-bd6e21fdc9ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Departments: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25\n",
            " 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n",
            " 50 51 52 54 55 56 58 59 60 67 71 72 74 77 78 79 80 81 82 83 85 87 90 91\n",
            " 92 93 94 95 96 97 98 99]\n",
            "\n",
            "Total Departments: 80\n"
          ]
        }
      ],
      "source": [
        "print(\"Departments:\", np.sort(train['Dept'].unique()))\n",
        "\n",
        "print(\"\\nTotal Departments:\", len(train['Dept'].unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nM6ZP2ZDy6sB",
        "outputId": "88027503-6533-423b-dc0d-2b46d6d13e60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Unique Weeks: 143\n"
          ]
        }
      ],
      "source": [
        "print(\"Total Unique Weeks:\", len(train['Date'].unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "-n3rPdMpzn_y",
        "outputId": "38db9622-9859-4846-c439-fbee783fd247"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date  Weekly_Sales  IsHoliday\n",
              "Store Dept                               \n",
              "1     1      143           143        143\n",
              "      2      143           143        143\n",
              "      3      143           143        143\n",
              "      4      143           143        143\n",
              "      5      143           143        143\n",
              "...          ...           ...        ...\n",
              "32    38     143           143        143\n",
              "      40     143           143        143\n",
              "      41     143           143        143\n",
              "      42     143           143        143\n",
              "      44     138           138        137\n",
              "\n",
              "[2404 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60b183ce-558b-4bd2-beb9-ae80406e2461\" 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></th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
              "      <th>1</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">32</th>\n",
              "      <th>38</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>138</td>\n",
              "      <td>138</td>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2404 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60b183ce-558b-4bd2-beb9-ae80406e2461')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60b183ce-558b-4bd2-beb9-ae80406e2461 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-60b183ce-558b-4bd2-beb9-ae80406e2461');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-91f485e8-b49a-46d5-9100-813e520c1448\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-91f485e8-b49a-46d5-9100-813e520c1448')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "train.groupby(['Store','Dept']).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "fZa8b2MY51nz"
      },
      "outputs": [],
      "source": [
        "# @title Analysis/ Result\n",
        "# @markdown From the above analyis, it can be analyzed that we have **32 Stores** and *almost* each store is having **80 Departments**. And *almost* each department is having data for **143 weeks**.\n",
        "\n",
        "# @markdown It seems it has many columns which can be treated as exogenous variables. Will use them later.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLr3a2SE7KFc"
      },
      "source": [
        "##Data Preprocessing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing NaN rows"
      ],
      "metadata": {
        "id": "3R9lBAK8wD2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = train.dropna()"
      ],
      "metadata": {
        "id": "FbtzDuZAwG3r"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setting up the index"
      ],
      "metadata": {
        "id": "JqSgE0x-wC3_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "z4qvIQZt7Wzp"
      },
      "outputs": [],
      "source": [
        "train.index = train['Date']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "X9X9LAkA7q0c"
      },
      "outputs": [],
      "source": [
        "train = train.drop(['Date'], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "NIHmaaTz7x2R"
      },
      "outputs": [],
      "source": [
        "train.index = pd.to_datetime(train.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "kykmc02e8J8i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "305649de-caf4-43c8-9acb-d8119b26e069"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('<M8[ns]')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "train.index.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Label Encoding of 'IsHoliday' column."
      ],
      "metadata": {
        "id": "GpHXU_CbvsQX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l_e = LabelEncoder()\n",
        "train['IsHoliday']= l_e.fit_transform(train['IsHoliday'])\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "eRDePa2Qvx8r",
        "outputId": "9a2efe9a-a5b4-4b40-eda1-1f907890217e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Store  Dept  Weekly_Sales  IsHoliday\n",
              "Date                                            \n",
              "2010-02-05      1     1      24924.50          0\n",
              "2010-02-12      1     1      46039.49          1\n",
              "2010-02-19      1     1      41595.55          0\n",
              "2010-02-26      1     1      19403.54          0\n",
              "2010-03-05      1     1      21827.90          0\n",
              "...           ...   ...           ...        ...\n",
              "2012-08-17     32    44       5656.19          0\n",
              "2012-08-24     32    44       4533.20          0\n",
              "2012-08-31     32    44       3767.29          0\n",
              "2012-09-07     32    44       4152.52          1\n",
              "2012-09-14     32    44       4394.31          0\n",
              "\n",
              "[309678 rows x 4 columns]"
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              "      <td>4394.31</td>\n",
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              "<p>309678 rows × 4 columns</p>\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Resample the data\n",
        "Merge all the rows on the basis of weeks."
      ],
      "metadata": {
        "id": "eWjMkhtysuzr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(train.resample(rule = 'W').mean())\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "GS4QLYZ1s5Qe",
        "outputId": "046b15d6-9148-45b6-d493-81398bd49758"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Store       Dept  Weekly_Sales  IsHoliday\n",
              "Date                                                     \n",
              "2010-02-07  16.172761  43.250914  18117.921143        0.0\n",
              "2010-02-14  16.163777  43.412865  17529.432641        1.0\n",
              "2010-02-21  16.157059  43.386745  17409.342306        0.0\n",
              "2010-02-28  16.148352  43.070971  15903.966896        0.0\n",
              "2010-03-07  16.141020  43.022967  16971.829812        0.0\n",
              "...               ...        ...           ...        ...\n",
              "2012-09-30  16.160277  43.429099  15738.767607        0.0\n",
              "2012-10-07  16.199172  43.791168  17115.639894        0.0\n",
              "2012-10-14  16.181361  43.915487  16422.246725        0.0\n",
              "2012-10-21  16.181650  43.379518  16294.627405        0.0\n",
              "2012-10-28  16.177994  43.426722  16453.238673        0.0\n",
              "\n",
              "[143 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49eea47a-05ac-4fd9-ac37-828bb18fc940\" 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>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-02-07</th>\n",
              "      <td>16.172761</td>\n",
              "      <td>43.250914</td>\n",
              "      <td>18117.921143</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-14</th>\n",
              "      <td>16.163777</td>\n",
              "      <td>43.412865</td>\n",
              "      <td>17529.432641</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-21</th>\n",
              "      <td>16.157059</td>\n",
              "      <td>43.386745</td>\n",
              "      <td>17409.342306</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-28</th>\n",
              "      <td>16.148352</td>\n",
              "      <td>43.070971</td>\n",
              "      <td>15903.966896</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-03-07</th>\n",
              "      <td>16.141020</td>\n",
              "      <td>43.022967</td>\n",
              "      <td>16971.829812</td>\n",
              "      <td>0.0</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>2012-09-30</th>\n",
              "      <td>16.160277</td>\n",
              "      <td>43.429099</td>\n",
              "      <td>15738.767607</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-07</th>\n",
              "      <td>16.199172</td>\n",
              "      <td>43.791168</td>\n",
              "      <td>17115.639894</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-14</th>\n",
              "      <td>16.181361</td>\n",
              "      <td>43.915487</td>\n",
              "      <td>16422.246725</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-21</th>\n",
              "      <td>16.181650</td>\n",
              "      <td>43.379518</td>\n",
              "      <td>16294.627405</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-28</th>\n",
              "      <td>16.177994</td>\n",
              "      <td>43.426722</td>\n",
              "      <td>16453.238673</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>143 rows × 4 columns</p>\n",
