Data_Mining_Project_Fall_2018.ipynb

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        "![uncc_bg.png](attachment:uncc_bg.png)"
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        "###                                ITSC 3162:  Introduction to Data Mining\n",
        "###                                UNC Charlotte Fall 2018 Semester\n",
        "###                                             Dr. Pamela Thompson\n",
        "###                                        Project Description\n",
        "\n",
        "\n",
        "\n"
      ]
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      "source": [
        "## Goal:\n",
        "\n",
        "<p> Apply the CRISP-DM process and data mining algorithms and techniques covered in class to a real world.  You will conduct your study following the CRISP-DM process and jupyter notebook. Make use of resources provided in class and install different libraries for visualization. Students must perform all steps of the CRISP-DM process and report on results. Specifically, students will apply regression, classification, clustering and association rule analysis as they develop a prediction model for Rossman Sales Data.  Students will turn in all work via Jupyter notebook for the project.  </p>\n"
      ]
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        "<h2>The Project</h2>\n",
        "<p>Analysis of Rossman Store Data.  Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.</p><p>\n",
        "\n",
        "Rossmann is challenging you to predict 6 weeks of daily sales for 1,115 stores located across Germany. Reliable sales forecasts enable store managers to create effective staff schedules that increase productivity and motivation. By helping Rossmann create a robust prediction model, you will help store managers stay focused on what’s most important to them: their customers and their teams!</p>\n",
        "<p>Note:  Students can use resources to help them with the project.  This was a popular Kaggle competition.  To get started, you will want to look at:  https://serhanaya.github.io/kaggle-rossmann-sales-prediction/  </p>\n",
        "<p>This example will give some insight into combining the train and test data with the store data for added insight. Also, the times series component of the data is addressed in this example for analysis of the Rossman Sales Prediction data.  Please remember that while you can use this example, you need to add original insight and results of modeling to your project.</p>"
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        "## Deliverable 1:  Due Friday November 9th end of day\n",
        "    \n",
        "<p>Each group will submit this jupyter notebook(html) detailing their progress on the project (following the CRISP-DM process). Submit the zipped folder with this file and the data. <br>\n",
        "<br>\n",
        "1)  Team Name<br>\n",
        "  -BinaryBro's <br>\n",
        "2)  Group Members<br>\n",
        "  -Jake Stevens <br>\n",
        "  -Thomas Winterfield <br>\n",
        "  -Nicholas Higgins <br> \n",
        "  -Brandon fierros <br>\n",
        "3)  Business Understanding (document resources)<br>\n",
        "  -Thomas <br> \n",
        "  - Nicholas<br>\n",
        "  \n",
        "4)  Data Understanding and Exploratory Data Analysis<br>\n",
        "  -Jake (We'll replicate code from previous notebooks)<br> \n",
        "  -Thomas<br>\n",
        "  -Nicholas<br>\n",
        "  -Brandon<br>\n",
        "5)  Plan for modeling:  students will need to show examples for three of the following:  regression, classification, clustering, association rule analysis<br>\n",
        " -Thomas<br>\n",
        " - Nicholas<br>\n",
        "  </p>\n",
        "  \n",
        "  \n",
        "\n",
        " "
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        "## Deliverable 2:  Due End of Day Wednesday December 5th, 2018 (last day of class)\n",
        "    \n",
        "<p>\n",
        "Submit Final Notebook that includes all previous work plus the results obtained through application of the CRISP-DM process.  Include an evaluation and discussion of results plus documentation of any sources used for the project.</p><p>\n",
        "Include all python code with comments and interpretation/evaluation of results and graphs.  </p>"
      ]
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        "<h2>Business Understanding</h2>\n",
        "<p>Our main goal is to predict the sales of over 1000 Rossman stores across Germany. We want to produce an accurate model that will detail the amount of sales obtained by the stores over the course of 6 weeks. Doing so will allow the owners of these stores to make informed decisions about scheduling. </p>\n",
        "\n",
        "<p>Our plan is to first explore the data and search for any missing values. After that, we will clean the data using standard techniques, such as replacing missing data or removing those entries (if appropriate). Then we will perform an exploratory data analysis to gather an initial understanding of our data and search for any obvious patterns. After that, we will use three different analysis techniques to obtain the best model for predicting sales. To do so, we will use the following tools:</p>\n",
        "\n",
        "<ul>\n",
        "  <li>Python with Jupyter Notebook for scripting and collaboration</li>\n",
