Customer_Transaction_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrR06-TzMpwz"
      },
      "source": [
        "# Customer Transaction Analysis\n",
        "##### Author: Nguyen Vo Thuy Hang\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0ucBtvrMpw1"
      },
      "source": [
        "## 1. Imports\n",
        "### 1.1. Import relevant libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ISNS3VdGMpw2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRCiojU5Mpw2"
      },
      "source": [
        "### 1.2 Load the dataset into a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
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        "id": "FRXaiC4EMpw3",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>...</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>11/08/2016</td>\n",
              "      <td>11/11/2016</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>...</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-BO-10001798</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Bush Somerset Collection Bookcase</td>\n",
              "      <td>261.9600</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>41.9136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2016-152156</td>\n",
              "      <td>11/08/2016</td>\n",
              "      <td>11/11/2016</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>...</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.9400</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "      <td>219.5820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2016-138688</td>\n",
              "      <td>06/12/2016</td>\n",
              "      <td>6/16/2016</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>...</td>\n",
              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.6200</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.8714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>US-2015-108966</td>\n",
              "      <td>10/11/2015</td>\n",
              "      <td>10/18/2015</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>...</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>957.5775</td>\n",
              "      <td>5</td>\n",
              "      <td>0.45</td>\n",
              "      <td>-383.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>US-2015-108966</td>\n",
              "      <td>10/11/2015</td>\n",
              "      <td>10/18/2015</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>...</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>OFF-ST-10000760</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Storage</td>\n",
              "      <td>Eldon Fold 'N Roll Cart System</td>\n",
              "      <td>22.3680</td>\n",
              "      <td>2</td>\n",
              "      <td>0.20</td>\n",
              "      <td>2.5164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
              "0       1  CA-2016-152156  11/08/2016  11/11/2016    Second Class    CG-12520   \n",
              "1       2  CA-2016-152156  11/08/2016  11/11/2016    Second Class    CG-12520   \n",
              "2       3  CA-2016-138688  06/12/2016   6/16/2016    Second Class    DV-13045   \n",
              "3       4  US-2015-108966  10/11/2015  10/18/2015  Standard Class    SO-20335   \n",
              "4       5  US-2015-108966  10/11/2015  10/18/2015  Standard Class    SO-20335   \n",
              "\n",
              "     Customer Name    Segment        Country             City  ...  \\\n",
              "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
              "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
              "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
              "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
              "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
              "\n",
              "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
              "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
              "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
              "\n",
              "                                        Product Name     Sales  Quantity  \\\n",
              "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
              "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
              "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
              "\n",
              "   Discount    Profit  \n",
              "0      0.00   41.9136  \n",
              "1      0.00  219.5820  \n",
              "2      0.00    6.8714  \n",
              "3      0.45 -383.0310  \n",
              "4      0.20    2.5164  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('C:/Users/Welcome/Downloads/US_Retail_Data.csv', encoding='ISO-8859-1')\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VP-fPadpMpw3"
      },
      "source": [
        "## 2. Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9994 entries, 0 to 9993\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Row ID         9994 non-null   int64  \n",
            " 1   Order ID       9994 non-null   object \n",
            " 2   Order Date     9994 non-null   object \n",
            " 3   Ship Date      9994 non-null   object \n",
            " 4   Ship Mode      9994 non-null   object \n",
            " 5   Customer ID    9994 non-null   object \n",
            " 6   Customer Name  9994 non-null   object \n",
            " 7   Segment        9994 non-null   object \n",
            " 8   Country        9994 non-null   object \n",
            " 9   City           9994 non-null   object \n",
            " 10  State          9994 non-null   object \n",
            " 11  Postal Code    9994 non-null   int64  \n",
            " 12  Region         9994 non-null   object \n",
            " 13  Product ID     9994 non-null   object \n",
            " 14  Category       9994 non-null   object \n",
            " 15  Sub-Category   9994 non-null   object \n",
            " 16  Product Name   9994 non-null   object \n",
            " 17  Sales          9994 non-null   float64\n",
            " 18  Quantity       9994 non-null   int64  \n",
            " 19  Discount       9994 non-null   float64\n",
            " 20  Profit         9994 non-null   float64\n",
            "dtypes: float64(3), int64(3), object(15)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pAsGc7FdMpw4",
        "outputId": "ee241d32-73fd-40ae-d203-6f818c597e7f"
      },
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        {
          "data": {
            "text/plain": [
              "(9994, 21)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7loBCWBMpw4",
        "outputId": "cc134b78-92ab-4018-9904-e12a16eaabd5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Row ID             int64\n",
              "Order ID          object\n",
              "Order Date        object\n",
              "Ship Date         object\n",
              "Ship Mode         object\n",
              "Customer ID       object\n",
              "Customer Name     object\n",
              "Segment           object\n",
              "Country           object\n",
              "City              object\n",
              "State             object\n",
              "Postal Code        int64\n",
              "Region            object\n",
              "Product ID        object\n",
              "Category          object\n",
              "Sub-Category      object\n",
              "Product Name      object\n",
              "Sales            float64\n",
              "Quantity           int64\n",
              "Discount         float64\n",
              "Profit           float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAlBwrfBMpw4",
        "outputId": "d47aa73f-c6ae-4200-e7d8-8ad42f9dae7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9994, 21)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drop_duplicates().shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "5TkU7Oq0Mpw5",
        "outputId": "b1d55c47-9b90-41cc-f861-6a911b94226c"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ne3xFEhAMpw5",
        "outputId": "a5a84bfc-926a-46a2-93c9-b4c369e25a83"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Row ID           0\n",
              "Order ID         0\n",
              "Order Date       0\n",
              "Ship Date        0\n",
              "Ship Mode        0\n",
              "Customer ID      0\n",
              "Customer Name    0\n",
              "Segment          0\n",
              "Country          0\n",
              "City             0\n",
              "State            0\n",
              "Postal Code      0\n",
              "Region           0\n",
              "Product ID       0\n",
              "Category         0\n",
              "Sub-Category     0\n",
              "Product Name     0\n",
              "Sales            0\n",
              "Quantity         0\n",
              "Discount         0\n",
              "Profit           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dfjxvW7Mpw5",
        "outputId": "68795a82-231c-4263-d536-3e89dfa7c57e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of outliers: 50\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "def detect_outliers(dataframe,contamination):\n",
        "\n",
        "    a=list(dataframe.select_dtypes(['int64']).columns)+list(dataframe.select_dtypes(['float64']).columns)\n",
        "    model=IsolationForest(contamination=contamination)\n",
        "    model.fit(dataframe[a])\n",
        "    outliers = model.predict(dataframe[a]) ==-1\n",
        "    return outliers\n",
        "\n",
        "contamination=0.005\n",
        "index=detect_outliers(df[['Sales','Quantity','Discount','Profit']],contamination)\n",
        "lign,col=df[index].shape\n",
        "print(\"Number of outliers: {}\".format(lign))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "r5SzNZS7Mpw5",
        "outputId": "0e4a1aa7-de69-422d-deca-586776f07412"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ce969e52-1b6a-460c-b3ee-f00c3d5f4eb4\" class=\"colab-df-container\">\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9994.000000</td>\n",
              "      <td>9994.000000</td>\n",
              "      <td>9994.000000</td>\n",
              "      <td>9994.000000</td>\n",
              "      <td>9994.000000</td>\n",
              "      <td>9994.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4997.500000</td>\n",
              "      <td>55190.379428</td>\n",
              "      <td>229.858001</td>\n",
              "      <td>3.789574</td>\n",
              "      <td>0.156203</td>\n",
              "      <td>28.656896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2885.163629</td>\n",
              "      <td>32063.693350</td>\n",
              "      <td>623.245101</td>\n",
              "      <td>2.225110</td>\n",
              "      <td>0.206452</td>\n",
              "      <td>234.260108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1040.000000</td>\n",
              "      <td>0.444000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6599.978000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2499.250000</td>\n",
              "      <td>23223.000000</td>\n",
              "      <td>17.280000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.728750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4997.500000</td>\n",
              "      <td>56430.500000</td>\n",
              "      <td>54.490000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>8.666500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7495.750000</td>\n",
              "      <td>90008.000000</td>\n",
              "      <td>209.940000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>29.364000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9994.000000</td>\n",
              "      <td>99301.000000</td>\n",
              "      <td>22638.480000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>8399.976000</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-ce969e52-1b6a-460c-b3ee-f00c3d5f4eb4')\"\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-ce969e52-1b6a-460c-b3ee-f00c3d5f4eb4 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-ce969e52-1b6a-460c-b3ee-f00c3d5f4eb4');\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-bf677687-9392-4032-a1e9-4b0d336bc260\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bf677687-9392-4032-a1e9-4b0d336bc260')\"\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-bf677687-9392-4032-a1e9-4b0d336bc260 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            Row ID   Postal Code         Sales     Quantity     Discount  \\\n",
              "count  9994.000000   9994.000000   9994.000000  9994.000000  9994.000000   \n",
              "mean   4997.500000  55190.379428    229.858001     3.789574     0.156203   \n",
              "std    2885.163629  32063.693350    623.245101     2.225110     0.206452   \n",
              "min       1.000000   1040.000000      0.444000     1.000000     0.000000   \n",
              "25%    2499.250000  23223.000000     17.280000     2.000000     0.000000   \n",
              "50%    4997.500000  56430.500000     54.490000     3.000000     0.200000   \n",
              "75%    7495.750000  90008.000000    209.940000     5.000000     0.200000   \n",
              "max    9994.000000  99301.000000  22638.480000    14.000000     0.800000   \n",
              "\n",
              "            Profit  \n",
              "count  9994.000000  \n",
              "mean     28.656896  \n",
              "std     234.260108  \n",
              "min   -6599.978000  \n",
              "25%       1.728750  \n",
              "50%       8.666500  \n",
              "75%      29.364000  \n",
              "max    8399.976000  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rJB2dhuMpw6"
      },
      "source": [
        "## 3. Exploratory data analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHJK65m4Mpw6"
      },
      "source": [
        "### 3.1 Orders, Products, and Customers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QA9wMhe2Mpw6",
        "outputId": "29f6dc6f-dcae-4c26-aed1-0b8cfaaa244f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of orders:  5009\n",
            "Total number of products:  1862\n",
            "Total number of customers:  793\n"
          ]
        }
      ],
      "source": [
        "print('Total number of orders: ',df['Order ID'].nunique())\n",
        "print('Total number of products: ', df['Product ID'].nunique())\n",
        "print('Total number of customers: ', df['Customer ID'].nunique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vop9YrLhMpw6"
      },
      "source": [
        "### 3.2 Category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp1a9NYNMpw6",
        "outputId": "9c21c247-9f40-4572-9440-cd32fc017693"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Office Supplies    6026\n",
              "Furniture          2121\n",
              "Technology         1847\n",
              "Name: Category, dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Category'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "8Sz3p48bMpw6",
        "outputId": "ffe2a9d3-9a9e-4116-cfb2-d56186092716"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "df_sorted=df['Category'].value_counts().sort_values(ascending=False).index\n",
        "sns.countplot(x='Category', data=df,order=df_sorted)\n",
        "plt.title('Number of products by Category')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YxGmMR6-Mpw7"
      },
      "source": [
        "### 3.3 Sales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_NkLJQjMpw7",
        "outputId": "b9ddec72-7717-4d0d-8e8a-60fa1cd43f20"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count     9994.000000\n",
              "mean       229.858001\n",
              "std        623.245101\n",
              "min          0.444000\n",
              "25%         17.280000\n",
              "50%         54.490000\n",
              "75%        209.940000\n",
              "max      22638.480000\n",
              "Name: Sales, dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Sales'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9sK74kIMpw7"
      },
      "source": [
        "In general, these statistics indicate that there is a large variability in the sales values between transactions, with some transactions having very high values compared to the majority of the other transactions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "ee39qeYvMpw7",
        "outputId": "7f6e273b-30fe-430d-f5f2-cc6e46a1c544"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([9.618e+03, 2.580e+02, 6.800e+01, 2.300e+01, 1.200e+01, 1.000e+00,\n",
              "        1.000e+00, 5.000e+00, 3.000e+00, 2.000e+00, 0.000e+00, 0.000e+00,\n",
              "        1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n",
              "        0.000e+00, 1.000e+00]),\n",
              " array([4.44000000e-01, 1.13234580e+03, 2.26424760e+03, 3.39614940e+03,\n",
              "        4.52805120e+03, 5.65995300e+03, 6.79185480e+03, 7.92375660e+03,\n",
              "        9.05565840e+03, 1.01875602e+04, 1.13194620e+04, 1.24513638e+04,\n",
              "        1.35832656e+04, 1.47151674e+04, 1.58470692e+04, 1.69789710e+04,\n",
              "        1.81108728e+04, 1.92427746e+04, 2.03746764e+04, 2.15065782e+04,\n",
              "        2.26384800e+04]),\n",
              " <BarContainer object of 20 artists>)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df.Sales, bins=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BeYHBLfUMpw7"
      },
      "source": [
        "### 3.4 Quantity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "t4GxXK7KMpw7",
        "outputId": "8fa54c76-6973-4071-9cad-ba48a13b7c2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "sns.countplot(x='Quantity',data=df)\n",
        "plt.title('Number of transactions by Quantity')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dj6ufo2Mpw8"
      },
      "source": [
        "The most common quantities were 2 and 3, accounting for approximately 50%. Transactions involving 4 and 5 quantities were less popular, but their observations were in mid-level, at around 1250 observations. Higher than 10 quantities were less common, just occuring in a small number of transactions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BcU1pVEMpw8"
      },
      "source": [
        "### 3.5 Ship Mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hV7LHzJpMpw8",
        "outputId": "4d33d426-04a4-42f9-939d-093ac629e039"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Standard Class    5968\n",
              "Second Class      1945\n",
              "First Class       1538\n",
              "Same Day           543\n",
              "Name: Ship Mode, dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Ship Mode'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "RyQ9zkn_Mpw8",
        "outputId": "b23234cc-fd3f-4814-f6aa-a90632a0e2b4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(6,4))\n",
        "sorted_data=df['Ship Mode'].value_counts().sort_values(ascending=False).index\n",
        "sns.countplot(x='Ship Mode',data=df,order=sorted_data)\n",
        "plt.title('Number of transactions by Ship Mode')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDj_76I1Mpw8"
      },
      "source": [
        "Standard Class is the most popular ship mode, with 5968 observations, followed by Second Class and First Class at 1945 observations and 1538 observations respectively. The least popular ship mode is Same Day option, which the values at 543. This would suggest that most customer selected Standard Class as their primary shipping mode when shipping online."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxsLZ05kMpw8"
      },
      "source": [
        "## 4. Descriptive Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6VxoPCvPMpw8"
      },
      "source": [
        "###  4.1 Total orders by States"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "gFtaMubnMpw8",
        "outputId": "b3308f81-bd30-4a24-f2c5-b8fb49e12a02"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "dd=df.groupby('State')['Order ID'].nunique().sort_values(ascending=False).reset_index()\n",
        "sns.barplot(data=dd,x='State',y='Order ID')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xtNGPqxQMpw9",
        "outputId": "7596f35e-2a6a-4044-e0ce-a889d9bb94bb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f84babfb-09f9-4246-9957-74d8aa2b76df\" 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>State</th>\n",
              "      <th>Order ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>California</td>\n",