              "</div>\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "        document.querySelector('#df-49eea47a-05ac-4fd9-ac37-828bb18fc940 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-49eea47a-05ac-4fd9-ac37-828bb18fc940');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78b50300-6f8b-40cd-8b69-1532eae79457')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78b50300-6f8b-40cd-8b69-1532eae79457 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['Weekly_Sales'])\n",
        "plt.xticks(rotation=45);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "-MBk7Pdxy3Gy",
        "outputId": "38057f5a-c283-4c11-ed54-3df0f7646603"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Stationarity"
      ],
      "metadata": {
        "id": "4O4vWQU2xiwJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adf = adfuller(df['Weekly_Sales'])\n",
        "print(\"P-Value:\",adf[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbFhJW_txk2B",
        "outputId": "2b4ff830-c50d-498d-9bb3-f01033a139ba"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-Value: 1.603633935400217e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is already stationary."
      ],
      "metadata": {
        "id": "jQzC59tczPOB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Train Test Split"
      ],
      "metadata": {
        "id": "IPwb76JlYHd-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_eval = df[:-33]\n",
        "test_eval = df[-33:]\n",
        "len(train_eval)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xNL1vQ4YJPc",
        "outputId": "cdaac648-3aeb-44f3-c73b-a146c3f1faf7"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_eval['Weekly_Sales'], label='Train')\n",
        "plt.plot(test_eval['Weekly_Sales'], label='Test')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "FkCX-kdOZvyL",
        "outputId": "de398e4c-18dc-41c5-ca2a-ec4493be8be1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Forecasting with SARIMA"
      ],
      "metadata": {
        "id": "aFh0t_UMxPz4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Auto_Arima"
      ],
      "metadata": {
        "id": "E9bzeGV0zSV2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is for finding the right parameters for the ARIMA based models."
      ],
      "metadata": {
        "id": "Pn-osHQUzX_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auto_arima(train_eval['Weekly_Sales'], seasonal = True, m=52)"
      ],
      "metadata": {
        "id": "TVxm-pwazWax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auto_arima(train_eval['Weekly_Sales'], seasonal = True, m=7)"
      ],
      "metadata": {
        "id": "CrUxNcZM0pRX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Fitting"
      ],
      "metadata": {
        "id": "kbA4r7vf1ZPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_sarima = SARIMAX(train_eval['Weekly_Sales'],order=(2,0,2),seasonal_order=(1,0,1,52)).fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glCYzDtn1cOd",
        "outputId": "6d2bd11f-adb3-4f89-86ef-8124dea48664"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-stationary starting autoregressive parameters'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-invertible starting MA parameters found.'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
            "  warn('Too few observations to estimate starting parameters%s.'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start=len(train_eval)\n",
        "end=len(train_eval)+len(test_eval)-1\n",
        "pred_sarima = model_sarima.predict(start=start, end=end, dynamic=False)"
      ],
      "metadata": {
        "id": "FO1cZlbuX9eq"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Plotting"
      ],
      "metadata": {
        "id": "8qEAeYDmjbJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_eval['Weekly_Sales'], label='Actual')\n",
        "plt.plot(pred_sarima, label='Prediction')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "69-cSw6ai3rl",
        "outputId": "bcf36c70-53e5-4b2d-e495-de7fa7bf1cc7"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluation"
      ],
      "metadata": {
        "id": "AgpRgq9Xjezs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"MAE:\", MAE(pred_sarima, test_eval['Weekly_Sales']))\n",
        "print(\"RMSE:\", MSE(pred_sarima, test_eval['Weekly_Sales'])**0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Aln_jX1jOH_",
        "outputId": "0aea78f5-c870-4b88-d366-7d73da53a8fe"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 510.803437608378\n",
            "RMSE: 743.9273044570964\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Forecasting with SARIMAX"
      ],
      "metadata": {
        "id": "paWm0isPktl6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this we'll pass **holiday** column as an exogneous variable."
      ],
      "metadata": {
        "id": "oZqZh5jxkzqV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Auto_Arima"
      ],
      "metadata": {
        "id": "GpaLVS4GrzTk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auto_arima(train_eval['Weekly_Sales'], exog=train_eval['IsHoliday'], seasonal = True, m=52)"
      ],
      "metadata": {
        "id": "eaGEm0kPr30b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Fitting"
      ],
      "metadata": {
        "id": "YIUgTmLbnBm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_sarimax = SARIMAX(train_eval['Weekly_Sales'],exog=train_eval['IsHoliday'],order=(2,0,2),seasonal_order=(1,0,1,52)).fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXLWjbqLkxIt",
        "outputId": "927c222c-5e44-455c-b252-1df622cce920"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-stationary starting autoregressive parameters'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-invertible starting MA parameters found.'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:866: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
            "  warn('Too few observations to estimate starting parameters%s.'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start=len(train_eval)\n",
        "end=len(train_eval)+len(test_eval)-1\n",
        "pred_sarimax = model_sarimax.predict(start=start, end=end, exog=test_eval['IsHoliday'], dynamic=False)"
      ],
      "metadata": {
        "id": "Q3LMBB2fm0e7"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Plotting"
      ],
      "metadata": {
        "id": "XSONrUCrm-T9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_eval['Weekly_Sales'], label='Actual')\n",
        "plt.plot(pred_sarimax, label='Prediction')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "W4vHUBwQm8pH",
        "outputId": "b3999e95-6945-4ec1-9752-d05ffd0fc971"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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uzP79+623+fj4WIMhgObNm5OSklLt5xVCuDBLQOTpD3lGCYiEXSQgqodGjBjBJ598goeHB2FhYbi5Ff8zN2pkG1hlZmbSr18/vvvuu1LnadasWbWe39vbu1qPqw53d3eb6zqdrtxATQhRz1kqVfuGSEAk7CYBUVW5+6iZGmc9tx0aNWpEhw4dqnRs3759+fHHHwkODi63QV7z5s35559/GDZsGAAFBQVs27aNvn37lnl8jx49MJvNrFu3zrpkVpJlhqqwsLDccXXt2pWCggL++ecf65LZ+fPniY+PJyIiokqvTQjRwFgCIN9gOH9IAiJhF0mqriqdTi1bOeOrGvlDVXXHHXfQtGlTrr/+ejZs2EBCQgJr167l8ccf59SpUwA88cQTvPHGGyxZsoQDBw7w6KOPlqohVFKbNm2YNGkS9913H0uWLLGec+HChQC0bt0anU7HsmXLOHv2LJmZpRMfO3bsyPXXX88DDzzAX3/9xc6dO7nzzjtp0aIF119/fa38LIQQLq5kQFTyuhBVIAFRA+fj48P69etp1aoVEyZMoGvXrkyePJnc3FzrjNGTTz7JXXfdxaRJk4iMjMTPz48bbrihwvN+8skn3HTTTTz66KN06dKFBx54gKws9cepRYsWvPLKKzzzzDOEhIQwZcqUMs/x1Vdf0a9fP6655hoiIyPRNI3ffvut1DKZEEIAxa07fEPVd5MERKLqdFoDTrgwGo0EBASQnp5earkoNzeXhIQE2rZti5eXl5NGKGqD/NsKUU992A/OH4aRL8GqVyCoHTy+w9mjErWgovfv6pIZIiGEEPVDyaRqkCUzYRcJiIQQQtQP1hwiCYiE/SQgEkII4fo0rbgOUcmk6oabFSLsJAGREEII12fKBoqCH7+ipGo0MOU4a0TCxUhAJIQQwvVZl8d04NOkjNuFqJgERJVowJvw6i35NxWiHrIsl3k0Ar2huKCtNHgVVSQBUTkMBgOAteO7qD+ys1WTXqlnJEQ9Ytlh5uFb9L2oTZGzmnILlyOtO8rh5uaGj48PZ8+exd3dHb1eYkdXp2ka2dnZpKSkEBgYaA16hRD1gGVpzBIIeTSCrLOyZCaqTAKicuh0Opo3b05CQgLHjx939nCEAwUGBhIaGlr5gUII12HtdF80Q+TeyPZ2ISohAVEFPDw86Nixoyyb1SPu7u4yMyREfZRfzpKZzBCJKpKAqBJ6vV7aOwghRF1X1pIZQL7kEImqkcQYIYQQrq+8pGpZMhNVJAGREEII11dy233J77JkJqpIAiIhhBCuT3KIRA1JQCSEEML1WQIfz4vrEElAJKpGAiIhhBCur1RSta/t7UJUwq6AaNasWQwYMAA/Pz+Cg4MZP3488fHx1vsvXLjAY489RufOnfH29qZVq1Y8/vjjpKen25znxIkTjBs3Dh8fH4KDg3nqqacoKCiwOWbt2rX07dsXT09POnTowLx580qNZ86cObRp0wYvLy8GDRrE5s2b7Xk5Qggh6ou8DPXdEghZW3dIQCSqxq6AaN26dURHR7Np0yZiYmIwmUyMHj2arCz1C3fmzBnOnDnDO++8w549e5g3bx4rVqxg8uTJ1nMUFhYybtw48vPz2bhxI/Pnz2fevHm8+OKL1mMSEhIYN24cI0aMIC4ujqlTp3L//fezcuVK6zE//vgj06dP56WXXmL79u306tWLqKgoUlJSavozEUII4WqsM0SSQySqSauBlJQUDdDWrVtX7jELFy7UPDw8NJPJpGmapv3222+aXq/XkpKSrMd88sknmr+/v5aXl6dpmqY9/fTTWrdu3WzOM3HiRC0qKsp6feDAgVp0dLT1emFhoRYWFqbNmjWryuNPT0/XAC09Pb3KjxFCCFEHfT5a017y17S9v6jrcT+o6/Ovd+qwRO2ojffvGuUQWZbCgoKCKjzG398fNzdVAzI2NpYePXoQEhJiPSYqKgqj0cjevXutx4waNcrmPFFRUcTGxgKq4eq2bdtsjtHr9YwaNcp6jBBCiAbk4tYdMkMk7FTtStVms5mpU6cyZMgQunfvXuYx586d47XXXuPBBx+03paUlGQTDAHW60lJSRUeYzQaycnJITU1lcLCwjKPOXDgQLljzsvLIy8vz3rdaDRW4ZUKIYSo80ptu5ccImGfas8QRUdHs2fPHn744Ycy7zcajYwbN46IiAhefvnl6j6NQ82aNYuAgADrV3h4uLOHJIQQwhFKVaq27DKTStWiaqoVEE2ZMoVly5axZs0aWrZsWer+jIwMxowZg5+fH4sXL8bd3d16X2hoKMnJyTbHW65bOpCXd4y/vz/e3t40bdoUg8FQ5jEVdTGfOXMm6enp1q+TJ0/a98KFEELUTeX1MjNJLzNRNXYFRJqmMWXKFBYvXszq1atp27ZtqWOMRiOjR4/Gw8ODpUuXlmqMGhkZye7du212g8XExODv709ERIT1mFWrVtk8LiYmhsjISEB1oe/Xr5/NMWazmVWrVlmPKYunpyf+/v42X0IIIVycuRAKctRl2WUmqsmugCg6Oppvv/2WBQsW4OfnR1JSEklJSeTkqF9ESzCUlZXFF198gdFotB5TWFgIwOjRo4mIiOCuu+5i586drFy5kueff57o6Gg8PT0BePjhhzl69ChPP/00Bw4c4OOPP2bhwoVMmzbNOpbp06fz2WefMX/+fPbv388jjzxCVlYW9957r6N+NkIIIVxByWUxS1K1e4kZInPhpR+TcD32bEkDyvz66quvNE3TtDVr1pR7TEJCgvU8x44d08aOHat5e3trTZs21Z588knrtnyLNWvWaL1799Y8PDy0du3aWZ+jpA8//FBr1aqV5uHhoQ0cOFDbtGmTXVvsZNu9EELUA+mn1Rb7V4I0zWxWt+Vlqdte8te0XKNzxyccrjbev3WapmnOCMTqAqPRSEBAgLU0gBBCCBd09iDMGQBegfDMcXWbpsErjQENnjwIfiEVnUG4mNp4/5ZeZkIIIVzbxVvuAXQ62Wkm7CIBkRBCCNd28Q4zC6lFJOwgAZEQQgjXdnGVagvZaSbsIAGREEII11buDJFlp5kERKJyEhAJIYRwbWXlEJW8LjNEogokIBJCCOHaLm7bYeEuOUSi6iQgEkII4doqWzKTgEhUgQREQgghXFt+hvpeKqlalsxE1UlAJIQQwrVZZ4hkl5moPgmIhBBCuDapQyQcQAIiIYQQrq28pGqpVC3sIAGREEII11butvsSHe+FqIQEREIIIVybVKoWDiABkRBCCNdWXg6RuyUgkiUzUTkJiIQQQrg2qUMkHEACIiGEEK4tr6gOkYef7e3WgEhyiETlJCASQgjh2sqdIZLCjKLqJCASQgjhugrywGxSl8utQyQ5RKJyEhAJIYRwXSVnf6RStagBCYiEEEK4Lsvsj5sXGNxs77MESIV5UFhwacclXI4EREIIIVxXeVWqwXYJzSSzRKJiEhAJIYRwXeUlVAMYPEBnsD1OiHJIQCSEEMJ1lde2A0Cnk51mosokIBJCCOG6ymvbYSGJ1aKKJCASQgjhuipaMit5uwREohISEAkhhHBd1irV5c0QWWoRSUAkKiYBkRBCCNdlnSEqLyCy5BBJcUZRMQmIhBBCuK6qLpmZpJ+ZqJgEREIIIVyXJFULB5GASAghhOuybrsvZ4bIvZHtcUKUQwIiIYQQrquiStUgM0SiyiQgEkII4boqTaq2BESSQyQqJgGREEII11XlOkSyZCYqZldANGvWLAYMGICfnx/BwcGMHz+e+Ph4m2M+/fRThg8fjr+/PzqdjrS0tFLnuXDhAnfccQf+/v4EBgYyefJkMjNtf1l37drF5ZdfjpeXF+Hh4bz11lulzrNo0SK6dOmCl5cXPXr04LfffrPn5QghhHB1+UV1iDz9yr5flsxEFdkVEK1bt47o6Gg2bdpETEwMJpOJ0aNHk5VV/IuWnZ3NmDFjePbZZ8s9zx133MHevXuJiYlh2bJlrF+/ngcffNB6v9FoZPTo0bRu3Zpt27bx9ttv8/LLL/Ppp59aj9m4cSO33XYbkydPZseOHYwfP57x48ezZ88ee16SEEIIVyaVqoWjaDWQkpKiAdq6detK3bdmzRoN0FJTU21u37dvnwZoW7Zssd72+++/azqdTjt9+rSmaZr28ccfa40bN9by8vKsx8yYMUPr3Lmz9fott9yijRs3zubcgwYN0h566KEqjz89PV0DtPT09Co/RgghRB3yTmdNe8lf087ElX3/7v+p+78aV/b9wiXVxvt3jXKI0tPTAQgKCqryY2JjYwkMDKR///7W20aNGoVer+eff/6xHjNs2DA8PDysx0RFRREfH09qaqr1mFGjRtmcOyoqitjY2HKfOy8vD6PRaPMlhBDChVW6y0wqVYuqqXZAZDabmTp1KkOGDKF79+5VflxSUhLBwcE2t7m5uREUFERSUpL1mJCQEJtjLNcrO8Zyf1lmzZpFQECA9Ss8PLzK4xZCCFHHaFqJOkTlBETu0stMVE21A6Lo6Gj27NnDDz/84Mjx1KqZM2eSnp5u/Tp58qSzhySEEKK6TNmApi5XWqlatt2LirlV50FTpkyxJkO3bNnSrseGhoaSkpJic1tBQQEXLlwgNDTUekxycrLNMZbrlR1jub8snp6eeHp62jVeIYQQdZR11kcHbt5lHyNLZqKK7Joh0jSNKVOmsHjxYlavXk3btm3tfsLIyEjS0tLYtm2b9bbVq1djNpsZNGiQ9Zj169djMpmsx8TExNC5c2caN25sPWbVqlU2546JiSEyMtLuMQkhhHBBJdt26Mt5O/OQJTNRNXYFRNHR0Xz77bcsWLAAPz8/kpKSSEpKIicnx3pMUlIScXFxHD58GIDdu3cTFxfHhQsXAOjatStjxozhgQceYPPmzfz9999MmTKFW2+9lbCwMABuv/12PDw8mDx5Mnv37uXHH3/kgw8+YPr06dbneeKJJ1ixYgWzZ8/mwIEDvPzyy2zdupUpU6bU+IcihBDCBVSWUA3FS2ZmExTk1/6YhOuyZ0saarG21NdXX31lPeall16q9Jjz589rt912m+br66v5+/tr9957r5aRkWHzXDt37tSGDh2qeXp6ai1atNDeeOONUuNZuHCh1qlTJ83Dw0Pr1q2btnz5cru22Mm2eyGEcGHHNqot9R/0Kf8YU5465iV/Tcu+cOnGJmpVbbx/6zRN05wSidUBRqORgIAA0tPT8ff3d/ZwhBBC2ONQDHx3E4T2hIc3lH/ca82gMB+m7YUA+/JeRd1UG+/f0stMCCGEa7LkEJXXtsNCtt6LKpCASAghhGuqrG2Hhew0E1UgAZEQQgjXVJWkapBaRKJKJCASQgjhmkpuu6+INHgVVSABkRBCCNdkXTKr6gyRLJmJ8klAJIQQwjVZk6qrGhDJDJEonwREQgghXFOVk6qL7jdJDpEonwREQgghXFNehvouS2bCASQgEkII4ZqqmkPkLktmonISEAkhhHBN9i6ZSUAkKiABkRBCCNdkd1K15BCJ8klAJIQQwjXlV7Uwo1SqFpWTgEgIIYRrqnKlaullJionAZEQQgjXJDlEwoEkIBJCCOF6zIVQkKMuV3XJzCQBkSifBERCCCFcT8l8IKlULRxAAiIhhBCuxxLc6N3A4FHxse6SQyQqJwGREEII15NXotO9TlfxsdZdZhIQifJJQCSEEML1WLfc+1V+bMklM02rvTEJlyYBkRBCCNdT1R1mJY/RCqEgr/bGJFyaBERCCCFcT1WrVINt0CTLZqIcEhAJIYRwPfbMEOkN4OZV9DipVi3KJgGREEII11PVth0WlsDJJP3MRNkkIBJCCOF6qtq2w0JqEYlKSEAkhBDC9dizZAbgbgmIZMlMlE0CIiGEEK4nP0N9r0pSNcgMkaiUBERCCCFcj3WGyN6ASHKIRNkkIBJCCOF67F0ys1arliUzUTYJiIQQQrgeu5OqpZ+ZqJgEREIIIVxPdbfdS0AkyiEBkRBCCNeTX6K5a1VYAieTBESibBIQCSGEcD2WmR7ZZSYcxK6AaNasWQwYMAA/Pz+Cg4MZP3488fHxNsfk5uYSHR1NkyZN8PX15cYbbyQ5OdnmmBMnTjBu3Dh8fHwIDg7mqaeeoqCgwOaYtWvX0rdvXzw9PenQoQPz5s0rNZ45c+bQpk0bvLy8GDRoEJs3b7bn5QghhHBVdtchkhwiUTG7AqJ169YRHR3Npk2biImJwWQyMXr0aLKyin/Bpk2bxq+//sqiRYtYt24dZ86cYcKECdb7CwsLGTduHPn5+WzcuJH58+czb948XnzxResxCQkJjBs3jhEjRhAXF8fUqVO5//77WblypfWYH3/8kenTp/PSSy+xfft2evXqRVRUFCkpKTX5eQghhHAFeUV1iDz8qna8hxRmFJXQaiAlJUUDtHXr1mmapmlpaWmau7u7tmjRIusx+/fv1wAtNjZW0zRN++233zS9Xq8lJSVZj/nkk080f39/LS8vT9M0TXv66ae1bt262TzXxIkTtaioKOv1gQMHatHR0dbrhYWFWlhYmDZr1qwqjz89PV0DtPT0dDtetRBCCKd7pYmmveSvaWmnqnb8jgXq+K9vqN1xiUuiNt6/a5RDlJ6eDkBQUBAA27Ztw2QyMWrUKOsxXbp0oVWrVsTGxgIQGxtLjx49CAkJsR4TFRWF0Whk79691mNKnsNyjOUc+fn5bNu2zeYYvV7PqFGjrMeUJS8vD6PRaPMlhBDCxRTkg9mkLlc5qVpyiETFqh0Qmc1mpk6dypAhQ+jevTsASUlJeHh4EBgYaHNsSEgISUlJ1mNKBkOW+y33VXSM0WgkJyeHc+fOUVhYWOYxlnOUZdasWQQEBFi/wsPD7X/hQgghnKvkspfUIRIOUu2AKDo6mj179vDDDz84cjy1aubMmaSnp1u/Tp486ewhCSGEsJclIHLzAoNb1R4jlapFJar4m2RrypQpLFu2jPXr19OyZUvr7aGhoeTn55OWlmYzS5ScnExoaKj1mIt3g1l2oZU85uKdacnJyfj7++Pt7Y3BYMBgMJR5jOUcZfH09MTT09P+FyyEEKLusLdKNRQvmZmkl5kom10zRJqmMWXKFBYvXszq1atp27atzf39+vXD3d2dVatWWW+Lj4/nxIkTREZGAhAZGcnu3bttdoPFxMTg7+9PRESE9ZiS57AcYzmHh4cH/fr1sznGbDazatUq6zFCCCHqKXu33Jc8VpbMRDnsmiGKjo5mwYIF/PLLL/j5+VnzdQICAvD29iYgIIDJkyczffp0goKC8Pf357HHHiMyMpLBgwcDMHr0aCIiIrjrrrt46623SEpK4vnnnyc6Oto6e/Pwww/z0Ucf8fTTT3PfffexevVqFi5cyPLly61jmT59OpMmTaJ///4MHDiQ999/n6ysLO69915H/WyEEELURfa27QBwLxEQmc2gl7rE4iL2bEkDyvz66quvrMfk5ORojz76qNa4cWPNx8dHu+GGG7TExESb8xw7dkwbO3as5u3trTVt2lR78sknNZPJZHPMmjVrtN69e2seHh5au3btbJ7D4sMPP9RatWqleXh4aAMHDtQ2bdpkz8uRbfdCCOGK9i1VW+g/v6rqj8nNUI95yV/T8jJrb2zikqiN92+dpmma88Ix5zIajQQEBJCeno6/v7+zhyOEEKIqdv4Aix+C9lfCXYur9hizGV5trC7/6zD4Nqu98YlaVxvv3zJnKIQQwrVYq1TbkUOk15dYNpOdZqI0CYiEEEK4FmtSdRXbdlhILSJRAQmIhBBCuJbq7DIrebwERKIMEhAJIYRwLZYlL087dplB8a40kwREojQJiIQQQrgW67Z7mSESjiMBkRBCCNdiXTKzc4bIXXKIRPkkIBJCCOFaqtO6A0rMEMkuM1GaBERCCCFcS7WTqi0NXqWfmShNAiIhhBCuJb+oDpGnvdvuJYdIlE8CIiGEEK6l2jNElhwiWTITpUlAJIQQwrXUeMlMZohEaRIQCSGEcC01Tao2SQ6RKE0CIiGEEK5D00rUIaruLjOZIRKlSUAkhBDCdZiyAU1dtrdStTR3FRWQgEgIIYTrsM7u6MDN277HygyRqIAEREIIIVxHybYdejvfwqwBkeQQidIkIBJCCOE6qptQXfIxsmQmyiABkRBCCNdR3S33UKIOkSyZidIkIBJCCOE6qtvpvuRjJCASZZCASAghhOuwBET2tu2A4iWzghwwFzpuTKJekICoAdtxIpUx769n/cGzzh6KEEJUTY2WzEo8RooziotIQNSALduVyIGkDBbvOO3soQghRNXUJKnazQvQqcuybCYuIgFRA5aUngvA6dQcJ49ECCGqqCY5RDqd9DMT5ZKAqAE7k64CodNpEhAJIVyEdcmsGjNEIInVolwSEDVglhmixPQcTIVmJ49GCCGqwJpULQGRcCwJiBqogkIzyUYVEJm14uBICCHqtJokVYPUIhLlkoCogUrJyMOsFV8/JXlEQghXkJehvld7yazocSYJiIQtCYgaqMSLZoQkj0gI4RIkh0jUEgmIGqjEdNsA6FSq1OQQQriAGi+ZSUAkyiYBUQN1cc6QLJkJIVxCTZOq3S0BkTR4FbYkIGqgzqSpgCjYzxOQWkRCCBeRX4PCjFBihkhmxYUtCYgaKMuS2YA2QQCcSpM/DkIIF5BXg8KMJR8nS2biInYHROvXr+faa68lLCwMnU7HkiVLbO5PTk7mnnvuISwsDB8fH8aMGcOhQ4dsjsnNzSU6OpomTZrg6+vLjTfeSHJyss0xJ06cYNy4cfj4+BAcHMxTTz1FQUGBzTFr166lb9++eHp60qFDB+bNm2fvy2mwLEnV/ds0VtfTciksue1MCCHqohonVVsqVcuSmbBld0CUlZVFr169mDNnTqn7NE1j/PjxHD16lF9++YUdO3bQunVrRo0aRVZWcTQ+bdo0fv31VxYtWsS6des4c+YMEyZMsN5fWFjIuHHjyM/PZ+PGjcyfP5958+bx4osvWo9JSEhg3LhxjBgxgri4OKZOncr999/PypUr7X1JDZJlhqhXeCBueh0FZs1al0gIIeokc6HqVA81CIikDpEoh1YDgLZ48WLr9fj4eA3Q9uzZY72tsLBQa9asmfbZZ59pmqZpaWlpmru7u7Zo0SLrMfv379cALTY2VtM0Tfvtt980vV6vJSUlWY/55JNPNH9/fy0vL0/TNE17+umntW7dutmMZ+LEiVpUVFSVx5+enq4BWnp6etVfdD2QX1CotXlmmdZ6xjIt2ZijDX1zldZ6xjJtc8J5Zw9NCCHKl5OmaS/5qy9TbvXOsfUr9fgFtzp0aOLSqo33b4fmEOXl5QHg5eVlvU2v1+Pp6clff/0FwLZt2zCZTIwaNcp6TJcuXWjVqhWxsbEAxMbG0qNHD0JCQqzHREVFYTQa2bt3r/WYkuewHGM5R3njMxqNNl8NUUpGHpoG7gYdTRt50jJQfWKSrfdCiDrNMqujdwODR/XOIUtmohwODYgsgc3MmTNJTU0lPz+fN998k1OnTpGYmAhAUlISHh4eBAYG2jw2JCSEpKQk6zElgyHL/Zb7KjrGaDSSk1P2jqlZs2YREBBg/QoPD6/xa3ZFSUXLZSH+Xuj1Olo09gZkp5kQoo4rWYNIp6veOSSpWpTDoQGRu7s7P//8MwcPHiQoKAgfHx/WrFnD2LFj0eudv6Ft5syZpKenW79Onjzp7CE5hWXLfViACoRaFgVEUotICFGnWdt2+FX/HO6SQyTK5uboE/br14+4uDjS09PJz8+nWbNmDBo0iP79+wMQGhpKfn4+aWlpNrNEycnJhIaGWo/ZvHmzzXktu9BKHnPxzrTk5GT8/f3x9vYuc2yenp54eno65HW6MktCdWiAWtpsEVg0QyTtO4QQdVlNq1RDiSUzSREQtmpt2iYgIIBmzZpx6NAhtm7dyvXXXw+ogMnd3Z1Vq1ZZj42Pj+fEiRNERkYCEBkZye7du0lJSbEeExMTg7+/PxEREdZjSp7DcozlHKJ8li33zQNVQNSysSWHSAIiIUQdVtMq1VBiyUxyiIQtu2eIMjMzOXz4sPV6QkICcXFxBAUF0apVKxYtWkSzZs1o1aoVu3fv5oknnmD8+PGMHj0aUIHS5MmTmT59OkFBQfj7+/PYY48RGRnJ4MGDARg9ejQRERHcddddvPXWWyQlJfH8888THR1tneF5+OGH+eijj3j66ae57777WL16NQsXLmT58uWO+LnUa4lFS2bN/S0BUfEMkdmsoddXc21eCCFqk0NmiCSHSJTN7oBo69atjBgxwnp9+vTpAEyaNIl58+aRmJjI9OnTSU5Opnnz5tx999288MILNud477330Ov13HjjjeTl5REVFcXHH39svd9gMLBs2TIeeeQRIiMjadSoEZMmTeLVV1+1HtO2bVuWL1/OtGnT+OCDD2jZsiWff/45UVFRdv8QGppEo2WGSAVCoQFe6HWQX2DmXGYewf5eFT1cCCGco6ZtO6A4ICrMg8ICMDg8c0S4KLt/E4YPH46mlV/R+PHHH+fxxx+v8BxeXl7MmTOnzOKOFq1bt+a3336rdCw7duyoeMCilMSiXCFLUrW7QU+ovxdn0nM5lZYjAZEQom7Kc2BABGDKAkNAzcYk6g3nb/0Sl1R+gZmzmapelCWpGiSPSAjhAhyxZGbwUHWMSp5PCCQganBSMnLRNPAw6GnSqLiwmdQiEkLUefmWbfc1CIh0OskjEmWSgKiBsewwCwnwtEmeLq5FJFtRhRB1lCWA8axBHSIAd9lpJkqTgKiBsW65D7Ct1SS1iIQQdZ4jlsxKPl5qEYkSJCBqYIoTqm0TpyWHSAhR5zkiqRpkyUyUSQKiBsYyQxR68QxRiRyiinYRCiGE0zhi233Jx8uSmShBAqIGxtK2IyzQdobIcj3HVMiFrPxLPi4hhKiUw5bMpJ+ZKE0CogbGOkN0Ua0hTzcDwX6qCrjkEQkh6iRHtO6A4oDKJDlEopgERA2MJSAKCyzdAFe63gsh6jSHJ1XLkpkoJgFRA2JpzQHQPKB0NeoWRYnVUotIuLr0bBOT523hl7jTzh6KcKQ8Sx2iGm67t+YQyZKZKCYBUQOSbCwqyuimJ6hEUUYLqUUk6oulO0+z6kAKH64+XPnBwnU4aobIXXKIRGkSEDUgxTWIvNDpSne0l1pEor7YcSINgIRzWeQVFDp3MMIxCvLBbFKXHbZkJgGRKCYBUQNi2WF2cUK1heQQifpix8k0AArNGkfPyptevVAy38dh2+7ld0MUk4CoAakooRpsAyKpRSRc1YWsfBLOFb/RxSdlOHE0wmEsAZGbFxjcanYumSESZZCAqAGxVKkOLSOhGqBFoFpXz8wrwJhTcMnGJYQjxZ1Mtbl+QAKi+sFapbqGy2UgdYhEmSQgakCsM0TlBETeHgaaFCVbn5TEauGiLPlDHm7qz9vBZAmI6gVrQnUNl8tKnsMkAZEoJgFRA1JeY9eSLMtmklgtXJUlIBrbPRSQJbN6w1FtO0CWzESZJCBqQKxJ1eXMEEFxTzNJrBauqNCsEVeUUD1xQDiggvuMXJMTRyUcwlFVqkECIlEmCYgaiLyCQs5lqh5l5SVVQ3HXeynOKFzRkbOZZOYV4ONhYGCbIOuOSlk2qwccVYMIwF0qVYvSJCBqIJLTVYVqTzc9jX3cyz1OijMKV7bjhEqo7tkyADeDnk6hqqJxfJK88bm8fEcmVVsCIvk7J4pJQNRAWJbLyivKaCHFGYUrs+QP9WnVGIAu1oDI6KwhCUex7jKrYdsOKA6IzCZV8FEIJCBqMKqSUA3FS2aSQyRckTUgCg8EoFOIevOUrff1gCOXzEqeQ5bNRBEJiBqIMyVmiCpiSapOzzFJIqpwKcZcEwdTVODTu1UgUDxDdDA5Q4qNujpHJlUb3MFQ1M9REqtFEQmIGogkywxRYMUBka+nG4FFOUaybCZcya6T6WiayoML9lO/5x2CfdHrIDXbxNmMPCePUNSII3OISp7HJHlEQpGAqIE4k6YCotBKlsygRB6RLJsJF2JJqLbkDwF4uRto00S98cXLTjPX5sjCjCXPI0tmoogERA1EklEFN+VVqS5JmrwKV2Rp6GrJH7LobE2sloDIpeU5sDAjSC0iUYoERA1EYlrVkqqhuKeZLJkJV6FpmnWGqG/rxjb3WRKrJSBycY5MqgZwl35mwpYERA1ArqmQ81lqa2llSdUgtYiE6zl+PpvUbBMebnoimvvb3Gfdei9LZq4tv+jfz9MB2+5BZohEKRIQNQDJRjU75OWutyZMV8Sy00xyiISr2FHU4b57mL+1qatFpxI7zQrNstPMZTl6hsiaQyQBkVAkIGoAzpRYLquoKKOF5BAJV3NxQcaS2jRphIebnlyTmZMXZNbTZTk8IJIZImFLAqIGwJJQXZXlMoCWRTlE57PyyckvrLVxCeEo2607zAJL3WfQ6+gYrGYDpECjC3N4UrXkEAlbEhA1AMVb7qsWEPl7u+Hn6QbA6TT5RC3qtpz8QvYnqkCnrBkiKN5pJk1eXZSmlahD5OBt9yYJiIRid0C0fv16rr32WsLCwtDpdCxZssTm/szMTKZMmULLli3x9vYmIiKCuXPn2hyTm5tLdHQ0TZo0wdfXlxtvvJHk5GSbY06cOMG4cePw8fEhODiYp556ioKCAptj1q5dS9++ffH09KRDhw7MmzfP3pfTIFiKMoZVYYcZgE6ns+YRybKZqOt2n06n0KwR7OdZblmJLrL13rWZsoGi/C9ZMhO1xO6AKCsri169ejFnzpwy758+fTorVqzg22+/Zf/+/UydOpUpU6awdOlS6zHTpk3j119/ZdGiRaxbt44zZ84wYcIE6/2FhYWMGzeO/Px8Nm7cyPz585k3bx4vvvii9ZiEhATGjRvHiBEjiIuLY+rUqdx///2sXLnS3pdU71kbu1ZSpbokySMSrmJHieWy8nLkrFvvZYbINVmDFl3xdvmakoBIXMTN3geMHTuWsWPHlnv/xo0bmTRpEsOHDwfgwQcf5L///S+bN2/muuuuIz09nS+++IIFCxZw5ZVXAvDVV1/RtWtXNm3axODBg/njjz/Yt28ff/75JyEhIfTu3ZvXXnuNGTNm8PLLL+Ph4cHcuXNp27Yts2fPBqBr16789ddfvPfee0RFRVXjR1F/FSdVVz0gkq73wlVYEqr7lrNcBtAlVG3FTziXRV5BIZ5uhksxNOEoJdt26B2U6eFuCYikUrVQHJ5DdNlll7F06VJOnz6NpmmsWbOGgwcPMnr0aAC2bduGyWRi1KhR1sd06dKFVq1aERsbC0BsbCw9evQgJCTEekxUVBRGo