        "  <li>Pandas package for loading/modifying our data</li>\n",
        "  <li>Numpy package and Scipy Statistics package for analyzing our data</li>\n",
        "  <li>Pyplot and Seaborn packages for exploratory data analysis and other visual displays of data</li>\n",
        "</ul>\n",
        "\n",
        "<p>This project will be considered a success if we can provide a model that allows the owners of Rossman stores to make informed decisions regarding scheduling. If done correctly, our model will allow managers to optimize productivity and motivations. This should improve profits by lowering the cost of operation at any given time.  </p>"
      ]
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        "<h2>Data Understanding and Exploratory Data Analysis</h2>\n",
        "<p>Perform Data Understanding, preprocessing and exploration in this section.</p><p>Some help is given to get you started</p>\n",
        "<p>1.  Import necessary libraries - Pandas for fetching and manipulating data, and plotting; Matplotlib and Seaborn for plotting; Numpy and Scipy for manipulating data.</p>\n",
        "<p>2.  Provide a high-level overview of the data next.</p>"
      ]
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      "source": [
        "######## TODO:  DELETE CELL BEFORE SUBMITTING ########\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "# Modify the data_path if necessary (it should work as-is if your gdrive folder has the same name)\n",
        "data_path = \"gdrive/My Drive/ITSC3162/\"\n",
        "######## TODO: DELETE CELL BEFORE SUBMITTING ####\n",
        "#print(data_path)"
      ],
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          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
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      "source": [
        "# import necessary libraries.\n",
        "! pip install apyori #<- COMMAND FOR PACKAGE INSTALL\n",
        "!pip show apyori # <- VERIFIES INSTALL\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as stats\n",
        "import seaborn as sns\n",
        "from apyori import apriori\n",
        "import itertools as it"
      ],
      "execution_count": 0,
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          "text": [
            "Collecting apyori\n",
            "  Downloading https://files.pythonhosted.org/packages/25/fd/0561e2dd29aeed544bad2d1991636e38700cdaef9530490b863741f35295/apyori-1.1.1.tar.gz\n",
            "Building wheels for collected packages: apyori\n",
            "  Running setup.py bdist_wheel for apyori ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/7b/2a/35/c0c3749c1a36d4f454ea22d8396e1b854b86340d63cbbb7949\n",
            "Successfully built apyori\n",
            "Installing collected packages: apyori\n",
            "Successfully installed apyori-1.1.1\n",
            "Name: apyori\n",
            "Version: 1.1.1\n",
            "Summary: Simple Apriori algorithm Implementation.\n",
            "Home-page: https://github.com/ymoch/apyori\n",
            "Author: Yu Mochizuki\n",
            "Author-email: ymoch.dev@gmail.com\n",
            "License: UNKNOWN\n",
            "Location: /usr/local/lib/python3.6/dist-packages\n",
            "Requires: \n",
            "Required-by: \n"
          ],
          "name": "stdout"
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        "# fetch data - the files are in your director for this startup project folder\n",
        "# train will provide the data for training your model\n",
        "# test will be the test data\n",
        "# store provides additional information about the stores in train and test\n",
        "# data types for certain attributes are set to string and date\n",
        "\n",
        "store = pd.read_csv(data_path + \"data/store.csv\", sep=\",\", dtype={'StoreType': str,\n",
        "                                                      'Assortment': str,\n",
        "                                                      'PromoInterval': str})\n",
        "\n",
        "#Changed objects to type 'category'\n",
        "store.PromoInterval = store.PromoInterval.astype('category')\n",
        "store.StoreType = store.StoreType.astype('category')\n",
        "store.Assortment = store.Assortment.astype('category')\n",
        "\n",
        "\n",
        "#Computes means for missing values, as well as mode for the categorical column\n",
        "mean_CompetitionOpenSinceMonth = np.nanmean(store.CompetitionOpenSinceMonth)\n",
        "mean_CompetitionOpenSinceYear = np.nanmean(store.CompetitionOpenSinceYear)\n",
        "mean_Promo2SinceWeek = np.nanmean(store.Promo2SinceWeek)\n",
        "mean_Promo2SinceYear = np.nanmean(store.Promo2SinceYear)\n",
        "mode_PromoInterval = store.PromoInterval.mode() \n",
        "\n",
        "#loops to replace all missing PromoInterval with PromoInterval mode\n",
        "for index in range (0,1114):\n",
        "    if isinstance(store.PromoInterval[index],float):\n",
        "            store.PromoInterval[index] = mode_PromoInterval\n",
        "#3 values for compdistance were present, after looking at the data \n",
        "#I assume they the store where next to each other, if you all have any recommendation that wold \n",
        "#be better then just zero, feel free to implement. \n",