              "      <td>1021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York</td>\n",
              "      <td>562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Texas</td>\n",
              "      <td>487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Washington</td>\n",
              "      <td>256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ohio</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Florida</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>North Carolina</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Michigan</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Virginia</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Tennessee</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Colorado</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Indiana</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Massachusetts</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>New Jersey</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Kentucky</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Oregon</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Maryland</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Minnesota</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Delaware</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Oklahoma</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Missouri</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Mississippi</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Utah</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Rhode Island</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Nevada</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Nebraska</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>New Mexico</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Louisiana</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>South Carolina</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Iowa</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>New Hampshire</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Kansas</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Idaho</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Montana</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Vermont</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
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              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Maine</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>North Dakota</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>West Virginia</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Wyoming</td>\n",
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            "text/plain": [
              "                   State  Order ID\n",
              "0             California      1021\n",
              "1               New York       562\n",
              "2                  Texas       487\n",
              "3           Pennsylvania       288\n",
              "4               Illinois       276\n",
              "5             Washington       256\n",
              "6                   Ohio       236\n",
              "7                Florida       200\n",
              "8         North Carolina       136\n",
              "9               Michigan       117\n",
              "10              Virginia       115\n",
              "11               Arizona       108\n",
              "12             Tennessee        91\n",
              "13               Georgia        91\n",
              "14              Colorado        79\n",
              "15               Indiana        73\n",
              "16         Massachusetts        62\n",
              "17            New Jersey        61\n",
              "18              Kentucky        61\n",
              "19                Oregon        56\n",
              "20             Wisconsin        53\n",
              "21              Maryland        46\n",
              "22           Connecticut        45\n",
              "23             Minnesota        44\n",
              "24              Delaware        44\n",
              "25               Alabama        34\n",
              "26              Oklahoma        34\n",
              "27              Missouri        30\n",
              "28              Arkansas        27\n",
              "29           Mississippi        26\n",
              "30                  Utah        26\n",
              "31          Rhode Island        25\n",
              "32                Nevada        23\n",
              "33              Nebraska        23\n",
              "34            New Mexico        22\n",
              "35             Louisiana        21\n",
              "36        South Carolina        20\n",
              "37                  Iowa        17\n",
              "38         New Hampshire        17\n",
              "39                Kansas        14\n",
              "40                 Idaho        11\n",
              "41               Montana         8\n",
              "42               Vermont         6\n",
              "43          South Dakota         5\n",
              "44  District of Columbia         4\n",
              "45                 Maine         3\n",
              "46          North Dakota         2\n",
              "47         West Virginia         2\n",
              "48               Wyoming         1"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a=df.groupby('State')['Order ID'].nunique().sort_values(ascending=False).reset_index()\n",
        "a"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iTrAE4TMpw9"
      },
      "source": [
        "The chart shows that California had the highest orders, which values more than 1021. New York and Texas were in second and third place, with 562 orders and 487 orders, respectively. The remaining states had a small number of orders.\n",
        "\n",
        "This indicates that shopping activities were highly frequent in large states, particularly California, New York, and Texas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dDtc1CIMpw9"
      },
      "source": [
        "### 4.2 Total Orders by quarters and years"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "raZdbMh2Mpw9",
        "outputId": "33f4071c-1c68-487f-f9ec-e1244b1ffc01"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "df['Order Date']=pd.to_datetime(df['Order Date'])\n",
        "\n",
        "\n",
        "df['Year_Quarter']=df['Order Date'].dt.to_period('Q')\n",
        "df['Year_Quarter']=df['Year_Quarter'].astype(str)\n",
        "\n",
        "orders_by_quarter_year=df.groupby(['Year_Quarter'])['Order ID'].nunique().reset_index()\n",
        "sns.lineplot(data=orders_by_quarter_year, x='Year_Quarter', y='Order ID')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkPCloZYMpw9"
      },
      "source": [
        "Overall, the line chart shows an upward trend in total orders. To be more detailed, the figure was always low in the first quarter, then increased gradually and peaked in the fourth quarter.\n",
        "\n",
        "It means that customers' shopping habits increased at the end of the year. Therefore, we should launch appropriate plans to boost this metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjDbC7d2MpxC"
      },
      "source": [
        "### 4.3 Sales over months and years"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "1rNj9ZphMpxC",
        "outputId": "4f09735b-0fcb-458a-a49e-64a2769ac485"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "df['Order Date']=pd.to_datetime(df['Order Date'])\n",
        "\n",
        "df['Year_Month']=df['Order Date'].dt.strftime('%b %Y')\n",
        "df['Year_Month']=pd.to_datetime(df['Year_Month'])\n",
        "df_sorted=df.sort_values('Year_Month')\n",
        "\n",
        "df.groupby(['Year_Month'])['Sales'].sum().plot(kind='line')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xSGPCiIMpxC"
      },
      "source": [
        "It can be seen from the chart that the sales had an upward trend over years. In a particular year, the sales of first six months tend to be less than that of last six months. During the end of year in 2017, the line chart increased dramatically in October, then significantly declined in November.\n",
        "\n",
        "This would suggest that customers had a tendency to spend more at the end of year, while shopping activities in the first half of year were less frequent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IBOkE0dMpxC"
      },
      "source": [
        "### 4.4 Category sales over quarters and years"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "SLHyrG5CMpxD",
        "outputId": "ae77481c-00d9-4f34-eca8-494c330e6091"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Order Date']=pd.to_datetime(df['Order Date'])\n",
        "df['Years'], df['Quarters']=df['Order Date'].dt.year, df['Order Date'].dt.quarter\n",
        "df.groupby(['Years','Quarters','Category'])['Sales'].sum().unstack().plot(kind='bar')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WmgRW-FMpxD"
      },
      "source": [
        "The diagram illustrates that all categories had an uptrend from the first to the fourth quarter. In the last quarter of each year, Technology consistently ranked in the first position, meaning that customers purchased more technology products at the end of the year."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQYFJFKiMpxD"
      },
      "source": [
        "### 4.5 Profit by Quarter and Category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "l-R4SOttMpxD",
        "outputId": "31dfa979-a3da-4a3a-ddeb-152adc947966"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
        "\n",
        "df['Year_Quarter'] = df['Order Date'].dt.to_period('Q')\n",
        "df['Year_Quarter'] = df['Year_Quarter'].astype(str)\n",
        "profit_by_quarter_category = df.groupby(['Year_Quarter', 'Category'])['Profit'].sum().reset_index()\n",
        "\n",
        "sns.lineplot(data=profit_by_quarter_category, x='Year_Quarter', y='Profit', hue='Category')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kX3aNTMhMpxD"
      },
      "source": [
        "While total sales was always a positive number, profit can be a negative value. The line chart describes the profit of 3 categories from Q1/2014 to Q4/2018. Overall, the profits grew over the given period in all categories.\n",
        "\n",
        "Office Supplies reached a peak at 20000 USD in Q4/2016, then significantly declined more than 5000 USD in Q1/2017. The profit increased lightly in Q3/2017 after dropped under 10000 USD in Q4/2017. In constrast, Furniture achieved the lowest profit in almost the given time. Its profits fluctuated through the period and the profit under 0 USD in Q4/2017.\n",
        "\n",
        "Regarding Technology, the figure had an upward trend from more than 1000 USD in Q1/2014 to nearly 20000 USD in Q4/2017\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQM7ZC9zMpxD"
      },
      "source": [
        "### 4.6 Time Delivery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "T7MkeKJMMpxD",
        "outputId": "bf42010f-3b83-4b3f-e232-9c68c5fa4162"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Ship Date'] = pd.to_datetime(df['Ship Date'])\n",
        "df['Delivery Time'] = (df['Ship Date'] - df['Order Date']).dt.days\n",
        "df['Delivery Time'].hist(bins=30)\n",
        "plt.title('Time Delivery Distribution')\n",
        "plt.xlabel('Count of Time Delivery')\n",
        "plt.ylabel('Time Delivery')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAns5Z3fMpxE"
      },
      "source": [
        "Time Delivery by calculating the days difference between ship date and order date. The delivery time distribution shows that more than 2500 transactions have been shipped in 4 days after ordering, which is the most common delivery duration. The following are 5 days and 2 days, with the transactions being around more than 2000 and nearly 1500 respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gbRndsnMpxE"
      },
      "source": [
        "### 4.7 Time delivery by Ship Mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "AyUtLkB6MpxE",
        "outputId": "fdf9dd64-4ab8-4b90-db98-c1b3e8274011"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ship_mode_delivery_time = df.groupby(['Ship Mode', 'Delivery Time'])['Order ID'].count().unstack()\n",
        "\n",
        "ship_mode_delivery_time.plot(kind='bar', stacked=False)\n",
        "\n",
        "plt.title('Count of Ship Mode by Delivery Time')\n",
        "plt.xlabel('Delivery Time (Days)')\n",
        "plt.ylabel('Count of Ship Mode')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VcOmm4WMpxE"
      },
      "source": [
        "There are 500 transactions shipped by the same date as order date and a few transactions in 1 days, labeled by Same Day. The First Class transactions could be shipped from 1 to 3 days, which was faster than the Second Class. The Standard Class transactions were delivered slowest, from 4 to 7 days. This means customers should allow more waiting time to receive products when selecting Standard Class as Ship Mode."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ss2v71AaMpxE"
      },
      "source": [
        "### 4.8 Sales vs Profit per Category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Ez5-vCi0MpxE",
        "outputId": "3c953665-78e8-4e56-c9de-7efa3eedf51d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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302Kx8Mknn9gcExISQp06dZgzZ47N9fbt28eqVausCe71qFWrFrVq1eKLL77ghx9+oGfPnrlmJUVEbiX9P46ISBH03HPPkZqayiOPPEKVKlXIzMxk06ZNLFy4kHLlytGvXz8AHnjgAeus0zPPPENycjKff/45gYGB1tm1/Dz11FN89913DB48mHXr1tG0aVPMZjOHDh3iu+++Y+XKldSvX5/x48fz+++/07FjR8qWLUt0dDTTp0+nVKlSNGvW7Jr30r17d0aOHMnIkSPx8/PLNWMycOBA4uLiaNWqFaVKleLUqVNMmzaNOnXqULVq1Zv/EK/B0dGRiRMn0q9fP1q0aMHjjz9uLaVfrlw5XnzxxRs+p6urK9WqVWPhwoVUqlQJPz8/atSocc3tEDIzM2ndujXdu3fn8OHDTJ8+nWbNmvHQQw8BOcsPZ8yYwVNPPUXdunXp2bMnAQEBnD59mmXLltG0aVM+/vjjm/ocKlSowLx58+jRowdVq1ald+/e1KhRw/qdW7RokXVftxv5vtWrV48ZM2bw1ltvER4eTmBgIK1ateLll19myZIldOrUib59+1KvXj1SUlLYu3cv33//PSdPnqREiRJ06dKFhg0bMmLECI4dO0aVKlVYsmSJ9ZcFV87Mvffee3To0IHGjRszYMAAayl9b2/vq+4zl5fevXszcuRIAC1pFJHbr5CrRYqISB6WL19u9O/f36hSpYrh4eFhODk5GeHh4cZzzz1nXLhwwWbskiVLjFq1ahkuLi5GuXLljIkTJxpfffWVARgRERHWcf8upW8YOeXkJ06caFSvXt1wdnY2fH19jXr16hlvvPGGkZCQYBiGYaxdu9Z4+OGHjdDQUMPJyckIDQ01Hn/88Vyl0K+madOmBmAMHDgwV9/3339vPPDAA0ZgYKDh5ORklClTxnjmmWeM8+fPX/O8gDF06NCrjrlcSn/RokV59i9cuNC45557DGdnZ8PPz8/o1auXcfbsWZsx11tK3zAMY9OmTUa9evUMJyena5bVv3zeDRs2GE8//bTh6+treHh4GL169bIpC3/lvbRr187w9vY2XFxcjAoVKhh9+/Y1duzYYROTu7v7VT+TvBw5csQYNGiQUa5cOcPJycnw9PQ0mjZtakybNs1IT0+3jrve71tUVJTRsWNHw9PT0wBsvntJSUnG6NGjjfDwcMPJyckoUaKE0aRJE+P999+32VIgJibGeOKJJwxPT0/D29vb6Nu3r7Fx40YDMBYsWGAT/5o1a4ymTZsarq6uhpeXl9G5c2fjwIEDNmMul9K/2rYQ58+fN+zt7Y1KlSrd8GcoIvJfmQwjj3UzIiIicstd3gR7+/bt1ucJ5ep++uknHnnkEf7880+aNm1a4OePjY0lJCSEMWPG8Prrrxf4+UVErkbPnImIiEiRlJaWZvPebDYzbdo0vLy8qFu37i255uzZszGbzTz11FO35PwiIlejZ85ERESkSHruuedIS0ujcePGZGRksHjxYjZt2sQ777xT4Fsj/Pbbbxw4cIC3336bLl26UK5cuQI9v4jI9VByJiIiIkVSq1atmDx5MkuXLiU9PZ3w8HCmTZvGsGHDCvxa48ePZ9OmTTRt2pRp06YV+PlFRK6HnjkTEREREREpAvTMmYiIiIiISBGg5ExERERERKQI0DNnt4DFYiEyMhJPT0+bTTJFREREROTuYhgGSUlJhIaGYmd39bkxJWe3QGRkJKVLly7sMEREREREpIg4c+YMpUqVuuoYJWe3gKenJ5DzA/Dy8irkaEREREREpLAkJiZSunRpa45wNUrOboHLSxm9vLyUnImIiIiIyHU97qSCICIiIiIiIkWAkjMREREREZEiQMmZiIiIiIhIEaBnzkRERETkjmcYBtnZ2ZjN5sIORe4w9vb2ODg4FMgWWkrOREREROSOlpmZyfnz50lNTS3sUOQO5ebmRkhICE5OTv/pPErOREREROSOZbFYiIiIwN7entDQUJycnApkhkMEcmZkMzMziYmJISIigooVK15zo+mrUXImIiIiIneszMxMLBYLpUuXxs3NrbDDkTuQq6srjo6OnDp1iszMTFxcXG76XCoIIiIiIiJ3vP8ymyFyLQX1/dK3VEREREREpAhQciYiIiIiIlIEKDkTEREREREpApSciYiIiIgUYVFRUTz33HOUL18eZ2dnSpcuTefOnVm7du11HT979mx8fHxubZBSIFStUURERESkiDp58iRNmzbFx8eH9957j5o1a5KVlcXKlSsZOnQohw4dKuwQb1hWVhaOjo6FHUaRpJkzERERESlQl9IvcTrxNOeSz5GSmVLY4RRrQ4YMwWQysW3bNh577DEqVapE9erVeemll9iyZQsAU6ZMoWbNmri7u1O6dGmGDBlCcnIyAOvXr6dfv34kJCRgMpkwmUyMGzcOgIyMDEaOHEnJkiVxd3enUaNGrF+/3ub6n3/+uXUbgkceeYQpU6bkmoWbMWMGFSpUwMnJicqVK/P111/b9JtMJmbMmMFDDz2Eu7s7b731FuHh4bz//vs243bv3o3JZOLYsWMF9wEWM0rORERERKRAZJoz2Ruzl8GrB9Pxx448uPhBXtv4GqeTThd2aMVSXFwcK1asYOjQobi7u+fqv5wk2dnZ8dFHH7F//37mzJnDb7/9xqhRowBo0qQJU6dOxcvLi/Pnz3P+/HlGjhwJwLBhw9i8eTMLFizg77//plu3brRv356jR48CsHHjRgYPHswLL7zA7t27adu2LW+//bZNDD/++CMvvPACI0aMYN++fTzzzDP069ePdevW2YwbN24cjzzyCHv37mXAgAH079+fWbNm2YyZNWsWzZs3Jzw8vEA+v+LIZBiGUdhB3GkSExPx9vYmISEBLy+vwg5HRERE5LY4EneEHkt7kG1k27QHuAbw7YPfEuIRcttjSk9PJyIigrCwsP+0OXBh2LZtG40aNWLx4sU88sgj133c999/z+DBg4mNjQVynjkbPnw48fHx1jGnT5+mfPnynD59mtDQUGt7mzZtaNiwIe+88w49e/YkOTmZpUuXWvuffPJJli5daj1X06ZNqV69Op999pl1TPfu3UlJSWHZsmVAzszZ8OHD+eCDD6xjIiMjKVOmDJs2baJhw4ZkZWURGhrK+++/T58+fW7ocyoKrvY9u5HcQDNnIiIiIvKfJWcmM233tFyJGUBMWgxbo7YWQlTF2/XOoaxZs4bWrVtTsmRJPD09eeqpp7h48SKpqan5HrN3717MZjOVKlXCw8PD+tqwYQPHjx8H4PDhwzRs2NDmuH+/P3jwIE2bNrVpa9q0KQcPHrRpq1+/vs370NBQOnbsyFdffQXAL7/8QkZGBt26dbuue75TKTkTERERkf8sJSuFXRd25du/7vQ6si25EzfJX8WKFTGZTFct+nHy5Ek6depErVq1+OGHH9i5cyeffPIJAJmZmfkel5ycjL29PTt37mT37t3W18GDB/nwww8L/F7yWpY5cOBAFixYQFpaGrNmzaJHjx64ubkV+LWLEyVnIiIiIvKf2dvZ4+Psk29/kFsQ9ib72xfQHcDPz4927drxySefkJKSu7BKfHw8O3fuxGKxMHnyZO69914qVapEZGSkzTgnJyfMZrNN2z333IPZbCY6Oprw8HCbV3BwMACVK1dm+/btNsf9+33VqlXZuHGjTdvGjRupVq3aNe/vwQcfxN3dnRkzZrBixQr69+9/zWPudErOREREROQ/K+Fagn41+uXb37VSV0wm022M6M7wySefYDabadiwIT/88ANHjx7l4MGDfPTRRzRu3Jjw8HCysrKYNm0aJ06c4Ouvv2bmzJk25yhXrhzJycmsXbuW2NhYUlNTqVSpEr169aJ3794sXryYiIgItm3bxoQJE6zPij333HP8+uuvTJkyhaNHj/Lpp5+yfPlym5/jyy+/zOzZs5kxYwZHjx5lypQpLF682Fp05Grs7e3p27cvo0ePpmLFijRu3LhgP7xiSMmZiIiIiBSIlqVa0rZsW5s2Eyb+1+h/hHqE5nOUXE358uXZtWsX999/PyNGjKBGjRq0bduWtWvXMmPGDGrXrs2UKVOYOHEiNWrU4Ntvv2XChAk252jSpAmDBw+mR48eBAQEMGnSJCCnOmLv3r0ZMWIElStXpkuXLmzfvp0yZcoAOc+OzZw5kylTplC7dm1WrFjBiy++aFPwokuXLnz44Ye8//77VK9enU8//ZRZs2bRsmXL67q/AQMGkJmZSb9++Sf2dxNVa7wFVK1RRERE7laX0i9xIfUCWyK34OboRqOQRpRwKYG7U+5njm6H4lytsSgaNGgQhw4d4o8//iiQ8/3xxx+0bt2aM2fOEBQUVCDnLAwFVa3R4VYGKSIiIiJ3F18XX3xdfKniV6WwQ5EC8P7779O2bVvc3d1Zvnw5c+bMYfr06f/5vBkZGcTExDBu3Di6detWrBOzgqRljSIiIiIikqdt27bRtm1batasycyZM/noo48YOHDgfz7v/PnzKVu2LPHx8dZllqKZMxERERERycd33313S87bt29f+vbte0vOXZwVm5kzs9nM66+/TlhYGK6urlSoUIE333zTZnM+wzAYM2YMISEhuLq60qZNG44ePWpznri4OHr16oWXlxc+Pj4MGDCA5ORkmzF///039913Hy4uLpQuXVrZvIiIiIiI3HLFJjmbOHEiM2bM4OOPP+bgwYNMnDiRSZMmMW3aNOuYSZMm8dFHHzFz5ky2bt2Ku7s77dq1Iz093TqmV69e7N+/n9WrV7N06VJ+//13nn76aWt/YmIiDzzwAGXLlmXnzp289957jBs3js8+++y23q+IiIiIiNxdik21xk6dOhEUFMSXX35pbXvsscdwdXXlm2++wTAMQkNDGTFihHVfhYSEBIKCgpg9ezY9e/bk4MGDVKtWje3bt1O/fn0AVqxYwYMPPsjZs2cJDQ1lxowZvPbaa0RFReHk5ATAq6++yk8//XTV3dmvpGqNIiIiIkWDqjXK7VBQ1RqLzcxZkyZNWLt2LUeOHAFgz549/Pnnn3To0AGAiIgIoqKiaNOmjfUYb29vGjVqxObNmwHYvHkzPj4+1sQMoE2bNtjZ2bF161brmObNm1sTM4B27dpx+PBhLl26lGdsGRkZJCYm2rxERERERERuRLEpCPLqq6+SmJhIlSpVsLe3x2w28/bbb9OrVy8AoqKiAHKV4QwKCrL2RUVFERgYaNPv4OCAn5+fzZiwsLBc57jc5+vrmyu2CRMm8MYbbxTAXYqIiIiIyN2q2Mycfffdd3z77bfMmzePXbt2MWfOHN5//33mzJlT2KExevRoEhISrK8zZ84UdkgiIiIiIlLMFJvk7OWXX+bVV1+lZ8+e1KxZk6eeeooXX3yRCRMmABAcHAzAhQsXbI67cOGCtS84OJjo6Gib/uzsbOLi4mzG5HWOK6/xb87Oznh5edm8RERERESKm3HjxlGnTp3CDuOuVWySs9TUVOzsbMO1t7fHYrEAEBYWRnBwMGvXrrX2JyYmsnXrVho3bgxA48aNiY+PZ+fOndYxv/32GxaLhUaNGlnH/P7772RlZVnHrF69msqVK+e5pFFERERE5Fbo27cvJpMp1+vYsWO37JojR460+fd037596dKlyy27ntgqNslZ586defvtt1m2bBknT57kxx9/ZMqUKTzyyCMAmEwmhg8fzltvvcWSJUvYu3cvvXv3JjQ01PqFqlq1Ku3bt2fQoEFs27aNjRs3MmzYMHr27EloaCgATzzxBE5OTgwYMID9+/ezcOFCPvzwQ1566aXCunURERERKQLMFoPNxy/y8+5zbD5+EbPl1hc9b9++PefPn7d5/bs+wvXIzMy8rnEeHh74+/vf8PmvxWw2WydVJH/FJjmbNm0aXbt2ZciQIVStWpWRI0fyzDPP8Oabb1rHjBo1iueee46nn36aBg0akJyczIoVK2zKWX777bdUqVKF1q1b8+CDD9KsWTObPcy8vb1ZtWoVERER1KtXjxEjRjBmzBibvdBERERE5O6yYt95mk38jcc/38ILC3bz+OdbaDbxN1bsO39Lr+vs7ExwcLDNa8CAAblms4YPH07Lli2t71u2bMmwYcMYPnw4JUqUoF27dqxfvx6TycTatWupX78+bm5uNGnShMOHD1uPu3JZ47hx45gzZw4///yzddZu/fr11vPEx8dbj9u9ezcmk4mTJ08CMHv2bHx8fFiyZAnVqlXD2dmZ06dPk5GRwciRIylZsiTu7u40atSI9evX35oPrxgqNtUaPT09mTp1KlOnTs13jMlkYvz48YwfPz7fMX5+fsybN++q16pVqxZ//PHHzYYqIiIiIneQFfvO8+w3u/j3PFlUQjrPfrOLGU/WpX2NkEKJ7WrmzJnDs88+y8aNGwE4fz4nkXzttdeYPHkyAQEBDB48mP79+1vHXGnkyJEcPHiQxMREZs2aBeT8W3rTpk3Xdf3U1FQmTpzIF198gb+/P4GBgQwbNowDBw6wYMECQkND+fHHH2nfvj179+6lYsWKBXTnxVexSc5ERERERG43s8XgjV8O5ErMAAzABLzxywHaVgvG3s5U4NdfunQpHh4e1vcdOnTA3d39uo6tWLEikyZNsr6/nJy9/fbbtGjRAsjZrqpjx46kp6fn2jzZw8MDV1dXMjIy8i2MdzVZWVlMnz6d2rVrA3D69GlmzZrF6dOnrY8UjRw5khUrVjBr1izeeeedG77GnUbJmYiIiIhIPrZFxHE+IT3ffgM4n5DOtog4Glco+Ge17r//fmbMmGF97+7uzujRo6/r2Hr16uXZXqtWLet/h4TkzPhFR0dTpkyZ/xBpbk5OTjbX2rt3L2azmUqVKtmMy8jIuCXPuRVHSs5ERERERPIRnZR/YnYz426Uu7s74eHhNm12dnYYhu1c3pWVxq88Ni+Ojo7W/zaZcmb7bqRYx+UK6lfGkNf1XV1drecHSE5Oxt7enp07d2Jvb28z9srZwbuZkjMRERERkXwEerpce9ANjCsIAQEB7Nu3z6Zt9+7dNklXQXFycsJsNue6PuQsk7y81dTu3buvea577rkHs9lMdHQ09913X4HHeicoNtUaRURERERut4ZhfoR4u5Df02QmIMTbhYZhfrctplatWrFjxw7mzp3L0aNHGTt2bK5kraCUK1eOv//+m8OHDxMbG0tWVhbh4eGULl2acePGcfToUZYtW8bkyZOvea5KlSrRq1cvevfuzeLFi4mIiGDbtm1MmDCBZcuW3ZL4ixslZyIiIiIi+bC3MzG2czWAXAna5fdjO1e7JcVA8tOuXTtef/11Ro0aRYMGDUhKSqJ379635FqDBg2icuXK1K9fn4CAADZu3IijoyPz58/n0KFD1KpVi4kTJ/LWW29d1/lmzZpF7969GTFiBJUrV6ZLly5s3769wJ93K65Mxr8XrMp/lpiYiLe3NwkJCXh5eRV2OCIiIiJ3rfT0dCIiIggLC8tVjfBGrNh3njd+OWBTHCTE24WxnasVyTL6cntd7Xt2I7mBnjkTEREREbmG9jVCaFstmG0RcUQnpRPombOU8XbOmMmdT8mZiIiIiMh1sLcz3ZJy+SKX6ZkzERERERGRIkDJmYiIiIiISBGg5ExERERERKQIUHImIiIiIiJSBCg5ExERERERKQKUnImIiIiIiBQBSs5ERERERESKACVnIiIiIiLFXGpqKo899hheXl6YTCbi4+PzbCtXrhxTp04t7HBvi/Xr11vvG2D27Nn4+PgUakzXouRMREREROR6WMwQ8Qfs/T7nfy3mW37JM2fO0L9/f0JDQ3FycqJs2bK88MILXLx40WbcnDlz+OOPP9i0aRPnz5/H29s7z7bt27fz9NNP37J4zWYz7777LlWqVMHV1RU/Pz8aNWrEF198ccuueb169OjBkSNHCjuMq3Io7ABERERERIq8A0tgxSuQGPlPm1cotJ8I1R66JZc8ceIEjRs3plKlSsyfP5+wsDD279/Pyy+/zPLly9myZQt+fn4AHD9+nKpVq1KjRg3r8Xm1BQQE3JJYL3vjjTf49NNP+fjjj6lfvz6JiYns2LGDS5cu3dLrXg9XV1dcXV0LO4yr0syZiIiIiMjVHFgC3/W2TcwAEs/ntB9YcksuO3ToUJycnFi1ahUtWrSgTJkydOjQgTVr1nDu3Dlee+01AFq2bMnkyZP5/fffMZlMtGzZMs82INeyxvj4eJ555hmCgoJwcXGhRo0aLF261Nr/559/ct999+Hq6krp0qV5/vnnSUlJyTfmJUuWMGTIELp160ZYWBi1a9dmwIABjBw50jomr6WVderUYdy4cdb3JpOJGTNm0KFDB1xdXSlfvjzff/+9tf/kyZOYTCYWLFhAkyZNrLFv2LAh39jyWtb4888/U7duXVxcXChfvjxvvPEG2dnZABiGwbhx4yhTpgzOzs6Ehoby/PPP53v+gqDkTEREREQkPxZzzowZRh6d/79txasFvsQxLi6OlStXMmTIkFyzPcHBwfTq1YuFCxdiGAaLFy9m0KBBNG7cmPPnz7N48eI823LdmsVChw4d2LhxI9988w0HDhzg3Xffxd7eHsiZeWvfvj2PPfYYf//9NwsXLuTPP/9k2LBh+cYdHBzMb7/9RkxMzH/+DF5//XUee+wx9uzZQ69evejZsycHDx60GfPyyy8zYsQI/vrrLxo3bkznzp1zLfnMzx9//EHv3r154YUXOHDgAJ9++imzZ8/m7bffBuCHH37ggw8+4NNPP+Xo0aP89NNP1KxZ8z/f19UoORMRERERyc+pTblnzGwYkHguZ1wBOnr0KIZhULVq1Tz7q1atyqVLl4iJicHPzw83NzecnJwIDg7Gz88vz7Z/W7NmDdu2bWPx4sW0bduW8uXL06lTJzp06ADAhAkT6NWrF8OHD6dixYo0adKEjz76iLlz55Kenp5nXFOmTCEmJobg4GBq1arF4MGDWb58+U19Bt26dWPgwIFUqlSJN998k/r16zNt2jSbMcOGDeOxxx6jatWqzJgxA29vb7788svrOv8bb7zBq6++Sp8+fShfvjxt27blzTff5NNPPwXg9OnTBAcH06ZNG8qUKUPDhg0ZNGjQTd3L9VJyJiIiIiKSn+QLBTvuBhlGXjN2BWP37t2UKlWKSpUq5dm/Z88eZs+ejYeHh/XVrl07LBYLEREReR5TrVo19u3bx5YtW+jfvz/R0dF07tyZgQMH3nB8jRs3zvX+3zNnV45xcHCgfv36ucbkZ8+ePYwfP97m/gYNGsT58+dJTU2lW7dupKWlUb58eQYNGsSPP/5oXfJ4qyg5ExERERHJj0dQwY67TuHh4ZhMpnwTjYMHD+Lr6/ufCnxcqzhGcnIyzzzzDLt377a+9uzZw9GjR6lQoUK+x9nZ2dGgQQOGDx/O4sWLmT17Nl9++aU1obOzs8uVdGZlZd30fdys5ORk3njjDZv727t3L0ePHsXFxYXSpUtz+PBhpk+fjqurK0OGDKF58+a3NFYlZyIiIlKkJWUkEZkcSWRyJKlZqYUdjtxtyjbJqcqIKZ8BJvAqmTOuAPn7+9O2bVumT59OWlqaTV9UVBTffvstPXr0wGTKL65rq1WrFmfPns23vHzdunU5cOAA4eHhuV5OTk7XfZ1q1aoBWAuJBAQEcP78eWt/YmJinjNxW7ZsyfX+38s8rxyTnZ3Nzp07810K+m9169bl8OHDed6fnV1OmuTq6krnzp356KOPWL9+PZs3b2bv3r3Xdf6boVL6IiIiUiSZLWYiEiN4f/v7bIrchJ3JjtZlWvN83ecp61W2sMOTu4WdfU65/O96k5OgXTnj8/8To/bv5owrYB9//DFNmjShXbt2vPXWWzal9EuWLGktXHGzWrRoQfPmzXnssceYMmUK4eHhHDp0CJPJRPv27XnllVe49957GTZsGAMHDsTd3Z0DBw6wevVqPv744zzP2bVrV5o2bUqTJk0IDg4mIiKC0aNHU6lSJapUqQJAq1atmD17Np07d8bHx4cxY8ZYi5BcadGiRdSvX59mzZrx7bffsm3btlzPk33yySdUrFiRqlWr8sEHH3Dp0iX69+9/Xfc/ZswYOnXqRJkyZejatSt2dnbs2bOHffv28dZbbzF79mzMZjONGjXCzc2Nb775BldXV8qWvXX//6OZMxERESmSziWf44llT7AxciMGBmbDzKpTq+i9vDeRyVcr0CBSwKo9BN3ngleIbbtXaE77LdrnrGLFiuzYsYPy5cvTvXt3KlSowNNPP83999/P5s2b8yzycaN++OEHGjRowOOPP061atUYNWoUZnNO5clatWqxYcMGjhw5wn333cc999zDmDFjCA0Nzfd87dq145dffqFz585UqlSJPn36UKVKFVatWoWDQ8680OjRo2nRogWdOnWiY8eOdOnSJc9lkm+88QYLFiygVq1azJ07l/nz51tn4S579913effdd6lduzZ//vknS5YsoUSJEtd17+3atWPp0qWsWrWKBg0acO+99/LBBx9Yky8fHx8+//xzmjZtSq1atVizZg2//PIL/v7+13X+m2EybuVThnepxMREvL29SUhIwMvLq7DDERERKXYyzZm8t/09FhxekGf/iHoj6F29N3Ym/Z5Zri49PZ2IiAjCwsJwcXH5byezmHOqMiZfyHnGrGyTWzJjJjn7nP3444906dIlz/6TJ08SFhbGX3/9RZ06dW5rbHm52vfsRnIDLWsUERGRIicxM5GNkRvz7V93Zh1dK3XFw8njNkYldz07ewi7r7CjkDuYft0kIiIiRY6jyRFvZ+98+/1c/HC0c7yNEYmI3HqaORMREZEix9vFm37V+zFiw4g8+5+s+iTODs63OSoRuV2u9eRVuXLlbukecIVFM2ciIiJSJNULqkfHsI652vtW70sFn/z3WBIRKa40cyYiIiJFkr+rP680fIU+1fuw/sx6HOwcaFm6JUFuQXg5q+CWiNx5lJyJiIhIkeXr4ouviy9V/a9vU1kRkeJMyxpFRERERESKACVnIiIiIiIiRYCSMxERERERkSJAyZmIiIiIyF1k3Lhx1KlT5465zp1EyZmIiIiIyHUwW8xsj9rOryd+ZXvUdswW8y27lslkuupr3Lhxt+zaUnhUrVFERERE5BrWnFrDu9ve5ULqBWtbkFsQrzZ8lTZl2xT49c6fP2/974ULFzJmzBgOHz5sbfPw8Cjwa0rh08yZiIiIiMhVrDm1hpfWv2STmAFEp0bz0vqXWHNqTYFfMzg42Pry9vbGZDLZtC1YsICqVavi4uJClSpVmD59us3xZ8+e5fHHH8fPzw93d3fq16/P1q1bbcZ8/fXXlCtXDm9vb3r27ElSUpK1r2XLljz//POMGjUKPz8/goODc83WnT59mocffhgPDw+8vLzo3r07Fy7YfkZXslgsjB8/nlKlSuHs7EydOnVYsWKFzZhNmzZRp04dXFxcqF+/Pj/99BMmk4ndu3djGAbh4eG8//77Nsfs3r0bk8nEsWPHbuQjLpKUnImIiIiI5MNsMfPutncxMHL1XW6buG3iLV3i+G/ffvstY8aM4e233+bgwYO88847vP7668yZMweA5ORkWrRowblz51iyZAl79uxh1KhRWCwW6zmOHz/OTz/9xNKlS1m6dCkbNmzg3XfftbnOnDlzcHd3Z+vWrUyaNInx48ezevVqICfRevjhh4mLi2PDhg2sXr2aEydO0KNHj3zj/vDDD5k8eTLvv/8+f//9N+3ateOhhx7i6NGjACQmJtK5c2dq1qzJrl27ePPNN3nllVesx5tMJvr378+sWbNszjtr1iyaN29OeHj4f/tgiwAtaxQRERERyceu6F25ZsyuZGAQlRrFruhdNAhucFtiGjt2LJMnT+bRRx8FICwsjAMHDvDpp5/Sp08f5s2bR0xMDNu3b8fPzw8gV+JisViYPXs2np6eADz11FOsXbuWt99+2zqmVq1ajB07FoCKFSvy8ccfs3btWtq2bcvatWvZu3cvERERlC5dGoC5c+dSvXp1tm/fToMGuT+L999/n1deeYWePXsCMHHiRNatW8fUqVP55JNPmDdvHiaTic8//xwXFxeqVavGuXPnGDRokPUcffv2ZcyYMWzbto2GDRuSlZXFvHnzcs2mFVeaORMRERERyUdMakyBjvuvUlJSOH78OAMGDMDDw8P6euuttzh+/DiQs8zvnnvusSZmeSlXrpw1MQMICQkhOjraZkytWrVs3l855uDBg5QuXdqamAFUq1YNHx8fDh48mOt6iYmJREZG0rRpU5v2pk2bWscfPnyYWrVq4eLiYu1v2LChzfjQ0FA6duzIV199BcAvv/xCRkYG3bp1y/deixPNnImIiIiI5CPALaBAx/1XycnJAHz++ec0atTIps/e3h4AV1fXa57H0dHR5r3JZLJZ9ni9YwrDwIEDeeqpp/jggw+YNWsWPXr0wM3NrbDDKhDFaubs3LlzPPnkk/j7++Pq6krNmjXZsWOHtd8wDMaMGUNISAiurq60adPGuob1sri4OHr16oWXlxc+Pj4MGDDA+iW/7O+//+a+++7DxcWF0qVLM2nSpNtyfyIiIiJStNQNrEuQWxAmTHn2mzAR7BZM3cC6tyWeoKAgQkNDOXHiBOHh4TavsLAwIGfGa/fu3cTFxd2yOKpWrcqZM2c4c+aMte3AgQPEx8dTrVq1XOO9vLwIDQ1l48aNNu0bN260jq9cuTJ79+4lIyPD2r99+/Zc53rwwQdxd3dnxowZrFixgv79+xfUbRW6YpOcXbp0iaZNm+Lo6Mjy5cs5cOAAkydPxtfX1zpm0qRJfPTRR8ycOZOtW7fi7u5Ou3btSE9Pt47p1asX+/fvZ/Xq1SxdupTff/+dp59+2tqfmJjIAw88QNmyZdm5cyfvvfce48aN47PPPrut9ysiIiIihc/ezp5XG74KkCtBu/z+lYavYG9nf9tieuONN5gwYQIfffQRR44cYe/evcyaNYspU6YA8PjjjxMcHEyXLl3YuHEjJ06c4IcffmDz5s0FFkObNm2oWbMmvXr1YteuXWzbto3evXvTokUL6tevn+cxL7/8MhMnTmThwoUcPnyYV199ld27d/PCCy8A8MQTT2CxWHj66ac5ePAgK1eutD5LZjL989nb29vTt29fRo8eTcWKFWncuHGB3VdhKzbJ2cSJEyldujSzZs2iYcOGhIWF8cADD1ChQgUgZ9Zs6tSp/N///R8PP/wwtWrVYu7cuURGRvLTTz8BOWtjV6xYwRdffEGjRo1o1qwZ06ZNY8GCBURGRgI51W8yMzP56quvqF69Oj179uT555+3ftlFRERE5O7SpmwbprScQqBboE17kFsQU1pOuSX7nF3NwIED+eKLL5g1axY1a9akRYsWzJ492zpz5uTkxKpVqwgMDOTBBx+kZs2avPvuu9ZljwXBZDLx888/4+vrS/PmzWnTpg3ly5dn4cKF+R7z/PPP89JLLzFixAhq1qzJihUrWLJkCRUrVgRyZtd++eUXdu/eTZ06dXjttdcYM2YMgM1zaAADBgwgMzOTfv36Fdg9FQUmwzBy1wUtgqpVq0a7du04e/YsGzZsoGTJkgwZMsRaveXEiRNUqFCBv/76izp16liPa9GiBXXq1OHDDz/kq6++YsSIEVy6dMnan52djYuLC4sWLeKRRx6hd+/eJCYmWhM6gHXr1tGqVSvi4uJsZuouy8jIsJl+TUxMpHTp0iQkJODl5VXwH4aIiIiIXJf09HQiIiIICwvL9Q/8G2W2mNkVvYuY1BgC3AKoG1j3ts6Y3Y2+/fZb+vXrR0JCgs2zdH/88QetW7fmzJkzBAUFFWKEOa72PUtMTMTb2/u6coNiUxDkxIkTzJgxg5deeon//e9/bN++neeffx4nJyf69OlDVFQUQK4fTlBQkLUvKiqKwEDb33g4ODjg5+dnM+bybx2uPMflvrySswkTJvDGG28UzI2KiIiISJFkb2d/28rl363mzp1L+fLlKVmyJHv27OGVV16he/fu1sQsIyODmJgYxo0bR7du3YpEYlaQis2yRovFQt26dXnnnXe45557ePrppxk0aBAzZ84s7NAYPXo0CQkJ1teVD0aKiIiIiMj1iYqK4sknn6Rq1aq8+OKLdOvWzab2w/z58ylbtizx8fF3ZNG+YjNzFhISkqvyS9WqVfnhhx8ACA4OBuDChQuEhIRYx1y4cMG6zDE4ODjX/g3Z2dnExcVZjw8ODubCBduNBi+/vzzm35ydnXF2dr7JOxMREREREYBRo0YxatSofPv79u1L3759b19At1mxmTlr2rQphw8ftmk7cuQIZcuWBXJ2Rg8ODmbt2rXW/sTERLZu3Wqt4NK4cWPi4+PZuXOndcxvv/2GxWKx7hPRuHFjfv/9d7KysqxjVq9eTeXKlfNc0igiIiIiIlIQik1y9uKLL7Jlyxbeeecdjh07xrx58/jss88YOnQokFMxZvjw4bz11lssWbKEvXv30rt3b0JDQ+nSpQuQM9PWvn17Bg0axLZt29i4cSPDhg2jZ8+ehIaGAjklPJ2cnBgwYAD79+9n4cKFfPjhh7z00kuFdesiIiIi8h8Vkxp4UkwV1Per2CxrbNCgAT/++COjR49m/PjxhIWFMXXqVHr16mUdM2rUKFJSUnj66aeJj4+nWbNmrFixwqZiyrfffsuwYcNo3bo1dnZ2PPbYY3z00UfWfm9vb1atWsXQoUOpV68eJUqUYMyYMTZ7oYmIiIhI8eDo6AhAamqqTbU/kYKUmpoK/PN9u1nFppR+cXIj5TJFRERE5NY6f/488fHxBAYG4ubmZrOhsch/YRgGqampREdH4+PjY1P74rI7spS+iIiIiMjNuFzU7d+F4UQKio+PT77FA2+EkjMRERERuaOZTCZCQkIIDAy0KfomUhAcHR2xty+YzciVnImIiIjIXcHe3r7A/hEtcisUm2qNIiIiIiIidzIlZyIiIiIiIkWAkjMREREREZEiQMmZiIiIiIhIEaDkTEREREREpAhQciYiIiIiIlIEKDkTEREREREpApSciYiIiIiIFAFKzkRERERERIoAJWciIiIiIiJFgJIzERERERGRIkDJmYiIiIiISBHgUNgBiIiI3E5p2WkkZCQA4O3kjaujayFHJCIikkPJmYiI3DXOJJ1hxp4ZrIhYgWEYtCnbhmH3DKOMZxlMJlNhhyciInc5JWciInJXiEyO5Klfn+Ji+kVr24qTK9gUuYmFnRZSyrNUIUYnIiKiZ85ERKSoy86C+DNwahMc/w3iIiAj+YZOYTEsLI9YbpOYXZaYmcgPR38g25xdUBGLiIjcFM2ciYhI0ZWVDif/gO/7QUZSTpudPdz3MjR6Gtz8r+s0SZlJrD29Nt/+9WfW81S1p/Cz9yuAoEVERG6OZs5ERKToSjgD83v8k5gBWMyw4d2cmbTr5GjniKeTZ779Ho4eOJj0+0oRESlcSs5ERKTo2rMgJxnLy4aJkBJ7Xadxc3Sjd7Xe+fb3qd4HL2evm4lQRESkwCg5ExGRosmcDTEH8++PPw3ZGdd9uqp+VXk0/NFc7e3KteOewHtuJkIREZECpTUcIiJSNNk7QJnGcGhZ3v2B1cHJ7bpP5+fqx/B6w+leuTsrTq7AYlhoH9aekh4l8XPRs2YiIlL4lJyJiEjRVaVzzvLFK585u6z1GHD1vaHT+br44uviS/US1QsoQBERkYKjZY0iIlJ0+ZSGvssgoPI/be4loNtsCKpRaGGJiIjcCpo5ExGRosvOHkJqQ5+lkHoRLNng6geeIWCn3y+KiMidRcmZiIjcHtlZkBQJZ7bmbCpdugH4VwSvkGsf6xGY8xIREbmDKTkTEZFbz5wNZ7fBN49Cdvo/7f7h8NRPOcsXRURE7nJaEyIiIrdeUiTM626bmAFcPAYrXs274IeIiMhdRsmZiIjcetGHIDM5777Dv173ZtIiIiJ3MiVnIiJy66XE5N9nWMB8/ZtJi4iI3KmUnImIyK0XUiv/Po8gcPa6fbGIiIgUUUrORETk1vMMgQqt8u574M2cfhERkbuckjMREbn13EvAw9OhyfPg5J7T5lsOus2Fiu3AZCrU8ERERIoCldIXEZHbwysEWr0OjZ4BcyY4uoFncGFHJSIiUmQoORMRkdvHwQm8SxV2FCIiIkWSljWKiIiIiIgUAUrOREREREREigAlZyIiIiIiIkWAkjMREREREZEiQMmZiIiIiIhIEaDkTEREREREpAhQciYiIiIiIlIEKDkTEREREREpAoptcvbuu+9iMpkYPny4tS09PZ2hQ4fi7++Ph4cHjz32GBcuXLA57vTp03Ts2BE3NzcCAwN5+eWXyc7Othmzfv166tati7OzM+Hh4cyePfs23JGIiIiIiNzNimVytn37dj799FNq1apl0/7iiy/yyy+/sGjRIjZs2EBkZCSPPvqotd9sNtOxY0cyMzPZtGkTc+bMYfbs2YwZM8Y6JiIigo4dO3L//feze/duhg8fzsCBA1m5cuVtuz8REREREbn7mAzDMAo7iBuRnJxM3bp1mT59Om+99RZ16tRh6tSpJCQkEBAQwLx58+jatSsAhw4domrVqmzevJl7772X5cuX06lTJyIjIwkKCgJg5syZvPLKK8TExODk5MQrr7zCsmXL2Ldvn/WaPXv2JD4+nhUrVlxXjImJiXh7e5OQkICXl1fBfwgiIlKkXEi5wKG4Q6w8uRJfF18eqvAQIe4heDnr7wARkbvdjeQGxW7mbOjQoXTs2JE2bdrYtO/cuZOsrCyb9ipVqlCmTBk2b94MwObNm6lZs6Y1MQNo164diYmJ7N+/3zrm3+du166d9Rx5ycjIIDEx0eYlIiJ3h6iUKAavGcyw34bxy4lfmHtgLl1/6cp3R74jKTOpsMMTEZFipFglZwsWLGDXrl1MmDAhV19UVBROTk74+PjYtAcFBREVFWUdc2Vidrn/ct/VxiQmJpKWlpZnXBMmTMDb29v6Kl269E3dn4iIFC9Z5iy+OfANx+KP5er7cNeHRKVEFUJUIiJSXBWb5OzMmTO88MILfPvtt7i4uBR2ODZGjx5NQkKC9XXmzJnCDklERG6DuPQ4vj/6fb79K05e33J4ERERKEbJ2c6dO4mOjqZu3bo4ODjg4ODAhg0b+Oijj3BwcCAoKIjMzEzi4+Ntjrtw4QLBwcEABAcH56reePn9tcZ4eXnh6uqaZ2zOzs54eXnZvERE5M5nYJCWnfeqCoCEjITbGI2IiBR3xSY5a926NXv37mX37t3WV/369enVq5f1vx0dHVm7dq31mMOHD3P69GkaN24MQOPGjdm7dy/R0dHWMatXr8bLy4tq1apZx1x5jstjLp9DRETkMncHdxqH5P/3Q9uybW9jNCIiUtw5FHYA18vT05MaNWrYtLm7u+Pv729tHzBgAC+99BJ+fn54eXnx3HPP0bhxY+69914AHnjgAapVq8ZTTz3FpEmTiIqK4v/+7/8YOnQozs7OAAwePJiPP/6YUaNG0b9/f3777Te+++47li1bdntvWEREijxPZ09eqvcS26O2k2nJtOmrVaIW5b3LF1JkIiJSHBWbmbPr8cEHH9CpUycee+wxmjdvTnBwMIsXL7b229vbs3TpUuzt7WncuDFPPvkkvXv3Zvz48dYxYWFhLFu2jNWrV1O7dm0mT57MF198Qbt27QrjlkREpIgL8w5jQacFtCzVEic7J/xd/Blaeygf3P8BAW4BhR2eiIgUI8Vun7PiQPuciYjcfZIzk0nJSsFkMuHv4o+9nX1hhyQiIkXAjeQGxWZZo4iISFHm4eSBh5NHYYchIiLF2B21rFFERERERKS4UnImIiIiIiJSBCg5ExERERERKQKUnImIiIiIiBQBSs5ERERERESKACVnIiIiIiIiRYCSMxERERERkSJAyZmIiIiIiEgRoORMRERERESkCFByJiIiIiIiUgQ4FHYAIiK3lWFASmzOf7v5g51+RyUiIiJFg5IzEbl7JJyDAz/BrrmAAbWfgJrdwLtkYUcmIiIiouRMRO4SCefgm0ch5tA/bWvGwq450OcX8C5VeLGJiIiIoGfORORucWytbWJ2WdwJOLAkZ7mjiIiISCFSciYid760BNj9df79u7+F1LjbF4+IiIhIHpScicidz2QCu6us4rZzANPtC0dEREQkL0rOROTO5+IF9Qfk319/QE7lRhEREZFCpORMRO4O5ZpCmca520PvgYptbn88IiIiIv+iao0icnfwDIFus+HMNtjxJRgWqNcPytwLXqGFHZ2IiIiIkjMRuYt4BkO1h6BCq5z3zh6FG4+IiIjIFZScicjdR0mZiIiIFEFKzkREJJfkzGQupl8kMTMRD0cP/Fz88Hb2LuywRERE7mhKzkRExEZ0ajTvb3+fFSdXYJCzOfe9Ifcyvsl4QjxCCjk6ERGRO5eqNYqIiFVKVgpTd05l+cnl1sQMYMv5LYz6fRRx6dqsW0RE5FZRciYiIlYX0y6yLGJZnn27Y3YTl6bkTERE5FZRciYiIlbJWclYDEu+/bFpsbcxGhERkbuLkjMREbHycPTAhCnffn9X/9sYjYiIyN1FBUFE5LYzW8xcSL3AifgTXEy/SGXfygS6BeLn6lfYod31/Fz8aFO2DatPrc7VV82vGv4uSs5ERERuFSVnInJbZVuy2R+7n8FrBpOclWxtbxjckAn3TSDQLbAQoxMPJw9eafAK6dnp/HHuD2t7zRI1ea/5e0qgRUREbiGTYRjGtYfJjUhMTMTb25uEhAS8vLwKOxyRIiUyOZJHfn6E1OzUXH2PV36cEfVH4OzgXAiRyZUSMhKIS48jISMBTydP/Fz88HXxLeywREREip0byQ00cyYit9WBiwfyTMwAFh9bTN+qvQj1Lnubo5J/83b21qbTIiIit5kKgojIbXU+5Xy+fRnmDDLjT0HqxdsYkYiIiEjRoJkzEblp0anRpGal4mjviK+zL26Obtc8prp/9Xz7gtyCcI0/DS7+4KbCEyIiInJ3UXImIjcsKTOJ7VHbmbR9EueSz+FgcqBDWAeeq/scIe4hVz22tGdpKvpU5Gj80Vx9L1R5isCts6CuBYLyT+KkaMk0ZxKbFkuGOQMXexcC3AJwsNNfLyIiIjdKyxpF5IbturCLF9a9wLnkc7g6uGJg8MuJXxiyZggxqTFXPTbALYBPWn7AA6Xvx95kD4C/iz/j73mR+2JPYzq7A1xVeKK4iEmN4aNdH9Hl5y489NNDPLbkMWbtm8XFNC1NFRERuVH61aaI3JCY1Bje3/4+T1Z9kiahTYhLj8PN0Y3UrFS+3PclJxNPEuAWcNVzhDj7Mt6jOsMbdyATC+7pyQTumI3dqU3g4AzBtW7T3ch/kZiZyOQdk1kWsczalpSVxEd/fURKVgqDaw/GxcGlECMUEREpXpScicgNSc9OZ0CtAWyJ3MLQtUMxyNmNI8A1gDGNx3Ay4SQNghtc/SQuXriHP4D7nE6QcPafdjt76DYHPINv4R1IQYlLi7NJzK709YGv6VqpK6U8S93mqERERIovJWcikqf07HRi02K5lH4JZ3tn/Fz8KOFWAhd7F04mnMz1j/KYtBj+98f/+KLdF9d3Ab8w6L8KInfBiQ3gUxaqPAheoTmzZ8VAQkYCUSlRrDy5krTsNB4o9wBlPMvg73p3FDOJTo3Oty/TkkliZuJtjEZERKT4U3ImIrnEpcex4NACvtj7BVmWLADKepVlSsspeDp6sujIojyPS8pK4nTiaar5V7u+C3mXzHlV7VxQod82CekJfLX/K77a95W17ZuD39A0tClvNn3zmks77wSeTp5X7Xex15JGERGRG6GCICJiwzAM1p9Zz4w9M6yJGcCpxFP0X9mftOy0q86InEw8eeuDLAJOJZ2yScwu2xi5kfVn1t/2eAqDv6t/vssWawfUxs/F7zZHJCIiUrwpORMRGzFpMUzfPT3PvoSMBNKy0wh0C8z3+KvtY3anyLZkM//Q/Hz7vz7w9V1RrTDQLZCPW31MCdcSNu2lPUsz4b4J+Lj4FE5gIiIixZSWNYqIjSxzFhdSL+TbvzdmL0PrDGXsprG5+gLdAqnoW/FWhlckmC1m4jPi8+1PykrCbJhvX0CFqIJPBeZ3nM/JhJOcSTpDeZ/ylPYsfdUEXkRERPKm5ExEbDjaOxLoFphvsQdvZ2/uDb2X+LrxzPx7JmnZaQBU86/GxPsmEux+51dadHZwpl25dvx57s88+5uVbIaXk9dtjqrwBLsHE+wezL3cW9ihiIiIFGtKzkTuIJnmTNKz03F1dMXRzvGmzhHgGsDgWoMZv2V8rj4vJy9qB9bG18WXJ6s9Sfuw9sRnxONi74Kviy++LnfP5tH3htxLqHsokSmRNu2uDq4MqDFA+3uJiIjIDSs2z5xNmDCBBg0a4OnpSWBgIF26dOHw4cM2Y9LT0xk6dCj+/v54eHjw2GOPceGC7fKs06dP07FjR9zc3AgMDOTll18mOzvbZsz69eupW7cuzs7OhIeHM3v27Ft9eyL/SVpWGkcvHeWtLW/x7JpneXfruxyLP0ZGdsYNn8tkMtG6bGsG1RyEg90/v78p5VGKr9p9RYh7CABO9k6EeoRSzb8a5X3K31WJGeTMFn3V/iu6VeqGs70zdiY7WpZqyfyO87W3l4iIiNwUk2EYRmEHcT3at29Pz549adCgAdnZ2fzvf/9j3759HDhwAHd3dwCeffZZli1bxuzZs/H29mbYsGHY2dmxceNGAMxmM3Xq1CE4OJj33nuP8+fP07t3bwYNGsQ777wDQEREBDVq1GDw4MEMHDiQtWvXMnz4cJYtW0a7du2uK9bExES8vb1JSEjAy+vuWdokhSPbks0fZ/9g+PrhWAyLtd3eZM/01tO5N/Re7Ew3/nuYtOw0LqZdJC49zrrP2d1QHv5GZWRncCnjEgAejh54OHkUckQiIiJSlNxIblBskrN/i4mJITAwkA0bNtC8eXMSEhIICAhg3rx5dO3aFYBDhw5RtWpVNm/ezL333svy5cvp1KkTkZGRBAUFATBz5kxeeeUVYmJicHJy4pVXXmHZsmXs27fPeq2ePXsSHx/PihUrris2JWdyS5mzITkKUmLBzoF0Zw8Gbvo/9sTuyTXU38WfBZ0W3N7nwNLiITUWMpLA2Qs8AsH56vthiYiIiNypbiQ3KDbLGv8tISEBAD+/nH10du7cSVZWFm3atLGOqVKlCmXKlGHz5s0AbN68mZo1a1oTM4B27dqRmJjI/v37rWOuPMflMZfPkZeMjAwSExNtXiK3RHoSHPgZZjaDz1rAzKa4zOnMlPCeNAysm2v4xfSLXEq/dPviSzgHPz4D0+rBZy3h4/rwy3BIOn/7YhAREREppoplcmaxWBg+fDhNmzalRo0aAERFReHk5ISPj4/N2KCgIKKioqxjrkzMLvdf7rvamMTERNLS0vKMZ8KECXh7e1tfpUuX/s/3KJKn6APwQ39IuyLhij9N4KL+jK3aL8/liwa3aXI8NQ5+ehaOXDHDbFhg3/ew8v9yZtJEREREJF/FMjkbOnQo+/btY8GCBYUdCgCjR48mISHB+jpz5kxhhyR3orQEWPdW3n3ZGfgdXkmjoIY2zd7O3vi5+N2G4ICUGIjYkHff/sWQnHdpfhERERHJUeySs2HDhrF06VLWrVtHqVL/VEQLDg4mMzOT+Ph4m/EXLlwgODjYOubf1Rsvv7/WGC8vL1xdXfOMydnZGS8vL5uXSIHLSoWYQ/l2e0QfJswj1PrehIlxjcdRwrXE7YgOUi/m32dYIEPLfUVERESuptgkZ4ZhMGzYMH788Ud+++03wsLCbPrr1auHo6Mja9eutbYdPnyY06dP07hxYwAaN27M3r17iY7+5zf4q1evxsvLi2rVqlnHXHmOy2Mun0Ok0Di6gG/5fLvNgVXxcw+mnFc52pRpw4JOC2gS2sSmHP4t5XqNUvpOKgoiIiIicjXFplrjkCFDmDdvHj///DOVK1e2tnt7e1tntJ599ll+/fVXZs+ejZeXF8899xwAmzZtAv4ppR8aGsqkSZOIioriqaeeYuDAgblK6Q8dOpT+/fvz22+/8fzzz6uUvhQNx9fD1w/nbrezhyFbSPEuSXp2Om4Obrg65j3Te8ukxMK87nBuZ+6+im3h0S/A1ef2xiQiIiJSyO7IUvomkynP9lmzZtG3b18gZxPqESNGMH/+fDIyMmjXrh3Tp0+3LlkEOHXqFM8++yzr16/H3d2dPn368O677+Lg8M/swvr163nxxRc5cOAApUqV4vXXX7de43ooOZPrlZyVTExqDGtOrSE+I54WpVoQ5h2W/35iaZdgz0JYMwYubzDt6guPfg7l7suZXfuvUmLBnAmObjeeTMWfhu/6QOSuf9rKNYdHZoJ3yf8em4iIiEgxc0cmZ8WJkjO5HslZySw7sYy3ttgW+ajuX50P7/+QIPegvA/MSssprpEUCXaO4BkMHsFg/x+XL6ZehFObYMNEiD8DwTWg1RgIqg7ON7CxckpMToKXGgfuJXJebv7/LTYRERGRYkrJWSFTcibX40T8CR7+OY8lisDAmgMZWnsoDv814bpeGcmwcSr8/l7uvh7fQOWOYFdsHlEVERERKTLuik2oRYq7VSdX5du34NACLqZfpfphQUuJhj8m59237CVIirp9sYiIiIjcpZSciRSSqyVfKVkpWAzLbQzmeE65+7wkR0Na3O2LRUREROQupeRM5BZJzUolIzuD6NRoDl48yK4LuziTdIbkzGQA7i99f77H1g+uj7uj++0KFRyuUUjkdpXjFxEREbmL6V9cIgUo05xJZHIkF9MvYhgGfi5+zNwzk/Vn15OWnYadyY5ulbrxbO1nCfcNp5JvJY5cOmJzDgeTAyPrj8TL+RY8r5iZkrNE8ehqSL4AFVpBiYrgGwZO7jn9/xZYTQU9RERERG4DFQS5BVQQ5O50Me0i3xz4hm8OfkO6OR07kx0tS7dkQI0BXEy7yImEE3z818dkG9kMrzucvtX7EpMWw9cHvmbRkUWkZadRL6geL9d/mQo+FXC51mzWjcpMgcPLYfEg2yWMwbWg+9cQcxAWPGHb5+wJfX+FkFoFG4uIiIjIXULVGguZkrM736X0S8Slx5GSlYKPsw8+zj7M3j+bz/d+nmts7YDatCnbhu1R22lTpg1jNo3By8mLHx76gWD3YLLMWdaZNjdHN7ydvW9N0BePw8f1IK8/8nV7Q4OnwcEJds+H2ENQtilU7QzeZf5zpcb07HQMw7j9G2OLiIiIFLIbyQ20rFHkBp1NOsvIDSPZf3E/AHYmO7544Au+PvB1nuP3xOxhQI0BfLjzQ+oH1aeyb2UOXzpMpjkTAEd7R4Ldg/M8tkAdW5N3YgawdxGEtYTQe6DtODBngb3jf75kbFosBy8eZP6h+WRbsukS3oV6QfXy38NNRERE5C6m5EzkBsSmxTJk7RAiEiKsbRbDwqX0S6Sb0/M97kLqBbydvfn5+M90Kt+J2LRYnO2db0fI/0iOzr8vKw3s7OHoSvB/tsASszc2vcH6s+utbZvPb6aSTyWmt5muBE1ERETkX25qrdLp06fJazWkYRicPn36PwclUqgsFog/kzPTtHMunNuZU0Qj7iTnE07aJGaXOdo5YmfK/4+Tn4sfyVnJxKfH4+7ozjO1nyHALeBW3kVuFfKvDklIHYg7AQlnC+xyhy4esknMLjsSf4QVJ1fc3q0CRERERIqBm0rOwsLCiImJydUeFxdHWFjYfw5KpNBYLBC1Bz69D755DH55Dj5vBfN7QPwpomIP5XnY5vObaVmqZZ59Ie4hpGSlkGHOoF5QPfyd/Wlfrv1Vk7lbwr9iThL2byYTNHsRds4is3oXIpMj2R+7nyOXjhCTmvvP+fXIyM5g4eGF+fZ/f+R74tK1d5qIiIjIlW5qWaNhGJhMplztycnJuLgUcIU5kdspKRK+fgTSLtm2R+6G7V9Q477heR7249Efeb/F+8RnxLMrepe1PdQ9lNcbv85bW97Cyc6JwbUHU8azDM4Ot3lJI4BnEDw+H/6cCn99DVmpOclasxdh/48k3PssvyQc5KMNQ0nLTgOgrFdZJreYTEXfijeUTBoYZFmy8u3PsmSBShGJiIiI2Lih5Oyll14CwGQy8frrr+Pm5mbtM5vNbN26lTp16hRogCK3Veyx3InZZYeXUeK+FwnzCiMi0XZpY7o5nU3nNvFUtacYWHMgTiZHyjm4Y7Zk8v3pNZT2LM3U+6cS5hWGg/2tedQzKTOJuPQ4zqecx8PRgwDXAALdAm1/keIVCve/BvX6QMIZuLAf1oyFql3YHlSBib+PtDnnqcRT9FvRj0UPLaKkR8nrjsXFwYUu4V3YGLkxz/4Hwx7Ex9nnZm5TRERE5I51Q/9K/Ouvv4CcmbO9e/fi5ORk7XNycqJ27dqMHDkyv8NFihSzxUxMWgwZ5gyc7Z0JcA3APvl8/gdYzDgmnOOTe0YwYu8nHIw7CIAJEw+GPUjfGn3ZeWEnwRaDMmc24/XXPMhMYlh4W7jvZex8KuQU3bgFYtNi+WjXR/x07CeM/z8lFeQWxMetPqayX2XbBM3VO+flXiJnqWONrsQ6ufDRqkF5njspK4lt57fxSMVHbiimukF1qepX1fo5XRboFkjXSl1vWZIq+cvIziA2LZbEzERcHFzwc/bD2+UWbd0gIiIiN+ym9jnr168fH374ofbwyof2OSv6LqZdZMnxJXy570sSMhLwdvbm6ZpP08u3Jvaftcz7IFdf6DARVv6PuGbDiQuuSqqdA96eofh7hOLh5IGRFAXf98N0apPtsU4eMGgdBFQq8HvJtmQza98sPvrro1x9Xk5eLOq8iFCP0KueIyolirbft823v2ulroxtPPaGY7uQcoHVp1az6MgiMi2ZdAzryKMVH71mPFLw4tLi+ObgN8w9MJcMcwYA9QLr8VaztyjlWaqQoxMREblz3fJ9zmbNmnVTgYkUBalZqXy17yvmHphrbUvISOC9He9R7/6PqVa6MaYzm3Mf2HgY7J4HKbH4rfw//EwmeHIxOHrnJF+AKfYI/DsxA8hMhnVv5yR3cSfgwBIMRzfM1R8izsmdeDsDX2ffm6rgGJsWy+z9s/PsS8xM5ODFg9dMhuxN9pTyKMXZ5LyrNVb1q3rDcQEEuQfRq2ovOoR1wDAMfFx8cLDTjNntlm3O5oejP+TaJH1n9E6eXfMsX7X76vZXDxUREZFcrvtfSY8++iizZ8/Gy8uLRx999KpjFy9e/J8DE7lVLqZfZN7BeXn2DfpzNOsfnYfThvdg78KczZjd/KH5y2DOhBPrwc2fuJajiCvbmFSTBU9zKo6JZ3FxcMF/7/fkLpXz/x3+Fe55Er7tCoAJcPhzMm4Nn2Z5YCgLT69mSospVPKrdEPFNzLNmSRmJubbfyLhBK1pnWffpdRM4lIySct04r2ms/j60Mf8eupnmzGuDq40CW1y3fH8m8lkwt/V/6aPl/8uJi2Gr/Z9lWffycSTnE0+q+RMRESkCLju5Mzb29v63IqXl1ee1RpFioP49Hiyjew8+5KykjicnUTNju9Bi5chOyNnVsw9AJIvQFgLzrp6MGLjaxw4+AkOJgcsWHig7AN0r9wdL3snnPI8M+DgAim5S9N7bPuMBx7/hhnpC+m3sh/fP/T9DRXfcLZ3poRrCWLTYvPsz2/W63RcKi99t5sdJ3MKoDg72PFk4+68Vr8xb+94FYASriX48P4PCXYPvu54pOhJy04jOSs53/4T8Se4J/Ce2xiRiIiI5OW6k7NHHnnEWiZ/9uzZtyoekVvuWmXsne2dwdENfMvZdviUJsbJhY92vMfY+q+AnT3JmUn4uPhxMO4gq0+uJrxaJ5y2f57neanZNWf2LA8B+36mRWgzfj218oaLbwS4BTCkzhDGbx6fqy/ILYhw3/Bc7RcS0+n95VZOXky1tmVkW/jyjzOMcK3ID51/JNvIxN/FP3fFRyl2nB2ccbZ3tj5r9m965kxERKRouKHkLCoqioCAAOzt7Tl//jyBgYG3MjaRm3K5CmOmORMne6ecKoxXVEn0c/YjzDuMiISccvjhPuGU8SrDpfRLJKQn4Ofil++5kzOTeLb6AF788xWOxx+3trcufT9P1xhIomHBqX4/3Hf867lMv/JQpRPM657neR0yEqngdQ8Tm0/Ey8mLbee3EeIRgr+LP26Obnkec5mdyY7WZVoTnx7P53s/t+5RVt2/OhPvm5jnrNfpuFSbxOxKn/8ewaP3NKekn+tVryvFRwmXEnSr1I1vDn6Tu8+1BGW9yhZCVCIiIvJv152cBQQEsGXLFjp37pzvJtQihe3fVRh9nX0ZVGsQHcM64ueak3SVcCvB1JZTGbtpLK9V6U3AhUN4R+4hLaAGzvUfxsnRI9/zuwADNrzImaQzNu1rz6zDy8mbnlUe54+Q8jz45CJ89y7GlHYJanaD0o1g4zSw5L2c8mLtnoR5ePLG5jdIyEgAcop09K/Rn6eqPYWvi+9V79vPxY/e1XvzYPkHSchIwMXeBV8X33yPO3Yh/yVuienZpGWZr3o9KV6cHZzpV6Mf0anRrDq1ytpe0qMkH7f+WMtWRUREiojrTs4GDx7Mww8/jMlkwmQyERyc/1/mZrP+YSe3WFJUzmbRpzdDVgaUa0qGmx+fHprL/EPzrcMuZVxi0vZJXEy7yDO1n8HVIWc2qLxPeWbVH43D7E6kVmzL+Xse52TGRRzi9lPGzqCEZ2mcndxzXTY6JSpXYnbZLxFL6Ve9NxP3fU5Mjf68+PAnYDGDw/9/Cq3RINj9NWT9a8bKpyyXgqvx6q+9yLRkWpvNhpnP935OuE84D5Z/8JofibO9MyU9Sl7X82pl/fOfjXNxtMPF8foLkkjxEOgWyJjGYxh2zzAupFzAy9mLEq4lCHTTCggREZGi4rqTs3HjxtGzZ0+OHTvGQw89xKxZs/Dx8bmFoYnkI+Ec7Pse1oyFK7bpc6zfj9pl72F+HofMPTCXrpW6/vNsTcpFHH4eSkLDASzx8iE9djf3+VfHZFjYcW4TXt5laRTaONeSwqikvEvNQ85+Y5eXFHYq3ylnw+krN532DcvZ62ztG3BkBTg4k1bjUS42HMjy06ttErMrTd8znYYhDSnhWuL6Pp/rEFbCnQBPZ2KScj+D1KtRWQI9XQrsWlJ0eDt74+3sTZh3WGGHIiIiInm4oQ2HqlSpQpUqVRg7dizdunXDze3qz8KI/GcZyViSozGbMzA7OJPtEYh7YiSm1WNyDbXbMYvWZRozq+kkki3plHb0xDErgywHJ5ZFbychI+Gf5CwtDpKiOF6mAffbORHy+wfYn8gpqFE5uBbRrV7lYvIFjib7kJltoaSvG+7O9pS6yrM5zvbOONi78HX72XhmpkFKLLhfkVDZO0BgFXj0M0hPwGwYpDk4kG3OIDI5Mt/znks6R3Y+yyFvVoiPK/MGNqL/nO2ciUuztneqFcIzzcvj5KCZMxEREZHbzWQYV0w93KCYmBgOHz4MQOXKlQkI0D45cGO7gMtVxJ/GsvkT7HbNzVkK6FWSrFb/h53JhP2Pg/M+plRDaP8uxtaZmPb/kLOs0GSHuUpnTG3GYpeVhuHiRXZWGml/zyex0gOUmv8kpF60PY+9I+n9f2P1WWfqB2ThEbsHZ2dnEsrW55kNL3E0/miuS3er1I2UrBR+jfiV8t7lmdLw/6jgXwWcPa95q4sOL2L8ltzVFgEq+1bms7afWZ+ZK0gXEtOJScogKT2LIC8X/D2c8XZ1LPDriIiIiNytbiQ3uKlfj6emptK/f39CQ0Np3rw5zZs3JzQ0lAEDBpCamncFOJEbknAWY8lz2G2d+c8zWonncPzpWezTEqBM47yPK98CtkzHtPe7nMQMwLBgf/Bn7Fa8AunxmOY/genoaszVuuB1clPuxAzAnIXjH+/R1ulvQuY2xfPXITj9OICAz9vwcZO3qB9UzzrUweTAwxUepk5gHVacXAHkbPw84I+XiUyLtRb4uJomoU3wdMw7iRteb/gtScwAgrxcqFHSm8YVSlA+wEOJmYiIiEghuqnk7MUXX2TDhg0sWbKE+Ph