5G9e/dajyl5DssxlnOUJS8vD6PRaPPVECQZq16U0UK63tdv7/95kJ4vr+Tvw+ecPZQa0TStREJ1+QFRiL8n/l5uFJo1jqTIjIDLcXRCNZSYIZI8SaE4PCD68MMPiYiIoGXLlnh4eDBmzBjmzJnDsGHDAEhKSsLDw4PAwECbx4WEhJCUlGQ9pmQwZLnfcl9FxxiNRnJyyn4TnzVrFgEBAdav8PDwGr/eui7XVMgFO4oyWhTXIpI/FvXN34fP8f6fhzDmFvDEDztIych19pCqLTE9l5SMPAx6HT1aBJR7nE6ns84SxSc3jA9C9Yqjt9yXPJcsmYkitRIQbdq0iaVLl7Jt2zZmz55NdHQ0f/75p6Ofym4zZ84kPT3d+nXy5ElnD6nWWRKqvd0NBHhXXpTRQnKI6qf0bBNPLtwJgLtBx7nMfJ5cuBOzixYstCyXdW3uh7dHxctgnUJl673LqpWASJq7ClsODYhycnJ49tlneffdd7n22mvp2bMnU6ZMYeLEibzzzjsAhIaGkp+fT1pams1jk5OTCQ0NtR5z8a4zy/XKjvH398fbu+ylIU9PT/z9/W2+6rszJRKqq1KU0cKSQ5SSkUdegdQiqg80TePZJbtJMubSrmkjfnrkMrzc9Ww4dI5PNxx19vCqxZpQHV7+cplF56IZooMSELkeR7ftAKlDJEpxaEBkMpkwmUzoL0p6MxgMmM1mAPr164e7uzurVq2y3h8fH8+JEyeIjIwEIDIykt27d5OSkmI9JiYmBn9/fyIiIqzHlDyH5RjLOYSSWI2EaoCgRh54u6tP3JakbOHalsSdZvmuRNz0Ot6b2JueLQN5+dpuALyzMt4aXLiSigoyXqyzNHl1XbW5ZGaStACh2B0QZWZmEhcXR1xcHKC2v8fFxXHixAn8/f254ooreOqpp1i7di0JCQnMmzePr7/+mhtuuAGAgIAAJk+ezPTp01mzZg3btm3j3nvvJTIyksGDBwMwevRoIiIiuOuuu9i5cycrV67k+eefJzo6Gk9PTwAefvhhjh49ytNPP82BAwf4+OOPWbhwIdOmTXPQj6Z+qE5CNVxci0j+YLi6U6nZvLhEbUh4YmRHeoUHAjBxQDjjejanwKzx2Pc7MOaanDhK++QVFLLnjMoHqiih2sISEJ1Jz3Wp1ymopaRq6WUmbNkdEG3dupU+ffrQp08fQNUd6tOnj7VG0A8//MCAAQO44447iIiI4I033uD111/n4Ycftp7jvffe45prruHGG29k2LBhhIaG8vPPP1vvNxgMLFu2DIPBQGRkJHfeeSd33303r776qvWYtm3bsnz5cmJiYujVqxezZ8/m888/ly33FzmTZl/bjpJaSpPXeqHQrDF94U4y8gro2yqQR4a3t96n0+mYNaEHLRt7cyo1h5k/70bTXCOfaH9iBvkFZhr7uNOmSeW1aQJ83K3/D2TZzMWU3HbvKB4ltt27yO+8qF121yEaPnx4hX8wQ0ND+eqrryo8h5eXF3PmzCm3uCNA69at+e233yody44dOyoecANnSaq2d4YIivOIJLHatX26/iibEy7QyMPAexN742aw/Rzk7+XOh7f14ea5sSzflcjlHZpy68BWThpt1e0osd2+qvlxnUL8SEzPJT45g/5tgmpzeMKRrEtmDpwhshR41MxQkAvu9v+NFPWL9DKr586kVy+HCIprEUlxRte153Q678bEA/DSdd1o3aTsT9h9WjXmydGdAXj5170ccoGKztYO90XLf1UhLTxclGWGyLMW6hCB1CISgARE9V5SNdp2WEgOkWvLNRUy9cc4TIUaUd1CuLlfywqPf2hYOy7v2JRck5nHvt9Brqlu7y7ccbLygowXs7TwkK33LqY2kqr1BnArmhWSrfcCCYjqtZz8QlKzVfJodZbMJIfItb3x+wEOp2TSzM+TWRN6VrqspNfrmH1LL5r6enAgKYPXl++/RCO139mMPE5eyEGng57h5RdkvFjJrveukislgLyiANaRS2YgxRmFDQmI6jFLU1cfDwP+Xnani1kDoiRjLqZCs0PHJmrXuoNnmbfxGADv3NyLoEYeVXpcsJ8X797SG4BvNh1nxZ7EWhphzcSdTAOgY7Av/l5VLzjaIdgXvQ7Ssk2kZOTV0uiEw9VGDhFILSJhQwKieiypRP6QPUUZLZo28sTDTY9ZKz6XqPsuZOXzr0WqGvWkyNZc0amZXY8f1qkZD13RDoCn/7erTuaQbbejIGNJXu4G2jRVswKSR+RCamPJDIoDLJMEREIConrtTA12mIFaQmlZtNPspOQRuQRN03j2592czcijQ7Avz4ztWq3z/Gt0Z3qFB6p+Z9/voKCOzRDusKMg48UksdoF1UZSNciSmbAhAVE9Zk2orsYOM4sWkkfkUhZtO8WKvUm4G3S8P7F3pf29yuNu0PPhrX3w83Rj6/FUPlh1yMEjrb6CQjO7TqUD9iVUW1gSq+NdYCedKJJfC4UZQQIiYUMConrMOkMUWP36GtLk1XWcOJ/NK0tVNeppV3WiewXd36uiVRMf/m9CDwA+WnOYjUfO1XiMjnAwOZPs/EL8PN3oGGz/G6TMELmg2loyc5eASBSTgKgeS6xBlWoLS3HGuphHIooVFJqZtjCOrPxCBrYJ4qFh7St/UBVc2yuMif3D0TSY9mMc5zOdn4hs2W7fKzwQvd7+3DjLDNGhlAwKzbLTzCXURusOkBkiYUMConossQZFGS0sxRmlFlHd9snaI2w7noqfpxuzb+mFoRqBQnleui6C9s0akWzM46n/7XL6dnVrQcZq5A8BtG7SCE83PbkmMycuyO91nWcuhIKiD2QSEIlaJAFRPZZYw6RqKJFDJDNEddbOk2m8X5Tj88r13QgPqryvlz18PNz46Pa+eLjpWX0ghS//PubQ89urJgnVAAa9jo4h6o01PsnoqGGJ2lKyaGKtJVVLYUYhAVG9lZ1fQHpOUVHGalSptrDkECWm5cryQh2UnV/AtB/jKDRrjOvZnBv6tKiV5+na3J8Xxqkda2/8vp+/Djknnyg928SRs+rTfG87t9yX1DnEH4D4JHkjrPMsszd6NzBUrZ5WlVkCIpPMFAoJiOoty+yQr6ebXYXrLhbs54WbXkeBWSPZKLWI6gJjronVB5KZ9dt+xs/5m6Pnsgj19+L18d2rVW+qqu4c3JqobiGYCjXu+vIf3o05eMm348edSgOgTROfKhebLEvn0KIZomSZIarzSiZUO/r3W5bMRAn2ly8WLiExTQUvoTXIHwK1vBAW6M2JC9mcSs0hrAY71kT1GHNNbEm4wD8JF9h09Dx7TqdTcrLO003Pu7f0ItDHwZ+eL6LT6Xh/Yh9eWrqHhVtP8Z9Vh9h05Dwf3Na7Rsuy9th+3P7+ZWXpHGqZIZKdZnWetW2Hn+PPLUtmogQJiOqpRAfUILJoURQQnU7LBoJqfD5RsfQcSwB0nk1HL7D3jG0ABGqGZHC7JgxqF8SQ9k0J9q/5v3NVeHsYeOumXgzp0JRnf97N5mMXGPvBBt65qRejIkJq/fl3FLXsqG7+kIVl6/2x89nkmgrxcq9evSZxCdTWlnsoTtKWGSKBBET1liN2mFlYaxFdkMTq2nL0bCYL/jnBpoTz7D1j5OKNXG2bNmJwuyAVBLVtUuOZv5q6vncLerUMZMr329lz2sj9X2/l3iFteGZsFzzdaie4MJs14qrZsuNiwX6eBHi7k55j4sjZTLqF1axmk6hF1qKMtRAQuVt6mUkOkZCAqN5yxA4zC9lpVrsuZOVz09xYLmTlW29r17QRg9o1sQZBIZdoBsgebZo24qdHLuOtFfF88VcCX/19jC3HLvDhbX1p29Txb15Hz2VhzC3Ay11Pl+Y1Wz7R6XR0DvVjc8IF4pMyJCCqyyyzN47eYQaSQyRsSEBUTzlyyay4FpEERLXh9eX7uZCVT/tmjXhiVCcGtw26ZEtgNeXpZuCFayK4rH0T/rVoJ3tOG7nmPxt4/YYejHfwjjfLdvueLQJxN9R8P0jnkOKASNRhtdW2o+Q5JYdIILvM6i1LUnVN2nZYSLXq2vP34XP8tP0UOh28c3MvrusV5jLBUEkju4bw+xPDGNQ2iKz8Qqb+GMe/Fu0kO7/AYc/hqPwhi86h0tPMJdRWlWqQGSJhQwKiesoyQxTmwByi06k5mKUWkcPkmgp5dvFuACZFtqnxzilnCw3wYsEDg5k6qiN6Hfxv2ymu+fAv9ic6Zmt7TStUX6yz9DRzDdVIqq5yOQiPohwiqUMkkICoXsrKK8CYqz6ZOyL5NjTAC70O8gvNnKsDvazqi/+sOsTx89k0D/DiX1GdnT0chzDodUwd1YkFDwwmxN+To2ezuH7O33yz6XiNWn5k5RVYq0o7KnC09DRLTM+1FjEVdZAdSdWapvHBn4eIeGkli7aerPzcJXeZmS9tTS1R90hAVA9ZEqr9PN3wq0FRRgt3g96anH1S8ogcYn+ikU/XHwXg1eu74+tZv9L5Brdrwu9PDOPKLsHkF5h5YckeHvl2O2nZ+ZU/uAw7T6Vh1tSMp6MSzAO83a05dgdl2azusgREnhUn0heaNZ5dvIf3/jxIfoGZX3clVn5ua5ClFfdLEw2WBET1kGW5zJFbsyWPyHEKzRozf95NgVljbPdQrroE9XucIaiRB19M6s8L10TgbtCxYm8SI2evY8mO03bPFhUvlzl2WVGWzVxAFZbMck2FPPrdNr7ffMJ6286TaZX/nrl5Azrb5xENlgRE9ZB1y70Dq0pbaxFJ1/sa+3bTceJOpuHn6cbL13Vz9nBqlU6nY/LQtvz0yGV0DPblfFY+U3+M4+4vN3P8fNXfgBydP2QhAZELqCSpOj3HxN1fbmbl3mQ8DHr+c1sfPN30pOeYSDhXye+YXl+iFpEERA2dBET1kGWHmSMSqi1aWAMimSGqiTNpOby14gAAM8Z2qZP1hWpDz5aBLH/8cp6K6oyHm54Nh84x+r31zFlzmPyCinM3NE0j7qRjWnZcrHOIBER1XgXb7pONuUz8byybEy7g5+nG/PsGcl2vMLq3UHWl4op2JlZIdpqJIhIQ1UO1sWRWcqeZqB5N03jxl71k5RfSr3Vjbh/YytlDuqQ83PREj+jAH1OHMbRDU/IKzLy9Mp5rPtzA1mMXyn3cqdQczmXm427Q0S3M36FjKrn1viZJ36IWlbNkduRsJhM+3siBpAya+Xny40ORRLZvAkDv8EBAAiJhHwmI6iHLklmYAxtutgi0FGeUJbPqWrEniT/3J+Nu0DFrQg/0+trrTF+XtWnaiG8mD+T9ib1p0siDg8mZ3DQ3lpk/7yY9u/Rur+1FBRkjwgIc3nOsfTNfDHod6Tkmko2yg7JOsiZVF88QxZ1M46ZPNnI6LYe2TRvx8yOXEVEiWLYvIJLijEKRgKgeqtUZorQc+SRdDek5Jl5auheAR65ob93y3VDpdDrG92nBqievYGL/cAC+33yCke+uY+nOMza/Y9b8oaI3OUfycjfQpokK9qVAYx110QzR2vgUbvt0E6nZJnq2DOB/D0cSHuRj8xBLQLQ/0UiuqbDi80stIlFEAqJ6yDpDFOi4gKh50blyTWbOZ1Vv63RD9taKA6Rk5NGuaSMeHdHB2cOpMwJ9PHjzpp78+OBg2jdrxLnMPB7/fgeTvtrCifPqDcrRFaovVpxY7ZgCkpfKij1JvPrrPr78K4E/9yVzMDmDnPxK3vwvsRPns/k69hiv/rqPFXuSyMqrRuXyvKJA1cOPxTtOcf/8reSYCrm8Y1O+f2AwTXw9Sz2kZWNvmvp6YCrU2Humkn9XWTITRepX8RNBZl4BGdaijI5bMvN0MxDi70myMY/TqTk0LeOPkCjblmMX+O4ftR34/yb0cPiyT30wqF0Tfnvicv677igfrTnM+oNnueq9dTx2ZQf2nUkHoG8tVfLuHOLPb7uTiE9ynSWTzLwCHv9hR5kJ6U19PWkV5E2rIB9aBfkQXvS9VRMfQvy8anWpNtdUyD8JF1gbn8K6+LMcLbHL68u/E/Bw0zOkfRNGRYQwqmtI1TYVFAUqC3ac59nVhwC4vncYb9/UCw+3sj/T63Q6eocH8uf+FOJOptGvdQW/O9aAyHX+/UXtkIConkksqhPk5+Xm8GJ/LRv7kGzM41RqDr1qYfmiPsorKGTmz6o9x8T+4Qxu18TJI6q7PN0MPD6yI9f0bM7zS/aw8ch53vnjIABNfT2sy7aOVpxY7TozRH8dOkd+QSEhjdzo164ZJy5kc/x8Nhm5BZzLzONcZh7bi5YaS/Iw6GkZ5E3rIB/aNvWlXbNGtGvWiPbNfAn280Snsz9YOn4+i7XxZ1kbn0Ls0fPkmoqDNINeR7/WjekQ7MuGQ2c5eSGHNfFnWRN/lucW76FnywBGdVXBUdfmfqWfvyAfzCqv7I3VJ4FGTB7alueu7lppYFcyIKpQyWrVLsBs1nh12T6aB3jx0BXtnT2cekUConqmNhKqLVoEerPteCqn02Stvar+u+4oh1Myaerrwcyruzh7OC6hXTNfvrt/ED9vP82/l+8jNdtE/9ZB9r1ZZ52H1a9Cl2ug41UVHmoJiA4lZ1Jo1jC4QLL7mv3J/OjxGp0NaQRO2ATeagYkPdvEiQvZnLiQzcnUou9F10+n5pBfaObo2SyOns1iTfxZm3P6errRtqkKkNqVCJbaNfXF26N4VjPXVMimo+dZG3+WdQfPlqr1E+LvyfBOwQzv3IwhHZviX1QtX9M0DqVkErMvmT/3JxN3Mo1dp9LZdSqdd2MO0iLQm6uKZo4Gtg3Cw02PKceIpdZ+Fl7MHNuFB4e1q9LvQu9w9TOxlGwol7UOkWv8XdtxMpV5G48BcEXnZnQJdezOy4bM7oBo/fr1vP3222zbto3ExEQWL17M+PHjrfeX94v61ltv8dRTTwFw4cIFHnvsMX799Vf0ej033ngjH3zwAb6+xbsIdu3aRXR0NFu2bKFZs2Y89thjPP300zbnXLRoES+88ALHjh2jY8eOvPnmm1x99dX2vqR6pTYSqi1aSi0iuxw5m8lHqw8D8OK13Qj08XDyiFyHTqfjxn4tGdElmF/iTjOqqx3VvAvyYeFdcPxvOLwKntilCvCVo1WQD17uenJNZo6fz6Jds1roqu5AZrNGfPweBukPQD6w5ycYcD8AAT7u9PAJoEfLgFKPKyg0k5iey8kL2Rw7n83Rs5kcPZfF0bOZnLiQTWZeAbtPp7P7dHqpx4YFeNGumS96vY7NCbazQG5Fs0DDO6sgqEtoGTM9qH/TTiF+dArxI3pEB1IycllzIIWYfSn8dfgsp9NymLfxGPM2HsPP040rOjfDYDzJB0Cu5s6bN/flpn4tq/xz6hkegE4HJy/kcD4zr8xcI8Dllsw2JxQHeJ+sPcIHt/Zx4mjqF7sDoqysLHr16sV9993HhAkTSt2fmGjbP+b3339n8uTJ3Hjjjdbb7rjjDhITE4mJicFkMnHvvffy4IMPsmDBAgCMRiOjR49m1KhRzJ07l927d3PfffcRGBjIgw8+CMDGjRu57bbbmDVrFtdccw0LFixg/PjxbN++ne7du9v7suqN2kiotmghtYiqzFzUniO/0Mzwzs24tmdzZw/JJQU18uDeIW2r/gBNg9+fUsEQQPpJOLkJWl9W7kMMeh0dg/3YfTqd+KSMOh8Q7T1jpH32LrDE13HfWwOiirgZ9IQX5RRddlFef36BmRMXsjhSNHtUMlhKzTZxJj2XM0V/WwBC/b0Y3rmZmgXq0LRaPROD/byYOKAVEwe0Iie/kL8Pn+PP/cn8uT+Fc5l5LNuVSCddIniC3svXrmAIwN/LnfbNfDmckkncyTRGlhdUu9iS2ZYSNbt+3XmGJ6/qTKsmPhU8QlSV3QHR2LFjGTt2bLn3h4aG2lz/5ZdfGDFiBO3atQNg//79rFixgi1bttC/f38APvzwQ66++mreeecdwsLC+O6778jPz+fLL7/Ew8ODbt26ERcXx7vvvmsNiD744APGjBljnXV67bXXiImJ4aOPPmLu3Ln2vqx6w1KlOtTf8UtmLRtbahG5TkC053Q6mXkF9GkViKfbpUtmXrTtJJsTLuDtbuC167tXKzej1mganN4OKfug50Rwq0czV5s/g23zAB2EdIfk3bB7UYUBEahls92n04lPzmBsj7odvK4+kMJA/YHiG05vhbMHoVmnap/Tw01Ph2A/OgSXLgeRmpXP0XOZHDmbRXZeAYPbN6FzSNmzQNXl7WFQidYRIZjNGjtPpfHn/mTyEs5BEnh4V29ZqHd4YBUCItfZZWY2a9Yipi0be3MqNYdPNxzh3+N7OHlk9UOtbrtPTk5m+fLlTJ482XpbbGwsgYGB1mAIYNSoUej1ev755x/rMcOGDcPDo/gPdVRUFPHx8aSmplqPGTVqlM3zRUVFERsbW+548vLyMBqNNl+1ITE9hw9XHcJsvvT1es4ULZk1r40ZokDXqkV0KjWbGz7+m1s/3UTvV2KY9OVmPt9wlP2Jxlod/9mMPF5fvh+AJ0d3KlUjxWnys2H71/DpFfD5lbB0Cvz5krNH5ThH18KKZ9Tlq16B0a+qy3sXq2W0CrhSC4/V8SkM1KvfL3yKkvR3Lqi152vcyIN+rYO4pX849wxpS5dQ/1oN8PV6HX1aNeapqC48f1VRNfdy+phVpkoFGl2oDlF8cgbG3AJ8PAy8MaEnAAu3niIlI7eSR4qqqNWAaP78+fj5+dksrSUlJREcHGxznJubG0FBQSQlJVmPCQmxjeYt1ys7xnJ/WWbNmkVAQID1Kzw8vPovrhymQjPXfvgXs2MO8se+8sdSW5JqManakkOUmVdAek7pisJ1zZr4s5gKVeCTYypk3cGz/Hv5fsZ+sIEBr69i6g87WLT1pPVn5iivLtuHMbeA7i38ueeyNg49d7WcPQi/PwOzu8DSxyBxJxiKPmxs+gRObnbu+Bzh/BFYOAm0Quh5K1z2OLQZBo2CIScVjq6p8OElW3jUZecy80g6lUBbfTIaOhj1irpj549grls1iByijCrV9igZEJX7AdWFKlVblsv6tmrMkA5N6NMqkPwCM1/+dcy5A6snajUg+vLLL7njjjvw8qobDSxnzpxJenq69evkyZMOfw53g55bB6hPNR+vPXLJZ1IsOUS1kVTt5W6gqa96I3WFZbN1RbtonorqzIqpl/P8uK4M79wMb3cD5zLzWBJ3hqf+t4vBs1Yx6t11vLx0L3/uSybTjuJxmqaRayokNSufM2k5/BJ3ml93nkGvgzcm9MTN4KTap4Um2LsE5l0DcwbAP59AXjo0bgNXvQbTD0Cv2wENfokGkwt/wsxNh+9vhdw0aDkArv0AdDowuEH3otzF3YsqPIUlIDp2LqvyysZOtDb+LAN1anZIF9oDet4CXoGQcUbNkNU35fQxq6ouoX54uevJyC2wqYlkw4WWzLYcUyskA9qoXZePDlfJYN9uOu4SH1Lrulrbdr9hwwbi4+P58ccfbW4PDQ0lJSXF5raCggIuXLhgzT8KDQ0lOTnZ5hjL9cqOuTiHqSRPT088PWu/oOC9Q9rw+V9H2XUqnb8Pn2dox6a1/pwAGbkm65t581oIiABaNPbhXGY+p1JzrB2l66L8AjOxR84BcEUntTW1S6g/91/ejryCQrYfT+Pvw+fYcPgcu0+lcTglk8MpmczbeAw3vSrq1rZpI7JNheTmF5KdX0iOqZCc/EKyTQXk5JvJyS8gx1RIWR88Jw9t65yfT/pp2D4fts2HzKIZSp0eOo2BAZOh3ZXFO66iXofDf8K5g7D+LRj54qUfb02ZC+F/k9Vr8AuDid+Ce4nf/R43q2DwwHL1hlfOG2uwnyeBPu6kZZs4nJJZZ3+31xxIYbAlf6jNUHDzhB43wZbPYef30GGkcwfoaNZO99ULiNwMenq0CGDLsVTiTqbRIbiMmSYXCYg0TWNLgpohGtBWlRQY2SWYTiG+HEzO5NtNx4mWKvg1UmsfX7/44gv69etHr169bG6PjIwkLS2Nbdu2WW9bvXo1ZrOZQYMGWY9Zv