        "store.CompetitionDistance.fillna(0, inplace = True)\n",
        "store.CompetitionOpenSinceMonth.fillna(mean_CompetitionOpenSinceMonth,inplace=True)\n",
        "store.CompetitionOpenSinceYear.fillna(mean_CompetitionOpenSinceYear,inplace=True)\n",
        "store.Promo2SinceWeek.fillna(mean_Promo2SinceWeek,inplace=True)\n",
        "store.Promo2SinceYear.fillna(mean_Promo2SinceYear,inplace=True)\n",
        "#store.PromoInterval.fillna(mode_PromoInterval,inplace=True)\n",
        "\n",
        "store.info()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1115 entries, 0 to 1114\n",
            "Data columns (total 10 columns):\n",
            "Store                        1115 non-null int64\n",
            "StoreType                    1115 non-null category\n",
            "Assortment                   1115 non-null category\n",
            "CompetitionDistance          1115 non-null float64\n",
            "CompetitionOpenSinceMonth    1115 non-null float64\n",
            "CompetitionOpenSinceYear     1115 non-null float64\n",
            "Promo2                       1115 non-null int64\n",
            "Promo2SinceWeek              1115 non-null float64\n",
            "Promo2SinceYear              1115 non-null float64\n",
            "PromoInterval                1115 non-null category\n",
            "dtypes: category(3), float64(5), int64(2)\n",
            "memory usage: 64.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "#store.info()\n",
        "#test.info()\n",
        "#train.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "source": [
        "# Data Preperation and EDA for train\n",
        "\n",
        "train = pd.read_csv(data_path + \"data/train.csv\", sep=',', parse_dates=['Date'],\n",
        "                    dtype={'StateHoliday': str, 'SchoolHoliday':str})\n",
        "\n",
        "\n",
        "print(train.info())\n",
        "\n",
        "\n",
        "print(train.isnull().values.any())\n",
        "\n",
        "\n",
        "# Display unique values for StateHoliday and SchoolHoliday\n",
        "print(train['StateHoliday'].unique())\n",
        "print(train['SchoolHoliday'].unique())\n",
        "\n",
        "# Change object values to numberical values.\n",
        "train['StateHoliday'] = train['StateHoliday'].map({'0': 0, 'a' : 1, 'b' : 2, 'c' : 3})\n",
        "train['StateHoliday'] = train['StateHoliday'].astype(int, copy=False)\n",
        "# Note that this gives a weighting to the different holidays that could be reflected in the model\n",
        "\n",
        "# Repeat above process for SchoolHoliday column\n",
        "train['SchoolHoliday'] = train['SchoolHoliday'].map({'0': 0, '1' : 1})\n",
        "train['SchoolHoliday'] = train['SchoolHoliday'].astype(int, copy=False)\n",
        "\n",
        "# Standardize data\n",
        "store_min = np.min(train.Store)\n",
        "store_max = np.max(train.Store)\n",
        "store_norm = (train.Store - store_min) / (store_max - store_min)\n",
        "store_norm = pd.DataFrame(store_norm)\n",
        "train.Store = store_norm\n",
        "\n",
        "DayOfWeek_min = np.min(train.DayOfWeek)\n",
        "DayOfWeek_max = np.max(train.DayOfWeek)\n",
        "DayOfWeek_norm = (train.DayOfWeek - DayOfWeek_min) / (DayOfWeek_max - DayOfWeek_min)\n",
        "DayOfWeek_norm = pd.DataFrame(DayOfWeek_norm)\n",
        "train.DayOfWeek = DayOfWeek_norm\n",
        "\n",
        "customers_min = np.min(train.Customers)\n",
        "customers_max = np.max(train.Customers)\n",
        "customers_norm = (train.Customers - customers_min) / (customers_max - customers_min)\n",
        "customers_norm = pd.DataFrame(customers_norm)\n",
        "train.Customers = customers_norm\n",
        "\n",
        "StateHoliday_min = np.min(train.StateHoliday)\n",
        "StateHoliday_max = np.max(train.StateHoliday)\n",
        "StateHoliday_norm = (train.StateHoliday - StateHoliday_min) / (StateHoliday_max - StateHoliday_min)\n",
        "StateHoliday_norm = pd.DataFrame(StateHoliday_norm)\n",
        "train.StateHoliday = StateHoliday_norm\n",
        "\n",
        "# Histogram of Data\n",
        "train.hist()\n",
        "print(plt.show())\n",
        "\n",
        "# Draw heatmap of correlations\n",
        "plt.figure(figsize=(10,4))\n",
        "sns.heatmap(train.corr(),annot=True,cmap='cubehelix_r')\n",
        "print(plt.show())\n",
        "\n",
        "print(train.head())\n",
        "print(train.groupby('DayOfWeek').size())\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(1,6)\n",
        "train.boxplot(column = 'Store', ax=ax[0])\n",
        "train.boxplot(column = 'DayOfWeek', ax=ax[1])\n",
        "#test.boxplot(column = 'Date', ax=ax[2])\n",
        "train.boxplot(column = 'Open', ax=ax[2])\n",
        "train.boxplot(column = 'Promo', ax=ax[3])\n",
        "#train.boxplot(column = 'StateHoliday', ax=ax[5])\n",
        "#train.boxplot(column = 'SchoolHoliday', ax=ax[6])\n",
        "train.boxplot(column = 'Sales', ax=ax[4])\n",
        "train.boxplot(column = 'Customers', ax=ax[5])\n",
        "fig.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1017209 entries, 0 to 1017208\n",
            "Data columns (total 9 columns):\n",
            "Store            1017209 non-null int64\n",
            "DayOfWeek        1017209 non-null int64\n",
            "Date             1017209 non-null datetime64[ns]\n",