44uPj+fnnn9mwYQMjRowo6BjlDmbOzuJ88nl2Ru1kzak1HLt0jJiUGIzkaEwn1ud90B/vQ93eefeVvx/2L8677+jqnE2l24zB4a+vcbVzxOvU5nxjsz+7BZfAcGg8FC4XB0mJIfTLDkyu/yo/d/qObzrM5cNWH+Jo58jrG1/HYlisx19Mv8ihuEO8ueVNtp3fRmJGYq5rGIZBliWLQNcgPm3zpc2zQJ6Onoys+3+EeVbLN0YRERERuXPc0DNnl/3www98//33tGzZ0tr24IMP4urqSvfu3ZkxY0ZBxSd3qoRzZMcc5ICTI0M2vWYzu9S+XHtGVelNgMlkU/DDKiUGnPIod2/vmPNc1xUJUi7pifDrSOj0AS5/fYvFKzT/31C4B8DxNRB7FLrNhu8HQEYipMfjteF9Jpfwp1/13jy5dmi+lzuUcJzolGgGrBrA+Cbj6Vy+Mw72DmRkZ3Au5RwrIlbg6xRCgxL3c/aCD89V/gg7xwSyjGzMWa7M35TA7gOneLuLF27ON/XHVURERESKiZte1hgUFJSrPTAwUMsa5driz8CcTlwwWRj4x8u5lv2tOLmCHyI3YC7fKu/jTXZk+YVhLtfsn7bgmtBzPthdY1meZxCkxkLyBTi6CqNe3/zH3vMk7P0ejq6CP6dCq9esXQ6JkQyo9AwODt74Oue/B1mYdxhRqVEATNo+iZi0GLLN2Ww5v4WxG8dTw6stK7eF0H7qJl7+/m/WH0rCLjuE0fMvMnRuBH8ei+OXPee5mJJ3JUcRERERuXPcVHLWuHFjxo4dS3p6urUtLS2NN954g8aN83kWSAQgOz0n0TGZ+CvlnLX0/L99fWg+MfWetL43l7+f892+JKLXPM72/Znfkk4wt3obkgf/ieXZjVCzO1mxx4nCj/NP/EbSvSPB9V9JU0gdsP//e45dOgmBVThvZJPd/l3496bqNR4DeyeSy9zPqcd/549aE9jp/xCRXX8hO6QeiaHNOJ/szidrYuge3ifPe/B29sbd0ZPzKecBSM5KJi49jui0aMZtGsegKv/j6VnHWH/oItkWg+SMbL7depq3lh1kbOfq/3xkFoNM81VmA0VERETkjnBT66SmTp1K+/btKVWqFLVr1wZgz549uLi4sHLlygINUO4Q2RmQmZLz2vMthNTm1P+fUcpLYmYiaQGVwNGNuLZjWeqQxWd7p5KQkYCLvQudyneicWhjNqdHcT7uCK3Ldmbe7ni+WXWElIxs7qvQhle6dKX8xlE4nf4dgmpAm7EQFwFAVql67K76AN5uvmwMKEu5Pj9SMjYCh+RoCK0DZ7ZzMduNmdmP8eXss1gMgLN4uzry6aPTqeDvyolT6SzaEcnbZe/lsQpP8NOJhZiNnCIkpTxK8Xazt3lj8xs292VvZ8+55HO0KtWebzZeIj0rd9J1PCaZ5Ixsyvi5cToulWAvF9yd7HONK2gX0y6SZcnC3mRPgJsqr4qIiIjcbjeVnNWsWZOjR4/y7bffcujQIQAef/xxevXqhaura4EGKMVcdiYknIItn8LZ7RjtJ2CycwT/cGr5V8/3sFD3UA4nn6HEkM18d/IXPvn7M2tfujmd749+z4XUCwytM5TkNHsG/XCGA+eTrGPWHY1j44l4ljzzOVUy9sLFY3DyD4jaB+4BHHB2pv+6YZgw0T6sPU1Dm2IJrETY9i9zCo4EVmddtYf4fKvtptMJaVn0WRjBj0Oa8OexYwD83+KT9GjYlg+adiHdkoCznQuxiY6cT4jjRMIJ67Eh7iH4ufhxMe0i1f0asHBVfL73v+l4LLVKeXM6LpXRD1YhyOvWbQqdmJHI7pjdfLjrQ45eOkpJj5I8U/sZmpdqjp/LrakSKSIiIiK53XBylpWVRZUqVVi6dCmDBg26FTFJcWYxYzZnE5URy+G4w0SnRFHRuwIlGvbDpcbDuLr54PnoZ5gOLqGSkw/B7sFEpeSeQetTvQ8/HvuJCvXD+erA13le6o9zfzC0zlCyM4I4cP5krv5Ms4VJayL4sORWPCNWwv2j4fAKIrt9zv92vQ+AgcHyiOWEuoeS6RxPyRLhOF3YR3TtZ/nw9zxK7AMZ2RbWH46haognK/dHYRiwYOsFFmzNWR1pGBDi7cKwTv88f+ls78yk5pMIdAskw5zBmUt78XB2Jjkj782lPZwdwTCY9vg93FexBKZ/L7ssINmWbNacXsPYTWOtbWeTz/L6xtfpU60Pz9Z5FndH91tybRERERGxdcPJmaOjo82zZiJYzJAUhRF/GnbNxT4jAZeKbfD2CmDsnhnEZ8RTybcSnzWbiMPGaZh2zgYg6MQGvnxsBmP2f8XOmF0AeDl5MbT2s7TyLE+nAAvnstLyfS4NICEzgS1HnfPt//3YJZI79Mez1kOkWjJZd98zfLD9bS6kXrAZt+jIIkY3Gs2x+k9RKTkGs3swZy+l5HveE7HJ9G5cjqlrbDejvlxc8sl7S+PqdICWpVtSs0RN2pdrT6hHKAD+Lv44O2XRpV4IM9dF5nn+JxqWJsTbhSBv25noi8kZRMSm8N2OM2SZDbrWK0XFIA8CPW9uZi0mLYbJOybn2ff1wa/pXrm7kjMRERGR2+SmljUOHTqUiRMn8sUXX+DgoPLed62ESEiNweLojmnLdEw7vrR2+R9ahn+JSnzR6V1G/PUBDwQ3xi09Cdeovf8cn3iOMgv78mHDQVx64HnSHV3xMpsJXDMehxPrIbQOcX5jc1/3Ck52Tni45D+r5O7sQCYOpB5exzy3LD48OCfPcYmZiTjZOdF34/94uc4AGpUoQ+WgsxyKSspzfKMwf0r6utK/aTm+2njSpq92KW8eq1uGYO9KdK7YEcd/VZB0c3Tj/jItqONvYcuxFHafsa1W+XyrcMqVcMfHzcmmPTY5gzd/OcDPe/5J6H786xzNwkswpXttAvNZ+hiTGkNadhqOdo74u/rjZP/PeRMyEkjMzL3/GoDFsHAu+RxlvMrYtMenx5NuTsfB5EAJtxJ5HisiIiIiN+6mMqvt27ezdu1aVq1aRc2aNXF3t/3N+uLF+WwCLHeGlHjIjIfMVAzDwC4zGc5syT0u/hThFge+D++Ly85Z8PcKzDW7Y+n0EY4rRsGpjZAah/f6iXjvWYil+1zsPm1OQuNRRNUby59ns7nPJZhGwfeyNSr3+QPdAjEME82ruDJnY96hdrmnJG+tOoWXU2MG31+Kz45+l+dMXJBbUE4Rkuw0xv/9MZ2TzzLigaEMmrsz11gfN0eql3IgMu0IA1uUocs9JfluxxnK+DrToroLmUYSycZZ4tL88HPN+5mtTEsmU/e8zRP3P0if7DA2HUnDzdnEo/eUpoyvZ67EDOBAZKJNYnbZn8di+f1oLF3rlbJpT8xIZHvUdt7f+T5nk87i6uBKt0rd6FO9D4FugaRnp2Pi6sslXR3+mblLzkrmcNxhPtjxAQfiDhDkHsTTNZ+meanm+Lv6X/U8cptlJENaXM5Uros3uPoUdkQiIiJyHUyGkdcuv1fXr1+/q/bPmjXrpgO6EyQmJuLt7U1CQgJeXl6FHc5/lpFlJjoxhUtpWbg6mijLBRx/HYHp1P/PiHzKQstX4dga2PfDPwd2nAIHl8CJ9bYn9CtPao9FOCwfhdPJtTltNbtjcXInwb08nyU25ovtMXzeuz7vrTzMq52DmLR7BCcTT1pP4evsy/hG0zgX60TpgEy2HoVP1toW76ga4smIByrz9NwdWAxoWM6XR+6L450do3Pd4/81+j8OXzrM6cTTtCvdlXsCGpGSDrvPJDBl9RES0rIAqBLsyejOwUzZO4oTCcep4luFaa2m4eLgwi8nfuHjvz4mNTvnWbNKvpWY1HwSFXwq2Fwry5zFtL+mMWt/zp8Tfxd/qvlXI8uSxf7Y/SzqvIiSniVtjknLNDNk3k7WHYrJ82dUPdSLrwc0xM89Z4mnYRisPLmSl39/OdfYhsENeaPJG3z292dU9KnIj8d+5Gj80VzjvJ29WdR5ESHuIRiGwboz63hh3Qu5xnWr1I3hdYfj5Vz8v+t3hIvHYc1YOLQsZ0P2sJbQ/h0oUTlnk3YRERG5rW4kN7ihv6ktFgvvvfceR44cITMzk1atWjFu3DhVaLxDpWelk5KZzdbjCby6eB+J6dlsGFQBp8WdICX2n4Hxp+DnIdD965yKiMnR4BkCDq65EzOAuBM47ltAbOv3CZnVACzZZJW5F8cT6zledgAzVp2mY80QVu6PYn9kIh+ttOeNjjlLJs1GFvZ2Wfi6OzHs66M4OzhSPdSL9jX9aFO5FCv3RxOTlMH9VQIo6evEn8ciCfF25Vx8GttOXuLVB++lTmAddkfvBnJmhp6sPAhLci0eLtOCxbsiKelcikkrjrFqfzRNKvjzziM18HN3wt3ZATuTQUp2Et0rDODzA5M5dOkQsw/MplaJWkzaPsnmNo9cOkL/lf1Z0HEBIR4h1vbY9FgWHl5ofX8x/SJ/nPvD+v6v6L9yJWdmi0Fqhjnfn1Vqphmz5Z/fs0SnRvP+jvfzHLstahvH4o+xPGI5m102M+beMbz6x6s2yxud7Jz4sOWHBLoG5pwvLZp3tr6T5/kWHVlE72q9lZwVBfGn4asHbP98RqyHL9rA4D/AP7zQQhMREZFru6Hk7O2332bcuHG0adMGV1dXPvroI2JiYvjqq69uVXxSCNLSErBPjcEx4nd8z+ygsU9VfnyiFfbO7pQ+v9L2H36XGQZsmQ51noQ/p0BYczj8a77XcPx7PkmluxJQ4QEcTv9BfOl6OLmWYNbunCWH7WsE88oPf9O6aiAP1Q5l+Pz9nI7LmZGqGuLJ652qMblnOL+eXEJCZiznM+7FMbUUTzYKJjYzlnmHp7H90FZC3UN56eF+nIoM5aPVkZyITuOFmm/g7JQBJjDMznzzZzxTdpxgeq+6xCZaiElOZdX+aAD2nIln6P3hvLfqMLtOxVvjrxzkzeRuc3hpY2/KepVl2l/TAPB09KRHlR7UCqhFljkLezt7IlMibZKzbHO2dXYtL+dSzuVq83BxoHPtULZGxOV5TIcawTZLIVOzU3MVPbnSkUtHCHEPISIxgsk7J/NW07eISIzg+KXjVPGvQsvSLQlxC8HeLmd/taSMpHzP52LvwrH4Y5TzLpfv9eQ2MAw48HPefz6zUmHzJ9BuAjjeum0ZRERE5L+5oeRs7ty5TJ8+nWeeeQaANWvW0LFjR7744gvs7OxuSYBy+6QlXMRsysAlMRKHrx/JqQvv4Ixvyjx8O3pgJEZiij2S/wkid5PaYSJnyzUgxC0Yz9/znrkBwLDg5+lCcpUupDQbzEvb3mJyw3eIS8tJPkp6O+Jkb0evRmUZNHeHzazQwfNJDJyzg7mDqjD74AwMDBYf/44wrzCqlZrCwNWPk2HJACA2LZa/Y1+kX9UhjGrfjJIhF3hz+5vW/ceq+VdjcN1XOR7tjckE/e4L5f0Vp6zXGtyiAp/+ftwmMQM4fCGJ1384zcsd3sTfw5GzyWfxcfZhwn0TmL1vNl/s/QLIKaHfo3IPynmVsz6X5eLgQimPUpxN/mcZpoPJASd7J1KzU6kdUDvPj6xVlUBK+bpy9pLtM3N+7k483rAMjvb//Bl0tHPE3mRv3RT737ydvUnJzqlGeTz+OM+ve55wn3BKe5bmwbAHcz1D5mCX+/8q6gfU4aVKjxOQmY5velbOBt/uAeDskec15RbLTIYjK/LvP/4bpCcoORMRESnCbiijOn36NA8++KD1fZs2bTCZTERG5l0OXIoBw4DUSxgJkbhkxuGelYZd2iWyev9Eap8lXOjzM1HD95IUWgfT2W3gGZz/uTyDWRe9k8c2vsLy2D1QpXP+Y6s+jKe7C2tcoMsfIzlw6TAzjsyhTZWcpMDLLp3hrcuzYNtpm8TsstRMM6v3J9IwuJG1LSIxgq/2fckD5R7INX7R0bncX8OJIb8NstkY+sDFA7yy+RlefySYFXujcHQwk3LF8sEm4SX4/UgeMxHAgfOJlHWvhSWzBCVcS/DcPc8xecdktkZttY7JMGcw98BcFh1ZRJY557m1ALcAXqr/EgBhXuV5s9E0JjScz8vVv2Rmi8WUda9icx2zxQJAqI8rC56+l0H3heHr5oiXiwNPNCzDj0OaUNrPzeYYPxc/2pZtm2fcLvYu1PSvg7eTj037sfhj7InZk2dC5+PiY5M0tgltxvulO1Nz0dMEf9cP5/k94eN68OdUSMl7fzi5xeyd4GrVM139wN4x/34REREpdDeUnGVnZ+PiYvtbV0dHR7Kysgo0KLkNsjIxLp3EuHgcI3I3pnM7MWFgxB7BbulwHD9vhdunLQj8+QWc4+PZnerP2fvfh/A2YMr7a3OpwQDmnFoOwMW0C+DkDmXuzT3QuxRU6UhWZgJHko+Tbk7H38WfnlV70rKKPwGezqQkJXJfmBf7I/Mu8w7w9+lUynlWsmlbeXIlLUq3sGmrE1CPGa3mMufAbLItuTd9TstOY0PkcrrVD8XFOZv7qvxTfdRstuR7fYDYpEy+WBdHr0qD8HH24Vj8sTzHzd4/m5i0f4p5NApuxLRWn/Bijcm8/UM2g2cfZ+SCUzz56RGmrDxFTFI6Zy+lMmfTSYZ8u4tJKw5xPDoZf3cnXm5XmeUvNGfli80Z07kaZf1z70Pm5ujGi/VepIK3bTESJzsnxjWawuRllxhUaQL3BNSz6e9VpRf+LrkrL/o4+zC+yXh8nX2xM9nxUnh3/H98FjKu2GrAYoY/3oNTf171M5NbxMEZ7n02//6mz4Nb3tVDRUREpGi4oWWNhmHQt29fnJ3/2fQ3PT2dwYMH25TTVyn9oisjIxnHtEuw/0fsstPAuzSGoyvm4DqQHIX9/J42401nt+G78CHCuq1g3n4Lg2uWw6vjFFg+CsyZ1nGptbqz06cEB48cBGDthR30MPngV68fVO2cU8UxOwMqPgBlGpN9KQLDZKJFaFPmH5rPjDYz2BK5FQeOM6t/Y6JiUikZf4BgbxfOxee9CXVJXyfiM21ntbIsWTbl4esE1KNnuf+xPSKVv2P35Pu57IreQVbcfQTH29O0kgs/bHfmQmIG7s4O2NuZ8py9A/B1c2TfuSS61KtPbFrusvuXpWSl2JTw93L2ItyjPh2++IOkjH8SRsOARTvPUsbPjX2RCazcf/k5rwvM3HCcGU/W4/7KAQR7X3tpWqhHKLMf+AKSYzBS47BzcCbF3oc3f4tj7cEY/jx6ic/6v8ywmJyfef2g+jxS8RHrc2b/Vt6nPAs6LeBEwgkCDqyCPBJdADZMhLJNwV17oN12JSpBsxHw5782Fq/ZI+dnIiIiIkXaDSVnffr0ydX25JNPFlgwcutYUuLItrPDwbCQZGeHW3grjD+mYL9lBiZnL+zq94fgGuDoCln/SoZS4/A5/ycWowkH0n0JCHkQpyca4XLpEA5ZqTiVuYeDGbGM+PN56yGnk04TX78ePofXYHfpJJS/H+wc4PRmOLSUo22/xt3Rg3KedizsuBBLljdR52qx9kAS8+wP80jdQKpVq83wxlE8depSnvfUr64fyZZmrD69AouRM8NVxa8KZ5P+eZarb+UXGDb7BL2bBhLgGsSZpDN5nivANZhz0dks2xPPm939mNKrJD/vSCElM5tOtUL4eXfupbtNw/1xsLcjNiWD13+M4IPeZfP9/B3tHHGxt02o/j4Tb5OYXemLPyMY07naFckZWAx4YcFfrH6xRa5ljHlKvoDXsXXYrfofpOYsNfQOrsVr90/l6AV3jkWnsO+0PW80foMKPhUo5VnqmvuVhXqEEuoWBH/MyH9Q/GmbxF1uIze/nBmyWt3h6ArIzoJK7cC7JLhpLzoREZGi7oaSs7t9/7LiKOPSGezSE8CcRYajN1EWL0obF3Gc9cA/SVjaJUxrxmKUaQIPvAPLXsx1Ho+zv+Pg3ZSTcak8vngvhgEBnl44O/hwPuEUz7QMpX2Zh/j11E/0rfAYjwc3psTWL7FLisIo2wRTmXth2+fEl23PqYD76bvwDM0rluC+OmeoXaIpfWf/bTNDdnBZEj//Fc0nj9fkhZZmpm04xeXJKyd7O95pH0rZvdOwN2XQPawzC078jAkTI+qN5N3tEwAIdg8mMs6BtCwzP++6xKhHnmRX9I48P6cHSnXjxd9iSck0M2WpPS92CKBZzTjsHD14pnl57O1MLNkdSbbFwM4Ej9YtyaD7ypNtsfDJE3WZuuYISck+BLoFEp0anev8XcK7UMLVdibpeExKvj+3hLQsXBxyz2ClZ1k4EZt8fclZ5B7sfnrGti3qb0r99BjTHlpKhzkpHIxMZWjLR699rivZ2efMwhxamnd/YPWcJF8Kh6tPziuwyrVGioiISBGjHUnz8cknn/Dee+8RFRVF7dq1mTZtGg0bNizssK6bJeUSlvhTOC8eABdznoNydPbC4bG5OG2fmXt2DDCd3oS5+QjsPQJz9iq7Qrp3BTrVCqH/7B20qx5MSR9XIuPTWHswGrPF4LP1kax6fhgvV+yM15kdOH3T/Z/znlgHrr4kP76EF9emsW5FBADL9kbx4gPN+WFnVJ5LF/dHJvH7ibP0aeDHI1XdORyTjoORRUWPTAL/mozLkZ/B3pFuT8zjYNJJnr/nRdbutuelWu+yNXoNqZaLJKXmPB8XlZjOuQuleKLyAOYf/gqDnEzP3mTPszVH8OdBSMnMKYSx63QivT9LpHv9UjzbyhuTYfBUo7J0qVOS5PRsQn1cSMk088YvBzgek0z5Eh78X6dqpGVkM67Bh7y9cyTnkv8ph9+yVEsG1x6Ms4Ozzf3VKOWd788v2MuFxPS8n+VMy7z6c3AAJETC+rz3JSPtEoGxW6gSXIm6ZXyvfa68VHkQ1k+AjDyeCWwzDlxv8rwiIiIidzElZ3lYuHAhL730EjNnzqRRo0ZMnTqVdu3acfjwYQIDAws7vGuypCeTnXYJp7mdbf/xnJGIa1Y8HF+d77HGwaVkNxiEw7q3/2k02RFd7iEsBrzzaE2W7T3P7jPxlC/hzqdP1WPettOsPnCBc+eiKO+VCCtfy33itEu4rx1NlyqTWHc45zmxbIuB2ezAkj1R+cazel8qD5c0KDfrPsp5BIFhtt3HyZxFmIs/bzV9i6xMD+Zs2s5nv1u4t3xdKga5UrliCeAkAJNXnOWJe5sy7b4HiEg6jJO9A41K1mLqyvP8stt2Dy/DgKYV/Cnnm7M/WYiXmeikDEzAxmOxjPphr3XshcQMNp+4yJsPVyf1oisvVPuQUiWyScyMJ8Q9BH8Xf7xdcidiVYI8CfZyISoxPVffgGZhLNqRewmmyQSVgz3z/KyS0rKISc5g1+lLtAzOosSFffl+rj4XtlIpqB5tqt7k99m7DPT7FX4YCDGHctrcS8CD70NQ9Zs7p4iIiMhdTpuT5WHKlCkMGjSIfv36Ua1aNWbOnImbm1ux2WzbMGdiPrI671kNcxY45F9MItPRhYRyTf5pcHDhYscvWHQMIuPTGTBnB4t2nGXnqUss2nmWgXN30K56EHXL+ODp5gyxh8DIe2bHdOpPHghzpHxATvGYGiW9SMkw42BnynM8gL3JhIPz/09Gki/k3mDXZAJHDxZsTsTN0YEnG5XFMGDz8UvM3RTJufg0al0xQzVvSzR9P4/g8+XeBNg1IMg1lBohuQtXdK1bkiYVAqzvnRzsKeXrRqbZwvilB/OM9d3lh2hdNYi6JctSM6A6TUs2pbxP+TwTM4AQH1fmP92I2lfE5+Zkz+gOVbi3vB+7TsfnOqZ/03KUcHfK1X4pJZOZG07QavIGRi76m00nE8C7dJ7XBUj1rcxzrcIJ9bnJ5Yd2dhBcE/r8AkO2wOA/4enfoerD4OJ1c+cUERERuctp5uxfMjMz2blzJ6NHj7a22dnZ0aZNGzZv3pznMRkZGWRkZFjfJybmX/79dkjHCdfI7Xl3Hv4Vqj8Kf32dZ/eliq3I8giEDp+Sbe+GZ5ka7L7gQLNq7gz5ZleuqoVmi8Hbyw4y/uEaBDuch+yrF4KIT05nXvfSDPrxLC+3q8zM34/zYI0Qpq3LuwT9441CcY/9K6cKXR4bYFsqdSDB5EO9MgYZWQ5UDvbkqXvL8t2OM2RkW3hv5WG+6F2fH3ad5ae/Isk0WwjxcmF0hyo0DPPHw8WRJxqV5YFqwWw+cfH/tXff4VGV6RvHvzOZTEmZFNIhhNCl9wAqFpCgguJaQF0FZVEQV11RFAvqrv5QbKuuK5ZdwS423EVFEayIIEhv0ltISJ/0Nuf3xywjYyYICskkuT/XNZfkvO+cPAeOZG7OOc9LeVUNp7aPId5pJzKkdgjKKa6kuI4mHiWVNeSXVNI+7tgXYU6NCePlawaQV1JJRXUNEY5g4sJtVFUbvD95MLM+3cKGAy4SI+z8eWh7Tm0XQ7ij9lpVGzIKefbLn38Pn19VzKl9b6bFoptrf9OgYBzdL6BDnP8rcMclLM7zEhEREZHfTeHsF3JycqipqSE+Pt5ne3x8PFu2bPH7npkzZ/LAAw/UR3nHxOyuoqxFF2qvfgVs/i/88X3YsxTydvoMFQ28ni8KfmJAZHsu+iqe4opqurfMoU/rSLoH28kt8R+88kurSAgPJvqbh2DQ9X7nABDXhdU50Ne8hnljezHt8/18vD6TF67qS4e4MLYdKvaZflr7aHpXrYPPp8JFs+GTO3wCWk3rwezodx/7sg0mvrKKD6ecyrT31jGqRxJPje2N2zAIDjKzeMshzCb44IbBhNiCcFgtJDh/vnoYbg8m3B5Mauyvh6qjXeUDCAo6+rg/0aFWon9xNcxqgT4pUTz/x76UVtUQHGQmJszm9/1F5VX888sdPts2Zrj4tl8fhvaZRNjq5z33aQLYnHDxS1giWx53nSIiIiJycimcnQDTp0/n1ltv9X7tcrlITq77lrKTzUYF5aeMgqUPe9YWO5K7mvLSAg6MfpqIrC3E7PiCSnsEOV1G8mnhVpzhiazbU83evFLA83zVJX1bUVl99CYU1qoirLsWQ8Ip0OtKWPO67wSzhawzHmbe8jIGtM+jcvNC9ub1BOD2d9fx5Jie5BRV8v7q/VjMZq5Ma0WvpBDYt4KKXuMpy8/H/IfXqSguoKboEJWhiSzNNGMtdPDGDzsxmzy3BJqA/6zN4D9ra7e+v6xfa7q2rLsJx7GICrESG2Yju7ii1liLUCst/Nxy+HtEhFj5tYorqt0cctWu5+b/7OP6tIsZ98erSajag9lih6gUcLaCYP9BT0REREQajp45+4WYmBiCgoLIyvJtEJGVlUVCQoLf99hsNpxOp8+rQQWFUGVvgevSd3zXNjJbKBp4G58XtaMqpDX/qDrAPUkp3OG0MW7t37GGxdMurC/T3/v59rgwu6cVfXWNG6fdf5YPs1mIqf5fGPr+WU9DiJFPQmIvCE+kqtMFZIz9jLuXWzi/nZWYjXOIyFpBcrTneafCsipe+HonPVJM/PHMCsYMKeaLvKf4dm8G5y8IYuKeoWyJPpPpXxRx2iu5nPOfYC55Lw/DHkVmUSWr9uQzvGs80aFW0rv6/zNKjLCTFPnrCzcfVlFdQV5ZHsWVvlfzWkU6eOzSHgT/4gqZxWzisUt70jKi/lvIh9stDEyN9jv2/PJsXtsWhLvDCGh/NrRop2AmIiIiEqB05ewXrFYrffv2ZfHixYwePRoAt9vN4sWLufHGGxu2uGNkDnHiNBXiiu9B8fglUJRFsFGB29mSAreT7sF2nHaDSd3/TEl1ARXuSnDbmbesiLu/2+izr4t6t+Tj9QepqHIzdXgn7vvPxlrf72/ntiFu1V2eLwwDVr+K66LX+bS0L44gN6uzqnnrtWwGtwnnzPADkLUBV7vzyd71822SV6Ql89Tah/g241u6x3Tn7v4PseuglZ7JpVTVGOQUlXPvyC7cdk4HcksqySmp4l/f7uKH3fn0bh3JjJFdaRFm496RXThYWMaafYXefcc7bcy9dgAJxxCcqmuq2Ve8j9c2vcbKrJXEOeKY0GMCnaM7E2mLxGIx069NFAv+fDpvrdjLT4eK6BAXztgBybSKdGCx1P+/d9gsQUw4PZV3f9xPxS+ucIZagxjTPxlLkP4dRkRERCTQmQzDMH59WvPy9ttvM27cOJ5//nkGDBjA3//+d+bNm8eWLVtqPYvmj8vlIiIigsLCwoa7ilZwEIMaCInFZDbhNpkpceVRQzUbinfRMrwVhUVhXPniD7wxMY0lW7J5avE2n110TXJyy7COXP/qStwGXNq3FcO6xPP2D3vZkV1C25hQxvZvTXVlGaeEFhFSXUBIWARGSAzbSx2EmStZvGE/5dUwLNVGy/wVtPhiGphM7L5sCWfN2YdhwDmnxHHv+W3Jr9iBwxFJeZWbvKIgDKBFaDDr81bx1YFP+eupfyUhNIHMwjL255dxyFVBh/gQYp12Ih0/Xw3KKa4gs7CcXTklJETYSY5y/Gowc5VVUVxRTWl1Cc+tn8XifZ9RY9R4xyf1mMS4ruMIs/78XFpVVQ0lVTWEBgcRHFx7wej6VFXtZnOmi+nvr2djhqchTe/WkTw0ujudEsIJ+pVn5URERETk5DiebKBwVod//OMf3kWoe/XqxdNPP01aWtoxvTcgwllNNUbWFgy7E7MlmBLDSkVlFeE1+RTVBLOnMhyrLZhr5/xIfkkVj1zSndSYMD5ZfxBXeRUDU1tQUePmb//dRNER3QkjQ4J5fUIaK3fnkRBhJzUmjH35pVRUuzGbTCxYe4C/DG3L3oIqrJX59C75lpCt70PGGqgsBnskFX/4N68cSGTToQrGdI+gvet7YhbdhJHYizf6XMTD62cDEGIJwcCgrNqzQPVdaXcxttNYTCYTVFdBwR7PqzgL4rqAI5qq8Fbsyy/lrR/2sTO7mBZhNq4amEKbFiGE2Wt3OaysrmHboWJmfryF4ooqLh/QmtbRDiJC4d9bHuWzPQsxMDBh4r8X/ZcUZ0q9/PH9VnklFRSWVmEymYhwBBN1gp+BExEREZHjo3DWwAIinAGG6wCmg+th4Z2Qv8t3MCQao++1fJcyifEv/0BVjYE1yHPLXoLTzp9OT2XsC9/jKvdtG3/POa0Y1S2e4mozL32fyXs/HqCyxnMrnT3YzKvjevKfDTm8+r1nAeVxfWO4qkcIrUyZFLrLCIvpzNsHvqZzdCf6HtyK7cv/g9JcAAqG3Mb1pRtIcaZwfur5FFQUYDFbsAXZeGvLWxRXFfP8Oc8TERQCB1fDm2OhNBd3Um+yT72R3LAY3I5IMvJtzPk6j+92FHjrfuzSnozskYj9F1e4NmUUcuGzS5lyVnuc9mBe/GYnBwvLcdotXD04mY5tDnHf93+h2qjmodMe4oJ2F5zgPyURERERacoUzhpYoIQzSgowvnkUU9sh8MH1UJb/85gjmv3jv+X7fZW0j4/i3VX72XGomJQWIZzbPYHlO3IY0DaGNfsKWL4zj7hwG9f2b0Hqtrk49y2GofdxoCqM1XkWfsysoWN0EAPbtmBDZik3vr+jVin/mpjM2vzP2Vu0l/Q26bR2m+j65jioLgeTGVoPpLTPVXweGsZu125e3vAy1YYnGDosDm7vdzsZxRlc0/0anK5D8NJQKC+gomM6P/b/I3f++Dh55XkAhAWHMaX7NDZsb8Vbyw8BYLOY+fzWM0iODvHW5CqrYsobP2I2meibEsUTi2qvozaiewydOqzh35v/wczTZjKy3cgT+SckIiIiIk3c8WQDNQRpykIjMXU6F76eBeMWwN7vIHcHRmwnytucxZurXDz7xU6eGtMLh8VM+7gwslzl/GnuSqpqDNYdKGLc4BTySiopLKsiu7SGmM6XUJKYRrA7jPiYaM6LLOfsFBPYwrnnswO8vy63Vhn9UiIpd+fRLaYbxZXF7C7cTduwFAh2UN7jKjI6j+Oj7ZUU7AnmtJ6FvLj+bp/3l1WX8bfv/8br571OUI0dsjZAeQEEBXPg1Bu44au/eIMcQHFVMY/8OIPHT32JT9cHk19aRUW1m925JT7hrLiimqXbc3h6bG/u+mC939/ChetzGDtwCEGm5+gR2+PE/LmIiIiIiPihFm5NXYv20GccfDgFvvsHHFyL6du/k3twLy8v3QPAne+vp118GF2SnBjA2Z3jefHqvpzXPYEpr6/mlWV7+HBNBrfN30a+NYkdob3ZZWnL+a8eYGF2NANe2Medn2XTr11irW8f4Qjm/gtPoUNkZ6xmK06bk6UHluEO6cHX5y5iQdJN7KqJo9ISht1Rw783vuT3MAwMPtz+H7ZluzAKPHVXdxjOvH1f+ASzI727cw6j+/y8lIDb7XuR2GwyEW4Pxmw21bp980gZ+dXc3v92Wjha1DlHREREROT30pWzps6ZAAnd4OAaz9f/CzZlphBK/7eGV1lVDXe+t56kCDuD2rXAAPbnlzH3u93e58kACkqrMAEmk4mb3lrDwcJy3lqxl5uGdsBqMZNdVMFL4/rx6cZMsl0VdG8VQZ/WUWQVVJIcV0NiaEt25O/mitQZXDJ7AyWVP3dDPKdLPDcMjeXT7w7UeSi7Xbsoi6/EnXIaQUB5RCu2FO+pc/4e1076JXqeMQsOMpEaE+oz3iLMylUDUwj+lTbzbaLi6NpqJKHBoUedd1iNu4aDJQdZfnA5m/M20yOmB33j+5IUluRpZiIiIiIi4ofCWXMQVLtjn4NyHMFBlFX9HJAyCst570dPOBrSIZa8kkqf98SEWdmdW8obK/YwdXhHDrkqmPXpVjolhHNWpziueGk5NouZMzvFEue0s3R7Ls8s2U5qTCjPXtGLonI7ZyRezPlPf1trPa5Fm7L4Q98WdIjsRFap7wLgh7WP6MaryzJoPbwtLWM7YS88QKdW7ViVtcrv/BRnKgfzPd/nznM7ExPuu/hycJCZPw5M4dONBxmQGs2KXXm19hEVEkybFk4ibMe2uLRhGGzO28y1n17r7TL59ta3CQ8O5+URL9MputMx7UdEREREmh/d1tgcOCKhzWk+m+J2zefqga38Tm8Z6SA1ylrrVr9xg9vwyrLdLN2ey23vrGNffil/Oj2V73bk8t2OHAAqqt18ujGLeSv38eNeTwOSXTkl7Mop477/bGTRpqxaweywmR/t4roekzBR++qSPcjOqfEjWLghiy05VRhj38TirmFMq7MJMvlfY2xMh2vJLHDz5sQ0LumbTIi19r9FJETYOa97In+7sCutonwDWKg1iJevGUCC0+53//5kl2Zz8xc3e4PZYUVVRUz9aio5ZTnHvC8RERERaV4UzpoDRxSMeATskd5N1ohEJrRzcWnPWI5cn7hDXBivXhxPUs0+HP9rO2+zmJlwWiqhVgvLj7i69OaKfQxuF0NFVQ2RIXWvp2X9322DUaFWDhSU1zlvb14ZwYTz+BlPEeOI8W5Pcabwf4P+yd8X5uA2oNptkGFKhFF/p6UzhWfPeoooW5R3fmhwKPek3Ys1CO44L4Udh0pYsSuXA/ll/r4tseF2OiU4eXfSIOZe25+p53Tkmct7s/CWIXRvGYH5OBZwzi3P5VDpIb9je1x7vB0lA0VldQ3780vZlOFiV3YxhWWVv/4mERERETkpdFtjcxHRGq6cB1s+gv0roVU/4l4byYx+f2bK+IvIq7QQYjFoUbiB2P9cT2W/Cbx2/WW4q0PJL6lmwbqD/OvbXbV2u3pvPhEOK4PbtSA4yORdLy023EZxRTWFZVWM6JbAF1sPsSenhFE9kuossV1sKKuyfuT0pLO55ZRnsdvLCTIFkZFn5v8+yGVXTgmtohyE2yyYzLBkv4lvt5fSIzmVV0a8SVFVHmU1ZdjMNtwGZOTYuPSVtWQXVwAQHWrlzYlpdErw38I0IcJBQoSDMzrG/ebf5vKausMnQGVN4ISf3OIK3lyxl39+uYPS/z3/N7hdCx65uIdPV0sRERERqR8KZ82Fwwn2KMj+CeK7etYWA8JX/J3wFX+njckERyx5V2w2+L8fb+W6U+7i+lcz69xtqDWIqekdmbdyPw//oQfVxdkMjqvClrcVtyOG/JBUSmyxXPmvlVRUu3HaLSRF2MkorB1irj87ntd3PklcqBOHuRuT/r2KIxssWoPM/O3CbrSJCWHsC8vZm1fqHQsym5j9x74M6RBDaWUN17+6khW79/vsP6+kkomvrOLdSYOIO45bFY9HrCMWi8nit4Okw+Ig2h59Ur7v8aqpcTN/TQaPfea7ttt3O3IZ9+8VvHndQOJP0u+RiIiIiPin2xqbk5gOMPxv4K6G7Z9Dx/N+HjtyLXKTieJW/diWv42N+SsY1Dayzl0ObNeC0opq0lKjGd7a4NI9fyX57XOIW3QjCf8ZyynzzyWlfCunJHg6HT740Wb+fU0fBrf7+TbE2DAb949uw47yT9lZuBNXVQFdEsP55ObTuXpQCoPatmDi6aksuOk0eiZH8uBHm32CGUCN2+CG11eRVVRBVlE5K3bn48/evFJyS07e1asWjhZc2+1av2M39LohYNrxZxVV8MySbX7HduaU1Pr9FREREZGTT1fOmhOTyRPQRjwMpflQVQwZP4LLt3194dB7eWXvpxgYfLDzDR4cNpT1c4sprvC9GnT1oBQ+WneQF7/ZxUU9Yjhj17uYdy7x/Z7lBcR+MIYHR3/KyFeKyHSVs3pfFhcMzubas9pQXQPlRj7v7XyENdmrMWEiwdqF0x/9kjM7xjLrkh44rEE4LEEEBZnZm1fKpxv9d3OsqjFYu6+AdrFhR/1tKDuihf+J5rA4uLLLlbR2tuafa/5JRkkGyeHJ3NT7JgYmDsQWZPv1nfwWFUVQWQoWOzgifnV6WVUNBaVVdY5vyXTRv01gXOUTERERaS4Uzpoji82z/lnOdhj1FBTuhx1LqA6PJ6vTcN7MXMrbuz8GoKCigKc33M3sa/7GVxvdfLcjl+gQK1ektaba7SbUFkyrqBDOTa4i5JVX/X+/ymJii7fQMjKKU+JDOSuqjNLgKK767gZclS6fqRO63MhHa4oxDPhiazbT3l3Hk2N6EWTzXOStrnFT84vFpI+UX1pJVKgFa5DZZ422w4LMJlqE1d285ESItkdzYfsLGZw0mGp3NcHmYGJCYn79jb9FRTHk/ARfzoRDmyCyDZx5JyR093TprIPNYsYebKa8yn/nzNZ65kxERESk3imcNVeuDHh1NBTug9jOMOjPZCb35sKFV1Pp9r3t76f8LXy8+1+M7HAzVksQReVVOO0WokJtLNyYSbDZRHiwAVX+uyECtKg4wDuXdSX8p3cJn/ccRkQy8857mI8Lt/Fd5jKibbEMT76EH36yMG/FQe/7vtiaTV5JpbcbZJjNQkqLEPbk+r/trk/rKCIdViaensqzX+6oNX7VwBRiwk7S1atfiA2JPbnfwF0DO7+At//487bC/TB3JJw7C/pcDcH+12eLC7dz+YDWvLx0d62xqJBgOsSFn6SiRURERKQueuasuXJleIIZQPYWyN9F+M6vuSz1vFpTYxwxTG7/B3buO8SzX2znlWV7uPrlHwizBXHrOR3589AOOELCIDyxzm9niUwiad4Iwr9/DCpLMGVvoeXc0VxWE83N3WZSc+gypr6az0tfH6z13iPXW4tz2nnggq5+v8cZHWNJjLDjsFq49rRU7r+gCzH/u0oWFRLMXed15saz2hNqayL/JlGUCf+92f/Yonuh2H9LfwCrxcykM9oxvEu8z/Z4p403Jg4kMULNQERERETqWxP5lCrHreQXiyHn/EREySGui27D0EEP8dq+RRRWFXN2bG+GRnYheuNnrModQZ/WUaTGOIgKsZFXUknrFp5GH4QnwtAZMH9y7e8V0xGqyqG8oNaQOWstsa2GMHNIGI8NqABzMMVBEdzzZSELNmQD4LT7nqb92kTzxsQ0HvpoMxszXEQ4grn21DaMHdCaFv+7KtYizMbVA9uQ3jWBimo3NouZuHA7QcexZlnAK831vPyprvBcRYtKqfPt8U47sy7pwbTiCvbmlREVEkxChJ0Epx2TqQn9PomIiIg0EgpnzVVka9+vf/oELn+bqLeuoJ81lO6dzqPKGkvomv9iOvggWVcsZnR7K5Xmg4TbzSzdv4SPMsqpDD6H1IhUzy18HUfAyL/Dkr9CaZ6nXX/HETDwBnjnar9l2PpcSdL2NzF9OdN7W2RkWDxPXfQiSc4kduZXewPXYWE2C4PbxfDqhAGUVdYQZDYRHWKlosZNZbUbq8VzQdhsNpEY4f+2vmNVUlVCblku2wu2YzFbaBfRjhhHDDZL/dwaeVSmoKOPBwX/6i4iQ6xEhlhpr9sYRURERBqcyTCMursryG/icrmIiIigsLAQp9P/gscNriQX3pvgeWbpsNaDYPCNsOQhT3MJgLhTyD77MfaHRfNV1mdUG9XM3TjXZ1e9Y3vz2JmPERcS53kOquigp1GFxQahMXBoM/x7uG+7fqCm51WYO4/A9PaVteuz2Kie+DXZ9pSjBqzqGjf788t4+4d9rNyTR0qLEK45NZWUFqGEHcPti8Xl1VTW1BBuDyY4yPcu38KKQt7c/CbPrXsOt+FpnBFsDub+QfczNGUoocGhv7r/k8p10PP7WrC39pgtHCYvg8jk+q9LRERERLyOJxsonJ0EjSKcgee5s88fgA3vetY+s4bBWXfDKaO8V7HyjVC+OljCa3vu4oZeN3DzF/6fcfpLn78wvtt4zCY/jzFWllBzcA1Bn94NGavBGkpJtz9iHjgJx/xrPdv8MIZMw3T23Uc9hLX7Crjs+WVUVPt2HXzisp6c1z0Re7D/q0t5JRVsPODi+a93kFdSxRkdYxk7IJnkqBDM/7v1cemBpUz6fJLf97876l06RXc6am31Yu/38MoFntsYDzOZYcxr0H44WH796pmIiIiInDzHkw10W2Nz5kyCkU/AmdOhutQTzsISwPJzq/nwGjcdzdtJyEngq/1f1bmrN7a8wah2o/x3KLSG8gVl5PYfQ9fwO0gMS+GtjW6urKzCkbu9zn2aDm2C6kqoLoOyQjABjijPVSEgu6iCW+etqRXMAKa/v57+baJJ9tMSvqC0kqcXb2POd3u82zYddPHa93t4/4bBdIgPx1Xh4vl1zx/1eO8deC8WcwP/L9SyD0z+Dn581bNmXUwn6D8BIlN+fzBzuz1r4GVtgPzdkNgTolOP2vhFRERERH47hbPmzhoK0XXfnmcJMmM2u7EH2SmpLKlzXlFlkffWP39WZa3itc2vAZ5bA6d0v4NwxwBo0cETKvyJ7wZl+fDhDbBjMWCCDukw/G/Qoj35pZXsyPZfU0W1m505xX7DWZarwieYeY+hopoHP9rMM5f3psKoILMks87j2evaS0VNRcOHsyArtGgPZ8/whFiLHYJOQE1uN2SuhVcuhPLCn7fHdIA/vl/7mUURERER+d3USl9+VYQtgr3Fe+mX0M+7zWK2MCplOE/1m84Tfe9gQtdrCLfW3VSiY1RH76+r3FV8uPt1SrLWwpDb/L/BYoduf4B/9Iftn3ueVzPcnsYl/zoHCvb+8hG2WuparPqrn+puMf/1tmwKy6oIDQ6lW4tudc7rG98Xe1AAtZsPCgJb2IkJZgBFGfDaxb7BDCBnG3x0a+3tIiIiIvK7KZyJf9UVnqYhFcXEOmIZ32U8TpuTVGcqKc4UPhzyFPcWlnP2x/dyzmcPMSEvh5CiukPPgMQBhFh+vorVM7orzrVvQ00lDP8/zxW8w5xJcMU8z1iFnxBQlg/r3ibSYaFVlP9mIRazifaxYb/58EOCQ7i+5/UE+emI6LA4uKDdBQSZf6VbYmOWv6fuNv3bP6+9FIOIiIiI/G4KZ+Krugpyt8Nn93oaTbx7LZZ9KxgW15ekkCTuHXQvrwz8K63fHo9j9WueD+muDIK+fhTeuATydvndbUJIAv9O/7enoyNQWlNOld0J74z3rH92zUIY/zFM+AwuexU2fFBnoxAAflpIvK2Kh//Qw+/aZbendyImzH+7+zM6xtW52yEdYolweJ7VauNsw+xzZtMqrJV3vGNUR15Of5mksKS6a2sKSo8SvgwDqsvrrxYRERGRZkLdGk+CRtOt0Z8Dq+HlEbU/fA+9DwZMpMxwY//i/zAtn+3//aOehr7j/A4ZhsGh0kPkludSWVNJt4pKLP86x/9+RjwM2T/Bmteg9x+h/TBPR0mzBbYtguIsuPglyk12duWU8OwX21m3v5CWUQ7+fHZ7uiQ6iQyx+t11QWklf//8p1rPnYXbLN6GIEfKLs2msLIQM2YibZFEO6L919yUZG2C5wb5H7NHwKSlatMvIiIicgzUSr+BNdpwVpIDr46GzPW1x0xm+PMqz8LHc0f6X1sLoN3ZcPG/IOQYAkxpHnz3D/j2cd/tbU6Hi1+Cg2s9t1dumu95uWs84azrRTDkdoj9uZV9aUU1JZU12CxmnI5f71KYV1LBxgwXL3y9k7ySSs7sGMuY/sm0OqKVfrNWmgsfTIZtn9YeG/Ew9J944p5vExEREWnC1EpffpuyfP/BDDzNOA6s8ixUbfF/uyAAwQ5PgDsWIdFw6p89jT82vA+VxdB1NES3g/B4z/f86DbY+tHP73FXw/p3PGMj/w52zwkeYrMQcgyLTh8WHWrj9A6x9G4dRWW1m3C7pdYi1M1aSAu44Cn49in4cY5n3buwODjzLjjlAgUzERERkZNAn7Dk+DhbQu+rYdG9/sf7XA2OiGPfnyPK80rw0xmxqtQ3mB1p4/ue9dnsv+/KZJjNAkfJms1aeCKc8wAMmgI1FZ7gHZYIZoXYQFdcWUy1u5pwa3jTblwjIiLSxCicyc8cUZDQAzLX1R4zmSGpD5hM0OVC2PQBHPjF+mRdLoS4LieuntL8uscMA0qyPetuycljsenZskYktyyXDTkbmLNxDiVVJZydfDaj2o2iZXjLhi5NREREjoHCmfwsNAYueBr+7achyNn3em5rA4hKgUvmeLopbnjXsxByryshpuOJ/SBv+5VW+DVVUO763VfPRJqC/PJ8Hlv5GAt2LvBu25y3mTe2vMFr571Ga6cWDhcREQl0CmfiK747TP4OVrwAe5ZCeBKc9heI7Qy2I7oYRqV4Xu3P8VxNs4bUvc/fyh4FLfvUvkIH0HogZK33NAVROBPhQPEBn2B2WH5FPs+teY4Zg2fgsPhfF1BEREQCgx4eEV9BFmjRDs75G1z9IVz6MqQMgpAo//NtoZ5gVl3hWbj4p09h/XuQ8xOUFfy+WkJj4NxHIf4Xz6Ml9vR0a9yzDIJPQigUaYQ+2fVJnWML9yyk0N+C7iIiIhJQdOVM/LNYwdLi2OZWlcGOJfDutb63Q/YZ97/bIWN/Ww1BFs/Vsz5XQ0QrT6v/0BjI3w3v/Qkuf0tXzURERESkyVA4k9+vcD+8/UdPe/sj/TgXWvXzhKvfKqKl51m296+DmkqoLAKLA8579MQ2HxFp5M5NPZdXNr3if6zNuUTYjqOLqoiIiDQIhTP5/Ta8VzuYHfbNE9Ah3bNu2W8RbIfUIXDdl57ujO4aT2OS8HhPIxIRAaBlWEtGpo5kwS7f586ibFFM6jlJz5uJiIg0Agpn8vvlbKt7rCjDs3D072EO8lxBi1A7cJG6RNmjuK3/baSnpjN341yKq4oZmjxUrfRFREQaEYUz+f3ane1pqe9PYi/P4sXNUE5RBXkllVTUuIkKCSYu3IbVogWB5eRp4WjBmcln0i++nxahFhERaYQUzuT3Sx3iadRRklN77Jy/Qkh0/dfUgAzD4KesIqa8sZrth4oBCLEGMXV4J/7QuyVRobodU06uMOuvrBEoIiIiAUmt9OX3i0yGaz6B1oN+3hbRytNNMb5rw9XVQA4UlHHZ8997gxlAaWUNf1uwiWU7chuwMhEREREJZLpyJidGTEcY+yaU5UJNNdgjwJnY0FU1iB9251FYVuV3bNanW+ifGk1suK2eqxIRERGRQKdwJidOSFTdi1U3I2v2FtQ5tju3lKqaOjpbioiIiMjvYhgGh0oPUVRZRHBQMJG2yEa1nIzCmZx8lSVQmgdGDdicTf4ZtM6JdS+MnRRhx2I21WM1/pVV1QDgCFazCBEREWkaiiuLWZqxlEdWPEJ2WTYA/RP6c9+g+0hxpjRwdcdG4UxOrvzdsPhB2PSBp6V+qwFw3iyI6wqWptkY49T2MTiCg7wB6Eg3D+tAnNPeAFV5ZLnKWb23gDdX7AXgyrTW9EyOJL4BaxIRERE5EdZlr+O2r27z2fZD5g9cs/AaXj//dRJDA/+RG4UzOXkK98Oc8z3/PWz/CvjXOXDd1xDfpeFqO4mSIuy8eV0aE19ZRXZRBQBBZhN/Oi2VYaf8xsW4T4AsVzlTXv+RlXvyvdu++imbAW2ieeaK3gpoIiIi0mjllefx+KrH/Y5ll2WzPnu9wpk0c7u/9Q1mh9VUwRcPwUWzwRZe/3WdZJYgMz1aRvKfG08lu6iCsqoaEpx2YsJshNoa7n+5b7fl+ASzw1bszmP5zjwu6JXUAFWJiIiI/H4V1RX8lP9TneM/ZP7A8DbD67Gi30bhTE4OdzVs/m/d47u/gYqiJhnOAMxmE4kRDhIjAmMB7sLSSl77fk+d468s282ZnWJxOoLrsSoRERGREyPIHEQLewtyy/0vW9RYnjlrFOuc7d69mwkTJpCamorD4aBdu3bcd999VFZW+sxbt24dp59+Ona7neTkZGbNmlVrX++88w6dO3fGbrfTvXt3Pv74Y59xwzCYMWMGiYmJOBwOhg0bxrZt207q8TVJpiAIP8qlY0c0mNWMor64Dahy190lsqrGjdsw6rEiERERkRMnxhHDtd2u9TtmMVsY0mpIPVf02zSKcLZlyxbcbjfPP/88Gzdu5Mknn2T27Nncdddd3jkul4vhw4eTkpLCqlWrePTRR7n//vt54YUXvHO+++47Lr/8ciZMmMDq1asZPXo0o0ePZsOGDd45s2bN4umnn2b27NksX76c0NBQ0tPTKS8vr9djbvRMJug7ru7xQTdCaFz91dPMRYYE84fereocv6RvKyJ01UxEREQaKbPJzHltz2Nk6kif7Q6Lg2fPfpaE0IQGquz4mAyjcf5z+aOPPspzzz3Hzp07AXjuuee4++67yczMxGr1dAG88847mT9/Plu2bAFgzJgxlJSUsGDBAu9+Bg4cSK9evZg9ezaGYZCUlMTUqVO57TZPp5fCwkLi4+OZM2cOY8eOPabaXC4XERERFBYW4nTW3Va9ySsrhDWvwad3+W7vmA6jnoHwhmuO0RxlFJRx+Yvfsye31Gd7akwob/wpjcTIwLgFU0REROS3KqwoJLcsl5/yfyLcGk5qRCpxjjgsQQ33NNfxZING+8xZYWEh0dE/r5e1bNkyhgwZ4g1mAOnp6TzyyCPk5+cTFRXFsmXLuPXWW332k56ezvz58wHYtWsXmZmZDBs2zDseERFBWloay5YtqzOcVVRUUFFR4f3a5XKdiENs/BwR0Ptq6DActi2CymJoPwwikyE0tqGra3aSIh28OXEg7/+4n3dX7cdkMnFZ31Zc2LulgpmIiIg0CRG2CCJsEbSNbNvQpfwmjTKcbd++nWeeeYbHHnvMuy0zM5PU1FSfefHx8d6xqKgoMjMzvduOnJOZmemdd+T7/M3xZ+bMmTzwwAO//YCaMnu45xXToaErETwBbfKZ7RnTvzUA0aFWggJgUWwRERERaeBnzu68805MJtNRX4dvSTzswIEDjBgxgksvvZSJEyc2UOW+pk+fTmFhofe1b9++hi5JpE5BZhOx4TZiw20KZiIiIiIBpEGvnE2dOpXx48cfdU7btj9fkszIyOCss85i8ODBPo0+ABISEsjKyvLZdvjrhISEo845cvzwtsTERJ85vXr1qrNGm82GzWY76nGIiIiIiIgcTYOGs9jYWGJjj+3ZowMHDnDWWWfRt29fXn75Zcxm34t+gwYN4u6776aqqorgYE/XuUWLFtGpUyeioqK8cxYvXswtt9zifd+iRYsYNGgQAKmpqSQkJLB48WJvGHO5XCxfvpzJkyf/zqOV41ZdARUuCLKBvRk3VhERERGRZqFRtNI/cOAAZ555Jq1bt+axxx4jOzubzMxMn+fArrjiCqxWKxMmTGDjxo28/fbbPPXUUz4NQG6++WYWLlzI448/zpYtW7j//vtZuXIlN954IwAmk4lbbrmFBx98kP/85z+sX7+eq6++mqSkJEaPHl3fh9181VRD7g747F6YMxLeuhK2L4YS/4sKioiIiIg0BY2iIciiRYvYvn0727dvp1Ur37WaDq8EEBERwWeffcaUKVPo27cvMTExzJgxg+uuu847d/Dgwbzxxhvcc8893HXXXXTo0IH58+fTrVs375xp06ZRUlLCddddR0FBAaeddhoLFy7EbrfXz8EKZG+Bf50DVaU/f737axg0BYZMA0dkg5YnIiIiInIyNNp1zgKZ1jn7HUrz4M3LYd/3/senrIDYTvVbk4iIiIjIb3Q82aBR3NYozUh5Yd3BDGDX1/VXi4iIiIhIPVI4k8bFpFNWRERERJomfdKVwOKIhDan1T2eenq9lSIiIiIiUp8UziSwOKLgvMfAFl57bMjtEBpf/zWJiIiIiNSDRtGtUZqZmE5w/Tew+nXYudgTyAb/GeJOAUdEQ1cnIiIiInJSqFvjSaBujSdITTVUFIHFCtbQhq5GREREROS4HU820JUzCVxBFgiJqnPYbbjJKcvBbbgJCQ7BaVUQFhEREZHGS+FMGqXs0mw+3vUxr2x6hfzyfPon9OfmPjfTNqItdosWDBcRERGRxkfhTBqdvLI87l16L0szlnq3fZfxHcsPLmfuuXPpGduzAasTEREREflt1K1RGp0DJQd8gtlhNUYNM5fPJL88vwGqEhERERH5fRTOpNH5PuP7Osc25m6kpKqkHqsRERERETkxFM6k0QkNrrtzo8VswWzSaS0iIiIijY8+xUqjc2rLUzFh8jt2bptzibLX3eFRRERERCRQKZxJoxPriGXGoBm1trcMa8mUXlNwWBwNUJWIiIiIyO+jbo3S6IQEhzAidQS94nqxYMcCMksyuaj9aLqHJBFiCoaaGggKaugyRURERESOi8kwDKOhi2hqjmcVcDkBXBmw+b+w6mWoqYLul0GvKyAyuaErExEREZFm7niyga6cSePmyoA3xkDmup+3ffl/sPoVuOYTiGzdcLWJiIiIiBwHPXMmjdue73yD2WGF+2HNm55bHEVEREREGgGFM2m8Kktg9Wt1j697C0pz6q8eEREREZHfQeFMGjETmI9yCpstoDXPRERERKSR0CdXabysIdD/urrH+14DoTH1V4+IiIiIyO+gcCaNW1JvaDe09va4rtDlQjD5X6xaRERERCTQqFujNG7h8TD6n5CxGla8CO4q6P1HaHMaOFs2dHUiIiIiIsdM4Uwav/AE6HQupA4BA7CFNnRFIiIiIiLHTeFMmg6rQpmIiIiINF565kxERERERCQAKJyJiIiIiIgEAIUzkaOodlc3dAkiIiIi0kzomTORX3AbbjKKM/hi7xf8kPUDqRGpXNj+QpJCk7Bb7A1dnoiIiIg0USbDMIyGLqKpcblcREREUFhYiNPpbOhy5DhtzdvK+IXjKa4q9m4zm8z8/cy/c2rLU7EGWRuwOhERERFpTI4nG+i2RpEj5JXlcde3d/kEM/BcTbvjmzvIKctpoMpEREREpKlTOBM5QkFlAT/l/+R3rKy6jL2uvfVckYiIiIg0FwpnIkeocdccdby8pryeKhERERGR5kbhTOQITquTWEes3zGzyUzbiLb1XJGIiIiINBcKZyJHiAuJ456B9/gdu6brNUTbo+u5IhERERFpLhTORI5gMplIS0jjtXNfY0DCAJxWJx2jOvLEmU8wrus4wqxhDV2iiIiIiDRRaqV/EqiVfoAqOgiluZ5fO1qAM/Go010VLsqqywg2BxPt0BUzERERETl+x5MNtAi1NH3VFbB/Bcy/AQr+120xohVc+E9IToNg/wtLO21OnDaFaxERERGpH7qtUZq+/N3wyuifgxlA4X547SLI39VQVYmIiIiI+FA4k6atuhK+fw7c1bXH3DWw9BmoKqv/ukREREREfkG3NUrTVlkMGavrHj+4GipLINhRfzWJnABuw82h0kNkl2ZTWl1KYmgi0fZoNa0RERFpxBTOpGkLdkB0Wzi4xv94dKqCmTQ61e5qNuVu4qYlN5Fb7mlyYzaZGdt5LNd3v14NbERERBop3dYoTVuwAwbfVPf4qX8Ba2j91SNyAmSVZPGnz/7kDWbguZL2xuY3+GzPZ6gJr4iISOOkcCZNX4v2ns6MliO6MlpsMOopiO3UcHWJ/EYrMldQVu3/WckX1r1Adll2PVckIiIiJ4Jua5Smzx4O3S6GNqf9r2OjAZEpEBanWxqlUdqWv63OseyybKr9NcARERGRgNforpxVVFTQq1cvTCYTa9as8Rlbt24dp59+Ona7neTkZGbNmlXr/e+88w6dO3fGbrfTvXt3Pv74Y59xwzCYMWMGiYmJOBwOhg0bxrZtdX8QkkYi2A5RKZB6OqQO8fxawUwaqR6xPeocaxXeCmuQtR6rERERkROl0YWzadOmkZSUVGu7y+Vi+PDhpKSksGrVKh599FHuv/9+XnjhBe+c7777jssvv5wJEyawevVqRo8ezejRo9mwYYN3zqxZs3j66aeZPXs2y5cvJzQ0lPT0dMrLy+vl+EREfk3P2J5E2aL8jt3S+xZiHDH1XJGIiIicCCajET05/sknn3Drrbfy3nvv0bVrV1avXk2vXr0AeO6557j77rvJzMzEavX8q/Gdd97J/Pnz2bJlCwBjxoyhpKSEBQsWePc5cOBAevXqxezZszEMg6SkJKZOncptt90GQGFhIfHx8cyZM4exY8ceU50ul4uIiAgKCwtxOp0n8HdARMRjR8EOpn09jZ/yfwIgNDiUG3vdyMi2I4m0RzZscSIiIuJ1PNmg0TxzlpWVxcSJE5k/fz4hISG1xpctW8aQIUO8wQwgPT2dRx55hPz8fKKioli2bBm33nqrz/vS09OZP38+ALt27SIzM5Nhw4Z5xyMiIkhLS2PZsmV1hrOKigoqKiq8X7tcrt9zqCIiv6pdZDtePOdF8ivyqaypJMIWQawjluCg4IYuTURERH6jRnFbo2EYjB8/nkmTJtGvXz+/czIzM4mPj/fZdvjrzMzMo845cvzI9/mb48/MmTOJiIjwvpKTk4/j6EREfptoRzTtIttxSotTSApLUjATERFp5Bo0nN15552YTKajvrZs2cIzzzxDUVER06dPb8hy6zR9+nQKCwu9r3379jV0SSIiIiIi0sg06G2NU6dOZfz48Ued07ZtW5YsWcKyZcuw2Ww+Y/369ePKK69k7ty5JCQkkJWV5TN++OuEhATvf/3NOXL88LbExESfOYefbfPHZrPVqk1EREREROR4NGg4i42NJTY29lfnPf300zz44IPerzMyMkhPT+ftt98mLS0NgEGDBnH33XdTVVVFcLDn1p5FixbRqVMnoqKivHMWL17MLbfc4t3XokWLGDRoEACpqakkJCSwePFibxhzuVwsX76cyZMnn4hDFhERERER8atRNARp3bq1z9dhYWEAtGvXjlatWgFwxRVX8MADDzBhwgTuuOMONmzYwFNPPcWTTz7pfd/NN9/MGWecweOPP87555/PW2+9xcqVK73t9k0mE7fccgsPPvggHTp0IDU1lXvvvZekpCRGjx5dPwcrIiIiIiLNUqMIZ8ciIiKCzz77jClTptC3b19iYmKYMWMG1113nXfO4MGDeeONN7jnnnu466676NChA/Pnz6dbt27eOdOmTaOkpITrrruOgoICTjvtNBYuXIjdbm+IwxIRERERkWaiUa1z1lhonTMREREREYHjywaNopW+iIiIiIhIU6dwJiIiIiIiEgAUzkRERERERAKAwpmIiIiIiEgAUDgTEREREREJAApnIiIiIiIiAUDhTEREREREJAAonImIiIiIiAQAhTMREREREZEAoHAmIiIiIiISABTOREREREREAoDCmYiIiIiISABQOBMREREREQkACmciIiIiIiIBQOFMREREREQkACiciYiIiIiIBACFMxERERERkQCgcCYiIiIiIhIAFM5EREREREQCgMKZiIiIiIhIAFA4ExERERERCQAKZyIiIiIiIgFA4UxERERERCQAKJyJiIiIiIgEAIUzERERERGRAKBwJiIiIiIiEgAUzkRERERERAKAwpmIiIiIiEgAUDgTEREREREJAApnIiIiIiIiAUDhTEREREREJAAonImIiIiIiAQAhTMREREREZEAoHAmIiIiIiISABTOREREREREAoDCmYiIiIiISABQOBMREREREQkACmciIiIiIiIBQOFMREREREQkACiciYiIiIiIBACFMxERERERkQCgcCYiIiIiIhIAFM5EREREREQCgMKZiIiIiIhIAFA4ExERERERCQCNKpx99NFHpKWl4XA4iIqKYvTo0T7je/fu5fzzzyckJIS4uDhuv/12qqurfeZ8+eWX9OnTB5vNRvv27ZkzZ06t7/Pss8/Spk0b7HY7aWlprFix4iQelYiIiIiISCMKZ++99x5XXXUV11xzDWvXrmXp0qVcccUV3vGamhrOP/98Kisr+e6775g7dy5z5sxhxowZ3jm7du3i/PPP56yzzmLNmjXccsst/OlPf+LTTz/1znn77be59dZbue+++/jxxx/p2bMn6enpHDp0qF6PV0REREREmheTYRhGQxfxa6qrq2nTpg0PPPAAEyZM8Dvnk08+YeTIkWRkZBAfHw/A7NmzueOOO8jOzsZqtXLHHXfw0UcfsWHDBu/7xo4dS0FBAQsXLgQgLS2N/v37849//AMAt9tNcnIyf/7zn7nzzjuPqV6Xy0VERASFhYU4nc7fc+giIiIiItKIHU82aBRXzn788UcOHDiA2Wymd+/eJCYmcu655/qErGXLltG9e3dvMANIT0/H5XKxceNG75xhw4b57Ds9PZ1ly5YBUFlZyapVq3zmmM1mhg0b5p3jT0VFBS6Xy+clIiIiIiJyPBpFONu5cycA999/P/fccw8LFiwgKiqKM888k7y8PAAyMzN9ghng/TozM/Ooc1wuF2VlZeTk5FBTU+N3zuF9+DNz5kwiIiK8r+Tk5N93wCIiIiIi0uw0aDi78847MZlMR31t2bIFt9sNwN13383FF19M3759efnllzGZTLzzzjsNeQgATJ8+ncLCQu9r3759DV2SiIiIiIg0MpaG/OZTp05l/PjxR53Ttm1bDh48CECXLl282202G23btmXv3r0AJCQk1OqqmJWV5R07/N/D246c43Q6cTgcBAUFERQU5HfO4X34Y7PZsNlsRz0OERERERGRo2nQcBYbG0tsbOyvzuvbty82m42tW7dy2mmnAVBVVcXu3btJSUkBYNCgQTz00EMcOnSIuLg4ABYtWoTT6fSGukGDBvHxxx/77HvRokUMGjQIAKvVSt++fVm8eLG3Tb/b7Wbx4sXceOONJ+SYRURERERE/GkUz5w5nU4mTZrEfffdx2effcbWrVuZPHkyAJdeeikAw4cPp0uXLlx11VWsXbuWTz/9lHvuuYcpU6Z4r2pNmjSJnTt3Mm3aNLZs2cI///lP5s2bx1/+8hfv97r11lt58cUXmTt3Lps3b2by5MmUlJRwzTXX1P+Bi4iIiIhIs9GgV86Ox6OPPorFYuGqq66irKyMtLQ0lixZQlRUFABBQUEsWLCAyZMnM2jQIEJDQxk3bhx//etfvftITU3lo48+4i9/+QtPPfUUrVq14qWXXiI9Pd07Z8yYMWRnZzNjxgwyMzPp1asXCxcurNUkRERERERE5ERqFOucNTZa50xERERERKAJrnMmIiIiIiLS1CmciYiIiIiIBACFMxERERERkQCgcCYiIiIiIhIAFM5EREREREQCQKNppS8iJ1dFdQW55blUu6txWBzEhvz6AvEiIiIicuIonIkIWSVZvLDuBT7c8SEVNRUkhydze//b6Rffj3BreEOXJyIiItIs6LZGkWYupyyHW7+8lXk/zaOipgKAfUX7uGnJTazMXNnA1YmIiIg0HwpnIs3cgaIDrMtZ53ds1g+zyC7NrueKRERERJonhTORZm5N9po6x/YX76e0urT+ihERERFpxhTORJq5WEfdjT+CzcEEm4PrsRoRERGR5kvhTKSZ6xnbE6vZ6ndsVNtRtLC3qOeKRERERJonhTORZi42JJZnhj5T6wrZKdGnMLnXZGwWWwNVJiIiItK8qJW+SDNnDbLSL74f/x39X1Znr+ZQySF6x/cmOTyZGEdMQ5cnIiIi0mwonIkI1iArLcNb0jK8ZUOXIiIiItJs6bZGERERERGRAKBwJiIiIiIiEgAUzkRERERERAKAwpmIiIiIiEgAUDgTEREREREJAApnIiIiIiIiAUDhTEREREREJAAonImIiIiIiAQAhTMREREREZEAoHAmIiIiIiISABTOREREREREAoDCmYiIiIiISABQOBMREREREQkACmciIiIiIiIBQOFMREREREQkAFgauoCmyDAMAFwuVwNXIiIiIiIiDelwJjicEY5G4ewkKCoqAiA5ObmBKxERERERkUBQVFRERETEUeeYjGOJcHJc3G43GRkZhIeHYzKZGrQWl8tFcnIy+/btw+l0Nmgt0vTpfJP6onNN6pPON6lPOt+aHsMwKCoqIikpCbP56E+V6crZSWA2m2nVqlVDl+HD6XTqf3CpNzrfpL7oXJP6pPNN6pPOt6bl166YHaaGICIiIiIiIgFA4UxERERERCQAKJw1cTabjfvuuw+bzdbQpUgzoPNN6ovONalPOt+kPul8a97UEERERERERCQA6MqZiIiIiIhIAFA4ExERERERCQAKZyIiIiIiIgFA4UxERERERCQAKJw1cc8++yxt2rTBbreTlpbGihUrGrokCWD3338/JpPJ59W5c2fveHl5OVOmTKFFixaEhYVx8cUXk5WV5bOPvXv3cv755xMSEkJcXBy333471dXVPnO+/PJL+vTpg81mo3379syZM6c+Dk8a2Ndff82oUaNISkrCZDIxf/58n3HDMJgxYwaJiYk4HA6GDRvGtm3bfObk5eVx5ZVX4nQ6iYyMZMKECRQXF/vMWbduHaeffjp2u53k5GRmzZpVq5Z33nmHzp07Y7fb6d69Ox9//PEJP15pWL92vo0fP77W33cjRozwmaPzTY7FzJkz6d+/P+Hh4cTFxTF69Gi2bt3qM6c+f37qs18jZ0iT9dZbbxlWq9X497//bWzcuNGYOHGiERkZaWRlZTV0aRKg7rvvPqNr167GwYMHva/s7Gzv+KRJk4zk5GRj8eLFxsqVK42BAwcagwcP9o5XV1cb3bp1M4YNG2asXr3a+Pjjj42YmBhj+vTp3jk7d+40QkJCjFtvvdXYtGmT8cwzzxhBQUHGwoUL6/VYpf59/PHHxt133228//77BmB88MEHPuMPP/ywERERYcyfP99Yu3atccEFFxipqalGWVmZd86IESOMnj17Gt9//73xzTffGO3btzcuv/xy73hhYaERHx9vXHnllcaGDRuMN99803A4HMbzzz/vnbN06VIjKCjImDVrlrFp0ybjnnvuMYKDg43169ef9N8DqT+/dr6NGzfOGDFihM/fd3l5eT5zdL7JsUhPTzdefvllY8OGDcaaNWuM8847z2jdurVRXFzsnVNfPz/12a/xUzhrwgYMGGBMmTLF+3VNTY2RlJRkzJw5swGrkkB23333GT179vQ7VlBQYAQHBxvvvPOOd9vmzZsNwFi2bJlhGJ4PQ2az2cjMzPTOee655wyn02lUVFQYhmEY06ZNM7p27eqz7zFjxhjp6ekn+GgkkP3yw7Lb7TYSEhKMRx991LutoKDAsNlsxptvvmkYhmFs2rTJAIwffvjBO+eTTz4xTCaTceDAAcMwDOOf//ynERUV5T3fDMMw7rjjDqNTp07ery+77DLj/PPP96knLS3NuP7660/oMUrgqCucXXjhhXW+R+eb/FaHDh0yAOOrr74yDKN+f37qs1/jp9sam6jKykpWrVrFsGHDvNvMZjPDhg1j2bJlDViZBLpt27aRlJRE27ZtufLKK9m7dy8Aq1atoqqqyuec6ty5M61bt/aeU8uWLaN79+7Ex8d756Snp+Nyudi4caN3zpH7ODxH52XztmvXLjIzM33OjYiICNLS0nzOr8jISPr16+edM2zYMMxmM8uXL/fOGTJkCFar1TsnPT2drVu3kp+f752jc1DAc4tYXFwcnTp1YvLkyeTm5nrHdL7Jb1VYWAhAdHQ0UH8/P/XZr2lQOGuicnJyqKmp8fmfHCA+Pp7MzMwGqkoCXVpaGnPmzGHhwoU899xz7Nq1i9NPP52ioiIyMzOxWq1ERkb6vOfIcyozM9PvOXd47GhzXC4XZWVlJ+nIJNAdPj+O9ndWZmYmcXFxPuMWi4Xo6OgTcg7q78bmZcSIEbzyyissXryYRx55hK+++opzzz2XmpoaQOeb/DZut5tbbrmFU089lW7dugHU289PffZrGiwNXYCIBI5zzz3X++sePXqQlpZGSkoK8+bNw+FwNGBlIiIn1tixY72/7t69Oz169KBdu3Z8+eWXDB06tAErk8ZsypQpbNiwgW+//bahS5FGSlfOmqiYmBiCgoJqdQLKysoiISGhgaqSxiYyMpKOHTuyfft2EhISqKyspKCgwGfOkedUQkKC33Pu8NjR5jidTgXAZuzw+XG0v7MSEhI4dOiQz3h1dTV5eXkn5BzU343NW9u2bYmJiWH79u2Azjc5fjfeeCMLFizgiy++oFWrVt7t9fXzU5/9mgaFsybKarXSt29fFi9e7N3mdrtZvHgxgwYNasDKpDEpLi5mx44dJCYm0rdvX4KDg33Oqa1bt7J3717vOTVo0CDWr1/v84Fm0aJFOJ1OunTp4p1z5D4Oz9F52bylpqaSkJDgc264XC6WL1/uc34VFBSwatUq75wlS5bgdrtJS0vzzvn666+pqqryzlm0aBGdOnUiKirKO0fnoPzS/v37yc3NJTExEdD5JsfOMAxuvPFGPvjgA5YsWUJqaqrPeH39/NRnvyaioTuSyMnz1ltvGTabzZgzZ46xadMm47rrrjMiIyN9OgGJHGnq1KnGl19+aezatctYunSpMWzYMCMmJsY4dOiQYRieVsCtW7c2lixZYqxcudIYNGiQMWjQIO/7D7cCHj58uLFmzRpj4cKFRmxsrN9WwLfffruxefNm49lnn1Ur/WaiqKjIWL16tbF69WoDMJ544glj9erVxp49ewzD8LTSj4yMND788ENj3bp1xoUXXui3lX7v3r2N5cuXG99++63RoUMHn9bmBQUFRnx8vHHVVVcZGzZsMN566y0jJCSkVmtzi8ViPPbYY8bmzZuN++67T63Nm6CjnW9FRUXGbbfdZixbtszYtWuX8fnnnxt9+vQxOnToYJSXl3v3ofNNjsXkyZONiIgI48svv/RZmqG0tNQ7p75+fuqzX+OncNbEPfPMM0br1q0Nq9VqDBgwwPj+++8buiQJYGPGjDESExMNq9VqtGzZ0hgzZoyxfft273hZWZlxww03GFFRUUZISIhx0UUXGQcPHvTZx+7du41zzz3XcDgcRkxMjDF16lSjqqrKZ84XX3xh9OrVy7BarUbbtm2Nl19+uT4OTxrYF198YQC1XuPGjTMMw9NO/9577zXi4+MNm81mDB061Ni6davPPnJzc43LL7/cCAsLM5xOp3HNNdcYRUVFPnPWrl1rnHbaaYbNZjNatmxpPPzww7VqmTdvntGxY0fDarUaXbt2NT766KOTdtzSMI52vpWWlhrDhw83YmNjjeDgYCMlJcWYOHFirQ+wOt/kWPg7zwCfn231+fNTn/0aN5NhGEZ9X60TERERERERX3rmTEREREREJAAonImIiIiIiAQAhTMREREREZEAoHAmIiIiIiISABTOREREREREAoDCmYiIiIiISABQOBMREREREQkACmciIiIiIiIBQOFMRETkJJkzZw6RkZENXYaIiDQSCmciIiJ1yM7OZvLkybRu3RqbzUZCQgLp6eksXbq0oUsTEZEmyNLQBYiIiASqiy++mMrKSubOnUvbtm3Jyspi8eLF5ObmNnRpIiLSBOnKmYiIiB8FBQV88803PPLII5x11lmkpKQwYMAApk+fzgUXXADAE088Qffu3QkNDSU5OZkbbriB4uLio+73ww8/pE+fPtjtdtq2bcsDDzxAdXU1AIZhcP/993uv1CUlJXHTTTed9GMVEZHAoHAmIiLiR1hYGGFhYcyfP5+Kigq/c8xmM08//TQbN25k7ty5LFmyhGnTptW5z2+++Yarr76am2++mU2bNvH8888zZ84cHnroIQDee+89nnzySZ5//nm2bdvG/Pnz6d69+0k5PhERCTwmwzCMhi5CREQkEL333ntMnDiRsrIy+vTpwxlnnMHYsWPp0aOH3/nvvvsukyZNIicnB/A0BLnlllsoKCgAYNiwYQwdOpTp06d73/Paa68xbdo0MjIyeOKJJ3j++efZsGEDwcHBJ/34REQksCiciYiIHEV5eTnffPMN33//PZ988gkrVqzgpZdeYvz48Xz++efMnDmTLVu24HK5qK6upry8nJKSEkJCQmqFs9jYWIqLiwkKCvLuv6amxvue3NxcTj31VAzDYMSIEZx33nmMGjUKi0WPiIuINAcKZyIiIsfhT3/6E4sWLeKrr76ic+fOTJ48mTFjxhAdHc23337LhAkTyM/PJzIyslY4czgcPPDAA/zhD3+otd+2bdtiNpspKyvj888/Z9GiRbzzzjukpqby1Vdf6UqaiEgzoH+KExEROQ5dunRh/vz5rFq1CrfbzeOPP47Z7HmEe968eUd9b58+fdi6dSvt27evc47D4WDUqFGMGjWKKVOm0LlzZ9avX0+fPn1O6HGIiEjgUTgTERHxIzc3l0svvZRrr72WHj16EB4ezsqVK5k1axYXXngh7du3p6qqimeeeYZRo0axdOlSZs+efdR9zpgxg5EjR9K6dWsuueQSzGYza9euZcOGDTz44IPMmTOHmpoa0tLSCAkJ4bXXXsPhcJCSklJPRy0iIg1J3RpFRET8CAsLIy0tjSeffJIhQ4bQrVs37r33XiZOnMg//vEPevbsyRNPPMEjjzxCt27deP3115k5c+ZR95mens6CBQv47LPP6N+/PwMHDuTJJ5/0hq/IyEhefPFFTj31VHr06MHnn3/Of//7X1q0aFEfhywiIg1Mz5yJiIiIiIgEAF05ExERERERCQAKZyIiIiIiIgFA4UxERERERCQAKJyJiIiIiIgEAIUzERERERGRAKBwJiIiIiIiEgAUzkRERERERAKAwpmIiIiIiEgAUDgTEREREREJAApnIiIiIiIiAUDhTEREREREJAD8P+TFtews87RzAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.scatterplot(data=df, x='Sales', y='Profit', hue='Category')\n",
        "plt.title('Sales vs Profit per Category')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9NIQh5hHMpxE"
      },
      "source": [
        "As we can see that there are many transactions on Furniture at the bottom of the plot, indicating low sales and low profit or even loss. However, there are also some transactions with high sales and profit. Most transactions in Office Supplies Category have low sales and low profit, but there is a few transactions with high sales and profit. Turning to the remaining category, Technology seems to be the highest sales and profit compared to the other two categories. Many transations with high sales and profit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKZfmh5yMpxF"
      },
      "source": [
        "## 5. Sales prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imJXD6XhMpxF"
      },
      "source": [
        "#### Using Pycaret library to compare models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "NkZlpxe5MpxF"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "data=pd.read_csv('Sample - Superstore.csv', encoding='ISO-8859-1')\n",
        "data['Order Date']=pd.to_datetime(data['Order Date'])\n",
        "data.set_index('Order Date', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "46u82qKrMpxF"
      },
      "outputs": [],
      "source": [
        "# keep outliers out\n",
        "data = data[data.Sales<10000]\n",
        "\n",
        "data = data.loc[~data.index.duplicated(keep='first')]\n",
        "data = data.groupby(data.index).mean(numeric_only=True)\n",
        "data=data.asfreq('W')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "4b6ir_4CW_9J",
        "outputId": "b6ad8344-74f4-44c1-836d-5ed834f0429d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([137.,  20.,  15.,   6.,   4.,   3.,   3.,   1.,   2.,   2.,   0.,\n",
              "          0.,   0.,   1.,   0.,   1.,   0.,   1.,   0.,   1.]),\n",
              " array([1.0800000e+00, 1.5102350e+02, 3.0096700e+02, 4.5091050e+02,\n",
              "        6.0085400e+02, 7.5079750e+02, 9.0074100e+02, 1.0506845e+03,\n",
              "        1.2006280e+03, 1.3505715e+03, 1.5005150e+03, 1.6504585e+03,\n",
              "        1.8004020e+03, 1.9503455e+03, 2.1002890e+03, 2.2502325e+03,\n",
              "        2.4001760e+03, 2.5501195e+03, 2.7000630e+03, 2.8500065e+03,\n",
              "        2.9999500e+03]),\n",
              " <BarContainer object of 20 artists>)"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(data.Sales, bins=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V2aQturpMpxF",
        "outputId": "7bf6c8d0-ed7e-4a22-c8d1-a7a57283bcbc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d1679_row28_col1, #T_d1679_row31_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d1679\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d1679_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_d1679_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_d1679_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
              "      <td id=\"T_d1679_row0_col1\" class=\"data row0 col1\" >7886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_d1679_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_d1679_row1_col1\" class=\"data row1 col1\" >Sales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_d1679_row2_col0\" class=\"data row2 col0\" >Approach</td>\n",
              "      <td id=\"T_d1679_row2_col1\" class=\"data row2 col1\" >Univariate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_d1679_row3_col0\" class=\"data row3 col0\" >Exogenous Variables</td>\n",
              "      <td id=\"T_d1679_row3_col1\" class=\"data row3 col1\" >Present</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_d1679_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
              "      <td id=\"T_d1679_row4_col1\" class=\"data row4 col1\" >(208, 6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_d1679_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_d1679_row5_col1\" class=\"data row5 col1\" >(208, 6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_d1679_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_d1679_row6_col1\" class=\"data row6 col1\" >(198, 6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_d1679_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_d1679_row7_col1\" class=\"data row7 col1\" >(10, 6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_d1679_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n",
              "      <td id=\"T_d1679_row8_col1\" class=\"data row8 col1\" >5.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_d1679_row9_col0\" class=\"data row9 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_d1679_row9_col1\" class=\"data row9 col1\" >ExpandingWindowSplitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_d1679_row10_col0\" class=\"data row10 col0\" >Fold Number</td>\n",
              "      <td id=\"T_d1679_row10_col1\" class=\"data row10 col1\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_d1679_row11_col0\" class=\"data row11 col0\" >Enforce Prediction Interval</td>\n",
              "      <td id=\"T_d1679_row11_col1\" class=\"data row11 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_d1679_row12_col0\" class=\"data row12 col0\" >Splits used for hyperparameters</td>\n",
              "      <td id=\"T_d1679_row12_col1\" class=\"data row12 col1\" >all</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_d1679_row13_col0\" class=\"data row13 col0\" >User Defined Seasonal Period(s)</td>\n",
              "      <td id=\"T_d1679_row13_col1\" class=\"data row13 col1\" >52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_d1679_row14_col0\" class=\"data row14 col0\" >Ignore Seasonality Test</td>\n",
              "      <td id=\"T_d1679_row14_col1\" class=\"data row14 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_d1679_row15_col0\" class=\"data row15 col0\" >Seasonality Detection Algo</td>\n",
              "      <td id=\"T_d1679_row15_col1\" class=\"data row15 col1\" >user_defined</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_d1679_row16_col0\" class=\"data row16 col0\" >Max Period to Consider</td>\n",