349JlNxxBsTE0Pnzp1p3Lix9ZhVq1bZnDsmJobIyMjaeklV1sTXs8Qs0eFL9ryW2SF/LzcaObgoo0XLEnlEddm246lk5RfS1NeTiOa2CZmebgYi2zfhX1Gd+SV6CDteGM3cO/tyx6BWtG7iQ4FZY+vxVBZtO8XyXYmsOqAKzsWdTCM+OYOTF3I4l5lHVr5tMORh0OPv5cbgdkFMu6r6Ca52M5vhyGr44Q54vwese1MFQ42C4fJ/qW3nt30PHUbZbj/3CYJxs9Xlv95XS2mu5s+X4HAMuHnDbQvA76IPRC36QuO2Kj8k/vdyT6PT6ep8HpGp0Mz6g2cZZMkfsiSK975dfd+/TM2W1Sd5loCo+r3/ipfNyqlH5O4aAdGp1BySjLm4G3T0KaqxpNfreLioOOOXfyXU6dlNV2D3u2ZmZiaHDxe/ySckJBAXF0dQUBCtWqkgwGg0smjRImbPnl3q8V27dmXMmDE88MADzJ07F5PJxJQpU7j11lsJCwsD4Pbbb+eVV15h8uTJzJgxgz179vDBBx/w3nvvWc/zxBNPcMUVVzB79mzGjRvHDz/8wNatW/n000/t/iHUhgeGtePbTcfZeOQ8O06k0qeW2g6UVLzlvnYq+kLJWkR1OwFx3UG1XDasY9NKK9oG+LgzpntzxnRXu4tOXsjm78PnOJ+Vj4+HAW93A94eBnw83EpcVrf7eKjr3u4G5yyPHYqB32fAhSPFt7UeCgPugy7XVr6DLOI6iBgP+5aopbMH1oDB/i3UThG3ADZ+qC6PnwNhZdRj0enULNH6t9SyWY+byj1d51A//km4UGfziLYcu4B73gU6eZ1WN7QqCojC+kLTznAuXi2T9pvktDE6XA2XzMBSoDGh/MRqF5kh2lw0O9S9RYBNocxre4Ux+4+DnE7LYeHWk9wd2cZJI3R9dv8F37p1K3369KFPH/XHZ/r06fTp04cXXyyebv/hhx/QNI3bbrutzHN89913dOnShZEjR3L11VczdOhQm0AmICCAP/74g4SEBPr168eTTz7Jiy++aN1yD3DZZZexYMECPv30U3r16sX//vc/lixZUmdqELUI9GZ8nxaAyiW6FCxtO2pruQxcpxaRJSC6onMzux8bHuTDrQNbET2iA/cOacutA1txfe8WXBURwtCOTenXujFdm/vTpmkjgv298PNyd04wdCFBJRJfOKI+QQ98EB7dBPcuV7kzVd1Of/XbqtJx0m74+/1aHbLDnNwMvz6hLg97qjhXqCyWIOjwn5B9odzDipu81s2AaM2BFAbo49WVZl2hUdEOM50Oehf9rd35vXMGV1tqmFQN0LuoKfCBxIyyZ1BcJCCyJFQPbBNkc7u7Qc9DV6iyNv9ddxRTYen+dqJq7P4rPnz4cDRNK/U1b9486zEPPvgg2dnZBASUvQ4fFBTEggULyMjIID09nS+//NKmSjVAz5492bBhA7m5uZw6dYoZM2aUOs/NN99MfHw8eXl57Nmzp85VqX74ivbodBBT1Im6tp2xJlRfihmiuhsQpRhz2Z9oRKeDoR0uTf7WJWc2w5JHwZQFrYfAkwdUYBPc1f5z+QbDmDfV5XVvQcqBio93tvRTanmwMF+15hj+bMXHN+sMoT3BXKC24Jejri+Z2dQfuriuUs+JKlfsRKzacVdf1DCHCFSV7WZ+nhSYNfaUUYXbususIKdO79TbXBQQ9b8oIAK4pX84TX09OJ2Ww687z1zqodUbTtoC0zB0CPYlKkLlNMy9BLNESUU1iMJqc4YoUNXsqHYO0cnNFX5Kd4T1h1QydY8WAeWX63d1/3wCJzaq/IfxH9foEzSgdit1jFJBxtIpdfeNIT8bfrgdslJU4cUb/lthWw6rHjer77v/V+4hnYpmiJKMuaRn160dOyfOZ3PkbFbp/CEL/zBoN0Jd3vnDpR1cbbIumVX/91un01Vcj8ijRJ2wOlqL6HxmHkfPqp9F/9al0y+83A3Wiu6frD3ilBp49YEERLXs0REq4e2XnWc4eaF2/7PV5pZ7C8uSWXqOiYxcO980ts2DL66ChXc7fmAlWJfLOtm/XOYSzh6EVUVFB6NeV1vpa0qng2veA09/OLUF/vlvzc/paFpRiYDEnaog4a0Lqh4Idr8R0KkgMq3schv+Xu7WDxN1LY9o9YFk/MgmQn9c3dB6SOmDLMnVO39QM4j1gTWpumYBvyUg2lFWQOTmpWbXoM4um1m223cK8aVxo7KXwu+KbI2fpxuHUjJZdSClzGNExSQgqmU9WwYytENTCs0an64/WqvPdSmSqn093Qj0UUm3ds0SpZ+Clc+ry8c21NqyTKFZY8OhehwQFRbAkoehIBfaj4R+9zju3AEt4KqiQGvVq3Chdn9f7bb+Hdj7M+jd1fb6xq2r/tiAFmqbOqhmqOWoqwUaV8efpZ8+Hj0aBLUD/zLai3QZpwLa9BPFvdxcnQOSqgH6WGaITqSVvlOnq/P9zCz5QwPKWC6z8Pdy547B6v/Ex2sPu0Q3gbpGAqJLwDJLtHDrSc5m5NXKc2iadkmSqqFEHtGFKgZEmga/ToX8Em8y2+c7fmDA7tPppGWb8PNys34qrFf+fh9ObwPPALjuQ/XH3JH63QNtLlf5FEsfV/92dcH+X2HNv9XlcbMr7U1WJktydRWWzeKTjJgKzVzIyufYuSx2nUrjr0Pn+G13Ij9uOcFn648y+494XvplD9N+jOP++Vu45b+xzFnj+DIbWXkFbDpyvrj+UHmv3d0but2gLsfVQiuPwgLIOuf481bE8jejhjNEPVoGoNOpD3Fl/g2u4x3vrQnVbcsPiADuG9oGDzc9O06kselo7aYm1Ee1VphRFIts14Te4YHEnUzjy78TmDGmi8Ofw5hbQFa+yvtoXotJ1aB20O05baz6DNHOH1StGIOnKv73x3NqN8zIl2yL6DmApTr10A5NnVclurYk7Ya1b6jLV7+lZj0cTaeD6/4DnwxRM3nb5kH/ex3/PPZI2gM/P6QuD3q4+tvKu14Hy/+lGr6m7C8zAb1LUUD07aYTfLvphN1PsTnhAqO6hlhnmhzh78PnyC80M9Q7HjTKXi6z6H27+rCx7xeVZF/T3DILTYMfboNDf0D/+1TLEK/qNVy1i2XGpoavw8/LnY7BqoBh3Mk0roq4qNGre1EeUX7dyyHKyitg7xnVd7OiGSKAYD8vbunfkm83neDjtYeJbN/kUgyx3qhn7xh1kyqxrmaJvo09jtHe3JsqsPTjCvRxt6lRURuKu95X4Y9HRhKsKNohOPwZGPwI+LdU/aX2/+rwsa07qNbO691yWUE+LH4YzCa1s6rnxNp7rqB2cOUL6vIfL6jlzkutIE/NhG35HL6/Te2mazcCRr9e/XP6BEHHq9TlcmaJBrZtgqeb7Z9FX083wgK86Bzix4A2jRnZJZgb+rRgUmRrHruyA89d3ZU3JvSw7mj87zrHbqBYE5+CN7l01YrOW1FAFD5I/fuZshz7/2v71yoYAtj6JXw8GA7+4bjzl8dBS2ZQSYHGOrz1fvuJVArNGi0CvauUDvHQsPYY9Do2HDrH7lP1rFBnLZMZoktkVNcQOgb7ciglk29iHV9i3dLlPtS/9vvGtSrq3r7h0DkKCs3lz8RoGix/UlXPbd5bNdzUG6DvXbB2lpp96Hmzw8aVnm2y7iIZVt8CovVvQfIelUx8zXuOXyq72KCH1Bb1U5th2TS4fWHtPWdBPqTsgzM7IDFOfU/ep4I/i6D2cPNXqj9ZTfS4CeJ/U0Uar3y+1GtqEejNP8+OxJhTgL+3G76eblWeaYwI8+evj86xdOcZpo/uZP3gUBOaprHmwFn66A9joFB9mAhsVf4DdDrVo27NvyHuu+L6RDWRkaQCY4C+d0PCBkhNgAU3Q49bYMwbxTWRHM1BSdWgCjQu3HqqnJ1mdbfBa3H/sqoV9w0P8uHans1ZEneGT9Yd5uM7+tXm8OoVmSG6RPR6HY8MLy6xnpPv2G3NSZcgodri6h7NCfB250BSBp9tSCj/wL2L4cAy0LvB9XOK38z63Kl2dRz/C845Lufir8PnMGvQMdj3kvwcLplT22DDu+ryuHdV3aDapjfA9R+BwUPNDOxa6JjzFprU0t/2r1Wg9ekImNUCPr0Clk1VQXLiThUMeQepxPFhT8E9y1XxyJrqNFaVKkg7rnbTlSHQx4NWTXwI9PGwa9m1Z8tAhnRoQoFZ4/OK/l/YYV+ikSRjLkPdigoytr6s8sC0162ATi15ptm/7FfKb/9SjYHD+sC49+CRjXDZY+r/8O6FMGegSlR3dL6ZppWoQ+SIgCgQgF0n00tvS6/DM0TF/csqXi4r6eGi95rf9yRx9GzdC/LqKgmILqFre4XRsrE357PyWbi17K2/1WVJqK7NLfcWzfw8eeGaCADe+/Ng2f/hss7Bb0+py5f/C0JLVBAPaAkdipYuts9z2Ljq5XKZKUftKtMKVS2dbuMv3XM36wxXFC13rpgBmdXcypt6TAV0n18Fs1rC3KGw9DG19HJmu6p95BUI7YbD0Glwy9cwdTc8fRTu+lnN5JS1q6o6PHyg6zXq8u5FjjlnCY9coWZ+f9xykgtZ+TU+35qi7dMjfQ6pG9pUsFxmERgObS9Xl3f+WPGxldm3VC296d3guo/UhxoPHxj9b5j8JwRHQPY5+N99qj6UMbFmz1eSKQeVNIVDlsw6hfji7W4gI6+AIxf/zbLUIqpjdYjyC8zsKFriu7hCdUW6hPozskswmqaqV4uqkYDoEnI36HlomCqx/ul6x5ZYt1Sprs2ijCXd2LcFwzo1I7/AzIyfdpX+xPX7DPWHMjgCLn+y9Aks28XjFqh8kRrSNI31B9UOmHq1XLb636qTu28ojH3r0j//kCcgtIfK+bIEuFVhTIRNn8Dno+CDXrDqFbX8VpCrtoa3HaaWUG/6Ch6PgxnH4O5fYNTLEHG9WhaqrSW6Hreo73t+VjunHGhIhyZ0b+FPjqmQ+RuP1fh8qw+k4IGJDvmWHWZVCIhALZsB7FxQ/ZmbnDQ1OwQwZKrthxqAlv3gwXWqUrjeXS1FzhkE2+Y7ZrbIunylK056rgE3g54eLVX3hFL1iOroktmeM+nkmsw09nGnQ7B9s2SW3c0/7zhFYnrd7SxQl0hAdIndXKLE+tI4x5VYtyyZ1fYOMwudTsf/3dAdHw8DW46l8t0/x4vvPLAc9vxPTalfP6fsnlodR4Nfc8g+r46voYPJmSQZc/Fy11e6NdVlHPsbYueoy9d9qJKCLzWDu/o31BlUA9h9S8s/NvsCbP0K5l0D73aFFc8ULUvpVAB0zfvw2HaYcRwm/QqjX4PuEyCobe3nRJXU7grwaaoC9oS1Dj21TqezzhLNjz1Gdn71A64LWfnsOJlGL90RDOZ8aNQMmlQx9zDiOvUmf+EonPynegOIeQEyk6FJR7VsWRY3Dxg+Ax5aDy36qaW1Xx+Hr69TvfZqomTbjqpUI6+CPuVVrK6jS2aW5bL+bYLQ2fl/pF/rIAa2DcJU6Lgl3PpOAqJLzMvdwH1Di0qsr3NciXVLUnVt1yAqqWVjH2sJgTd+P6C24eekwrLp6oDLHocWfct+sMFN5RKByhupIcty2eB2TfByr91ddpdEXiYseQTQoM9d0Gm088bSvBcMnaouL3/StvVKrlGVVfj2Jnino8oDOrYB0NSOp7FvqT5rk35V2/ebtHfYm1u1GdyL6/VUUJOousZ0D6VNEx/Ssk38sLn6S+Nr41PQNLgmoOjNrCr5QxYejdRMG6jkanslrFd5XqDKMFRWHiMkAibHQNT/gZu3evzHkSqgr24bGAcmVFv0Lq9AY10NiKwFGauXP2fZ3fz95hOkOmAJt76TgMgJ7hysSqwfTsnkj33JNT6fpmnFM0SXOJn4rsGt6d+6MVn5hTz78260lc9BZpL6VDn8mYof3OcuQAcJ62pcFdnSrmNYx3qyXBbzokr8DQhXbzLONuxpaNpZ9RD7fYZKmP/xTni7Ayx+SNWZMheoJqqjXoEndsHkP9RuNb9QZ4++NEtvs/2/FuWqOI5Br+PBYeqN6PMN1V8aX12UPzTM86C6ofVQ+07Qq2iH2d4l9r3G/GxVlBOg/+SqF8HUGyAyGh7dWFzcc+Wz8MVoVffJXg7ccm9h6Xwfn5xhu7HFve4FRGazxtbjlh1m1ZsdvqJTMyKa+5OdX8j82GMOHF39JAGRE/h7uXP3ZarE+icOKLFuzCkgu+g/96XYdl+SXq/jzZt64uGmRzv8J7q47wCd2qHkXklw1rg1tL9SXbZ8Gq2G7PwCtiSoPxxXdK4HAdHhVbD1C3X5+jmXpgBeZdy91L8pOrWzaNE9KpgozCsKfmdC9BZ4eIOaTbKnrYYzhA9UeUr5mXBwhWPOef6I2qIOTOjbgqa+npxJz63W0nhBoZn1B8/iRgGtsnarG+2tzt16iHqNeUb7lqXXvaG21fuFqZwuewW1UzOC1/5H5Yud3gpzL4efHoBTW6t+nloIiJoHeBPi70mhWWP36RI1eurgDNHhs5mkZZvwdjfQvUVAtc6h0xXvbp638RhZeY7NmatvJCByknuHtMXTTc/OU+lsPHK+RudKNKpPf40vQVHGsrRv5su/rmjOLPfPAcju+wC0Gly1B1uqDu/4Tm3JroZNR8+TX2imZWNv2jV13B9Pp8hJUzuwAAY+pPJd6orwgSrJGtQb7dBp8PBfMGWLmg1s1sm547OHTgfdK2/lUWV7l8BHA+CzkVCQh5e7gclFS+Nzq7E0vu14KsbcAgZ7n8JQkK124QVH2Dcmvb54lqiqy2Zn4mDjR+ryNe9WPxjX6dT/7eh/oPPVqozC7oXw+UhVaiHu+8o3U1jadng6ruo3lFOgsQ4GRJuL8of6tArEvQZV96/u0dy6hPv9ZgeUYbBHfvalb/dSAxIQOUlTX09uHRAOqEZ8NZGYdmkTqssyOXc+LXTnOWFuxnPp46v+wM5XQ6NgtRQT/3u1ntvSrmNYp2Z2Jx7WOStmgvG0KkRYnU/ntW3UyzBtn1oSG/Wy2oHmqj9zy7LZoT9U7lt1xa+Anyar0gjGU9bmsXcMbmXtPr7azu7jq+PV8Tc3LXoDa31Z9XKvet2qvh9dC8ZKZqoKTbB0inod3SZA57H2P9/F/MPgtu/hgTVq55vBQ5VaWPIwvBsBq16D9NNlP7YWZohAFWiEixKr62Avs6o0dK0Kg17HQ1dYlnATyCtwbA28cmkauT89QtZHl6v2Oy5AAiInemBYO9z0Ov4+fL7s6qlV5IyEahsJGzBs+xKAZwsfZPHeNFbsSaraYw3u0OcOdbmaDV/XH1KfQFy+/tCB5WqbtE4P4z8pro1Sl+h0qoeaqwZBJYVEQHA3VQepot1zFTmyGhbepfKn/Fuq22I/Bk2rUfdxS/2hgYYSBRmrI6gdtLoMNDPsqqQmUexHqmimVyCMfbN6z1eeFn3hhk9g+n7Vz9C/hdrlt+EdeL8HLLxb7aos+TPKK7HLzIHKTKy2PEcdqkNk2WHmiF2zE/q2IMTfkyRjLkt2lBOAOlBieg6rP30Kr/gleGQnc/Rk7T+nI0hA5EQtG/twXe8wAD6uZpfsZGMuG4rq7zQPdEJAlJ+lPlUC9LuXnpdfB8ALv+whPbuKS2B971bfD6+C1OMVH3uR4+ezSDiXhZtex2Wu3Mgw6zz8WrQcddnj0GqQc8fTUFhax1SnSOOxv+D721VA1eUaeHCt2mGVvFvdB9w3RHUf334izdqCoTInL2RzMDkTN52ZkNTt6sbqBkRQ3L4jroKaROePFDcOHjOr9qqhN2qq6pI9sUsV4GxzuZqR2vcLzLtaFe3cNk8ttTiwSnVJPVsGoNep2m0pxtyi56hbS2anUrM5k56LQa+zBnA14elm4P6hqgbe7D8O8nXssar/fbbDifPZzPx5N6+9/RZXJn4GwKd+j5IeMtDhz1UbJCByskeHt0engz/2JXMoOaNKj8nJL+SXuNPc/eVmImetYsVeNRvTvplj/3BUyerXVSVi/xZw1as8PrIj7Zo14mxGHq//tq9q5whqB22vADTY8Y1dT7++aHdZ39aN8fNyt2/sdcmKGZB1Fpp1hRHPOns0DUf3G9X3Y39VvqRU0snNsGCi2knVcbQqMOnbrDj42PQxAMH+XtzYV80cza1i09c1RctlN4QZ0eelq4AgtFfVx3axiPEqUDt3EE5vL32/2ax2lRXkqga6lryj2mRwU2UB7lmmWoH0u0cVX0zeoz4YvNulOEh1cEDUyNONTiEqL8laoLGOBURbi4Ln7mH+NPJ0TMvR2wa1omVjb1Iy8njxl70M/L8/mfZjHJuOnq/xxp4jZzOZvjCOEbPXErdlA28bVP20M53u4tEnX6NPKwe03bkEJCBysg7BfoyOCAFUXaLymM0aG4+c41+LdtL/3zE88UMc6w+exaxB/9aNmTWhB3cNvsQ7e05utv7h59oPwMsfL3cDb93YE50OFm49xV+HqphQZ02u/tau6sHrDtaD5bKz8UV//HVqWcHN09kjajgCW0GrSEBTlaur4kycqruUn6kC+Vu+KS4+OugR9T3+dzXrAjw0rB16ndpGfyDJWOnpLflG1zU+pm4IH1SzprZe/tD1WnV554LS9+/4WvUVdPeBa9+/9MuhId3U34/p+2D069C4jWoIfbaoOren4z/o9b64QGMdq1S92UH5QyX5erqx/LHLeenaCDqH+JFXYGbxjtPc+ukmrpy9jk/WHiElI9euc+5PNDJlwXZGvbuOn7efJtCcxjc+79FIlwfthhM28X2XyuuUgMjRzGY4FAM/P1jlTxuPDlfVZ5fGneFUqu0a9uGUTN5eeYDL31rD7Z/9w/+2nSIrv5DwIG+eGNmRtf8azv8euYzbBrayqxFljZly4ZdoQFOfKDteZb2rf5sg7i4Kzp75eVfVtnp2uUZ1cs9IVEmuVZBfYGbjkXoQEP39H/W9yzjVQFNcWj0su82qsGyWvBe+Ga8qMreKVAnDJYsWNutU1KdPg82fAtCmaSPGdle92CrrK5WTX0hs0a7T3uaiRNSaLJdZWGaudv/PdneXMRH+eFFdvvJ5FYw4i3djuGyKqmZ++0LV2NfdxzGv/yKl8ogsrUHy60YOUaUNXXf/T+Uc2jmzE+Djzr1D2rJi6uUsiR7CbQPDaeRhIOFcFm+uOEDkrNU88PVWVu1PpqCC+lk7T6bxwNdbGfvBBpbtSkTTIKpLEOtaf0nTwhQ163/TVzUL5J3AtUbrCnQ61f8n9ZiqsWPZ5VGBXuGqS/bfh8/z2fqjPDGqE7/uPMPPO06zs0SytZ+XG9f0bM6Evi3p37qxcyPvdW+qKfhGwWUWDnxqTBf+3J/CqdQc3vkjnpeu7Vbx+dw8VWAV+5FKru5ydaVD2Hr8Atn5hTT19SSieR2o1VMd6aeLk12HTnPuWBqqiBtUscnEODh3CJp2LPu4swfh6+vVjrQW/dSbdlkJv4MfUYUqd3yrlj+9Anj4ivYs353I0p1nmH5VJ8KDyk6Y33jkHHkFZloEeOGbtFndWNX+ZRVpe4WqK5RxRs1edRuv3kytnez7wqCHa/48jqA3QKco9aVptTJjZSnQuOtUGoVmDYPl37EwT+22Mzhv+T01K59DKWqmqswZoqPr1K5GUKUjrnkXvOyrU6TTqdyk3uGBPD8uguW7E/lxy0m2HU8lZl8yMfuSCfH35OZ+4dzSP5xWTdTv65ZjF/hw9WFrqoJOp7b1R1/Rnoitz8KxLar21G0/OqfVUA3JDJGj6XTQu2jXlB0l8y2zRN/9c4JB//cnLy3dy86TaRj0OkZ2CWbO7X3Z8twoZk3oyYBq9LVxmKxzsO5t+PsDdf2ad8v8xff1dOP/JvQAVEGwbcerkFBqafh66I/yt+KWUFyduil6vQN+HqZctT10z8+w7i1VW6a2bfpY1WhpPRRa9q/95xOlNWpSXCC0vJpEF46q/lxZZ1WpgTt/Kr9GT/sroVkXtfyyXeXE9WgZwNAOTSk0a3zxV/l9pSzLZTe3zUGXfQ7cvMpvf2MPvaH4w9nO79X3/UvhwLKiTvYfqmPqmlr6O9cx2I9GHgay8gs5nJJpm6fk5Dwiy3b7DsG+BDUqow/k3+8XX97zP1X08tS2aj9fI083bukfzk+PXEbMtGHcP7QtQY08SDbm8dGawwx7ew13fL6Jif+N5ea5saw/eBaDXseEvi2ImXYFc27vS8SJ79QHAJ0ebvrStWqSlSABUW2w/OFJWA9pVSuEdVn7JvQKD6TArGEq1Ojewp8Xr4ngn2dH8sU9AxjXs7lze3Ql74VfpqjaIWv+rXaG9Li5ODehDFd0asaNfVuiaTDjp12V179o2lEFBppZ/eeqhKW7vd3VqbPOw/FY1ZV75XPw3c2qI/vroTB3CPzvXljzOiyaBGd22Hdue2RfKO7jJrNDztWjxG6zi5ch0k7C/OvUcm6zLnDXErW8Ux6dTs0SAfzzX2tOnKVi8A9bTnA+s3RRQk3TrNvtx/gW5RO2HOC4nLLet6vvh2LUbNdvRQ1bh04r3cm+njPodfRoqWZV4k6mqhwwfdGsUB0JiMrsX3YmTpV60Bngxi8goJVq8fPlaPjrfZWyUQMdQ/x4/poIYmdeyZzb+xbVdoO/D5/nn4QLuBt03DawFWueHM67t/SmQ7AvHP4T/nhOneCq12zSJ1yNLJnVhsBWqrt3wnrV+PKKpyt9iE6nY87tfVi5N5mhHZrSOdSx1VmrxWyGQyvVLEbC+uLbw/qo5FHLDp0KvHBNV9YdPMvhlEw+Wn2YJ0d3rvgB/SapBM/tX8Owf5X7qTXZmMv+RCM6HQzt0LT8853eDsc3wrl4tRxy7iBkV1AZ3CtA9ezKz4KUvaqn2N1La+eT6pYv1CxCSA/oMNLx5xdV1/lqlUdy4YgqHNiin7rdmAjzr4X0k6pY5t1L1dbxyvScCH++AuknIH45RFzPZe2b0KNFALtPpzN/4zGmX/R/IT45gzPpuXi56+mYs0vd6Mj8maYdoUV/1Upj/jWqk33TTuV3sq/neoc3ZtPRC8SdTGPigFaq7lduutNrEVnKM5S5XGaZme9+o8p96zBK7crbtwT+fEn1hRw/F/xCajQGTzcD43o2Z1zP5pxKzWbx9tOYzBq3DQy3LQB87hAsuk99iO19h+pl58Jkhqi2lFw2q2LiW8vGPkwe2tb5wVBeBmyaCx/2he9vVcGQTq+27963sqjq7MQqJcwF+njw2vUqf+iTtUfYd6aSXTZdr1OF4YynVF2icljWsHu0CKCJbxmfoM1mVVflsxHq08v2r+FEbHEwFNBKJW0OfhSueQ/uWQ7/OgQzjsP9MSpZ1uChXnsF46i2/Gz4Z666POSJ+lHo0JV5+qqgCIqXzTLPqmWy1AT1IWfS0qq/0bh7Q//71OVNnwC2faXmxx4vtdlg1X41O3RZuyYYTmxUNzoif6gkyyxRZlFT6Wv/02B3NVoSq3dYEqvrwE6z7PwC9hT1WCsVEJ0/ogIfKG6h4x0IN89Tu/TcvNXs0dwhDv2b1bKxD4+N7Mj0qzrZBkM5qer9IS9d7YS85j2X/zsmAVFt6Xqt+g+Weky9EbuCCwmqdcTsrqouTmqCmjEZ8kRRIbX5qkeZnb/0Y3s0Z0y3UArMGjN+2lXh7gXcvYrroFRQubrC6tR5mbDoblg7S13vGAVXzFBTzA+th2fPwLTdcNfPqghd//ugzVBVjM7y2hq3hoEPqst/vgRmB5e7j/tOVeoNbAXdbnDsuUX1WJbN9vykcuW+Ga9mFP1bqGalAS3tO9+A+9UyzIlYa/2fqG6htG3aiPSc0n2lLMtl17Y2qeRnvZtaMnOk7hPA4Fk8vtaRjj2/C+lTlFh9MDlDBad1oBZR3Ik0CswazQO8aNn4olZMGz9UMzEdR9sucep0Kv/ywbWq313WWfh2AvzxAhTk185ACwtg0b1w/rCq0D7x23oRWEtAVFs8GqmdHGBXcvUlp2lqFuT72+E/fdTyWH6GmkofN1uV2r/qVQgMr9HTvHp9N/y93Nh9Op1P1lZSoM5Skyj+d2v38JIKzRobDqkZolIBUeox+DJKdWI3eKhu8XcsVLt9etwEzXtVvRXA5U+qgDB5T+VtD+xRWAAbi7baX/a4y21NrbfaX6lygzKT4b/D1L97o2C1TFad7ej+zVUAAtZZIoNex4PDVMXgL/5KIL9AfThIzcpn+wm1VDLc86B6TFhfx7dv8W4MUa+r4G/kS449t4sJ8feieYAXZg12n06vEwFRyfpDNhtnMpJVpXGAIVPLfnBwF3hgtQp0Qf2N+TJKfdB1tD+eh6Nr1DLzbd/XXmXzS0wCotrU+071fe8SpyfqlZJrVDksc4eqHIn45YCm1qTv+Ake/Uf9x3JQH6Fgfy9euEZ1654dc5CFW05WcHBXCB+sErfLSK7edSqNtGwTfl5utmXtEzaoTtrJe8A3RC2D9bmz+oP2CYKh09Xl1a+DKaf65ypp3xKVbO/TtHhpVTifm4daFgbVYNc7CO7+BZp2qP45LcnVe39W+UiovlLBfp4kpufyS5zaTbn+kCqy2jnEj8Znt6rHtHHwcpnFwAfgxs+r38m+HrEp0Oju/IDImlB9cf2hfz5RJQFaDqw4r8zdW32Qnfit+jB3ZrvahVbe7snq2DZfjQfghrnQvKfjzu1kEhDVplaDoXFbtSa9/1dnj0bNBp3aqgoqzu4My6er4MHdRy0bRW9W24k7jqpeZ+1K3NSvJfcPbQvAjJ93Wd8MymSZJdr+damdE5bdZUM7NFXFKDUNNn+mljhyLqik7wfWQLgD+ucMekhNCRtPqR1DNaVpajcIqLovdbGBa0NmWa71CoC7l6gGsDUR1kc1VzUXwBbV28nTzcB9Rf8P/rv+KGazZt1uP6JLMBz/Wz3W0flDohSbAo1OniEyFZrZfjwNuGiHWW66+vAKMHRq1VIWul4LD/+tiofmZ6i6RUuiixvmVtfxjbD8SXV5+LOq/Uo9IgFRbapmTSKHy0lVb+afDIHPR6pZF1O2WhYb/TpM26sS4ppVsgOshnQ6Hc+N68odg1qhaTB94U5W7Cm9JAaoT+qeAWpLacJam7vWHVRvHld0aqbWyJdNVQXmzAXQ4xa493fVkd0R3L3hyqItpRveVVvla+LwKtX8070RDJhc8/EJx2o1SM0KPbReLa86gmWWaOtX1krIdwxqhZ+XG4dTMlm5N8laUysq3Kxy93R6xwT0okI2M0RODoj2njGSYyokwNudTsElNtZs/QryjGr3a6exVT9hYDhMWqbyJ3V6iPsWPh2utsmnHlN11+yRehx+vFPVTYsYX6Xd065GkhdqW69bVU2bhPXqF6rxJeo3pmlwYpOqc7NviWrcCKrQW8R4lYRXjQTpmtLpdLx2fXdyTWZ+2n6Kx77fzmd392d454vWoD18oOct6lP1tnnWwnlp2fnW/kPDW6IqB5/YCOjgqldUTo6jX1PPiRA7R82mbZitcjCq66/31Pf+97pkJdcGod1wx56vyziVPJ92QuWi9b8XPy937hzcmk/WHuGFX/aQlm0iwNudnoVF7TpCe9hdfVjYr0fLAAx6HUnGXLJ1XviA03aZbS1aLuvfunFxoVlTbnG/yCFP2D9zb3BT+ZNth8FPD8D5Q/BtiXIp3o3Brzn4hV70vXnxdd9g1e7lh9vVLt3mvWD8Jy6/o6wsds8QrV+/nmuvvZawsDB0Oh1Lliwpdcz+/fu57rrrCAgIoFGjRgwYMIATJ4p3VOTm5hIdHU2TJk3w9fXlxhtvJDk52eYcJ06cYNy4cfj4+BAcHMxTTz1FQYHtNtW1a9fSt29fPD096dChA/PmzbP35dS+wHD1ywiqJlFty76g3rznDIKvxsCuH1QwFNwNxr4NTx6ACf9Vu0uc9Aut1+t488YejOvRHFOhxkPfbLP2b7JhWTY7sBwy1azQX4fPYdZgTJMUQn+8WgVDnv6qjUJtbV/XG2DUK+ry5k9VYFsdp7aqGkt6d7XdXzQMekNxW4xNn1jLcNw7pA0ebnrOZaqdQFd0aobhZNGO1NZDnTHSBsfHo7jzfUpuUc0zJ9Uh2lxW/7JdP6gkf/8Wxbsgq6PNUHj4L+h1u9og4FbUfy8nFVL2qe36cd+pD3y//Qt+vAM+vxLei4DXmqkUC8smg1sX1NulfrsDoqysLHr16sWcOXPKvP/IkSMMHTqULl26sHbtWnbt2sULL7yAl1dxA8Rp06bx66+/smjRItatW8eZM2eYMGGC9f7CwkLGjRtHfn4+GzduZP78+cybN48XX3zRekxCQgLjxo1jxIgRxMXFMXXqVO6//35Wrlxp70uqfSWXzWpYSbRMmqYSiv83Wf3irnxWFSJ091FJxfevgkf+hkEPVlxh9xJyM+h5b2JvRnYJJq/AzOT5W6y7bKxCe6gCeeYC6w6L9QfPMk6/iQ9zZqhieU06qNfXaXTtDrjDSNUPqjAfVv+7euewzA71vMVxS3rCNfS5U5XhOBev3nyAYD8vbupXvJX/yi7BKkcDaqWhqSibZdnsdHbR26ETlsw0TWPr8YsKMpoLixs/R05RSf810agJ3PAJPLETnkuCGcfg0U1w12I14zPyRVVqpOu1qoCnf0tV+gFNzZoZPOHW7+wvP+FKtBoAtMWLF9vcNnHiRO3OO+8s9zFpaWmau7u7tmjRIutt+/fv1wAtNjZW0zRN++233zS9Xq8lJSVZj/nkk080f39/LS8vT9M0TXv66ae1bt26lXruqKioKo8/PT1dA7T09PQqP6Za8jI17fUWmvaSv6Yl/OX48695Q53b8jX3ck3b/Lmm5dTy63KAnPwC7Y7PNmmtZyzTur+0Qtt9Ks32gG3z1Wv6oLdmLjBpX7xyb/Hr/GaCpmWnXrrBnt5R/Nxn4ux7bEq8pr0UoB6bcqA2Rifqut9mFP/eFkk4m6m1m7lc6/jcb1pqyuni36/Mc04caMPy4+YTWusZy7QF7zyufvZLHr3kYziUbNRaz1imdXruNy3PVKhu3LNYjWdWK03LzbjkY9I0TdMKCzUtI0X9vUs94ZwxlKM23r8dmlRtNptZvnw5nTp1IioqiuDgYAYNGmSzrLZt2zZMJhOjRo2y3talSxdatWpFbKyaLo6NjaVHjx6EhBRXhY2KisJoNLJ3717rMSXPYTnGco6y5OXlYTQabb4uCZuaRAsce+7zR2DDO+pynztVca6H1quEXRfYVuvlbuDTu/sxoE1jMnILuPvLzRxKzig+oNsE8PCDC0fJnTuc+8w/AVAw+DG1TOYdeOkGG9a7eNo65sUKDy1l4weABp3H1XryuqijBj0I6FRS69l4ANo0bcT3Dwxmwf2DCLRstw+OUJ/mxSVh6Xx/NL3ohozkco+tLZsT1OxQ7/BAPNyKds5amrgOekhVUncGvR58m6m8oRrWonMFDg2IUlJSyMzM5I033mDMmDH88ccf3HDDDUyYMIF169YBkJSUhIeHB4GBgTaPDQkJISkpyXpMyWDIcr/lvoqOMRqN5OSUXS9m1qxZBAQEWL/Cwy/hP7Bl2Wzv4ppvfSxp5XNqGaf9SLjuI7XN18X4eLjx5T0D6NkygAtZ+dzx+T8cO1c0be3pqwoqAt5nd5OnufPfJjNwG/Nv53TnvvJ5VfDx6Nqql8dPPw07iwo7Dp1aWyMTdV1QO5VgDdZCjQAD2wbRv02QLJc5Sftmvvh6urHe1EXdcPhP1aOrCnJNhfy45QS3zI3lvZiDFVfhr4Cl/tBAS/5QwjrVWNrNGwY+VK1zCvs5fIYI4Prrr2fatGn07t2bZ555hmuuuYa5c+c68qmqZebMmaSnp1u/Tp6soDigo1lqEpmyHFeT6PCfcPB3tc475g2Xzvr383Ln6/sG0iXUj5SMPO74/B9OpRYlNw64H/RuXNA34Zb8F3Dvc5vzBtq4DQx4QF2OealqOWGbPlZbVVsPka3UDZ1lC/7OH0qXcDj+l/ouAdElZdDr6NkygHitFaeChwOaKrFRgdSsfD5cdYihb65mxk+72XzsAh+sOsStn27idJr9BVy3lKhQDRTnG/a9W2YLLyGHBkRNmzbFzc2NiAjbYmZdu3a17jILDQ0lPz+ftLQ0m2OSk5MJDQ21HnPxrjPL9cqO8ff3x9v7oh4wRTw9PfH397f5umQcXZOo0KT6joHawdKsU83P6WSBPh58M3kQ7Zo14nRaDnd+/g8pxlwI7U7OQ5sYmfc2O7UOXNG5jP5ll9Kwf6kaScm7YffCio/NSVVlA6D8kvui4Wg9BEJ7QkFO8e8FQE4aJO0pPkZcUpbE6sW+Rc1vd/1YZsuLhHNZPL9kN5FvrGJ2zEHOZeYTFuDFA5e3xc/Tja3HUxn7/vry66uVITE9h1OpOeh10Ld1YzUzdHQt6Awu3z3e1Tg0IPLw8GDAgAHEx8fb3H7w4EFat1b1d/r164e7uzurVhUvN8THx3PixAkiI1WjwcjISHbv3k1KSor1mJiYGPz9/a3BVmRkpM05LMdYzlEn9boV0MGxDdXfum2x+VPVeLJRs3pVIKuZnyff3T+I8CBvjp3P5o7P/+F8Zh6xqf6kFqqGh+2aOqadSLX5BMHl09Tl1f+uuMDZls/VDo3gbtDxqkszPlF36XTFJRc2f6Y+2ACc/AfQIKi9qv0iLilLQLTsfHOVfqAVWnN4NE1j67ELPPj1Vq6cvZZvN50g12Smewt/Pri1N+ueHsFz4yL47YnL6RUeiDG3gIe/3cYLS/aQa6q8KbRlu323sAB8Pd2KK9n3uOnS1a0TQDUCoszMTOLi4oiLiwPU9ve4uDjrDNBTTz3Fjz/+yGeffcbhw4f56KOP+PXXX3n0UfVHICAggMmTJzN9+nTWrFnDtm3buPfee4mMjGTw4MEAjB49moiICO666y527tzJypUref7554mOjsbTU3XUffjhhzl69ChPP/00Bw4c4OOPP2bhwoVMmzbNET+X2hEYDu2uUJd3fl/982SmwNo31OWRL9a7Am7NA7xZcP9gQv29OJSSyV1fbObXnaoP1LBOzWybHjrLoIdVbZD0kyo4LYspBzYVLRVXteS+qP+6T1C99jLOwL5f1G3Wdh2yXOYMlsTqgykZZF+mWlNoO75j9T/bueHjjdw0N5Y/9iWjaao8woIHBvHrlKFc37sF7gb1Nhoe5MOihyJ5qKh57zebjjN+zt8cTsko8zktbJbLzh+B/UvVHUOeqIVXKipid0C0detW+vTpQ58+Knl3+vTp9OnTx1oj6IYbbmDu3Lm89dZb9OjRg88//5yffvqJoUOLC4299957XHPNNdx4440MGzaM0NBQfv75Z+v9BoOBZcuWYTAYiIyM5M477+Tuu+/m1VdftR7Ttm1bli9fTkxMDL169WL27Nl8/vnnREVFVfuHcUlYl80WVL8m0apXVSn35r2LG8jWM+FBPnz3wCCa+nqwL9HI4h2q71mp7vbO4u4NIywtPd4pu6XHjm8h+xwEtFK75YQAcPMs7kgeO0ftKDom/cucKdjPixaB3mgaxOZ3ILFxf3RmE8d/fYO4k2l4uOm5dUA4f04fxpf3DOCy9k3L/GDm4aZn5tVdmX/fQJo08uBAUgbXfvg3C7eeRCsqyHmxLQmW+kONVYd6zQwdoyCkW62+ZlGaTivvX6kBMBqNBAQEkJ6efunyifKz4Z1OquHePctVBVF7nNmhOrqjwX1/qN5L9dj+RCO3frqJ9BwTbnodO168Cj8vd2cPSzEXqk7SKXvhssdgdImCjYUF8GFf1Ytt7NtFW66FKJJ5Ft7rpjqY3/kzLLhFFSCdulu1+RCXXPR321m+O1Gtaur28r3H6+Tizrz+v3DjFf1p5udp1/lSjLlMWxjH34dVFf7reoXx+g3dbf5+pWXn0/vVGAC2PdGDJp/3U7uG7/1dZgsrURvv39Lc9VLz8IHuN6jL9tYk0jT4fQagqSam9TwYAuja3J9vJg8kxN+TG/u2rDvBEKht/1cVtfT457+qV5XFviUqGPJpoupDCVGSbzNVsRzg1ydUMBQQLsGQE/Vtrar4axokNR5ASmAvvDDxsPtvdgdDAMH+Xnxz3yCeiuqMQa9j6c4zXPPhX+w6lWY9ZltRdep2TRvRZM8XKhgKH6S61ItLTgIiZ7DWJFpiX02i3YtU8qV7o+I34gagZ8tANs0cyZs39XT2UErrMEr1qivMh9VFTV81rTgxcuBD9bbvj6ghyxb89KLyHzIj4FR3DGrFk1d14r939ePPJ4cTPO4FdcfWLyGrjF6LVaDX64ge0YGFDw2mRaA3x89nc+MnG/l8w1HMZo3NRflDl4d7qOcBtRtV8g2dQgIiZwgfpIq0mbKKE+gqk5dZXB152JPgH1Z746uD6kQidVl0OriqKLdt14+QuAuOrFJb8t0bwcAHnDs+UXeFdIN2w4uvS/6QU3m5G3hsZEeiuoVi0OvUh53mvVWz101l9+6sqn6tg/jt8csZ0y0UU6HGv5fvZ/L8Law/eA6Am/hD5YU26wKdxjjg1YjqkIDIGXQ66F1U76Kqy2YbZkNGoioMOFhqU9QpYX2g+02ABn++VDw71G+S2qIvRHksW/BBAqK6RqeDYU+py/98qmqK1UCAjzuf3NmX18Z3x8NNz5r4s+xPNOJJPhHHi2rTDZmq2mUIp5CfvLP0LFmT6FjFx144CrEfqctR/wfuXrU9OmGvkS+A3l11Mj+2QVUPl6JqojIdrlJL6H0nQZP2zh6NuFjnq1VvufwMFRTVkE6n467BrfkleggdglV/snsabcKQnaK6yxe1KRLOIQGRs9jUJPqh4mNXPq9yVNqNUP9BRd3TuI3t8liPWyCgpdOGI1yEXg/jP4br/iN5I3WRXq8q04NqwZNXcU2hqura3J+lU4YwI6oDU31+VzdeNgUMdWjTSAMkAZEzVaUm0ZHVEL9clXF38X5l9d6wp8ArUM0ODXnc2aMRQjhCxHho0hFy01TleQfx8XDjkeB9eGccB+/Gqm+ZcCoJiJypyzXg6a+2Z1sq1ZZUaILfn1GXBz4IwV0u7fiEfXyC4IHVcP8qCO7q7NEIIRxBb4DLVfVqNn6kask5gqYVN3Ed+BB4OLklkZCAyKk8fKBbBTWJtnwO5+JVLZvhz1zasYnqadIewno7exRCCEfqcTMEtlaV50s25a0usxlWPgeJO8HNW33gFU4nAZGzWZbN9v1iW5Mo6xysmaUuX/kCeAde8qEJIYQADG5w+XR1eeN/Km7oXJnCAlg6pXgrf9Tr0KhJzccoakwCImcLH6g6XJuyihs9QlG/snQI7Slry0II4Wy9blcNnTMSIe7b6p3DlAuLJkHcdyovdPxcGDDZseMU1SYBkbOVVZPoTBxs/1pdHvuWWsMWQgjhPG4eqk4QqFpjhSb7Hp+XAQtuhgPLwOAJE7+B3rc5epSiBiQgqgt6FdUkOv6Xqjlk6VfW/SZoLT1thBCiTuh7F/iGqHYrlZVLKSn7Asy/DhLWg4cv3Pk/6DKu9sYpqkUCorogoGVxCf//3QcnN4G7T3FLCCGEEM7n7g2XPaYu//WuygeqTPpp+HIMnNkO3kEw6VfV/1DUORIQ1RWW5OozO9T3odMhoIXzxiOEEKK0/vepwObCUdj7c8XHnj+igqFz8eAXBvetgBZ9L804hd0kIKoruoxTNYkAAlupqqVCCCHqFo9GxW151r9TflHdxF3wZRSkn1AbZyavhGadL904hd0kIKorPHxU6we9O1w9W03NCiGEqHsGPgheAWrmZ//S0vcfj4V510DWWQjtoWaGAltd+nEKu0hAVJdc+QI8cwI6jXb2SIQQQpTHyx8GPawur39HVZ22OBQD39ygyqa0ioR7loNvsHPGKewiAVFdotOpmSIhhBB126CH1Y6x5N1wcIW6bff/4PtboSAHOo6GO39WM0nCJUhAJIQQQtjLJwgG3K8ur3sLtnwBP90P5gJVMuXWBfIB18VIQCSEEEJUR+QU1YvszHZYPh3QVJA04TMwuDt7dMJOEhAJIYQQ1eHbDPrfW3x92FNw9Tugl7dWV+Tm7AEIIYQQLmvYU5CTCq2HqErWwmVJQCSEEEJUl08Q3DDX2aMQDiDzekIIIYRo8CQgEkIIIUSDJwGREEIIIRo8CYiEEEII0eBJQCSEEEKIBk8CIiGEEEI0eBIQCSGEEKLBk4BICCGEEA2e3QHR+vXrufbaawkLC0On07FkyRKb+++55x50Op3N15gxY2yOuXDhAnfccQf+/v4EBgYyefJkMjMzbY7ZtWsXl19+OV5eXoSHh/PWW2+VGsuiRYvo0qULXl5e9OjRg99++83elyOEEEIIYX9AlJWVRa9evZgzZ065x4wZM4bExETr1/fff29z/x133MHevXuJiYlh2bJlrF+/ngcffNB6v9FoZPTo0bRu3Zpt27bx9ttv8/LLL/Ppp59aj9m4cSO33XYbkydPZseOHYwfP57x48ezZ88ee1+SEEIIIRo4naZpWrUfrNOxePFixo8fb73tnnvuIS0trdTMkcX+/fuJiIhgy5Yt9O/fH4AVK1Zw9dVXc+rUKcLCwvjkk0947rnnSEpKwsPDA4BnnnmGJUuWcODAAQAmTpxIVlYWy5Yts5578ODB9O7dm7lzq1ZG3Wg0EhAQQHp6Ov7+/tX4CQghhBDiUquN9+9aySFau3YtwcHBdO7cmUceeYTz589b74uNjSUwMNAaDAGMGjUKvV7PP//8Yz1m2LBh1mAIICoqivj4eFJTU63HjBo1yuZ5o6KiiI2NLXdceXl5GI1Gmy8hhBBCCIcHRGPGjOHrr79m1apVvPnmm6xbt46xY8dSWFgIQFJSEsHBwTaPcXNzIygoiKSkJOsxISEhNsdYrld2jOX+ssyaNYuAgADrV3h4eM1erBBCCCHqBYd3u7/11lutl3v06EHPnj1p3749a9euZeTIkY5+OrvMnDmT6dOnW68bjUYJioQQQgjh+IDoYu3ataNp06YcPnyYkSNHEhoaSkpKis0xBQUFXLhwgdDQUABCQ0NJTk62OcZyvbJjLPeXxdPTE09PT+t1S/qULJ0JIYQQrsPyvl2DNOhSaj0gOnXqFOfPn6d58+YAREZGkpaWxrZt2+jXrx8Aq1evxmw2M2jQIOsxzz33HCaTCXd3dwBiYmLo3LkzjRs3th6zatUqpk6dan2umJgYIiMjqzy2jIwMAJklEkIIIVxQRkYGAQEBDjmX3bvMMjMzOXz4MAB9+vTh3XffZcSIEQQFBREUFMQrr7zCjTfeSGhoKEeOHOHpp58mIyOD3bt3W2dnxo4dS3JyMnPnzsVkMnHvvffSv39/FixYAEB6ejqdO3dm9OjRzJgxgz179nDffffx3nvvWbfnb9y4kSuuuII33niDcePG8cMPP/B///d/bN++ne7du1fptZjNZs6cOYOfnx86nc6eH0OdYln6O3nyZIPZLdcQXzM0zNfdEF8zyOtuSK+7Ib5mqNnr1jSNjIwMwsLC0OsdlA6t2WnNmjUaUOpr0qRJWnZ2tjZ69GitWbNmmru7u9a6dWvtgQce0JKSkmzOcf78ee22227TfH19NX9/f+3ee+/VMjIybI7ZuXOnNnToUM3T01Nr0aKF9sYbb5Qay8KFC7VOnTppHh4eWrdu3bTly5fb+3LqhfT0dA3Q0tPTnT2US6YhvmZNa5ivuyG+Zk2T192QXndDfM2aVvded43qEIm6oSHWU2qIrxka5utuiK8Z5HU3pNfdEF8z1L3XLb3MhBBCCNHgSUBUD3h6evLSSy/Z7KCr7xria4aG+bob4msGed0N6XU3xNcMde91y5KZEEIIIRo8mSESQgghRIMnAZEQQgghGjwJiIQQQgjR4ElAJIQQQogGTwIiIUS9lZeX5+whCCFchAREDVxmZiY5OTnOHsYlc/LkSQ4ePOjsYTjF0aNH+fvvv509jEsmPj6eF198kYKCAmcP5ZLKz88nOzvb2cMQwuVIQNSAHTx4kKFDh7Jw4cIGERTt2LGD/v37s3v3bmcP5ZLbtWsXQ4cOZd68eZw9e9bZw6l1u3btonfv3rz99tv8+eefzh7OJbN//37uvfdeRowYwSOPPNKgftcPHz7M22+/zYwZM/jmm284d+6cs4dUqw4ePMjMmTOZPHkyH330kbXHaENw9uxZ0tLSHH5eCYgasK+++opdu3bx3HPPsWTJknq9vLBz504uv/xy7rjj/9u7+6Cq6jyO4597eTKe0wrxoYBRAp8isARWtzR8SGdc1qd2020fYtCmXdNaN1ODbbMaH2qHymnZNZw0gXFlx9bC1l2W2gV3ChUZBbbJxwxKDK5CQHC597N/sOd0MEvFC4dzv7/XP+W5oN/3nAP87jmXcxdj/vz5Zo/Tr06dOoWZM2diyZIlyM3Nxc0332z2SH2qqqoKycnJ+MUvfoGFCxciPz8f7e3t8PZbrlVXV2Py5MkICAjAnDlzsGfPHmzbts3ssfrFsWPHkJycjNLSUhw/fhwZGRl44IEHsHfvXrNH6xM1NTW4++67UVlZCYfDgTVr1uDRRx9FXl6e2aP1uZqaGowcORLLli1DS0uLZ/9yU99JTTHV3/72N65du5ZPPPEEAwICuGPHDrNH6hO1tbUMDAzkmjVrSJJOp5Pvv/8+9+zZw/LycpOn63tvvPEG58+fT7K7fePGjczIyGB2djZLS0vNHc7DDh06xJCQEK5du5Yk+eKLLzI8PJwnT54kSbrdbjPH6zPNzc287777uGrVKn3bH/7wB/70pz/9xhtnexuHw8HU1FT965skjx49Sh8fHyYlJXH79u0mTud5HR0dXLx4MTMzM/Vt//3vf7lo0SKmpKTw1VdfNXG6vvXZZ58xJSWFU6dO5eDBg/mjH/2Izc3NHvv71Rki4d566y1s3rwZS5YswdKlS/H222/jkUcewauvvmr2aB7R2dmJ1atXIzg4GOnp6QCAefPmYfny5cjMzMS9996Lxx57zKtPrx8+fFg/+5eWloY9e/agpaUFBQUFePrpp/HHP/7R5Ak948KFC5g8eTIyMzOxfv16AMCjjz6KmJgYPPvssyAJm81m8pR9p7m5GXFxcfqfjx49isrKSiQkJGDRokXIzc01cbq+43Q60d7ejhkzZsDtdqOtrQ1xcXFITU2Fy+XCjh07UFNTY/aYHuPv74/PP/8cdnv3j2+SuP3227FhwwbExsZi165deOedd0ye0vNIorKyEtHR0di4cSPefvttvPvuu8jMzPTYmSK1IBIsOTkZgwcPxldffYWtW7ciIyMDP/zhD1FYWIjk5GSzx/MIf39/rFu3DgkJCcjKykJ8fDycTify8vLw4YcfoqioCK+99hpycnLMHrXPTJgwAQEBASgsLISfnx/+8pe/oLCwEKWlpYiOjsbu3bvR1NRk9pjXLTw8HAcOHMDmzZsBdH8D9fX1xYwZM3Dw4EE0Njbq271NW1sbHA4HDhw4gOLiYmRnZ2Pbtm3IyMjAM888A39/f+Tn5+PIkSNmj+pxLS0tqK2tRUNDA+x2OwIDA1FXV4fOzk6sWrUKFRUVKCoqMntMjyAJp9OJESNGoKmpCR0dHSAJt9uNqKgorFu3Dh0dHcjPzzd7VI+z2WxISkpCRkYGJk6ciJSUlB6LoubmZv1je/017rFzTYolJSUlsaSkhCSZkZHB4OBgDho0iH/+85/Z3t5u8nSeU1FRwdTUVE6fPp2nTp3q8VhOTg5vvvlm1tXVeeUllQMHDjAgIIBJSUlcsGBBj8dqampos9n4j3/8w6TpPE/bh9p/6+vrGRQUxOeff97MsfpccXExR40axfT0dEZERDA/P19/7MSJEwwMDOTrr79u4oR9Z+XKlQwICGB2djZffvllhoWFcenSpSTJTZs28Xvf+x5bW1u95uu7pKSEdrudW7ZsIdl9rHd1dfV4rKamxswR+5zL5SJJlpeXMzw8XL981tnZyddee61X39N8PbR4Uwaws2fPora2FufPn8f06dMRHh4Of39/uFwuxMbGwuVyYfny5SguLsaxY8ewadMmLFq0CLt27cKCBQvMHv+aGXvT0tIQFhaGiRMnIjc3Fx999BFGjBgBAPolFJvNhsjISAwZMsTyl1Qu3dchISFISUnBiy++iOXLl4MkTp06hejoaADALbfcguTkZISHh5s7eC9917Ht4+MDl8uFyMhIZGZm4p133sGSJUswcuRIs8e+bpce46Ghobj//vtRXl6OgIAApKWl4bbbbgMAuN1u3HTTTbjzzjsRFhZm8uTX79J9PnjwYPzud79DaGgotm/fjoiICDz++OPIysoCAP3sZ2BgoJlj91pbWxv8/Pzg5+cHkiCJadOm4YUXXsCvfvUr3HDDDfj5z38OHx8fAEBYWBhuv/12y/YaGdsvpV0yTE1NRXFxMWbPno2lS5fihhtuwJtvvtm7y6SeXLEpA09VVRUjIiKYmJhIf39/jh07lqtWrWJjYyNJ8rnnnqPNZmNkZCQrKir0z1uxYgVra2vNGrvXLtf7xBNP6L2dnZ3f+JzHHnuMCxYsYGtra3+P61GXa//1r3/NpqYmkuSzzz5Lm83Ghx9+mGVlZWxoaOC6desYHR3N+vp6k6e/dt92bDscDpLUnzGT5L59+xgSEsK9e/eaNK3nXOkYr6+vZ0xMDLdt20ay+5jPzs7myJEjeebMGRMnv36XtsfHx/PJJ5/U9/n58+f1/9dkZmYyIyODnZ2dljtDVF1dzWnTpnHv3r10Op0kvz7z2dbWxqysLNpsNmZnZ/PIkSN0OBxcvXo1R48ezYaGBjNHv26Xa/8u77//Pm02GwcPHsxDhw716t9UCyIvduHCBSYmJurfLNvb2/nUU08xNTWVc+fOpcPhYFVVFX/2s5+xsrKSZM8fIlbzXb0/+MEP9B8YmpMnT/Lpp59meHg4jx07ZtLUnvFd7enp6fqiKC8vj5GRkRw6dCjHjBnDW2+9lYcPHzZ5+mt3tfvaeDzPmDGD99xzD10ul+V+MGqu1H3+/HmSXz/RmTRpEu+9914OHz7ckvvZ6NvaU1JSOHfuXH7xxRckv14wfPzxx/zNb37D0NBQS359nz59mnFxcfTx8WFUVBT3799/2YVBXl4eIyIiOHz4cMbHx3PYsGGW39dX267p6OjgsmXLGBISwurq6l7/u2pB5MVOnTrFmJgYvvfee/q2jo4O5uXlcdKkSXzooYfY0dFxVatvK/iu3pSUFC5evFj/Fc2jR49y7ty5jIqK0heDVnalfb148WJevHiRZPdCsLy8nKWlpayrqzNr5OtyLftae61BUVERjx8/bsq8nnKl7gcffFDv3r17N5ctW8YNGzbw448/Nmtkj/mu9uTk5B7tjY2NXLduHSdOnGjJxYHT6eTvf/97pqen88yZM5w1axaHDh36rQuDEydO8L333uO+fft49uxZEyb2nGttJ8kPPviAY8eO5Ycffnhd/7ZaEHmx8+fPc9y4cXzllVdIfv3MyeVyccuWLUxISNDvPWTVZ8xGV+pNTEzU70nS1tbGkpIS/f40Vnc17W+88YaZI3rUtexrK5/1vNSVuu+8806vu++O5lr2OUnW1dXx3Llzpsx6vdxuN8vKylhUVKRvmzlzJiMjI7l//3790r/b7faK791GV9tu5HA4vnEFoDfUgsiLdXZ2cv78+UxNTeXp06e/8fiMGTM4Z84cEybrG1fTO3v2bBMm63tqX/fkbb0adYzL2efamU2jmTNn6mdLtIX+3r17+eWXX/b3eH3qWto9eeNRtSDyUtqzhnPnzjEiIoJz587luXPnejybeOWVVzhp0iSv+PV6ab1G0tql9WqkdpOy20n2uFQ0a9YsRkZGsri4mJmZmRw1apQlfyniavVnu7oxo5ey2Wzo7OzELbfcgnfffRcffPABlixZgoMHD8LlcgEAjhw5giFDhui/vmhl0nqNpLVL69VI7QZkttNwc0FfX184nU4AwL59+5CQkIA5c+Zg586dKCwsRGRkpFlj9gmz2m2kF962VdHvw9LY2IjOzk60t7fj/vvvR3BwMLq6uhATE4OSkhKUlZVhwoQJZo973aT1Gklrl9arkdoNyGvXeh0OB7744guMHj0aANDV1QVfX1+sXLkS27dvx7///W+MGTPG5Gk9y8x271hKKzqS6Orqgo+PD06fPo0JEyagpKQEMTExqKiowIoVKzB9+nTcddddqKiosPw3D2m9RtLapfVqpHYD8tqNvWfOnMG4ceNw6NAh/XFfX19s3boVOTk52L9/v1cthgZCuzpDZGHHjx/Hrl278NFHH2HKlClIS0tDVFQUAODTTz/F+PHjsXDhQuTm5oKk5U8lS+s1ktYurVcjtRuQ134tvcY76B8/fhy+vr76x1rRQG1XCyKLOnbsGKZNm4apU6fi7Nmz6OrqQlJSEjZt2oSgoCC89dZbKCsrw6ZNmyz/dhSAvF4jae3SejVSuwF57dJ6jQZ0u8denq30m08++YRjxozh6tWr9W1btmxhTEyMfqO9y92rwaqk9RpJa5fWq5HaTcprl9ZrNNDbrX3OUSCSKC0tRWxsLJYtWwa32w0AePjhhwEA1dXVAHDZN8OzImm9RtLapfVqpHYD8tql9RpZoV29273F2Gw23HTTTZg1a5b+btYk4XQ60dHRgYsXL37jc/j/d3W3Imm9RtLapfVqpHYD8tql9RpZoV0tiCxE+3XE2bNn69u0AyY4OBhDhw6Fv7+//tj27duRnJyM2NhYM8a9btJ6jaS1S+vVSO0G5LVL6zWySru6ZGYBFy5cAAD4+Pigq6urx2PG1bPdbtdvaLV27Vr88pe/tOQzC2m9RtLapfVqpHYD8tql9RpZrV0tiAa42tpaJCYmIisrC0D3vRi0a69GX331FRobG0ESGzZswEsvvYTS0lL9plZWIa3XSFq7tF6N1G5AXru0XiNLtnv+ddqKp3zyySdMSEjg6NGjOW7cOD7zzDP6Y5e++Z3L5eLkyZM5duxYBgYGsqKior/HvW7Seo2ktUvr1UjtJuW1S+s1smq7eg3RAEUSBQUFGDZsGFasWIHy8nIUFBQAALKysmC32/XrsgDgdrvhdrtRX1+P//znP5a7Y6u0XiNp7dJ6NVK7AXnt0nqNrNyuFkQDlM1mw0MPPYSIiAhMnz4dd9xxBwCgoKAAJJGdnQ0fHx+43W7Y7Xb4+voiIyMDU6ZMwahRo0ye/tpJ6zWS1i6tVyO1G5DXLq3XyNLt/X9SSumt+vp6ZmdnMy4ujr/97W/17UVFRSZO1Xek9RpJa5fWq5HaTcprl9ZrZJV2dYZoAPnss89w9uxZOBwOpKWl9TilaLPZEBkZiczMTABAYWEhSOLixYvIycnBp59+imHDhpk5/jWT1mskrV1ar0ZqNyCvXVqvkde0m7cWU4yqqqp42223MTY2lmFhYYyLi2N+fj4bGxtJdr/wzO12k+xebWdlZdFms/HGG2/kwYMHzRy9V6T1Gklrl9arkdpNymuX1mvkTe1qQTQANDQ0MC4ujmvWrOGJEydYV1fHBx54gPHx8czOzmZDQwNJ6gcVSf7kJz9haGgoq6urzRq716T1Gklrl9arkdpNymuX1mvkbe1qQTQAVFdXMyoq6hur5SeffJLjx4/nxo0b2draqm/funUrw8PDefjw4f4e1SOk9RpJa5fWq5HaTcprl9Zr5G3takE0ABw5coQjRozgv/71L5JkW1ub/tjy5csZHR3Nqqoqfdvnn3/OkydP9vucniKt10hau7RejdRuUl67tF4jb2u3kf+/X7ZiqrvvvhvBwcH45z//CQDo6OhAQEAAAOCuu+7CqFGjUFBQ0OP+DVYmrddIWru0Xo3UbkBeu7ReI29qV2/dYYLW1la0tLSgublZ35abm4vq6mo8+OCDAICAgAD9vV++//3vo7W1FQAG/AF1OdJ6jaS1S+vVSO0G5LVL6zXy9na1IOpnNTU1mDdvHu655x7Ex8dj586dAID4+Hjk5OTg73//OxYuXAin0wm7vXv3NDQ0ICgoCF1dXbDaCT1pvUbS2qX1aqR2A/LapfUaiWg361qdRNXV1RwyZAhXrlzJnTt38vHHH6efn5/+ArPW1lb+9a9/5YgRIxgXF8f09HQuWrSIQUFBPHr0qMnTXztpvUbS2qX1aqR2k/LapfUaSWlXryHqJ01NTfjxj3+MuLg45OTk6NunTp2K8ePH4+WXX9a3tbS0YP369WhqasKgQYPwyCOPYMyYMWaM3WvSeo2ktUvr1UjtBuS1S+s1ktSu7lTdT5xOJy5cuIAFCxYAgP4+LtHR0WhqagLQ/aZ4JBESEoINGzb0+DirkdZrJK1dWq9Gajcgr11ar5GkdmtNa2ERERF48803MWXKFACAy+UCAAwfPlw/aGw2G+x2e48XrNlstv4f1gOk9RpJa5fWq5HaDchrl9ZrJKldLYj60ejRowF0r5z9/PwAdK+sGxoa9I954YUXsHXrVv1V+lY8qDTSeo2ktUvr1UjtBuS1S+s1ktKuLpmZwG63g6R+wGir7KysLKxfvx6VlZXw9fWeXSOt10hau7RejdRuQF67tF4jb29XZ4hMor2W3dfXFyNHjsTmzZuxceNGHDx4EHfccYfJ03metF4jae3SejVSuwF57dJ6jby53bpLOYvTVtZ+fn7405/+hNDQUJSVlSExMdHkyfqGtF4jae3SejVSuwF57dJ6jby5XZ0hMtnMmTMBAAcOHMDEiRNNnqbvSes1ktYurVcjtRuQ1y6t18gb29V9iAaA1tZWBAUFmT1Gv5HWayStXVqvRmo3IK9dWq+Rt7WrBZGiKIqiKOKpS2aKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooinFkSKoiiKooj3Pw3A12xSOcyDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluation"
      ],
      "metadata": {
        "id": "zJS629E7nXSv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"MAE:\", MAE(pred_sarimax, test_eval['Weekly_Sales']))\n",
        "print(\"RMSE:\", MSE(pred_sarimax, test_eval['Weekly_Sales'])**0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9DiEahsnMIr",
        "outputId": "ffaf6714-3198-44ec-e182-6f98990f6af8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 815.386909576506\n",
            "RMSE: 1325.1870486326934\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Analysis / Result\n",
        "# @markdown It can be seen the SARIMAX is not performing well on the dataset, in comparison to SARIMA. In plot, a clear spike can be seen on holiday. It seems it overestimated the effect of holidays.\"\""
      ],
      "metadata": {
        "cellView": "form",
        "id": "Vb2ZT9B0uH7V"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##VARMA based models"
      ],
      "metadata": {
        "id": "b5DitfRfwdWt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have one more table i.e. **Stores**, which consists stores size. Let's see if this influnces the overall sales."