            "Sales            1017209 non-null int64\n",
            "Customers        1017209 non-null int64\n",
            "Open             1017209 non-null int64\n",
            "Promo            1017209 non-null int64\n",
            "StateHoliday     1017209 non-null object\n",
            "SchoolHoliday    1017209 non-null object\n",
            "dtypes: datetime64[ns](1), int64(6), object(2)\n",
            "memory usage: 69.8+ MB\n",
            "None\n",
            "False\n",
            "['0' 'a' 'b' 'c']\n",
            "['1' '0']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+x97evsp2CwsLNBoNer0eOzs79diKcwghhDGpd8BfuXIlP/zwA6WlpcycOZMe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\nIvD/oRGp9UOlaRsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad6032d5c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SWZTkZtKmb3gVOYlMQVD4S5zd9hV/LJlFauwZOgybWknG0skD5zaB1bbTOnRU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\n2rRpSCQSmjdvzscffwwYo9fh4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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad64669860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "None\n",
            "      Store  DayOfWeek       Date  Sales  Customers  Open  Promo  \\\n",
            "0  0.000000   0.666667 2015-07-31   5263   0.075122     1      1   \n",
            "1  0.000898   0.666667 2015-07-31   6064   0.084597     1      1   \n",
            "2  0.001795   0.666667 2015-07-31   8314   0.111126     1      1   \n",
            "3  0.002693   0.666667 2015-07-31  13995   0.202761     1      1   \n",
            "4  0.003591   0.666667 2015-07-31   4822   0.075663     1      1   \n",
            "\n",
            "   StateHoliday  SchoolHoliday  \n",
            "0           0.0              1  \n",
            "1           0.0              1  \n",
            "2           0.0              1  \n",
            "3           0.0              1  \n",
            "4           0.0              1  \n",
            "DayOfWeek\n",
            "0.000000    144730\n",
            "0.166667    145664\n",
            "0.333333    145665\n",
            "0.500000    145845\n",
            "0.666667    145845\n",
            "0.833333    144730\n",
            "1.000000    144730\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:418: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
            "  \"matplotlib is currently using a non-GUI backend, \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YRHzGww8/ytSpk/jzn5/l/PnzjBz5Gq1bt2Xy5HF8/PFKbryxIffd1wez2cywYcMZOXI4\nJpOJwYOHYrFY6NmzFykpW3n++SEEBgaSkJAIQEJCAuPGFX/NKTo6mi5duhi8p1JTKIRFxGfUqlWb\n8eOTSs1PTp5fal5sbByxsXEl5vn7+zuD93K33HILH374ofsGKlJBOh0tIiJiEIWwiIiIQRTCIiIi\nBlEIi4iIGEQhLCIiYhCFsIiIiEEUwiIiIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgY\nRCEsIiJiEIWwiIiIQRTCIiIiBlEIi4iIGEQhLCIiYhCFsIiIiEEUwiIiIgZRCIuIiBhEISwiImIQ\nhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJiEHNFVpoyZQq7du3CZDKRkJBAVFSUc9nx48cZOXIkFy9e\npG3btkycONFjg/UVs2fPIC0tFZPJxEsvjaJNmwjnMtXLNdWsclQv8TT1mHeU+0p427ZtHDp0iOXL\nl5OUlERSUlKJ5dOmTWPw4MF89NFH+Pv7c+zYMY8N1hfs3LmD9PQjLFiwiPj4sSQnTy+xXPUqTTWr\nHNVLPE095j3lhvDmzZuJi4sDoGXLlpw5c4bc3FwAioqK2LFjB3fffTcAiYmJNGrUyIPDrfp27NhO\n1649AGjWrDk5OWfJy1O9yqKaVY7qJZ6mHvOeck9HZ2ZmEhHx79MQYWFhZGRkYLFYyMrKIigoiKlT\np5KWlkaHDh0YNWpUmfcXGloXs9n/+kdeQTZb8DXPuxb5+We56abbnPdns1lxOM5hszUkMzOz0vWC\nqlkzd9UL3F8zb9fLFXfW50rqMfE0u91Oq1atndMhIaHY7XaCgiycPp3tEz3mSlXsqQq9J3w5h8NR\n4u+TJ08yaNAgGjduzJ/+9CfWr19Pjx49rnr77Oz8axrotcrIyCkxbbMFl5rnar1rVVBwkbNnC5z3\nd+FCIVlZeVgsOdjtOZWuF1TNmrmrXlB2zaDq99iVrtZj7qIec7+qeHCuSq73uA/V/3FZke27Uu7p\n6PDwcDIzM53Tp06dwmazARAaGkqjRo246aab8Pf3p3Pnzvzyyy9uGrJvslqt2O1253RmZiZWqxWA\n+vVDVC8XyqqZeqw09Zh4mnrMe8oN4ZiYGFavXg1AWloa4eHhWCwWAMxmM02bNuXgwYPO5c2bN/fc\naH1Ax453sn791wD89NNerFYrdesGAarX1ahmlaN6iaepx7yn3NPR7du3JyIiggEDBmAymUhMTGTl\nypUEBwfTq1cvEhISiI+Px+Fw8Lvf/c75Zn1N1a5dNK1atWHYsMGYTCZGjnyNzz//lKAgC927x6pe\nLpRVs8cee0g1u4J6TDxNPeY9FXpP+JVXXikx3br1v9+wv/nmm1m6dKl7R+Xjnn9+RInpW2/9nfNv\n1cs11axyVC/xNPWYd+iKWSIiIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJi\nEIWwiIiIQRTCIiIiBlEIi4iIGEQhLCIiYhCFsIiIiEEUwiIiIgZRCIuIiBhEISwiImIQhbCIiIhB\nFMIiIiIGUQiLiIgYRCEsIiJiEIWwiIiIQRTCIiIiBjEbPQBPGnL4E35+9v0S8352tV5gCHC3V8ZU\n1VWkZqqXiIh7VOsQXnjTQ7wbXzIsbLZgMjJySsybNm0tMd4cWBVWkZqpXiIi7lGtQ1hEqp/582ex\na9f/cunSJQYO/COtW7dl0qRxFBUV0aCBlbFjJxIYGMiaNV+wYsVSTCYTffs+Qp8+D1NYWEhS0nhO\nnDiOv78/o0ePo3HjJuzdu5fx48cD0KpVKyZMmGDsTkqNofeERcRnfP99CgcO7GfBgkXMmDGbWbNm\nsHDhAvr1e4L58/9GkyZN+eyzTygoKGDRondITp7P3LkLWL78Q86ePcOXX67CYgnm7bcXMmjQYBYs\nmAdAUlISCQkJLFu2jNzcXDZs2GDwnkpNoRAWEZ8RHX0bkya9AYDFEsy5c+fYuXMHd93VDYCYmK6k\npGxlz55U2rSJwGKxUKtWbdq1i2b37l2kpGyjW7ceAHTo0JEfftjFxYsXOXr0KFFRUQDExsayefNm\nQ/ZPah6djhYRn+Hv70+dOnUA+Ne/PqZz5y5s3bqFwMBAAEJDw7Db7djtdkJCQpy3K56fSVaWnZCQ\nUAD8/PwwmUzY7Xbq1avnXLdBgwZkZGSUOY7Q0LqYzf7u3r1KsdmCDd2+uIdCWER8zjffrOdf//qY\nt96ax4ABjzjnOxwOl+tXZv7V1r1cdnZ+BUfqGa4+YOrt7Yt76HS0iPiUrVs38/777zJjxmwsFgt1\n6tTl/PlzAGRknMJqtWK1WrHb7c7bZGZmYLXasFptZGUVzy8sLMThcGC1Wjl9+rRz3ZMnTxIeHu7d\nnZIaSyEsIj4jNzeX+fNn8de/JlOvXn2g+L3d9evXArBhw1o6depCREQke/fuIScnh/z8fHbv3kV0\n9G3cccedrFv3FQCbNm2kffsOmM1mWrRoQUpKCgBr1qyha9euxuyg1Dg6HS0iPuPrr9dw+vRpxo6N\nd84bM2YC06ZN4uOPV3LjjQ25774+mM1mhg0bzsiRwzGZTAwePBSLxULPnr1ISdnK888PITAwkISE\nRAASEhIYN674a07R0dF06dLFqF2UGkYhLCI+o2/ffvTt26/U/OTk+aXmxcbGERsbV2Kev7+/M3gv\nd8stt/Dhhx+6b6AiFaTT0SIiIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJi\nEIWwiIiIQRTCIiIiBlEIi4iIGEQhLCIiYhCFsIiIiEEUwiIiIgapUAhPmTKF/v37M2DAAHbv3u1y\nnRkzZjBw4EC3Ds5XzZ49g+eee4Zhwwbz449pLtdRvUpSzSpH9RJPU495R7khvG3bNg4dOsTy5ctJ\nSkoiKSmp1Dr79u1j+/btHhmgr9m5cwfp6UdYsGAR8fFjSU6eXmod1ask1axyVC/xNPWY95Qbwps3\nbyYurvg3OVu2bMmZM2fIzc0tsc60adP4z//8T8+M0Mfs2LGdrl17ANCsWXNycs6Sl6d6lUU1qxzV\nSzxNPeY95vJWyMzMJCIiwjkdFhZGRkYGFosFgJUrV9KxY0caN25coQ2GhtbFbPa/xuFWns0WfM3z\nrkV+/lluuuk25/3ZbFYcjnPYbA2BytcLqmbN3FUvcH/NvF0vV9xZnyupx8TT7HY7rVq1dk6HhIRi\nt9sJCrq24z5U/8fltSo3hK/kcDicf58+fZqVK1eyaNEiTp48WaHbZ2fnV3aT1yUjI6fEtM0WXGqe\nq/WuVUHBRc6eLXDe34ULhWRl5WGx5HD27JlK1wuqZs3cVS8ou2YBAZeqfI9d6Wo95i7qMferigfn\nquTy476v9NiVPP24rMj2XSn3dHR4eDiZmZnO6VOnTmGz2QDYsmULWVlZPPXUUwwfPpy0tDSmTJni\npiH7JqvVit1ud05nZmZitVqB4lM8qldpZdVMPVaaekw8TT3mPeWGcExMDKtXrwYgLS2N8PBw56no\n3r178/nnn7NixQrmzp1LREQECQkJnh1xFdex452sX/81AD/9tBer1UrdukEAxMbGqV4ulFUz9Vhp\n6jHxNPWY95R7Orp9+/ZEREQwYMAATCYTiYmJrFy5kuDgYHr16uWNMfqUdu2iadWqDcOGDcZkMjFy\n5Gt8/vmnBAVZ6N491ujhVUll1eyxxx4yenhVjnpMPE095j0Vek/4lVdeKTHdunXrUus0adKExYsX\nu2dUPu7550eUmL711t+VWkf1Kkk1qxzVSzxNPeYdumKWiIiIQRTCIiIiBlEIi4iIGEQhLCIiYhCF\nsIiIiEEUwiIiIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIuIjunXrRHh4PUwm\nE+Hh9QgPr0e3bp2MHpZchwr9gIOIiBhv48atAAyetpZ34+82eDTiDnolLCIiYhCFsIiIiEEUwiIi\nIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJiEIWwiIiIQRTCIiIiBlEIi4iI\nGEQhLCIiYhCFsIiIiEEUwiIiIgZRCIuIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJi\nELPRAxARqYwDB/YRHz+K/v1/z6OP9ufkyRNMmjSOoqIiGjSwMnbsRAIDA1mz5gtWrFiKyWSib99H\n6NPnYQoLC0lKGs+JE8fx9/dn9OhxNG7chL179zJ+/HgAWrVqxYQJE4zdSakxqn0ID562ttx1gmpX\n+zKIVAsFBQW89dab3H57R+e8hQsX0K/fE9x9dxwLFszjs88+oXfvB1i06B3eeed9AgLMPPvsILp1\ni2XTpm+wWIJ5++3JbNu2hQUL5jFx4lSSkpJISEggKiqKUaNGsWHDBrp3727gnkpNUa3T5934u0vN\nGzxtrcv5IlL1BQQEMH36LD744O/OeTt37uCVV0YDEBPTlaVLF3PTTTfTpk0EFosFgHbtotm9excp\nKdvo3fsBADp06MjUqRO5ePEiR48eJSoqCoDY2Fg2b96sEBavqNYhLCLVi9lsxmwuedgqKCggMDAQ\ngNDQMOx2O3a7nZCQEOc6xfMzycqyExISCoCfnx8mkwm73U69evWc6zZo0ICMjAwv7I2IQlhEqhGH\nw3Hd86+27uVCQ+tiNvtXbnBuZrMFG7p9cQ+FsIj4tDp16nL+/Dlq1apNRsYprFYrVqsVu93uXCcz\nM4OIiHZYrTaysornFxYW4nA4sFqtnD592rnuyZMnCQ8PL3Ob2dn5ntmZSsjIyDFs23oC4D76ipKI\n+LQOHTqyfn3xBzA3bFhLp05diIiIZO/ePeTk5JCfn8/u3buIjr6NO+64k3XrvgJg06aNtG/fAbPZ\nTIsWLUhJSQFgzZo1dO3a1bD9kZpFr4RFxGfs3fsjc+e+xYkTxzGbzaxb9zWJiZNJShrPxx+v5MYb\nG3LffX0wm80MGzackSOHYzKZGDx4KBaLhZ49e5GSspXnnx9CYGAgCQmJACQkJDBuXPHXnKKjo+nS\npYvBeyo1hUJYRHxG69ZtmDv3v0rNT06eX2pebGwcsbFxJeb5+/s7g/dyt9xyCx9++KH7BipSQTod\nLSIiYhCFsIiIiEEqdDp6ypQp7Nq1C5PJ5LyqzG+2bNnCzJkz8fPzo3nz5iQlJeHnV7OzffbsGaSl\npWIymXjppVG0aRPhXKZ6uaaaVY7qJZ6mHvOOcqu2bds2Dh06xPLly0lKSiIpKanE8nHjxjF79myW\nLVtGXl4e33zzjccG6wt27txBevoRFixYRHz8WJKTp5dYrnqVpppVjuolnqYe855yXwlv3ryZuLji\nDze0bNmSM2fOkJub67wc3MqVK51/h4WFkZ2d7cHhVn07dmyna9ceADRr1pycnLPk5eUSFKR6XY1q\nVjmql3iaesx7yn0lnJmZSWhoqHM6LCysxCXdfvuPOHXqFJs2barx11u98nJ5ISGhJS4aoHqVpppV\njuolnqYe855Kf0XJ1SXd7HY7w4YNIzExsURgu1LdL/dWp04A9erVcW7DbPYjLCyoxDYrUy/wfs1c\n1efKee6sobtrph5Tj4l7Xe9xH4x5XEZGRpKWllZiXkREBKmpqV4dR1nKDeHw8HAyMzOd06dOncJm\nszmnc3NzGTp0KC+//DJ33XVXuRus7pd7Cwqqz6+/pju3cfz4Sfz86jin69QxVape4P2aXVkfmy24\n1Dx31rCsmtlswT7XY67q5U7qMfdT4JdU+rKfmVitVud0ZR+TYMzjct26zc6/L/8FPSMu+Xm1Hiv3\ndHRMTAyrV68GIC0tjfDwcOepCIBp06bx9NNP061bNzcN1bd17Hgn69d/DcBPP+3FarVSt26Qc7nq\nVZpqVjmql3iaesx7yn0l3L59eyIiIhgwYAAmk4nExERWrlxJcHAwd911F//85z85dOgQH330EQB9\n+vShf//+Hh94VdWuXTStWrVh2LDBmEwmRo58jc8//5SgIAudOnVWvVwoq2YPPNBLNbuCeqxmGZG8\nkbxzhaXmD562tsR0UG0zc152Tyiqx7ynQu8Jv/LKKyWmW7du7fy7Kp1bryqef35Eielbb/2d82/V\ny7Wr1axOnTqqmQvqsZoj71yh8zTqb1ydvr8ylK+Xesw79O1qERERgyiERUREDKIQFhERMYhCWERE\nxCD6PWEppbwPeATVVtuIiLhg1gqZAAATV0lEQVSDjqZSwpWfwoSSX3IXERH30eloERERgyiERURE\nDKIQFhERMYhCWERExCAKYREREYMohEVERAyiryiJiFRhQw5/ws/Pvl9i3s+u1gsMAfRVQl+jEBYR\nqcIW3vRQhX5Fadq0tcR4c2DiFjodLSIiYhCFsIiIiEEUwiIiIgZRCIuIiBhEH8wSEZFqZUTyRvLO\nFZaaf/kvxAXVNjPn5W7eHJZLCmEREalW8s4VlvuJ8vJ+stVbdDpaRETEIAphERERgyiERUREDKIQ\nFhERMYhCWERExCD6dLSISBVXkU/yBtXW4dwX6X9NRKQKu/KrNlAcyq7mi+9RCIuISLVSkZ9/rCo/\n/agQFhGRaqUiP/9YVX76UR/MEhERMYhCWERExCAKYREREYMohEVERAyiEBYRETGIPh0tIiLVTnkX\nOKkqFzepGqMQERFxE1+6wIlOR4uIiBhEISwiImIQhbCIiIhBFMIiIiIGUQiLiIgYRCEsIiJiEIWw\niIiIQRTCIiIiBlEIi4iIGEQhLCIiYpAKXbZyypQp7Nq1C5PJREJCAlFRUc5l3333HTNnzsTf359u\n3brxwgsveGywvmL27BmkpaViMpl46aVRtGkT4VymermmmlWO6uV+ZR3naiL1mHeU+0p427ZtHDp0\niOXLl5OUlERSUlKJ5ZMnT2bOnDksXbqUTZs2sW/fPo8N1hfs3LmD9PQjLFiwiPj4sSQnTy+xXPUq\nTTWrHNXL/co7ztU06jHvKTeEN2/eTFxcHAAtW7bkzJkz5ObmAnDkyBHq169Pw4YN8fPzo3v37mze\nvNmzI67iduzYTteuPQBo1qw5OTlnycsrrtfRo+mqlwtl1Uw9Vpp6zP3KOs7VROox7yn3dHRmZiYR\nEf8+DREWFkZGRgYWi4WMjAzCwsJKLDty5EiZ9xcaWhez2f86hnxtIiMjSUtLAyB8JkRERJCamur2\n7eTnn+Wmm27DZgsGwGaz4nCcw2ZryJEjBZWuFxhTs8vrBcbV7Pvvf/GZHrvcb/viCb7eY0MOf8LP\nz75fYt7PV6wztFYINltfr4wHyj7OuVJVjmPgmcelr/fYb7x5HLtWlf4pQ4fDcV0bzM7Ov67bX6t1\n64qfqdlswWRk5AA4/3WngoKLnD1b4LzvCxcKycrKw2LJ4fTpa9t3I2r2W73A2JpdC6N67DeX18sT\nfL3HYuYnl5rnqmaerGF5yjvOVaXjGLi/Vr7eY7/x5nGsPFd7Yl7u6ejw8HAyMzOd06dOncJms7lc\ndvLkScLDw693rD7NarVit9ud05mZmVit1v9bZlO9XCirZuqx0tRj7lfWca4mUo95T7khHBMTw+rV\nqwFIS0sjPDzceYqmSZMm5Obmkp6eTmFhIevWrSMmJsazI67iOna8k/Xrvwbgp5/2YrVaqVs3CICG\nDRupXi6UVTP1WGnqMfcr6zhXE6nHvMfkqMD55enTp5OSkoLJZCIxMZE9e/YQHBxMr1692L59O9On\nF39y7p577mHIkCFl3peRp5jA86cKAd5+ew67du3EZDIxcuRr/PLLTwQFWejePZaDB/dWql5Qs2v2\n2GMPqcdcUI+5f/tXHudat2591fVrQr3UY+7fvisVCmF30n9E5T+wU5Nrpnpd2/YrSzWrXM1UL/XY\ntWzfFV0xS0RExCAKYREREYMohEVERAyiEBYRETGIQlhERMQgCmERERGDKIRFREQMohAWERExiNcv\n1iEiIiLF9EpYRETEIAphERERgyiERUREDKIQFhERMYhCWERExCAKYREREYOYjR6AuyxZsoSPP/6Y\nwMBAzp07x8iRIwkLC6NWrVo0b97c6OG5XXp6Og8++CCRkZE4HA78/f0ZNmwYnTt3rtT9bNq0iTlz\n5uBwODh//jxPPPEEv//97wH49NNPmTt3Lg8++CCFhYW8/PLLALzzzjukpqYya9YsAL766is+//xz\nZs6cWeHtbt26lSVLljB79uxKjdcohw8fZsqUKWRkZFBUVET79u159dVXqV27ttFDq1Ku7MsLFy4w\ndOhQevXqZfTQqjxXx7AuXbqUWs/XHjuVcfDgQaZMmUJWVhZFRUXcdtttvPbaawQGBlbo9seOHSMz\nM5OoqCgPj9SNHNXAkSNHHA899JDjwoULDofD4fj1118dTz31lGP27NmOtWvXGjw6zzhy5IjjkUce\ncU4fOnTIcd999zl+/PHHCt9Henq6495773Wkp6c7HA6H4/z5847hw4c7VqxY4XA4HI74+HjHl19+\n6UhNTXU89dRTzts999xzjn79+jmnp02b5li2bFmlxr9lyxbHiBEjKnUbo1y6dMnx0EMPOb777jvn\nvIULFzpeeeUVA0dVNV3Zl9nZ2Y4ePXo4CgoKDBxV1Xe1Y5grvvTYqYzCwkJHnz59HFu3bnU4HA5H\nUVGRY+LEiY6ZM2dW+D7+8Y9/OBYvXuypIXpEtXglnJuby/nz57l48SIBAQE0a9aMsWPHMnjwYMLC\nwmjQoAEFBQW89dZbmM1mbrjhBqZOncq//vUvNm7cyKlTp3jrrbf46quv+PTTT/Hz8yMuLo7Bgwcb\nvWsVdtNNNzFs2DA+/PBD6tSpw+7duzl//jxPPvkk9957L48//jirVq3CZDLxySefkJaWRkBAAAMH\nDqRx48YABAYGMnr0aJ577jkaNWrExo0bSU1NZezYsRw8eJALFy4QEBCA3W6nWbNm/PrrrzRv3pyU\nlBT69+9Pbm4uCQkJnDlzhkuXLjFmzBhat25NSkoKM2fOxGw207BhQyZNmlRi7MuWLeOHH34gKSnJ\niNKV69tvv6VZs2YlzjI888wz9O7dm6FDh9K0aVMOHDhAdnY2U6dOpW3btixZsqRUL82ZM4ecnBx+\n/fVXDh8+TEJCAt27dzdwzzwvJCQEm81GYmIigYGBnD59mpkzZzJu3DiOHDnChQsXePHFF7nrrruI\ni4vjiSeeYNWqVdx8881EREQ4/54xYwYnTpwgISGBixcvYjKZSEpKomnTpkbvolu4OoZ98MEHfPfd\nd8yaNYuAgADq1atHcnJyidutWbOGd999F7PZTGRkJPHx8Rw7doxXX30VPz8/Ll26xJtvvul8jFdl\nmzZtokWLFnTs2BEAk8nEq6++yrFjx+jXrx8rV64EoF+/fsyePZuDBw+SnJxM7dq1adCgAYmJicyd\nO9d5nGnSpAkTJ07Ez8+PoKAgpk2bxk8//cT777+Pv78/e/bsYdiwYXzzzTf8+OOP/OUvfyEuLs5l\nTVeuXOnMijfffJM333yTjIwMLly4wIgRI+jWrds173e1COHWrVsTFRVFz5496d69O926deOee+6h\na9eu3HvvvURFRdG7d28WLVpEw4YNmThxIp9++ikmk4njx4+zbNky0tPTWbVqFUuXLgXgySefpHfv\n3jRq1Mjgvau4yMhI/v73v9O3b19Gjx7NuXPniIuL4/HHH6dVq1bs3LmT9u3b8/XXX/Pss8/y9ttv\n07NnzxL30ahRI7Kzs+ncubOzfh07diQqKordu3cTHBxMy5YtadeuHSkpKdxwww3OUJ43bx5du3bl\n8ccfZ9++fSQlJbFo0SImT57Me++9R0hICH/9619ZtWoVN9xwAwDff/89a9asYcGCBUaUrEIOHDhA\n27ZtS8wzmUzceuut5OXlUVhYyHvvvcfatWuZN28e8fHxLnsJ4MSJE7zzzjts3LiRZcuWVfsQTk9P\n5/Tp01y6dIn69eszadIk/vnPfxIYGMgHH3zAyZMnGTRoEKtXr6aoqIi2bdsydOhQevTowT333MNH\nH31Ejx49OHv2LLNmzeKxxx7j/vvvZ9WqVcydO5c33njD6F10i6sdw86cOcP06dNp2rQpf/nLX/j2\n228JCgoCIC8vj7fffpvly5cTGBjISy+9xI4dO9i9ezddunThhRdeIC0tjYyMDJ8I4QMHDtCmTZsS\n82rXrn3VU9EffPAB8fHxdOjQgTVr1nDp0iUeeeQRQkND6dmzJ4MGDeIvf/kL0dHRLFy4kPfff59O\nnTrx448/smrVKrZv384rr7zC119/za5du1i8eDGdO3d2WVPAmRV79uwhOzubJUuWcPbsWTZs2HBd\n+10tQhjgr3/9K/v37+ebb77hb3/7G0uXLnUG6OnTpzGZTDRs2BCATp06sX37dtq2bUu7du0wmUz8\n8MMPHDp0iEGDBgHFDX706FGfCuG8vDzq1q3LmTNnGDBgAAEBAWRnZwPQt29fPv/8cyIjI0lPT3fu\n96VLl1zel8lkKjH9W83q1avH7bffTrt27Vi0aBGNGzfm9ttvB2Dnzp1kZWXxySefAFBQUEBmZiaH\nDh1ixIgRAOTn5xMaGsoNN9zAqVOnGDVqFCtWrCAgIMBTZbluV6uTw+HAz8/P+b7df/zHfzB9+vSr\n9hJA+/btAbjxxhvJycnx0h5416+//srAgQNxOBzUqlWLN954g+XLlzvfp0tNTaVTp04A3HDDDc5X\nyABRUVGYTCYaNGjgfOITFhZGTk4OqampjBo1Cijux3nz5hmwd57j6hj2wgsvMGbMGC5dusSRI0e4\n8847nSG8b98+jh07xpAhQwDIycnh2LFjxMTEMHz4cHJycrj33nu57bbbjNytCivreORK7969SUxM\n5MEHH+SBBx7AZrOVWL5//36io6OB4n6ZO3cunTp1onXr1gQGBmKz2WjWrBl169alQYMG5OTkXLWm\ngPOY2aJFC/Ly8nj11Vfp1asXDzzwwHXtd7UIYcf/fQCkZcuWtGzZkoEDB3Lfffc5l5tMJhyXXSL7\nt9NZgPPgHxAQQI8ePZg4caJ3B+9GqampAGzZsoXFixcTEBDgfAB269aNWbNmsWXLFmJjYwFo0aIF\nqampdOjQwXkfR48exWazuQzhGTNmYLFYGDFiBM2aNWP//v2kpKQ4D6gBAQGMHTu2xIP+zJkzhIeH\ns3jx4hL3t3XrVtLT0+ncuTP//d//zZ///Gf3F8RNWrRo4XxV+xuHw8G+ffto3rw5RUVFzvkmk+mq\nvbRlyxbM5mrxkCtT8+bNS/1/L1++vMQTrcsfjxcuXMDPr/iLGv7+/s75l//tcDhKPI4vXrzovE11\ncLVjWEJCAu+88w4tW7Ys1U8BAQFERkaycOHCUvf38ccfs2nTJmbOnMmjjz7Kww8/7K1duWYtWrRg\nyZIlJeZduHDBGYK/KSwsBODhhx+ma9eufPXVVzz//PPOD4q6cnm/XP4YvPLxeLWarly50tm/derU\nYcWKFXz//ff8z//8D+vWrWPq1KmV3Nt/qxZd/NFHHzF27FjnAzQnJ4eioiKaNGniPA1mMpmc/5nb\ntm0jMjKyxH1ERESwdetWCgoKcDgcTJ48mXPnznl9X67V4cOHee+99+jXrx833ngjAQEBfP3111y6\ndMn5Xu4dd9zB7NmzefDBB4Hi06RLlizh8OHDQHGjTps2jaeffrrU/bdq1YrDhw9z/PhxWrRogZ+f\nH2FhYXz77bfOEI6Ojuarr74Cip+lL1q0iPr16zunARYvXszevXuB4leFkydP5osvvuCXX37xbIGu\nQ0xMDOnp6SVOO7333nvcfvvthISEOE9X7dy5k5YtW/p8L3lau3bt2Lp1K1B8is/Pz4969epV6nbb\nt28v9Rj2ZVc7huXl5dGwYUPOnj3L1q1buXjxovM2zZs3Z//+/djtdgBmz57NyZMn+eyzz/jll1+I\ni4vjpZdecj45r+piYmI4evQoa9euBaCoqIg333yT1atXY7fbcTgcZGRkcOTIEQDmzZuH2Wymf//+\n3H///ezfvx+TyeQM6VtvvZWdO3cCFe+Xq9X0cmlpaXz66ad06NCB8ePHs3///uva72rxtLxfv34c\nOHCAxx9/nLp161JYWMiYMWOw2+1MnjyZoKAgJk2axKhRozCbzTRt2pQHHnjAedoUit8LHTRoEE89\n9RT+/v7ExcVV+a+f/Hba78KFC1y6dIlx48YRFRXFkiVL+MMf/kBcXBw9evRg/PjxTJkyhfvuu4/d\nu3dz8803A8X7PH36dF599VXnM/GHHnrI5bNmk8lEq1atSryCad++PStWrHB+OOYPf/gDo0eP5ve/\n/z1FRUW8/vrrACQlJTF69GgCAgIIDw+nf//+zgdHrVq1mDBhAq+//jpLly4t8eqnqvDz82PhwoUk\nJiYya9YsHA4HkZGRjBkzhokTJ3L+/Hmee+45jh8/zptvvumTveRNDzzwANu2bWPgwIFcvHixwmef\nXnzxRV5//XXn2xdTpkzx8Ei952rHsF27dvHkk0/SrFkznn32WebMmcPIkSOB4ldkCQkJDB06lMDA\nQNq2bUt4eDjNmjUjMTGRunXr4u/vz5gxYwzeu4r57XE2btw45s6dS2BgIF26dGH48OHk5+fz6KOP\n0rp1a+f7xo0aNeKZZ56hXr161KtXj2eeeYagoCBee+01wsLCGDNmDBMmTMBkMlG/fn2mTp1KWlpa\nmWO4Wk0v16RJE2bOnMny5cvx9/d3nrq+Vvopwxpk9uzZNG7cmEcffdTooVQb8fHx3Hvvvc5T/CIi\nlVEtXglL+f70pz9Ru3ZtXnjhBaOHIiIi/0evhEVERAxSLT6YJSIi4osUwiIiIgZRCIuIiBhEISwi\nImIQhbCIiIhBFMIiIiIG+f99Aukw+WABHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad61cca0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rrjKq0tuxmuC",
        "colab_type": "code",
        "outputId": "c35958b1-f58a-46b3-a12b-adf955ae086d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        }
      },
      "cell_type": "code",
      "source": [