              "      <td id=\"T_d1679_row16_col1\" class=\"data row16 col1\" >60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_d1679_row17_col0\" class=\"data row17 col0\" >Seasonal Period(s) Tested</td>\n",
              "      <td id=\"T_d1679_row17_col1\" class=\"data row17 col1\" >[52]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_d1679_row18_col0\" class=\"data row18 col0\" >Significant Seasonal Period(s)</td>\n",
              "      <td id=\"T_d1679_row18_col1\" class=\"data row18 col1\" >[1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_d1679_row19_col0\" class=\"data row19 col0\" >Significant Seasonal Period(s) without Harmonics</td>\n",
              "      <td id=\"T_d1679_row19_col1\" class=\"data row19 col1\" >[1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_d1679_row20_col0\" class=\"data row20 col0\" >Remove Harmonics</td>\n",
              "      <td id=\"T_d1679_row20_col1\" class=\"data row20 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_d1679_row21_col0\" class=\"data row21 col0\" >Harmonics Order Method</td>\n",
              "      <td id=\"T_d1679_row21_col1\" class=\"data row21 col1\" >harmonic_max</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_d1679_row22_col0\" class=\"data row22 col0\" >Num Seasonalities to Use</td>\n",
              "      <td id=\"T_d1679_row22_col1\" class=\"data row22 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_d1679_row23_col0\" class=\"data row23 col0\" >All Seasonalities to Use</td>\n",
              "      <td id=\"T_d1679_row23_col1\" class=\"data row23 col1\" >[1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "      <td id=\"T_d1679_row24_col0\" class=\"data row24 col0\" >Primary Seasonality</td>\n",
              "      <td id=\"T_d1679_row24_col1\" class=\"data row24 col1\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
              "      <td id=\"T_d1679_row25_col0\" class=\"data row25 col0\" >Seasonality Present</td>\n",
              "      <td id=\"T_d1679_row25_col1\" class=\"data row25 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
              "      <td id=\"T_d1679_row26_col0\" class=\"data row26 col0\" >Seasonality Type</td>\n",
              "      <td id=\"T_d1679_row26_col1\" class=\"data row26 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
              "      <td id=\"T_d1679_row27_col0\" class=\"data row27 col0\" >Target Strictly Positive</td>\n",
              "      <td id=\"T_d1679_row27_col1\" class=\"data row27 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
              "      <td id=\"T_d1679_row28_col0\" class=\"data row28 col0\" >Target White Noise</td>\n",
              "      <td id=\"T_d1679_row28_col1\" class=\"data row28 col1\" >Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
              "      <td id=\"T_d1679_row29_col0\" class=\"data row29 col0\" >Recommended d</td>\n",
              "      <td id=\"T_d1679_row29_col1\" class=\"data row29 col1\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
              "      <td id=\"T_d1679_row30_col0\" class=\"data row30 col0\" >Recommended Seasonal D</td>\n",
              "      <td id=\"T_d1679_row30_col1\" class=\"data row30 col1\" >0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
              "      <td id=\"T_d1679_row31_col0\" class=\"data row31 col0\" >Preprocess</td>\n",
              "      <td id=\"T_d1679_row31_col1\" class=\"data row31 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
              "      <td id=\"T_d1679_row32_col0\" class=\"data row32 col0\" >Numerical Imputation (Target)</td>\n",
              "      <td id=\"T_d1679_row32_col1\" class=\"data row32 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
              "      <td id=\"T_d1679_row33_col0\" class=\"data row33 col0\" >Transformation (Target)</td>\n",
              "      <td id=\"T_d1679_row33_col1\" class=\"data row33 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
              "      <td id=\"T_d1679_row34_col0\" class=\"data row34 col0\" >Scaling (Target)</td>\n",
              "      <td id=\"T_d1679_row34_col1\" class=\"data row34 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
              "      <td id=\"T_d1679_row35_col0\" class=\"data row35 col0\" >Feature Engineering (Target) - Reduced Regression</td>\n",
              "      <td id=\"T_d1679_row35_col1\" class=\"data row35 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
              "      <td id=\"T_d1679_row36_col0\" class=\"data row36 col0\" >Numerical Imputation (Exogenous)</td>\n",
              "      <td id=\"T_d1679_row36_col1\" class=\"data row36 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
              "      <td id=\"T_d1679_row37_col0\" class=\"data row37 col0\" >Transformation (Exogenous)</td>\n",
              "      <td id=\"T_d1679_row37_col1\" class=\"data row37 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
              "      <td id=\"T_d1679_row38_col0\" class=\"data row38 col0\" >Scaling (Exogenous)</td>\n",
              "      <td id=\"T_d1679_row38_col1\" class=\"data row38 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
              "      <td id=\"T_d1679_row39_col0\" class=\"data row39 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_d1679_row39_col1\" class=\"data row39 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
              "      <td id=\"T_d1679_row40_col0\" class=\"data row40 col0\" >Use GPU</td>\n",
              "      <td id=\"T_d1679_row40_col1\" class=\"data row40 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
              "      <td id=\"T_d1679_row41_col0\" class=\"data row41 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_d1679_row41_col1\" class=\"data row41 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
              "      <td id=\"T_d1679_row42_col0\" class=\"data row42 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_d1679_row42_col1\" class=\"data row42 col1\" >ts-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d1679_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
              "      <td id=\"T_d1679_row43_col0\" class=\"data row43 col0\" >USI</td>\n",
              "      <td id=\"T_d1679_row43_col1\" class=\"data row43 col1\" >37d1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x27223a5a940>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pycaret.time_series import setup\n",
        "s=setup(data=data, target='Sales', fh=10, fold_strategy='expanding', seasonal_period=52, fold=3,\n",
        "        numeric_imputation_target='mean', numeric_imputation_exogenous='mean' )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783,
          "referenced_widgets": [
            "b421add5066e416bae76b08b4a60214b",
            "35c27eb165e0417fb4c2c4b11140639f",
            "065a48ce0b7f43c88d7d5ab509a0b613",
            "91a0e1a6376a4964b54116437ad708a6",
            "57d03811b1aa48faa68e3c905609a5dd",
            "d3cc077537684d5c9c34b8ee4420acce",
            "96c582ac440d4d7ab757f398e5f62d05",
            "7411cf32da264c30ac2d9419216a93ea",
            "27ac3c0faa3841ec8538b87db482a331",
            "8b7439f31dd44dd788be282ab53d2b75",
            "1e2cab51bbc740f6bf45cb41abf1ee6c"
          ]
        },
        "id": "kU04Q9PLMpxF",
        "outputId": "2553ac07-1174-4554-9b05-16c426697bdf"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "  <thead>\n",
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              "      <th>Initiated</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>17:46:01</td>\n",
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              "    <tr>\n",
              "      <th>Status</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>Fitting 3 Folds</td>\n",
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              "    <tr>\n",
              "      <th>Estimator</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>Naive Forecaster</td>\n",
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            "text/plain": [
              "                                                                \n",
              "                                                                \n",
              "Initiated  . . . . . . . . . . . . . . . . . .          17:46:01\n",
              "Status     . . . . . . . . . . . . . . . . . .   Fitting 3 Folds\n",
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          "data": {
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              "<style type=\"text/css\">\n",
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              "  text-align: left;\n",
              "}\n",
              "#T_d7c2a_row0_col0, #T_d7c2a_row0_col5, #T_d7c2a_row1_col0, #T_d7c2a_row1_col1, #T_d7c2a_row1_col2, #T_d7c2a_row1_col3, #T_d7c2a_row1_col4, #T_d7c2a_row1_col5, #T_d7c2a_row1_col6, #T_d7c2a_row1_col7, #T_d7c2a_row2_col0, #T_d7c2a_row2_col1, #T_d7c2a_row2_col2, #T_d7c2a_row2_col3, #T_d7c2a_row2_col4, #T_d7c2a_row2_col6, #T_d7c2a_row2_col7, #T_d7c2a_row3_col0, #T_d7c2a_row3_col1, #T_d7c2a_row3_col2, #T_d7c2a_row3_col3, #T_d7c2a_row3_col4, #T_d7c2a_row3_col5, #T_d7c2a_row3_col6, #T_d7c2a_row3_col7, #T_d7c2a_row4_col0, #T_d7c2a_row4_col1, #T_d7c2a_row4_col2, #T_d7c2a_row4_col3, #T_d7c2a_row4_col4, #T_d7c2a_row4_col5, #T_d7c2a_row4_col6, #T_d7c2a_row4_col7, #T_d7c2a_row5_col0, #T_d7c2a_row5_col1, #T_d7c2a_row5_col2, #T_d7c2a_row5_col3, #T_d7c2a_row5_col4, #T_d7c2a_row5_col5, #T_d7c2a_row5_col6, #T_d7c2a_row5_col7, #T_d7c2a_row6_col0, #T_d7c2a_row6_col1, #T_d7c2a_row6_col2, #T_d7c2a_row6_col3, #T_d7c2a_row6_col4, #T_d7c2a_row6_col5, #T_d7c2a_row6_col6, #T_d7c2a_row6_col7, #T_d7c2a_row7_col0, #T_d7c2a_row7_col1, #T_d7c2a_row7_col2, #T_d7c2a_row7_col3, #T_d7c2a_row7_col4, #T_d7c2a_row7_col5, #T_d7c2a_row7_col6, #T_d7c2a_row7_col7, #T_d7c2a_row8_col0, #T_d7c2a_row8_col1, #T_d7c2a_row8_col2, #T_d7c2a_row8_col3, #T_d7c2a_row8_col4, #T_d7c2a_row8_col5, #T_d7c2a_row8_col6, #T_d7c2a_row8_col7, #T_d7c2a_row9_col0, #T_d7c2a_row9_col1, #T_d7c2a_row9_col2, #T_d7c2a_row9_col3, #T_d7c2a_row9_col4, #T_d7c2a_row9_col5, #T_d7c2a_row9_col6, #T_d7c2a_row9_col7, #T_d7c2a_row10_col0, #T_d7c2a_row10_col1, #T_d7c2a_row10_col2, #T_d7c2a_row10_col3, #T_d7c2a_row10_col4, #T_d7c2a_row10_col5, #T_d7c2a_row10_col6, #T_d7c2a_row10_col7, #T_d7c2a_row11_col0, #T_d7c2a_row11_col1, #T_d7c2a_row11_col2, #T_d7c2a_row11_col3, #T_d7c2a_row11_col4, #T_d7c2a_row11_col5, #T_d7c2a_row11_col6, #T_d7c2a_row11_col7, #T_d7c2a_row12_col0, #T_d7c2a_row12_col1, #T_d7c2a_row12_col2, #T_d7c2a_row12_col3, #T_d7c2a_row12_col4, #T_d7c2a_row12_col5, #T_d7c2a_row12_col6, #T_d7c2a_row12_col7, #T_d7c2a_row13_col0, #T_d7c2a_row13_col1, #T_d7c2a_row13_col2, #T_d7c2a_row13_col3, #T_d7c2a_row13_col4, #T_d7c2a_row13_col5, #T_d7c2a_row13_col6, #T_d7c2a_row13_col7, #T_d7c2a_row14_col0, #T_d7c2a_row14_col1, #T_d7c2a_row14_col2, #T_d7c2a_row14_col3, #T_d7c2a_row14_col4, #T_d7c2a_row14_col5, #T_d7c2a_row14_col6, #T_d7c2a_row14_col7, #T_d7c2a_row15_col0, #T_d7c2a_row15_col1, #T_d7c2a_row15_col2, #T_d7c2a_row15_col3, #T_d7c2a_row15_col4, #T_d7c2a_row15_col5, #T_d7c2a_row15_col6, #T_d7c2a_row15_col7, #T_d7c2a_row16_col0, #T_d7c2a_row16_col1, #T_d7c2a_row16_col2, #T_d7c2a_row16_col3, #T_d7c2a_row16_col4, #T_d7c2a_row16_col5, #T_d7c2a_row16_col6, #T_d7c2a_row16_col7, #T_d7c2a_row17_col0, #T_d7c2a_row17_col1, #T_d7c2a_row17_col2, #T_d7c2a_row17_col3, #T_d7c2a_row17_col4, #T_d7c2a_row17_col5, #T_d7c2a_row17_col6, #T_d7c2a_row17_col7, #T_d7c2a_row18_col0, #T_d7c2a_row18_col1, #T_d7c2a_row18_col2, #T_d7c2a_row18_col3, #T_d7c2a_row18_col4, #T_d7c2a_row18_col5, #T_d7c2a_row18_col6, #T_d7c2a_row18_col7, #T_d7c2a_row19_col0, #T_d7c2a_row19_col1, #T_d7c2a_row19_col2, #T_d7c2a_row19_col3, #T_d7c2a_row19_col4, #T_d7c2a_row19_col5, #T_d7c2a_row19_col6, #T_d7c2a_row19_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_d7c2a_row0_col1, #T_d7c2a_row0_col2, #T_d7c2a_row0_col3, #T_d7c2a_row0_col4, #T_d7c2a_row0_col6, #T_d7c2a_row0_col7, #T_d7c2a_row2_col5 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_d7c2a_row0_col8, #T_d7c2a_row1_col8, #T_d7c2a_row2_col8, #T_d7c2a_row3_col8, #T_d7c2a_row4_col8, #T_d7c2a_row5_col8, #T_d7c2a_row6_col8, #T_d7c2a_row7_col8, #T_d7c2a_row8_col8, #T_d7c2a_row9_col8, #T_d7c2a_row10_col8, #T_d7c2a_row11_col8, #T_d7c2a_row12_col8, #T_d7c2a_row13_col8, #T_d7c2a_row14_col8, #T_d7c2a_row15_col8, #T_d7c2a_row16_col8, #T_d7c2a_row17_col8, #T_d7c2a_row19_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_d7c2a_row18_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d7c2a\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d7c2a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_d7c2a_level0_col1\" class=\"col_heading level0 col1\" >MASE</th>\n",
              "      <th id=\"T_d7c2a_level0_col2\" class=\"col_heading level0 col2\" >RMSSE</th>\n",
              "      <th id=\"T_d7c2a_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
              "      <th id=\"T_d7c2a_level0_col4\" class=\"col_heading level0 col4\" >RMSE</th>\n",
              "      <th id=\"T_d7c2a_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "      <th id=\"T_d7c2a_level0_col6\" class=\"col_heading level0 col6\" >SMAPE</th>\n",
              "      <th id=\"T_d7c2a_level0_col7\" class=\"col_heading level0 col7\" >R2</th>\n",
              "      <th id=\"T_d7c2a_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row0\" class=\"row_heading level0 row0\" >rf_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row0_col0\" class=\"data row0 col0\" >Random Forest w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row0_col1\" class=\"data row0 col1\" >0.4108</td>\n",
              "      <td id=\"T_d7c2a_row0_col2\" class=\"data row0 col2\" >0.3065</td>\n",
              "      <td id=\"T_d7c2a_row0_col3\" class=\"data row0 col3\" >122.1357</td>\n",
              "      <td id=\"T_d7c2a_row0_col4\" class=\"data row0 col4\" >176.2149</td>\n",
              "      <td id=\"T_d7c2a_row0_col5\" class=\"data row0 col5\" >4.1357</td>\n",
              "      <td id=\"T_d7c2a_row0_col6\" class=\"data row0 col6\" >0.7786</td>\n",
              "      <td id=\"T_d7c2a_row0_col7\" class=\"data row0 col7\" >0.7431</td>\n",
              "      <td id=\"T_d7c2a_row0_col8\" class=\"data row0 col8\" >0.6800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row1\" class=\"row_heading level0 row1\" >et_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row1_col0\" class=\"data row1 col0\" >Extra Trees w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row1_col1\" class=\"data row1 col1\" >0.4619</td>\n",
              "      <td id=\"T_d7c2a_row1_col2\" class=\"data row1 col2\" >0.3356</td>\n",
              "      <td id=\"T_d7c2a_row1_col3\" class=\"data row1 col3\" >137.4712</td>\n",
              "      <td id=\"T_d7c2a_row1_col4\" class=\"data row1 col4\" >192.9768</td>\n",
              "      <td id=\"T_d7c2a_row1_col5\" class=\"data row1 col5\" >4.4931</td>\n",
              "      <td id=\"T_d7c2a_row1_col6\" class=\"data row1 col6\" >0.8685</td>\n",
              "      <td id=\"T_d7c2a_row1_col7\" class=\"data row1 col7\" >0.6686</td>\n",
              "      <td id=\"T_d7c2a_row1_col8\" class=\"data row1 col8\" >0.5933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row2\" class=\"row_heading level0 row2\" >gbr_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row2_col0\" class=\"data row2 col0\" >Gradient Boosting w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row2_col1\" class=\"data row2 col1\" >0.4683</td>\n",
              "      <td id=\"T_d7c2a_row2_col2\" class=\"data row2 col2\" >0.3642</td>\n",
              "      <td id=\"T_d7c2a_row2_col3\" class=\"data row2 col3\" >139.0119</td>\n",
              "      <td id=\"T_d7c2a_row2_col4\" class=\"data row2 col4\" >209.3683</td>\n",
              "      <td id=\"T_d7c2a_row2_col5\" class=\"data row2 col5\" >3.1891</td>\n",
              "      <td id=\"T_d7c2a_row2_col6\" class=\"data row2 col6\" >0.8431</td>\n",
              "      <td id=\"T_d7c2a_row2_col7\" class=\"data row2 col7\" >0.6677</td>\n",
              "      <td id=\"T_d7c2a_row2_col8\" class=\"data row2 col8\" >0.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row3\" class=\"row_heading level0 row3\" >dt_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row3_col0\" class=\"data row3 col0\" >Decision Tree w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row3_col1\" class=\"data row3 col1\" >0.6078</td>\n",
              "      <td id=\"T_d7c2a_row3_col2\" class=\"data row3 col2\" >0.5624</td>\n",
              "      <td id=\"T_d7c2a_row3_col3\" class=\"data row3 col3\" >179.8406</td>\n",
              "      <td id=\"T_d7c2a_row3_col4\" class=\"data row3 col4\" >323.6541</td>\n",
              "      <td id=\"T_d7c2a_row3_col5\" class=\"data row3 col5\" >3.2893</td>\n",
              "      <td id=\"T_d7c2a_row3_col6\" class=\"data row3 col6\" >0.8002</td>\n",
              "      <td id=\"T_d7c2a_row3_col7\" class=\"data row3 col7\" >0.1239</td>\n",
              "      <td id=\"T_d7c2a_row3_col8\" class=\"data row3 col8\" >0.3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row4\" class=\"row_heading level0 row4\" >huber_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row4_col0\" class=\"data row4 col0\" >Huber w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row4_col1\" class=\"data row4 col1\" >0.6575</td>\n",
              "      <td id=\"T_d7c2a_row4_col2\" class=\"data row4 col2\" >0.6119</td>\n",
              "      <td id=\"T_d7c2a_row4_col3\" class=\"data row4 col3\" >195.3429</td>\n",
              "      <td id=\"T_d7c2a_row4_col4\" class=\"data row4 col4\" >351.4730</td>\n",
              "      <td id=\"T_d7c2a_row4_col5\" class=\"data row4 col5\" >6.7387</td>\n",
              "      <td id=\"T_d7c2a_row4_col6\" class=\"data row4 col6\" >0.9661</td>\n",
              "      <td id=\"T_d7c2a_row4_col7\" class=\"data row4 col7\" >-0.0270</td>\n",
              "      <td id=\"T_d7c2a_row4_col8\" class=\"data row4 col8\" >0.3467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row5\" class=\"row_heading level0 row5\" >ada_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row5_col0\" class=\"data row5 col0\" >AdaBoost w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row5_col1\" class=\"data row5 col1\" >0.6808</td>\n",
              "      <td id=\"T_d7c2a_row5_col2\" class=\"data row5 col2\" >0.4566</td>\n",
              "      <td id=\"T_d7c2a_row5_col3\" class=\"data row5 col3\" >202.2588</td>\n",
              "      <td id=\"T_d7c2a_row5_col4\" class=\"data row5 col4\" >262.5323</td>\n",
              "      <td id=\"T_d7c2a_row5_col5\" class=\"data row5 col5\" >10.4186</td>\n",
              "      <td id=\"T_d7c2a_row5_col6\" class=\"data row5 col6\" >1.0301</td>\n",
              "      <td id=\"T_d7c2a_row5_col7\" class=\"data row5 col7\" >0.4695</td>\n",
              "      <td id=\"T_d7c2a_row5_col8\" class=\"data row5 col8\" >0.4567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row6\" class=\"row_heading level0 row6\" >auto_arima</th>\n",
              "      <td id=\"T_d7c2a_row6_col0\" class=\"data row6 col0\" >Auto ARIMA</td>\n",
              "      <td id=\"T_d7c2a_row6_col1\" class=\"data row6 col1\" >0.7430</td>\n",
              "      <td id=\"T_d7c2a_row6_col2\" class=\"data row6 col2\" >0.5837</td>\n",
              "      <td id=\"T_d7c2a_row6_col3\" class=\"data row6 col3\" >220.9147</td>\n",
              "      <td id=\"T_d7c2a_row6_col4\" class=\"data row6 col4\" >335.1915</td>\n",
              "      <td id=\"T_d7c2a_row6_col5\" class=\"data row6 col5\" >14.7460</td>\n",
              "      <td id=\"T_d7c2a_row6_col6\" class=\"data row6 col6\" >1.0500</td>\n",
              "      <td id=\"T_d7c2a_row6_col7\" class=\"data row6 col7\" >0.0871</td>\n",
              "      <td id=\"T_d7c2a_row6_col8\" class=\"data row6 col8\" >1.8267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row7\" class=\"row_heading level0 row7\" >arima</th>\n",
              "      <td id=\"T_d7c2a_row7_col0\" class=\"data row7 col0\" >ARIMA</td>\n",
              "      <td id=\"T_d7c2a_row7_col1\" class=\"data row7 col1\" >0.7436</td>\n",
              "      <td id=\"T_d7c2a_row7_col2\" class=\"data row7 col2\" >0.5853</td>\n",
              "      <td id=\"T_d7c2a_row7_col3\" class=\"data row7 col3\" >221.0970</td>\n",
              "      <td id=\"T_d7c2a_row7_col4\" class=\"data row7 col4\" >336.1134</td>\n",
              "      <td id=\"T_d7c2a_row7_col5\" class=\"data row7 col5\" >14.8302</td>\n",
              "      <td id=\"T_d7c2a_row7_col6\" class=\"data row7 col6\" >1.0487</td>\n",
              "      <td id=\"T_d7c2a_row7_col7\" class=\"data row7 col7\" >0.0823</td>\n",
              "      <td id=\"T_d7c2a_row7_col8\" class=\"data row7 col8\" >3.5367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row8\" class=\"row_heading level0 row8\" >lightgbm_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row8_col0\" class=\"data row8 col0\" >Light Gradient Boosting w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row8_col1\" class=\"data row8 col1\" >0.7736</td>\n",
              "      <td id=\"T_d7c2a_row8_col2\" class=\"data row8 col2\" >0.5811</td>\n",
              "      <td id=\"T_d7c2a_row8_col3\" class=\"data row8 col3\" >229.7981</td>\n",
              "      <td id=\"T_d7c2a_row8_col4\" class=\"data row8 col4\" >334.0139</td>\n",
              "      <td id=\"T_d7c2a_row8_col5\" class=\"data row8 col5\" >4.9794</td>\n",
              "      <td id=\"T_d7c2a_row8_col6\" class=\"data row8 col6\" >1.0427</td>\n",
              "      <td id=\"T_d7c2a_row8_col7\" class=\"data row8 col7\" >0.1347</td>\n",
              "      <td id=\"T_d7c2a_row8_col8\" class=\"data row8 col8\" >0.5167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row9\" class=\"row_heading level0 row9\" >lr_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row9_col0\" class=\"data row9 col0\" >Linear w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row9_col1\" class=\"data row9 col1\" >0.8069</td>\n",
              "      <td id=\"T_d7c2a_row9_col2\" class=\"data row9 col2\" >0.5970</td>\n",
              "      <td id=\"T_d7c2a_row9_col3\" class=\"data row9 col3\" >240.0371</td>\n",
              "      <td id=\"T_d7c2a_row9_col4\" class=\"data row9 col4\" >342.8434</td>\n",
              "      <td id=\"T_d7c2a_row9_col5\" class=\"data row9 col5\" >17.3883</td>\n",
              "      <td id=\"T_d7c2a_row9_col6\" class=\"data row9 col6\" >1.0919</td>\n",
              "      <td id=\"T_d7c2a_row9_col7\" class=\"data row9 col7\" >0.0322</td>\n",
              "      <td id=\"T_d7c2a_row9_col8\" class=\"data row9 col8\" >0.7067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row10\" class=\"row_heading level0 row10\" >llar_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row10_col0\" class=\"data row10 col0\" >Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row10_col1\" class=\"data row10 col1\" >0.8075</td>\n",
              "      <td id=\"T_d7c2a_row10_col2\" class=\"data row10 col2\" >0.5959</td>\n",
              "      <td id=\"T_d7c2a_row10_col3\" class=\"data row10 col3\" >240.1900</td>\n",
              "      <td id=\"T_d7c2a_row10_col4\" class=\"data row10 col4\" >342.2314</td>\n",
              "      <td id=\"T_d7c2a_row10_col5\" class=\"data row10 col5\" >17.0916</td>\n",
              "      <td id=\"T_d7c2a_row10_col6\" class=\"data row10 col6\" >1.0952</td>\n",
              "      <td id=\"T_d7c2a_row10_col7\" class=\"data row10 col7\" >0.0375</td>\n",
              "      <td id=\"T_d7c2a_row10_col8\" class=\"data row10 col8\" >0.3900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row11\" class=\"row_heading level0 row11\" >lasso_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row11_col0\" class=\"data row11 col0\" >Lasso w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row11_col1\" class=\"data row11 col1\" >0.8075</td>\n",
              "      <td id=\"T_d7c2a_row11_col2\" class=\"data row11 col2\" >0.5959</td>\n",
              "      <td id=\"T_d7c2a_row11_col3\" class=\"data row11 col3\" >240.1900</td>\n",
              "      <td id=\"T_d7c2a_row11_col4\" class=\"data row11 col4\" >342.2314</td>\n",
              "      <td id=\"T_d7c2a_row11_col5\" class=\"data row11 col5\" >17.0916</td>\n",
              "      <td id=\"T_d7c2a_row11_col6\" class=\"data row11 col6\" >1.0952</td>\n",
              "      <td id=\"T_d7c2a_row11_col7\" class=\"data row11 col7\" >0.0375</td>\n",
              "      <td id=\"T_d7c2a_row11_col8\" class=\"data row11 col8\" >0.3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row12\" class=\"row_heading level0 row12\" >ridge_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row12_col0\" class=\"data row12 col0\" >Ridge w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row12_col1\" class=\"data row12 col1\" >0.8076</td>\n",
              "      <td id=\"T_d7c2a_row12_col2\" class=\"data row12 col2\" >0.5949</td>\n",
              "      <td id=\"T_d7c2a_row12_col3\" class=\"data row12 col3\" >240.2293</td>\n",
              "      <td id=\"T_d7c2a_row12_col4\" class=\"data row12 col4\" >341.6788</td>\n",
              "      <td id=\"T_d7c2a_row12_col5\" class=\"data row12 col5\" >16.8334</td>\n",
              "      <td id=\"T_d7c2a_row12_col6\" class=\"data row12 col6\" >1.0972</td>\n",
              "      <td id=\"T_d7c2a_row12_col7\" class=\"data row12 col7\" >0.0424</td>\n",
              "      <td id=\"T_d7c2a_row12_col8\" class=\"data row12 col8\" >0.6400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row13\" class=\"row_heading level0 row13\" >naive</th>\n",
              "      <td id=\"T_d7c2a_row13_col0\" class=\"data row13 col0\" >Naive Forecaster</td>\n",
              "      <td id=\"T_d7c2a_row13_col1\" class=\"data row13 col1\" >0.8077</td>\n",
              "      <td id=\"T_d7c2a_row13_col2\" class=\"data row13 col2\" >0.7026</td>\n",
              "      <td id=\"T_d7c2a_row13_col3\" class=\"data row13 col3\" >239.6460</td>\n",
              "      <td id=\"T_d7c2a_row13_col4\" class=\"data row13 col4\" >403.9363</td>\n",
              "      <td id=\"T_d7c2a_row13_col5\" class=\"data row13 col5\" >12.4919</td>\n",
              "      <td id=\"T_d7c2a_row13_col6\" class=\"data row13 col6\" >1.0873</td>\n",
              "      <td id=\"T_d7c2a_row13_col7\" class=\"data row13 col7\" >-0.1909</td>\n",
              "      <td id=\"T_d7c2a_row13_col8\" class=\"data row13 col8\" >4.2333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row14\" class=\"row_heading level0 row14\" >grand_means</th>\n",
              "      <td id=\"T_d7c2a_row14_col0\" class=\"data row14 col0\" >Grand Means Forecaster</td>\n",
              "      <td id=\"T_d7c2a_row14_col1\" class=\"data row14 col1\" >0.8169</td>\n",
              "      <td id=\"T_d7c2a_row14_col2\" class=\"data row14 col2\" >0.6493</td>\n",
              "      <td id=\"T_d7c2a_row14_col3\" class=\"data row14 col3\" >242.2749</td>\n",
              "      <td id=\"T_d7c2a_row14_col4\" class=\"data row14 col4\" >373.2708</td>\n",
              "      <td id=\"T_d7c2a_row14_col5\" class=\"data row14 col5\" >12.1000</td>\n",
              "      <td id=\"T_d7c2a_row14_col6\" class=\"data row14 col6\" >1.1078</td>\n",
              "      <td id=\"T_d7c2a_row14_col7\" class=\"data row14 col7\" >-0.0129</td>\n",
              "      <td id=\"T_d7c2a_row14_col8\" class=\"data row14 col8\" >3.3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row15\" class=\"row_heading level0 row15\" >en_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row15_col0\" class=\"data row15 col0\" >Elastic Net w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row15_col1\" class=\"data row15 col1\" >0.8249</td>\n",
              "      <td id=\"T_d7c2a_row15_col2\" class=\"data row15 col2\" >0.5920</td>\n",
              "      <td id=\"T_d7c2a_row15_col3\" class=\"data row15 col3\" >245.2366</td>\n",
              "      <td id=\"T_d7c2a_row15_col4\" class=\"data row15 col4\" >339.9657</td>\n",
              "      <td id=\"T_d7c2a_row15_col5\" class=\"data row15 col5\" >13.1294</td>\n",
              "      <td id=\"T_d7c2a_row15_col6\" class=\"data row15 col6\" >1.1277</td>\n",
              "      <td id=\"T_d7c2a_row15_col7\" class=\"data row15 col7\" >0.0749</td>\n",
              "      <td id=\"T_d7c2a_row15_col8\" class=\"data row15 col8\" >0.6433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row16\" class=\"row_heading level0 row16\" >br_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row16_col0\" class=\"data row16 col0\" >Bayesian Ridge w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row16_col1\" class=\"data row16 col1\" >0.8755</td>\n",
              "      <td id=\"T_d7c2a_row16_col2\" class=\"data row16 col2\" >0.6414</td>\n",
              "      <td id=\"T_d7c2a_row16_col3\" class=\"data row16 col3\" >260.2714</td>\n",
              "      <td id=\"T_d7c2a_row16_col4\" class=\"data row16 col4\" >368.4471</td>\n",
              "      <td id=\"T_d7c2a_row16_col5\" class=\"data row16 col5\" >14.3082</td>\n",
              "      <td id=\"T_d7c2a_row16_col6\" class=\"data row16 col6\" >1.1405</td>\n",
              "      <td id=\"T_d7c2a_row16_col7\" class=\"data row16 col7\" >-0.0919</td>\n",
              "      <td id=\"T_d7c2a_row16_col8\" class=\"data row16 col8\" >0.4733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row17\" class=\"row_heading level0 row17\" >omp_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row17_col0\" class=\"data row17 col0\" >Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row17_col1\" class=\"data row17 col1\" >0.8976</td>\n",
              "      <td id=\"T_d7c2a_row17_col2\" class=\"data row17 col2\" >0.6595</td>\n",
              "      <td id=\"T_d7c2a_row17_col3\" class=\"data row17 col3\" >266.3438</td>\n",
              "      <td id=\"T_d7c2a_row17_col4\" class=\"data row17 col4\" >379.1297</td>\n",
              "      <td id=\"T_d7c2a_row17_col5\" class=\"data row17 col5\" >15.0366</td>\n",
              "      <td id=\"T_d7c2a_row17_col6\" class=\"data row17 col6\" >1.1518</td>\n",
              "      <td id=\"T_d7c2a_row17_col7\" class=\"data row17 col7\" >-0.0479</td>\n",
              "      <td id=\"T_d7c2a_row17_col8\" class=\"data row17 col8\" >0.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row18\" class=\"row_heading level0 row18\" >croston</th>\n",
              "      <td id=\"T_d7c2a_row18_col0\" class=\"data row18 col0\" >Croston</td>\n",
              "      <td id=\"T_d7c2a_row18_col1\" class=\"data row18 col1\" >1.0015</td>\n",
              "      <td id=\"T_d7c2a_row18_col2\" class=\"data row18 col2\" >0.6790</td>\n",
              "      <td id=\"T_d7c2a_row18_col3\" class=\"data row18 col3\" >296.9367</td>\n",
              "      <td id=\"T_d7c2a_row18_col4\" class=\"data row18 col4\" >390.4800</td>\n",
              "      <td id=\"T_d7c2a_row18_col5\" class=\"data row18 col5\" >18.3159</td>\n",
              "      <td id=\"T_d7c2a_row18_col6\" class=\"data row18 col6\" >1.1797</td>\n",
              "      <td id=\"T_d7c2a_row18_col7\" class=\"data row18 col7\" >-0.1451</td>\n",
              "      <td id=\"T_d7c2a_row18_col8\" class=\"data row18 col8\" >0.1667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d7c2a_level0_row19\" class=\"row_heading level0 row19\" >knn_cds_dt</th>\n",
              "      <td id=\"T_d7c2a_row19_col0\" class=\"data row19 col0\" >K Neighbors w/ Cond. Deseasonalize & Detrending</td>\n",
              "      <td id=\"T_d7c2a_row19_col1\" class=\"data row19 col1\" >1.1134</td>\n",
              "      <td id=\"T_d7c2a_row19_col2\" class=\"data row19 col2\" >0.8021</td>\n",
              "      <td id=\"T_d7c2a_row19_col3\" class=\"data row19 col3\" >330.5479</td>\n",
              "      <td id=\"T_d7c2a_row19_col4\" class=\"data row19 col4\" >461.1456</td>\n",
              "      <td id=\"T_d7c2a_row19_col5\" class=\"data row19 col5\" >15.7211</td>\n",
              "      <td id=\"T_d7c2a_row19_col6\" class=\"data row19 col6\" >1.2117</td>\n",
              "      <td id=\"T_d7c2a_row19_col7\" class=\"data row19 col7\" >-0.5810</td>\n",
              "      <td id=\"T_d7c2a_row19_col8\" class=\"data row19 col8\" >0.4400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x27223e823a0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-6c84df25-8980-45e5-b222-0b3a2c367390 {color: black;background-color: white;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 pre{padding: 0;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-toggleable {background-color: white;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-estimator:hover {background-color: #d4ebff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-item {z-index: 1;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-parallel-item:only-child::after {width: 0;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-6c84df25-8980-45e5-b222-0b3a2c367390 div.sk-text-repr-fallback {display: none;}</style><div id='sk-6c84df25-8980-45e5-b222-0b3a2c367390' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]},\n",
              "                                                   n_jobs=1)],\n",
              "                    regressor=RandomForestRegressor(n_jobs=-1, random_state=7886),\n",
              "                    window_length=1)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class='sk-label-container'><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('0e0e0553-9a29-47e6-a6c9-4de054f8f5e8') type=\"checkbox\" ><label for=UUID('0e0e0553-9a29-47e6-a6c9-4de054f8f5e8') class='sk-toggleable__label sk-toggleable__label-arrow'>BaseCdsDtForecaster</label><div class=\"sk-toggleable__content\"><pre>BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={&#x27;lag&#x27;: [1]},\n",
              "                                                   n_jobs=1)],\n",
              "                    regressor=RandomForestRegressor(n_jobs=-1, random_state=7886),\n",
              "                    window_length=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('0784a23c-b66f-4483-bb2e-415b0f13f297') type=\"checkbox\" ><label for=UUID('0784a23c-b66f-4483-bb2e-415b0f13f297') class='sk-toggleable__label sk-toggleable__label-arrow'>RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=7886)</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [1]},\n",
              "                                                   n_jobs=1)],\n",
              "                    regressor=RandomForestRegressor(n_jobs=-1, random_state=7886),\n",
              "                    window_length=1)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pycaret.time_series import compare_models\n",
        "best_model = compare_models()\n",
        "best_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "After evaluating various models, it turns out that Random Forest gets the lowest MASE, MAE, and highest R2 score. Though ARIMA and Seasonal Naive Forecaster are two popular models in terms of time-series forecasting, we can neither choose one of them because of their poor performances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447,
          "referenced_widgets": [
            "fed76f0c868346e3bfe6a50c15da7a1e",
            "8f122c563740422e94ddd1efa28163b9",
            "8e0394f5e75e4548972ad7db2b019121",
            "bd38abb19bcd415183c5dd2741339b8c",
            "f77dabc04eab4560ae522f2dcb113a44",
            "f0509efedbbb4f57ae2dbeb90d89424a",
            "dfd7267bdc8f4539bea78fdebfde22ef",
            "fdf7136a9c2241eca2040bc4bfe47a29",
            "e6f0e390411447b0911e5ad2f49d97d7",
            "442d21fddd3243e5b179f39081e3221e",
            "5f5dfa28c2fe45bfb94b8d2c270b5620",
            "6cc6ceebfe214dce8e084ee21d62fe9e",
            "9c2e48fc37094c859b89f9d827d9ac18",
            "1f09dd4850c741ef94a9bad12d98c07e",
            "a28c6b740f364aa9aece72051e4bef46",
            "eb3f312546e148a5af99b81fe87552af",
            "60520cf58a964359af1cff4f2febea08",
            "2df80cbfbb0b492b993167a34875884e",
            "73270c7456464d11869c6767021bde17",
            "8f688a317f124a50ace0183b9e567c61",
            "c0c303a2f32d4360a2a49be4142bae9b",
            "399b1abca260408bacad9151cb04bff2"
          ]
        },
        "id": "W4Q-S5fdT-UE",
        "outputId": "5facb213-815a-4619-8c3b-1c616b8d64ab"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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",
              "        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></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Initiated</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>17:47:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Status</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>Fitting 3 Folds</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estimator</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>RandomForestRegressor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                     \n",
              "                                                                     \n",
              "Initiated  . . . . . . . . . . . . . . . . . .               17:47:26\n",
              "Status     . . . . . . . . . . . . . . . . . .        Fitting 3 Folds\n",
              "Estimator  . . . . . . . . . . . . . . . . . .  RandomForestRegressor"
            ]
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              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7b9f5\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_7b9f5_level0_col0\" class=\"col_heading level0 col0\" >cutoff</th>\n",
              "      <th id=\"T_7b9f5_level0_col1\" class=\"col_heading level0 col1\" >MASE</th>\n",
              "      <th id=\"T_7b9f5_level0_col2\" class=\"col_heading level0 col2\" >RMSSE</th>\n",
              "      <th id=\"T_7b9f5_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
              "      <th id=\"T_7b9f5_level0_col4\" class=\"col_heading level0 col4\" >RMSE</th>\n",
              "      <th id=\"T_7b9f5_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "      <th id=\"T_7b9f5_level0_col6\" class=\"col_heading level0 col6\" >SMAPE</th>\n",
              "      <th id=\"T_7b9f5_level0_col7\" class=\"col_heading level0 col7\" >R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_7b9f5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_7b9f5_row0_col0\" class=\"data row0 col0\" >2017-03-13/2017-03-19</td>\n",
              "      <td id=\"T_7b9f5_row0_col1\" class=\"data row0 col1\" >0.3460</td>\n",
              "      <td id=\"T_7b9f5_row0_col2\" class=\"data row0 col2\" >0.2199</td>\n",
              "      <td id=\"T_7b9f5_row0_col3\" class=\"data row0 col3\" >101.1306</td>\n",
              "      <td id=\"T_7b9f5_row0_col4\" class=\"data row0 col4\" >126.3417</td>\n",
              "      <td id=\"T_7b9f5_row0_col5\" class=\"data row0 col5\" >1.8854</td>\n",
              "      <td id=\"T_7b9f5_row0_col6\" class=\"data row0 col6\" >0.7414</td>\n",
              "      <td id=\"T_7b9f5_row0_col7\" class=\"data row0 col7\" >0.9111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7b9f5_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_7b9f5_row1_col0\" class=\"data row1 col0\" >2017-05-22/2017-05-28</td>\n",
              "      <td id=\"T_7b9f5_row1_col1\" class=\"data row1 col1\" >0.3951</td>\n",
              "      <td id=\"T_7b9f5_row1_col2\" class=\"data row1 col2\" >0.2623</td>\n",
              "      <td id=\"T_7b9f5_row1_col3\" class=\"data row1 col3\" >118.3376</td>\n",
              "      <td id=\"T_7b9f5_row1_col4\" class=\"data row1 col4\" >152.1654</td>\n",
              "      <td id=\"T_7b9f5_row1_col5\" class=\"data row1 col5\" >7.8654</td>\n",
              "      <td id=\"T_7b9f5_row1_col6\" class=\"data row1 col6\" >0.7678</td>\n",
              "      <td id=\"T_7b9f5_row1_col7\" class=\"data row1 col7\" >0.6579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7b9f5_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_7b9f5_row2_col0\" class=\"data row2 col0\" >2017-07-31/2017-08-06</td>\n",
              "      <td id=\"T_7b9f5_row2_col1\" class=\"data row2 col1\" >0.4915</td>\n",
              "      <td id=\"T_7b9f5_row2_col2\" class=\"data row2 col2\" >0.4373</td>\n",
              "      <td id=\"T_7b9f5_row2_col3\" class=\"data row2 col3\" >146.9390</td>\n",
              "      <td id=\"T_7b9f5_row2_col4\" class=\"data row2 col4\" >250.1375</td>\n",
              "      <td id=\"T_7b9f5_row2_col5\" class=\"data row2 col5\" >2.6562</td>\n",
              "      <td id=\"T_7b9f5_row2_col6\" class=\"data row2 col6\" >0.8268</td>\n",
              "      <td id=\"T_7b9f5_row2_col7\" class=\"data row2 col7\" >0.6603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7b9f5_level0_row3\" class=\"row_heading level0 row3\" >Mean</th>\n",
              "      <td id=\"T_7b9f5_row3_col0\" class=\"data row3 col0\" >NaT</td>\n",
              "      <td id=\"T_7b9f5_row3_col1\" class=\"data row3 col1\" >0.4108</td>\n",
              "      <td id=\"T_7b9f5_row3_col2\" class=\"data row3 col2\" >0.3065</td>\n",
              "      <td id=\"T_7b9f5_row3_col3\" class=\"data row3 col3\" >122.1357</td>\n",
              "      <td id=\"T_7b9f5_row3_col4\" class=\"data row3 col4\" >176.2149</td>\n",
              "      <td id=\"T_7b9f5_row3_col5\" class=\"data row3 col5\" >4.1357</td>\n",