      ],
      "metadata": {
        "id": "Vs9mHAMs54WG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged = pd.merge(train, stores, on=['Store'], how='inner')\n",
        "df_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2_MAh-Gswhvk",
        "outputId": "c85ad8b0-bbf8-4a8d-afaf-371febe4c917"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Store  Dept  Weekly_Sales  IsHoliday Type    Size\n",
              "0           1     1      24924.50          0    A  151315\n",
              "1           1     1      46039.49          1    A  151315\n",
              "2           1     1      41595.55          0    A  151315\n",
              "3           1     1      19403.54          0    A  151315\n",
              "4           1     1      21827.90          0    A  151315\n",
              "...       ...   ...           ...        ...  ...     ...\n",
              "309673     32    44       5656.19          0    A  203007\n",
              "309674     32    44       4533.20          0    A  203007\n",
              "309675     32    44       3767.29          0    A  203007\n",
              "309676     32    44       4152.52          1    A  203007\n",
              "309677     32    44       4394.31          0    A  203007\n",
              "\n",
              "[309678 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-117ed16f-4360-4548-a550-d42549b03169\" 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>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>24924.50</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>46039.49</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>41595.55</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19403.54</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21827.90</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309673</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>5656.19</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309674</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4533.20</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309675</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>3767.29</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309676</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4152.52</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309677</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4394.31</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>309678 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-117ed16f-4360-4548-a550-d42549b03169')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-117ed16f-4360-4548-a550-d42549b03169 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-117ed16f-4360-4548-a550-d42549b03169');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5159999b-3965-42a7-ab00-1f85beafd54a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5159999b-3965-42a7-ab00-1f85beafd54a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5159999b-3965-42a7-ab00-1f85beafd54a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged.index = train.index\n",
        "df_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "aZMIGsJK2jJc",
        "outputId": "78251f4c-2f9e-4c47-fc86-848deb513b75"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Store  Dept  Weekly_Sales  IsHoliday Type    Size\n",
              "Date                                                         \n",
              "2010-02-05      1     1      24924.50          0    A  151315\n",
              "2010-02-12      1     1      46039.49          1    A  151315\n",
              "2010-02-19      1     1      41595.55          0    A  151315\n",
              "2010-02-26      1     1      19403.54          0    A  151315\n",
              "2010-03-05      1     1      21827.90          0    A  151315\n",
              "...           ...   ...           ...        ...  ...     ...\n",
              "2012-08-17     32    44       5656.19          0    A  203007\n",
              "2012-08-24     32    44       4533.20          0    A  203007\n",
              "2012-08-31     32    44       3767.29          0    A  203007\n",
              "2012-09-07     32    44       4152.52          1    A  203007\n",
              "2012-09-14     32    44       4394.31          0    A  203007\n",
              "\n",
              "[309678 rows x 6 columns]"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\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>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Type</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-02-05</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>24924.50</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-12</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>46039.49</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2010-02-19</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>41595.55</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-26</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19403.54</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-03-05</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21827.90</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>151315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-08-17</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>5656.19</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-08-24</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4533.20</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-08-31</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>3767.29</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-09-07</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4152.52</td>\n",
              "      <td>1</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-09-14</th>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>4394.31</td>\n",
              "      <td>0</td>\n",
              "      <td>A</td>\n",
              "      <td>203007</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>309678 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82b69bda-1a03-42fe-9b53-09b92388c0a1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-82b69bda-1a03-42fe-9b53-09b92388c0a1 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-82b69bda-1a03-42fe-9b53-09b92388c0a1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-071c8321-67c5-498e-9423-a31c4a96f77e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-071c8321-67c5-498e-9423-a31c4a96f77e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-071c8321-67c5-498e-9423-a31c4a96f77e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merged_reasampled = pd.DataFrame(df_merged.resample(rule = 'W').mean())\n",
        "df_merged_reasampled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "URT-3cNZ2bWv",
        "outputId": "814bccbb-d23d-4cf7-ab0f-6b8a41b7777e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-71-7d206671a82c>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_merged_reasampled = pd.DataFrame(df_merged.resample(rule = 'W').mean())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Store       Dept  Weekly_Sales  IsHoliday           Size\n",
              "Date                                                                    \n",
              "2010-02-07  16.172761  43.250914  18117.921143        0.0  147756.334095\n",
              "2010-02-14  16.163777  43.412865  17529.432641        1.0  147814.885493\n",
              "2010-02-21  16.157059  43.386745  17409.342306        0.0  147673.298684\n",
              "2010-02-28  16.148352  43.070971  15903.966896        0.0  147571.258700\n",
              "2010-03-07  16.141020  43.022967  16971.829812        0.0  147927.440974\n",
              "...               ...        ...           ...        ...            ...\n",
              "2012-09-30  16.160277  43.429099  15738.767607        0.0  147366.949654\n",
              "2012-10-07  16.199172  43.791168  17115.639894        0.0  147803.998160\n",
              "2012-10-14  16.181361  43.915487  16422.246725        0.0  147537.798995\n",
              "2012-10-21  16.181650  43.379518  16294.627405        0.0  147154.212697\n",
              "2012-10-28  16.177994  43.426722  16453.238673        0.0  147355.767915\n",
              "\n",
              "[143 rows x 5 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "      <th>Size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-02-07</th>\n",
              "      <td>16.172761</td>\n",
              "      <td>43.250914</td>\n",
              "      <td>18117.921143</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147756.334095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-14</th>\n",
              "      <td>16.163777</td>\n",
              "      <td>43.412865</td>\n",
              "      <td>17529.432641</td>\n",
              "      <td>1.0</td>\n",
              "      <td>147814.885493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-21</th>\n",
              "      <td>16.157059</td>\n",
              "      <td>43.386745</td>\n",
              "      <td>17409.342306</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147673.298684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-28</th>\n",
              "      <td>16.148352</td>\n",
              "      <td>43.070971</td>\n",
              "      <td>15903.966896</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147571.258700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-03-07</th>\n",
              "      <td>16.141020</td>\n",
              "      <td>43.022967</td>\n",
              "      <td>16971.829812</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147927.440974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-09-30</th>\n",
              "      <td>16.160277</td>\n",
              "      <td>43.429099</td>\n",
              "      <td>15738.767607</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147366.949654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-07</th>\n",
              "      <td>16.199172</td>\n",
              "      <td>43.791168</td>\n",
              "      <td>17115.639894</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147803.998160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-14</th>\n",
              "      <td>16.181361</td>\n",
              "      <td>43.915487</td>\n",
              "      <td>16422.246725</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147537.798995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-21</th>\n",
              "      <td>16.181650</td>\n",
              "      <td>43.379518</td>\n",
              "      <td>16294.627405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147154.212697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-10-28</th>\n",
              "      <td>16.177994</td>\n",
              "      <td>43.426722</td>\n",
              "      <td>16453.238673</td>\n",
              "      <td>0.0</td>\n",
              "      <td>147355.767915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>143 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [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-5666797b-34aa-43b9-af79-8318dce23197 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-5666797b-34aa-43b9-af79-8318dce23197');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6a51cfe8-876f-470b-863c-1d16f8cce0e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a51cfe8-876f-470b-863c-1d16f8cce0e6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6a51cfe8-876f-470b-863c-1d16f8cce0e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Plottting the Store sizes"
      ],
      "metadata": {
        "id": "rUheW95S60ZU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = pd.DataFrame(df_merged.groupby(['Store']).mean()['Size'])\n",
        "a.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "UfrD1-sB3A5Y",
        "outputId": "5f19223b-db3d-4629-bcf2-0a71dd03f19b"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-77-9b50e3635981>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  a = pd.DataFrame(df_merged.groupby(['Store']).mean()['Size'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Size\n",
              "Store          \n",
              "1      151315.0\n",
              "2      202307.0\n",
              "3       37392.0\n",
              "4      205863.0\n",
              "5       34875.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f823ed2-cd1b-4a96-805a-7a03a8c3b3e6\" 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",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Store</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>151315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>202307.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37392.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>205863.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34875.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f823ed2-cd1b-4a96-805a-7a03a8c3b3e6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9f823ed2-cd1b-4a96-805a-7a03a8c3b3e6 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-9f823ed2-cd1b-4a96-805a-7a03a8c3b3e6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-735fc90b-615f-48f9-891c-61bb44a373d5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-735fc90b-615f-48f9-891c-61bb44a373d5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-735fc90b-615f-48f9-891c-61bb44a373d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(a.index, a['Size'])\n",
        "plt.title(\"Store Size\")\n",
        "plt.xlabel(\"Store\")\n",
        "plt.ylabel(\"Store Size\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cuFhkVM83ncO",
        "outputId": "9b362bd4-5b80-49e6-a96e-1dd971c0efbe"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = pd.DataFrame(df_merged.groupby(['Store']).mean()['Weekly_Sales'])\n",
        "b.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "5_nbpZXh8ZSs",
        "outputId": "caba9d26-2708-4ebe-b03b-552045f00d59"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-92-5a2fc567c495>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  b = pd.DataFrame(df_merged.groupby(['Store']).mean()['Weekly_Sales'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Weekly_Sales\n",
              "Store              \n",
              "1      21710.543621\n",
              "2      26898.070031\n",
              "3       6373.033983\n",
              "4      29161.210415\n",
              "5       5053.415813"
            ],
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              "      <th>Weekly_Sales</th>\n",
              "    </tr>\n",
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              "      <th>Store</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "      <td>21710.543621</td>\n",
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              "      <td>6373.033983</td>\n",
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              "      <td>29161.210415</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>5053.415813</td>\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-41d6bf39-a112-4fa7-9157-63a1eac751b6 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-41d6bf39-a112-4fa7-9157-63a1eac751b6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e72d1e6a-fb2e-4a7f-917d-fa9e50af8711\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e72d1e6a-fb2e-4a7f-917d-fa9e50af8711')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e72d1e6a-fb2e-4a7f-917d-fa9e50af8711 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(b.index, b['Weekly_Sales'])\n",
        "plt.title(\"Weekly Sales\")\n",
        "plt.xlabel(\"Store\")\n",
        "plt.ylabel(\"Weekly Sales\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "FuIDlqR04GZc",
        "outputId": "89a384ad-3de2-48d3-b6a1-0e8ffa99417c"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the plots it looks like there is some causality between weekly sales and store sizes. Lets determine this by performing Granger Causality test."
      ],
      "metadata": {
        "id": "9qDDtxe99--M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Granger Causality Test\n"
      ],
      "metadata": {
        "id": "Iq6KWkol9is2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grangercausalitytests(df_merged_reasampled[['Weekly_Sales','Size']], maxlag = 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pm4UhiDK97V0",
        "outputId": "a6ea65d7-16fd-48e4-dbb0-4cef2327e791"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 1\n",
            "ssr based F test:         F=2.8614  , p=0.0930  , df_denom=139, df_num=1\n",
            "ssr based chi2 test:   chi2=2.9232  , p=0.0873  , df=1\n",
            "likelihood ratio test: chi2=2.8935  , p=0.0889  , df=1\n",
            "parameter F test:         F=2.8614  , p=0.0930  , df_denom=139, df_num=1\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 2\n",
            "ssr based F test:         F=1.2835  , p=0.2804  , df_denom=136, df_num=2\n",
            "ssr based chi2 test:   chi2=2.6614  , p=0.2643  , df=2\n",
            "likelihood ratio test: chi2=2.6366  , p=0.2676  , df=2\n",
            "parameter F test:         F=1.2835  , p=0.2804  , df_denom=136, df_num=2\n",
            "\n",
            "Granger Causality\n",
            "number of lags (no zero) 3\n",
            "ssr based F test:         F=1.0992  , p=0.3519  , df_denom=133, df_num=3\n",
            "ssr based chi2 test:   chi2=3.4711  , p=0.3245  , df=3\n",
            "likelihood ratio test: chi2=3.4288  , p=0.3301  , df=3\n",
            "parameter F test:         F=1.0992  , p=0.3519  , df_denom=133, df_num=3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: ({'ssr_ftest': (2.861418246295564, 0.09296823614585152, 139.0, 1),\n",
              "   'ssr_chi2test': (2.923175474632878, 0.08731587733721517, 1),\n",
              "   'lrtest': (2.8934942541914097, 0.08893784004101622, 1),\n",
              "   'params_ftest': (2.861418246295546, 0.09296823614585152, 139.0, 1.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef956710>,\n",
              "   <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef955720>,\n",
              "   array([[0., 1., 0.]])]),\n",
              " 2: ({'ssr_ftest': (1.2834936168931186, 0.28040199737222665, 136.0, 2),\n",
              "   'ssr_chi2test': (2.6613617644401426, 0.26429724472698835, 2),\n",
              "   'lrtest': (2.6365569276126735, 0.2675955811367387, 2),\n",
              "   'params_ftest': (1.2834936168931026, 0.28040199737223404, 136.0, 2.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef955ae0>,\n",
              "   <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef956350>,\n",
              "   array([[0., 0., 1., 0., 0.],\n",
              "          [0., 0., 0., 1., 0.]])]),\n",
              " 3: ({'ssr_ftest': (1.099177655253837, 0.3519102843956569, 133.0, 3),\n",
              "   'ssr_chi2test': (3.4710873323805385, 0.32453140257084495, 3),\n",
              "   'lrtest': (3.4287554400161753, 0.3301205319615809, 3),\n",
              "   'params_ftest': (1.0991776553072172, 0.3519102843735893, 133.0, 3.0)},\n",
              "  [<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef954e80>,\n",
              "   <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7af1ef955c90>,\n",
              "   array([[0., 0., 0., 1., 0., 0., 0.],\n",
              "          [0., 0., 0., 0., 1., 0., 0.],\n",
              "          [0., 0., 0., 0., 0., 1., 0.]])])}"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From Granger Causality Test, it can be determine that there is no causality exists between store size and weekly sales."
      ],
      "metadata": {
        "id": "sSU9mQtLDD-g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "CYRg8r81LG5e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Forecasting into the actual future with a SARIMA model.\n",
        "As SARIMA model showcased highest accuracy in the predictions. Hence we'll do the real forecasting with SARIMA model only."
      ],
      "metadata": {
        "id": "Wx6rKHOkLQkY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auto_arima(df['Weekly_Sales'], seasonal=True, m=52)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "10VwuOq8LJK4",
        "outputId": "18473819-f79f-4751-9efc-abfe49a55031"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ARIMA(order=(2, 0, 2), scoring_args={}, seasonal_order=(1, 0, 0, 52),\n",
              "      suppress_warnings=True)"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fitting the model"
      ],
      "metadata": {
        "id": "T5FlgpnKQP7P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SARIMAX(df['Weekly_Sales'],order=(2,0,2),seasonal_order=(1,0,0,52)).fit()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUqTStHeM397",
        "outputId": "90ae0e3e-fa74-4fc5-f016-8e00e2fed885"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-stationary starting autoregressive parameters'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
            "  warn('Non-invertible starting MA parameters found.'\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:997: UserWarning: Non-stationary starting seasonal autoregressive Using zeros as starting parameters.\n",
            "  warn('Non-stationary starting seasonal autoregressive'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we're resampling the test table to know the exact time period for doing forecasting."
      ],
      "metadata": {
        "id": "qBTck-ujQFSa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test.index=test['Date']\n",
        "test = test.drop(['Date'], axis = 1)\n",
        "test.index = pd.to_datetime(test.index)\n",
        "\n",
        "test_resampled = pd.DataFrame(test.resample(rule = 'W').mean())"
      ],
      "metadata": {
        "id": "eygZJ9blO6oy"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_resampled.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "9mV7QHLqPJ9R",
        "outputId": "d7fa4a5d-00cc-404f-90e5-2f7ce77a9682"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Store       Dept  IsHoliday\n",
              "Date                                       \n",
              "2012-11-04  22.198040  44.414667        0.0\n",
              "2012-11-11  22.237630  44.410300        0.0\n",
              "2012-11-18  22.211096  44.212449        0.0\n",
              "2012-11-25  22.163642  44.532930        1.0\n",
              "2012-12-02  22.235989  44.360230        0.0"
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            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making predictions"
      ],
      "metadata": {
        "id": "5sII0TB1QV-3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start=len(df)\n",
        "end=len(df)+len(test_resampled)-1\n",
        "pred = model_sarima.predict(start=start, end=end, dynamic=False)"
      ],
      "metadata": {
        "id": "Uf2OgScIOtJ0"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting the forecasting"
      ],
      "metadata": {
        "id": "VoEpyMf3QZKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['Weekly_Sales'], label='Actual')\n",
        "plt.plot(pred, label='Prediction')\n",
        "plt.xticks(rotation = 45)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Td3qtXQpPvrk",
        "outputId": "1dfb4882-0826-47d7-bf2c-5d7d6fa50482"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": [],
      "authorship_tag": "ABX9TyPCyY3VJ7SGH7s7JQd1+Pj2",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}