              "      <td id=\"T_7b9f5_row3_col6\" class=\"data row3 col6\" >0.7786</td>\n",
              "      <td id=\"T_7b9f5_row3_col7\" class=\"data row3 col7\" >0.7431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7b9f5_level0_row4\" class=\"row_heading level0 row4\" >SD</th>\n",
              "      <td id=\"T_7b9f5_row4_col0\" class=\"data row4 col0\" >NaT</td>\n",
              "      <td id=\"T_7b9f5_row4_col1\" class=\"data row4 col1\" >0.0604</td>\n",
              "      <td id=\"T_7b9f5_row4_col2\" class=\"data row4 col2\" >0.0941</td>\n",
              "      <td id=\"T_7b9f5_row4_col3\" class=\"data row4 col3\" >18.8931</td>\n",
              "      <td id=\"T_7b9f5_row4_col4\" class=\"data row4 col4\" >53.3237</td>\n",
              "      <td id=\"T_7b9f5_row4_col5\" class=\"data row4 col5\" >2.6560</td>\n",
              "      <td id=\"T_7b9f5_row4_col6\" class=\"data row4 col6\" >0.0357</td>\n",
              "      <td id=\"T_7b9f5_row4_col7\" class=\"data row4 col7\" >0.1188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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              "<style type=\"text/css\">\n",
              "#T_e0cb8_row3_col0, #T_e0cb8_row3_col1, #T_e0cb8_row3_col2, #T_e0cb8_row3_col3, #T_e0cb8_row3_col4, #T_e0cb8_row3_col5, #T_e0cb8_row3_col6, #T_e0cb8_row3_col7 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e0cb8\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e0cb8_level0_col0\" class=\"col_heading level0 col0\" >cutoff</th>\n",
              "      <th id=\"T_e0cb8_level0_col1\" class=\"col_heading level0 col1\" >MASE</th>\n",
              "      <th id=\"T_e0cb8_level0_col2\" class=\"col_heading level0 col2\" >RMSSE</th>\n",
              "      <th id=\"T_e0cb8_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
              "      <th id=\"T_e0cb8_level0_col4\" class=\"col_heading level0 col4\" >RMSE</th>\n",
              "      <th id=\"T_e0cb8_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "      <th id=\"T_e0cb8_level0_col6\" class=\"col_heading level0 col6\" >SMAPE</th>\n",
              "      <th id=\"T_e0cb8_level0_col7\" class=\"col_heading level0 col7\" >R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e0cb8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_e0cb8_row0_col0\" class=\"data row0 col0\" >2017-03-13/2017-03-19</td>\n",
              "      <td id=\"T_e0cb8_row0_col1\" class=\"data row0 col1\" >0.3460</td>\n",
              "      <td id=\"T_e0cb8_row0_col2\" class=\"data row0 col2\" >0.2199</td>\n",
              "      <td id=\"T_e0cb8_row0_col3\" class=\"data row0 col3\" >101.1306</td>\n",
              "      <td id=\"T_e0cb8_row0_col4\" class=\"data row0 col4\" >126.3417</td>\n",
              "      <td id=\"T_e0cb8_row0_col5\" class=\"data row0 col5\" >1.8854</td>\n",
              "      <td id=\"T_e0cb8_row0_col6\" class=\"data row0 col6\" >0.7414</td>\n",
              "      <td id=\"T_e0cb8_row0_col7\" class=\"data row0 col7\" >0.9111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e0cb8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_e0cb8_row1_col0\" class=\"data row1 col0\" >2017-05-22/2017-05-28</td>\n",
              "      <td id=\"T_e0cb8_row1_col1\" class=\"data row1 col1\" >0.3951</td>\n",
              "      <td id=\"T_e0cb8_row1_col2\" class=\"data row1 col2\" >0.2623</td>\n",
              "      <td id=\"T_e0cb8_row1_col3\" class=\"data row1 col3\" >118.3376</td>\n",
              "      <td id=\"T_e0cb8_row1_col4\" class=\"data row1 col4\" >152.1654</td>\n",
              "      <td id=\"T_e0cb8_row1_col5\" class=\"data row1 col5\" >7.8654</td>\n",
              "      <td id=\"T_e0cb8_row1_col6\" class=\"data row1 col6\" >0.7678</td>\n",
              "      <td id=\"T_e0cb8_row1_col7\" class=\"data row1 col7\" >0.6579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e0cb8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_e0cb8_row2_col0\" class=\"data row2 col0\" >2017-07-31/2017-08-06</td>\n",
              "      <td id=\"T_e0cb8_row2_col1\" class=\"data row2 col1\" >0.4915</td>\n",
              "      <td id=\"T_e0cb8_row2_col2\" class=\"data row2 col2\" >0.4373</td>\n",
              "      <td id=\"T_e0cb8_row2_col3\" class=\"data row2 col3\" >146.9390</td>\n",
              "      <td id=\"T_e0cb8_row2_col4\" class=\"data row2 col4\" >250.1375</td>\n",
              "      <td id=\"T_e0cb8_row2_col5\" class=\"data row2 col5\" >2.6562</td>\n",
              "      <td id=\"T_e0cb8_row2_col6\" class=\"data row2 col6\" >0.8268</td>\n",
              "      <td id=\"T_e0cb8_row2_col7\" class=\"data row2 col7\" >0.6603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e0cb8_level0_row3\" class=\"row_heading level0 row3\" >Mean</th>\n",
              "      <td id=\"T_e0cb8_row3_col0\" class=\"data row3 col0\" >NaT</td>\n",
              "      <td id=\"T_e0cb8_row3_col1\" class=\"data row3 col1\" >0.4108</td>\n",
              "      <td id=\"T_e0cb8_row3_col2\" class=\"data row3 col2\" >0.3065</td>\n",
              "      <td id=\"T_e0cb8_row3_col3\" class=\"data row3 col3\" >122.1357</td>\n",
              "      <td id=\"T_e0cb8_row3_col4\" class=\"data row3 col4\" >176.2149</td>\n",
              "      <td id=\"T_e0cb8_row3_col5\" class=\"data row3 col5\" >4.1357</td>\n",
              "      <td id=\"T_e0cb8_row3_col6\" class=\"data row3 col6\" >0.7786</td>\n",
              "      <td id=\"T_e0cb8_row3_col7\" class=\"data row3 col7\" >0.7431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e0cb8_level0_row4\" class=\"row_heading level0 row4\" >SD</th>\n",
              "      <td id=\"T_e0cb8_row4_col0\" class=\"data row4 col0\" >NaT</td>\n",
              "      <td id=\"T_e0cb8_row4_col1\" class=\"data row4 col1\" >0.0604</td>\n",
              "      <td id=\"T_e0cb8_row4_col2\" class=\"data row4 col2\" >0.0941</td>\n",
              "      <td id=\"T_e0cb8_row4_col3\" class=\"data row4 col3\" >18.8931</td>\n",
              "      <td id=\"T_e0cb8_row4_col4\" class=\"data row4 col4\" >53.3237</td>\n",
              "      <td id=\"T_e0cb8_row4_col5\" class=\"data row4 col5\" >2.6560</td>\n",
              "      <td id=\"T_e0cb8_row4_col6\" class=\"data row4 col6\" >0.0357</td>\n",
              "      <td id=\"T_e0cb8_row4_col7\" class=\"data row4 col7\" >0.1188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
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          "data": {
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              "<style type=\"text/css\">\n",
              "#T_81dc1_row3_col0, #T_81dc1_row3_col1, #T_81dc1_row3_col2, #T_81dc1_row3_col3, #T_81dc1_row3_col4, #T_81dc1_row3_col5, #T_81dc1_row3_col6, #T_81dc1_row3_col7 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_81dc1\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_81dc1_level0_col0\" class=\"col_heading level0 col0\" >cutoff</th>\n",
              "      <th id=\"T_81dc1_level0_col1\" class=\"col_heading level0 col1\" >MASE</th>\n",
              "      <th id=\"T_81dc1_level0_col2\" class=\"col_heading level0 col2\" >RMSSE</th>\n",
              "      <th id=\"T_81dc1_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
              "      <th id=\"T_81dc1_level0_col4\" class=\"col_heading level0 col4\" >RMSE</th>\n",
              "      <th id=\"T_81dc1_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "      <th id=\"T_81dc1_level0_col6\" class=\"col_heading level0 col6\" >SMAPE</th>\n",
              "      <th id=\"T_81dc1_level0_col7\" class=\"col_heading level0 col7\" >R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_81dc1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_81dc1_row0_col0\" class=\"data row0 col0\" >2017-03-13/2017-03-19</td>\n",
              "      <td id=\"T_81dc1_row0_col1\" class=\"data row0 col1\" >0.6318</td>\n",
              "      <td id=\"T_81dc1_row0_col2\" class=\"data row0 col2\" >0.3646</td>\n",
              "      <td id=\"T_81dc1_row0_col3\" class=\"data row0 col3\" >184.6616</td>\n",
              "      <td id=\"T_81dc1_row0_col4\" class=\"data row0 col4\" >209.4946</td>\n",
              "      <td id=\"T_81dc1_row0_col5\" class=\"data row0 col5\" >7.6144</td>\n",
              "      <td id=\"T_81dc1_row0_col6\" class=\"data row0 col6\" >0.9930</td>\n",
              "      <td id=\"T_81dc1_row0_col7\" class=\"data row0 col7\" >0.7556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_81dc1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_81dc1_row1_col0\" class=\"data row1 col0\" >2017-05-22/2017-05-28</td>\n",
              "      <td id=\"T_81dc1_row1_col1\" class=\"data row1 col1\" >0.9131</td>\n",
              "      <td id=\"T_81dc1_row1_col2\" class=\"data row1 col2\" >0.4871</td>\n",
              "      <td id=\"T_81dc1_row1_col3\" class=\"data row1 col3\" >273.5135</td>\n",
              "      <td id=\"T_81dc1_row1_col4\" class=\"data row1 col4\" >282.5999</td>\n",
              "      <td id=\"T_81dc1_row1_col5\" class=\"data row1 col5\" >29.1935</td>\n",
              "      <td id=\"T_81dc1_row1_col6\" class=\"data row1 col6\" >1.1850</td>\n",
              "      <td id=\"T_81dc1_row1_col7\" class=\"data row1 col7\" >-0.1800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_81dc1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_81dc1_row2_col0\" class=\"data row2 col0\" >2017-07-31/2017-08-06</td>\n",
              "      <td id=\"T_81dc1_row2_col1\" class=\"data row2 col1\" >0.9951</td>\n",
              "      <td id=\"T_81dc1_row2_col2\" class=\"data row2 col2\" >0.6627</td>\n",
              "      <td id=\"T_81dc1_row2_col3\" class=\"data row2 col3\" >297.4923</td>\n",
              "      <td id=\"T_81dc1_row2_col4\" class=\"data row2 col4\" >379.0410</td>\n",
              "      <td id=\"T_81dc1_row2_col5\" class=\"data row2 col5\" >9.1266</td>\n",
              "      <td id=\"T_81dc1_row2_col6\" class=\"data row2 col6\" >1.2632</td>\n",
              "      <td id=\"T_81dc1_row2_col7\" class=\"data row2 col7\" >0.2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_81dc1_level0_row3\" class=\"row_heading level0 row3\" >Mean</th>\n",
              "      <td id=\"T_81dc1_row3_col0\" class=\"data row3 col0\" >NaT</td>\n",
              "      <td id=\"T_81dc1_row3_col1\" class=\"data row3 col1\" >0.8467</td>\n",
              "      <td id=\"T_81dc1_row3_col2\" class=\"data row3 col2\" >0.5048</td>\n",
              "      <td id=\"T_81dc1_row3_col3\" class=\"data row3 col3\" >251.8892</td>\n",
              "      <td id=\"T_81dc1_row3_col4\" class=\"data row3 col4\" >290.3785</td>\n",
              "      <td id=\"T_81dc1_row3_col5\" class=\"data row3 col5\" >15.3115</td>\n",
              "      <td id=\"T_81dc1_row3_col6\" class=\"data row3 col6\" >1.1471</td>\n",
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              "      <td id=\"T_81dc1_row4_col4\" class=\"data row4 col4\" >69.4352</td>\n",
              "      <td id=\"T_81dc1_row4_col5\" class=\"data row4 col5\" >9.8355</td>\n",
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              "      <td id=\"T_81dc1_row4_col7\" class=\"data row4 col7\" >0.3833</td>\n",
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          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   16.9s finished\n"
          ]
        }
      ],
      "source": [
        "# get the final model from pycaret and tune it\n",
        "from pycaret.time_series import create_model\n",
        "from pycaret.time_series.forecasting.functional import tune_model\n",
        "\n",
        "create_model(best_model)\n",
        "final_model = create_model(best_model)\n",
        "tuned_final_model = tune_model(final_model)"
      ]
    },
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        "outputId": "482c7500-970f-4e52-826c-c52f1faa6a1b"
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                      "title": {
                        "standoff": 15
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                    "yaxis": {
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                },
                "title": {
                  "text": "Actual vs. Forecast (Out-of-Sample)"
                },
                "xaxis": {
                  "anchor": "y",
                  "domain": [
                    0,
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                "yaxis": {
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                    1
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                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pycaret.time_series import plot_model\n",
        "\n",
        "plot_model(estimator=tuned_final_model, plot = 'forecast')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Sales prediction: **This model is only capable of predicting shorter periods, such as the next week or two weeks, and can’t make predictions weeks or months into the future.**\n",
        "\n",
        "During analysis, I noticed that sales Prediction for 2018 seems to be fixed on one value. The primary reason the model might not be performing well is due to its reliance on past sales data for making future predictions. The model was trained using historical data up to October 2017. This limitation in the training data restricts its ability to make predictions for a longer time horizon. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgYWAj2RMpxH"
      },
      "source": [
        "## 6. Customer Segmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xxdbhg1TMpxH"
      },
      "source": [
        "Customer Segmentation is the process of dividing customers into groups that are similar in several characteristics. In this project, I use unsupervised learning, particularly K-means clustering method to perform this task. \n",
        "\n",
        "I select Sales, Profit, Quantity, and Discount are important for customer segmentation, as they capture purchasing behavior and profitability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "tXeMkVeOMpxH"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = pd.read_csv('C:/Users/Welcome/Downloads/Sample - Superstore.csv', encoding='ISO-8859-1')\n",
        "selected_features = ['Sales', 'Quantity', 'Discount', 'Profit']\n",
        "customer_data = data[selected_features]\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(customer_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sales       0\n",
              "Quantity    0\n",
              "Discount    0\n",
              "Profit      0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "a=customer_data.isnull().sum()\n",
        "a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "imputer=SimpleImputer(strategy='mean')\n",
        "scaled_data=imputer.fit_transform(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "wccs=[]\n",
        "for i in range(1,11):\n",
        "    kmeans=KMeans(n_clusters=i, init='k-means++', n_init=10, random_state=0, max_iter=300)\n",
        "    kmeans.fit(scaled_data)\n",
        "    wccs.append(kmeans.inertia_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(range(1,11), wccs, marker='o')\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsIdOHRHMpxI"
      },
      "source": [
        "To determine the best number of clusters, I try different values of k from 1 to 10 and plotted the Within Cluster Sum of Square (WCSS) for each value. Then I use the Elbow method to identify the point where the WCSS begins to level off. This point, which is located between 3 and 5 in this case, indicates that the optimal number of clusters is 4."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               Sales  Quantity  Discount       Profit\n",
            "Cluster                                              \n",
            "0         126.813585  2.533279  0.097487    21.516522\n",
            "1         407.908020  6.452784  0.094655    69.790464\n",
            "2         153.637349  3.836207  0.667960  -108.306796\n",
            "3        7685.179259  5.185185  0.070370  2610.220085\n"
          ]
        }
      ],
      "source": [
        "segment_means = data.groupby('Cluster')[selected_features].mean()\n",
        "print(segment_means)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for cluster_label in range(4):\n",
        "    plt.scatter(scaled_data[kmeans.labels_ == cluster_label, 0], \n",
        "                scaled_data[kmeans.labels_ == cluster_label, 1], \n",
        "                label=f'Cluster {cluster_label}')\n",
        "\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', label='Cluster Centers')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.title('Customer Segmentation Based on Customer Behavior')\n",
        "plt.xlabel('Sales')\n",
        "plt.ylabel('Profit')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Simple interpretation of result**\n",
        "\n",
        "|Cluster| Characteristics | Customer group |\n",
        "|---| ---| ---|\n",
        "|Cluster 0| These customers make small purchases with relatively low quantities and discounts. They contribute to a modest profit.| Low-value Customers |\n",
        "|Cluster 1| Customers in this group make moderately-sized purchases, with a slightly higher discount rate. They are reasonably profitable.| Moderate Customers |\n",
        "|Cluster 2| These customers purchase at a moderate level but frequently take advantage of discounts, resulting in negative profits. | Discount Customers |\n",
        "|Cluster 3|This cluster consists of customers with significantly higher sales, making fewer but larger purchases. They rarely use discounts and contribute substantially to profit.| High-value customers |\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of data points in Cluster 0: 1374\n",
            "Number of data points in Cluster 1: 2441\n",
            "Number of data points in Cluster 2: 35\n",
            "Number of data points in Cluster 3: 6\n"
          ]
        }
      ],
      "source": [
        "cluster_assignments = kmeans.labels_\n",
        "for i in range(4):\n",
        "    print(f\"Number of data points in Cluster {i}: {sum(cluster_assignments == i)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfECs4nSMpxJ"
      },
      "source": [
        "## 7. Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OguMh8IQMpxJ"
      },
      "source": [
        "After utilizing different techniques, I have discovered the following insights:\n",
        "\n",
        "• Shopping activities were more frequent in big cities such as California, New York, and Texas.\n",
        "\n",
        "• The number of orders were always highest in the fourth quarters, indicating an appropriate plan should be taken at the end of the year to maximize this metric.\n",
        "\n",
        "• Sales had an upward trend over years.\n",
        "\n",
        "• Customers had a habit of spending more at the end of year, especially purchasing Technology products.\n",
        "\n",
        "• The Standard Class was the most common ship mode, with time delivery from 4 to 7 days.\n",
        "\n",
        "• There is a clear difference in sales and profit per Categoty. These differences suggest that each Category has its own characteristics and needs to be carefully considered when planning for business.\n",
        "\n",
        "Classify customers into 4 groups to understand customer behaviors.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 8. Recommendation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Based on the data on customer segmentation, I think we should focus on **Cluster 1 and Cluster 3** to optimize marketing strategies because they have the potential to drive substantial revenue and profit. \n",
        "\n",
        "**Cluster 1 (Moderate Customers):**\n",
        "\n",
        "* Implement personalized marketing strategies based on historical purchase data\n",
        "\n",
        "* Consider loyalty programs designed for this cluster. Offer rewards, discounts, and exclusive rewards to encourage repeat purchasing activities.\n",
        "\n",
        "* Focus on efficient order processing and fast shipping to maintain customer satisfaction.\n",
        "\n",
        "**Cluster 3 (High-value Customers):**\n",
        "\n",
        "* Establish a VIP program with exclusive products and events.\n",
        "\n",
        "* Customize rewards and incentives based on customer preferences.\n",
        "\n",
        "* Gain insight into their purchase history and preferences.\n",
        "\n",
        "* Provide personalized support through dedicated teams or managers.\n",
        "\n"
      ]
    }
  ],
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