        "test = pd.read_csv(data_path + \"data/test.csv\", sep=\",\", index_col = 'Id', parse_dates=['Date'],\n",
        "                  dtype={'StateHoliday': str, 'SchoolHoliday':str})\n",
        "\n",
        "\n",
        "#checks to see if there are any misisng values. \n",
        "print(test.isnull().values.any())\n",
        "\n",
        "#this code replaces all missing values with 1\n",
        "test['Open'].fillna(1, inplace=True)\n",
        "\n",
        "\n",
        "print(test['StateHoliday'].unique())\n",
        "print(test['SchoolHoliday'].unique())\n",
        "\n",
        "test['StateHoliday'] = test['StateHoliday'].map({'0': 0, 'a' : 1})\n",
        "test['StateHoliday'] = test['StateHoliday'].astype(int, copy=False)\n",
        "\n",
        "test['SchoolHoliday'] = test['SchoolHoliday'].map({'0': 0, '1' : 1})\n",
        "test['SchoolHoliday'] = test['SchoolHoliday'].astype(int, copy=False)\n",
        "\n",
        "\n",
        "# Standardize data for test\n",
        "\n",
        "store_min = np.min(test.Store)\n",
        "store_max = np.max(test.Store)\n",
        "store_norm = (test.Store - store_min) / (store_max - store_min)\n",
        "store_norm = pd.DataFrame(store_norm)\n",
        "test.Store = store_norm\n",
        "\n",
        "DayOfWeek_min = np.min(test.DayOfWeek)\n",
        "DayOfWeek_max = np.max(test.DayOfWeek)\n",
        "DayOfWeek_norm = (test.DayOfWeek - DayOfWeek_min) / (DayOfWeek_max - DayOfWeek_min)\n",
        "DayOfWeek_norm = pd.DataFrame(DayOfWeek_norm)\n",
        "test.DayOfWeek = DayOfWeek_norm\n",
        "\n",
        "StateHoliday_min = np.min(test.StateHoliday)\n",
        "StateHoliday_max = np.max(test.StateHoliday)\n",
        "StateHoliday_norm = (test.StateHoliday - StateHoliday_min) / (StateHoliday_max - StateHoliday_min)\n",
        "StateHoliday_norm = pd.DataFrame(StateHoliday_norm)\n",
        "test.StateHoliday = StateHoliday_norm\n",
        "\n",
        "\n",
        "#Random EDA Stuff\n",
        "#test.groupby('Date').size()\n",
        "print(test.groupby('Open').size())\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "sns.heatmap(test.corr(),annot=True,cmap='cubehelix_r')\n",
        "plt.show()\n",
        "\n",
        "print(test.info())\n",
        "\n",
        "#test.plot(kind='box', subplots=True, layout=(7,7), sharex=False, sharey=False)\n",
        "#plt.show()\n",
        "\n",
        "# boxpolot\n",
        "fig, ax = plt.subplots(1,6)\n",
        "test.boxplot(column = 'Store', ax=ax[0])\n",
        "test.boxplot(column = 'DayOfWeek', ax=ax[1])\n",
        "#test.boxplot(column = 'Date', ax=ax[2])\n",
        "test.boxplot(column = 'Open', ax=ax[2])\n",
        "test.boxplot(column = 'Promo', ax=ax[3])\n",
        "test.boxplot(column = 'StateHoliday', ax=ax[4])\n",
        "test.boxplot(column = 'SchoolHoliday', ax=ax[5])\n",
        "fig.show()\n",
        "#print(test.info())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n",
            "['0' 'a']\n",
            "['0' '1']\n",
            "Open\n",
            "0.0     5984\n",
            "1.0    35104\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2LBlPxJm99Bg1DYXS8p+l3WMvcG7vagz6XIv1tRu359rJnRQW5GEsKeHaiR3U\nbdrx3oJRKGjaeTCXDm661+qIasRg0KPR2JiXNRobFAoFBX/qyczP1REfE06Dxq2YvXgz7Tv347Ml\nb1NcXFTZIVuFobAAjfq266S2QYGCgkK9RTmj0ci3Py9jbN9XUKssv6vGJEVyPuIkj3Z4slJirgxq\nGy1FRQUW64oKC9DY2N0qo7GlqMhQuoxGi1brSG3/Fpw+toWvlo4k6cZVHhs63aJsnYBWKFAQG3W2\n4ipiBUq1EmPxreTHWGLEaDSiVJf9cewW4kHjZ4Jx8HEk8agpkTVkFpB9PYvonyO5tu4KKhsl3p39\nyty/WlMb7/xVRVTonKpmzZrx/fffM3DgQCZPnoxer6d379488cQTNGzYkDNnztCqVSv27NnDc889\nx+eff06vXr0sjuHj40NGRgYdOnSgS5cuhIaG0q5dO0JCQjh//jw1atQgKCiI4OBgTp48Sa1atcxJ\n1qeffkqXLl144oknCA8PZ86cOXz33XfMnj2bFStW4OLiwsKFC/n111+pVcv0DfL06dPs3LmTL7/8\nsiIvDQAGfR56XSaxl029J9dO/krbR57Dyd2PrGTTWLlP/VbY2tUg8ty+UvvbaB1p1mUIDdv1A0Ch\nVKHPzcLeyY3QZ009eSmxf3Bo46J/jMWzdmMKC/RkJj/gY/T/j+3dsY59O9cBoFKpcXZxM28rNBRg\nNBrRai2HrezsHXFydqNFm+4AdOn5OBtXLSUpIQYfv8BKi70y7Tj+IztPmCbdq1UqnB1rmrcZigwY\nMaK9LXkA2Hv6Z3w96tCoTrDFeqPRyHe/fFxmslWdFRryUattLdapNVoMt83FLDToUd+WkN5epkCf\nS3LCNRLifgfgxOG1dOw2Bo1GS+HNhLVJ8z78fn5XBdekcrgFu1Mz2MO0UGIk+7YJ5gqVAoVCQUlh\n2b1MaedTSDufgnN9V4KGNODq6svkJeaSl3jrS3byqSQC+gfxoM1sVMjdf5Zyc3Oxt7cnKyuL4cOH\no9FoyMgwTbQeOHAgv/zyC82aNSMuLo7g4GAUCgXFxcVlHkvxp6vbvn17Tpw4gZOTE61btyY4OJjv\nvvsOX19fWrduDcCZM2dIT09n2zZTV3x+fj6pqalER0fz6qumiaZ5eXm4urpSq1YtkpOTmTRpEuvX\nr0ej0VTUZTHLzUxCY2tnajk3e+2MxhKMJbeuQd0mHanpE8SwyasAsLGrQY9R0zn+85fkZ6cRezms\nzOG+LUuev6tY/Bq1I/6qTOB+kPUMHWaeG7Vv53quXj5t3paUGIOzizv2DpZDWm7u3uj1uZSUlKBU\nKlEoFCiUSpTKKjSJoZyFthvkHpVgAAAgAElEQVREaLtBAOw6sZXL0efN2xLT4nBxdMPhT3f/nfzj\nCNdvXOX01ScAyM7LYto3LzOqzwvEJEWybOMHABQVF6E35PPOF+NZMOHrSqpR+UtLiaFx8K0vwLa2\nDmjtapCRGntbmWhcavpa7Ofq5ktqchRGoxFbrYN5vbHElFCUGG8lFkENO3L80JqKqkKlSruQar4z\nr2Yzdxx8brUfG2dbCnMLKTFYfvbZutqidrAhN840BJh1LQOfrn7Yumop1hdRUmSkWG/qMVYoFRhL\nqk5vTblRVr86VWhSdfGi6S62sLAwVq5ciUajoWXLlgB07dqVZcuWERYWRo8ePQAIDAzk4sWLtGnT\nxnyM+Ph4PDw8ykyqFi1ahKOjI6+++ir+/v5ERESYh/4ANBoN06dPN58TICsrC09PT/Pcr/85duwY\ncXFxdOjQgQ0bNvDSSy+V/wX5k4zEKPJy0mnQpi9XT/yXus06Y8jXWcxpOrr1E45u/cS83Pe5BZzd\n8wOJ1y9QWJBPcLcnuHZqJ8WFBTRo14/iwkIizpSecPxPanoHcv38b+VSL1H1tWjTnW0bviDxRhRe\nPv7s+mUV7TqGlirnW6ceLq4eHNr3I117DeZk2C7sHZws5mM9yFo37MSG/d9zIzUWH/fa/BK2iY7N\nepQq987IuRbLry0bxfSxi/Bw8aJr84fN63+POsumA/+p9o9UiIk8hdPgyfjVDSEu+jxtOw0j4soR\ncy8TQFpKFHm5mTQJMfU4Bbd8hOzMRDLSYtFlp+A4+F3867UjKvw4zdsOJC7mAsU3hwvtHVxwcHAh\nPS32r0KotrKvZ1GrnTc2LrYYMgtwb+FJ5rXSd3WrtGpq965L+PorFOUVYe/lgEKpwJBVgEfrWti6\naonZcR2MpiHCnOhsK9SmglXD724VllTFxMSwYsUKJkyYwKFDh9BoNOzZs4fi4mIMBgM2Nja0bduW\njz/+mEWLTMNTI0aMYOzYsfTs2ZM6depQWFjI/PnzGTt2bKnjN2zYkJiYGFxdXQkMNA1D1KxZk0OH\nDpmP17x5c3bv3k3Lli0JDw/n4MGDjBs3DoDw8HDq1avHypUradu2LQCtWrVi9uzZDB06lD59+lC/\nfv2Kujxm+1bPofOQNwnu9gR6XRb7V8/FztGVPuNms3XZi3+7b8zvR3CpVYcBr5jumslOS+DI5qVl\nlu3z9GwcXT1xcPbAyd2X5j1GcGrHCmJ+PwKAvZM7+br08q1cFePp6cmBA7fu3Nq/fz9FRUX06tWL\nGzdu/M2eDx7Xmp6MfOZdPl00iZKSYur4N2LE06bb4a+HX+THDZ8zcfKnKBQKJryxkO++mMl/t62g\nhpMrE15fgEql5typ39i4eimGAj1ZmWlMnzQYF1dPJk37wsq1Kz81ndx55pHXWLx+BsUlxQR41WNo\nv1cACI+/woZ93zF59IJ/OMqDp6jIwLZ1M3i4/5tobOzISIvj501zcHRyZ9jTS/j3x08BsH39TPoO\nepfOvZ4lV5fOtvWmxyUUFurZvGoKoQP/hVqtISszkZ83zTEfv4azJ3l5meYe/AdJUW4hN36LpW6/\nQBRKyE/JJ+Hm4xTsPO2p1d6bqO0R5CXkknwykYCB9UChwFhsJHZnFCWFJSSfTMS3a20ajGgMRshN\nzCXhyIM2+AeKajhRXWE0lk+rjYuLo3///jRr1gyDwUBxcTETJ04kJCSEcePGodVq6d27N6dPn8bR\n0ZG5c+dy8uRJFixYwIYNG8zHOXfuHHPnzsVoNGIwGBgwYADPPPMMAO+++y6hoaHmnq3XXnsNo9HI\n8uWmpOKLL75g/fr17N27FwCdTsfkyZNJS0ujpKSEqVOnmudeLViwAI1Gg6enJwsXLuTMmTOsWrWK\njz/+mNOnTzN//nzWrFmDSqUqs74rpvQrj8v2wBs371drh1AtHDiVY+0Qqg2Hy/f2rLb/b3b9Ptza\nIVQLj/p88s+FBADBL7f850LlyHv0nX/OJvzw3wqM5M6VW1J1Lz7++GN8fX0ZMmSItUK4Z5JU3RlJ\nqu6MJFV3TpKqOyNJ1Z2RpOrOVXZS5TPmzj9nb/ynaiRVVrsd5fnnn0er1fLyyy9bKwQhhBBCVFXV\ncPjPaknVV199Za1TCyGEEKKKU5Tj3X9z587l3LlzKBQKpkyZQkhISKkyixYt4uzZs6VuZLsbD86D\nU4QQQgjxwCivierHjx8nOjqadevWERERwZQpU1i3bp1FmfDwcE6cOHHfj1OqhjcsCiGEEOJBp1Aa\n7/j1d44ePUrv3r0BCAoKIisrC51OZ1Fm/vz5TJw48b5jlqRKCCGEEFWOUllyx6+/k5qaiqurq3m5\nZs2aFr8XvHnzZtq1a2f+ibz7ivm+jyCEEEIIUc7Kq6fqz25/6EFmZiabN282P8PyfsmcKiGEEEJU\nOeU1p8rT05PU1FTzcnJyMh4ept9iDAsLIz09nVGjRmEwGIiJiWHu3LlMmTLlns4lPVVCCCGEqHKU\nCuMdv/5Op06d2LFjBwCXLl3C09MTR0fT7y/27duXX375hfXr1/PJJ5/QtGnTe06oQHqqhBBCCFEF\nlVdPVatWrWjatCnDhw9HoVAwY8YMNm/eTI0aNejTp0+5nON/JKkSQgghRJXzTz1Qd+Ott96yWG7U\nqFGpMn5+fvf1jCqQpEoIIYQQVZDqH+7qq4okqRJCCCFElVOePVWVRZIqUaHkh4LvTLfWNawdQrXR\n9PUO1g6hWvjaobu1Q6gWJkSPt3YI1cZhTlbq+cprTlVlkqRKCCGEEFWO9FQJIYQQQpQDSaqEEEII\nIcqBWiaqCyGEEELcP6XC2hHcPUmqhBBCCFHlqJDhPyGEEEKI+yZzqoQQQgghyoEkVUIIIYQQ5UBl\n7QDugSRVQgghhKhyNNJTJYQQQghx/2T4TwghhBCiHMjwnxBCCCFEOVBJT5W4G16BzWnb7znUtlpy\nM5I5tGkJedmpFmVcvQLoMPAVbB2cKMjN5ujW5WQkRgEQENKNkB7DUSrVZCRFcXjTEgoL8gBoHTqO\nOk06AkaiLx3h9M4VADi5+dJh0CvYO7lRUlzExYObiTiz2+Kcfg3b0nvsB2xcOBZdZnJFX4ZydfzI\nDn7e8m+Ki4vwrR3E2Bfew96+9I8VZ6an8O3nM0hOjEFr58DIce/QoHErALKz0vnmk2mkpsQzd+nW\nyq5ClaBWq5k/fz6TJk3Cz8+P+Ph4a4dkFe3qt+atAa9gb2tHQkYi01bPISkrxaJMm6AWvDngZRy1\njugNehZsWcapyLMADOv0OCM6D0GlUhGflsDMdfNJrGbvqbvhFhyCX48eKJRK8pKSiNi8ieKCglLl\nFEoldUJD8enchVML5mPIzjZv8+vVC/fgEFAoyL1xg8itP1Ks11dmNSpUq6A2vPLYG9jZ2JGUkcic\nDe+TkmXZJoLrNufV/hNxsHVAX6jn4+2LOXf9DABP9Xiafq0fw2g0Ep18nY+2LCBdl2aNqlQ4pbUD\nuAfVMeYHglpjS7fh73J4y1K2LB5P7JVjdBj0Sqly3Ya/y8XfNrJl8XguHFhP1yf/BYCDswft+7/I\n7hXvsWXJeHQZSbR6eCxgSra8AkLY+vFLbP34JbwCQ6jbrDMAnYZOJOriIbYseZ6d302l7SPP4eTm\naz6fSmNL69Bx6POyS8VS1aWlJrBmxUJee2cZsxdvxs3dmx/XfVZm2W8/n0GzFh2Zv/wnho99i707\n1gGQq8viww/G41unXmWGXuVs3boVnU5n7TCsys5Gy4djPmDGunk8Nnc4+y8d5r2b77//sdXYsGTc\nXGZv+IgB80bw+Y5vWfT0LABa+Dfj6R4jGfPxi/SfO4LIpCjeGviqNapSKWycnQno35/L36/g7NIl\nFGRmUOfhh8ss23D0UxQbDKXWu4WE4BJUj/OfLOfs0iUolEp8u3Wv4Mgrj1aj5YNRc5m/cRYjPhzC\nocu/8fbgyRZlNCoN859exBf/Xc6oRU/wzc4veH/kHADa1m/Po20HMP6TsYxa9AQxqTG88tjr1qhK\npbC5i1dVUe2TqpiYGCZMmMCQIUN4/PHHmTVrFvpq8K3GK6gFuvRE0m9EAHDt1E586rVCbWNnLuNS\nyx8brSMxl48CEHvlGFpHF5w9alO7SQcSIs6Se/Nb87VTO/EP7gJA3WadCT+9i5LiQkqKi4g4swf/\nm0mVa60AEiJM36LzczLITo3HxbOO+Zwteo0i4sxeigryK/4ilLOzJw/QuFk73Ny9AejcYxAnw3aX\nKpeelkj09cv0DB0GQKOmbZnwxoKbWxW8PGkRLVp3raywq6RZs2Yxc+ZMa4dhVe3qtyYuLZ7LcVcB\n2Bz2Ex0btsPe1t5cRqPS8N7aefwe9wcAYVdP4u7khpNdDdJ0GUz+4X2y83MAOHb1JAG3vdceNDUb\nNyErIgJDVhYAySdPUbNZcJll4/btJW7PnlLr85OTidy2lZKiIjAayb4eiZ2He4XGXZla12vLjbR4\nrsab2svPJ7bRrv5DFm1KrVKzcNMcTkecAuDc9bN4OHviqHUk0KseV+Iuk6vPBeB0+AkCvYIqvyKV\nRKW481dVUa2TqpKSEl599VXGjh3Lpk2b2LJlC76+vkyfPt3aof0jZ3dfstMTzMtFBj0FeTk4uflY\nlMnJSLDYLyc9EWeP2qb9025ty0lLwM7RFRutY6lj56Qn4OxRG4CEiLMEhHQDhQInd18cXWuREnsF\nMCVxPvVacenwlgqpc0VLSojBo5afedmjlh852enk6ix73WKjr+Lu6cPmNcuZ9uZgPnx/PDHXTdfA\nwdEJLx//ygy7SgoLC7N2CFbn71GH2NRbw575hnwyc7Oo436rjen0uey7eNC8POSh/pyMOEt2fg6x\nqfGcjboImHq0Hm0Tyt7byj5otO7u6NNvDUPp09OwcXREpdWWKquLjS3zGHmJieQlJgKgsrXFrVkw\nGZcvV0zAVlDbow7x6XHm5XxDPll5Wfi61bZYd+DiPvNyh0YdiUmJRqfXcSbyJMF1Q/Bw9kSlVNG1\nWQ9OXDtWqXWoTKq7eFUV1XpO1aFDh/D396dDhw7mdePGjaNv376MHz+e2rVrExkZSUZGBvPmzaNJ\nkyasWrWK7du3o1Qq6d27N8888wzLly8nJyeH69evExMTw5QpU+jWrVuFxq7S2FJcZNn9XVxUgNpG\n+6cyhZZlCgtQ29iatukyzetLigsxlpSgttGi0mgtjl1caDAf9/jPX9Lv+Q9p0nEQGq09x7Z/Tr4u\nA4AOg17h2PbPMZYUl3t9K4PBoMfJ2dW8rNHYoFAoKCjIx8HRybw+P1dHfEw4/QeP58mn3uS3PZv5\nbMnbzFmyBZWqWr8lRDnS2thi+NN7VF9YgJ1N6SShT/MeTB3yJtn5Ot741nI4583+L/Fkp8c5HXmO\nb/esqtCYrUml0VCYe2vI2FhcjLGkBJWNzV3Piar/5DBcGzcm7fx5Us6cKe9QrUar0VJQaNmmCgr1\nZbYpgCCverzW/01mrp4GwNX4P/jvqZ/Y+O429AY9KVlJvPj5+AqP21qqUrJ0p6r1J0hkZCRNmjSx\nWKdQKKhfvz65ubkUFRWxYsUK9u7dy6effsq7777Lr7/+ypo1awAYMWIEffv2BSAxMZGvv/6a3377\njbVr11Z4UlVk0KNSW44EqzW2FBluDbsVFepRqTV/KqOlqEBfan+VWoNCqaTIkF9qm1pjax7O6zFq\nGmd3ryT89G7sndzp9/xC0m9E4OodQFZyDMnRlyqiuhVm74517Ntpmg+lUqlxdnEzbys0FGA0GtFq\n7S32sbN3xMnZjRZtugPQpefjbFy1lKSEGHz8AistdlG15Rv02PzpPWpnoyXPUHpofNe5few6t492\n9Vvz3SufMHjhGNJy0gFYvP0zlv38JWO6D+ebl5YxaunzlRJ/ZfB66CG8HjJ9qTUWF2PQ5Zi3KdRq\nFEplmXOn/sm19etQqNXUDe1LvSee5Nq6teUWszXlG/TYaizblNZGS34Z0y2a1Q1h1qh5zN84mzOR\npqHAzk260qFRZ/rPCiU7L4sxPZ9hxohZvPXtgzmvSpKqSqZQKCguLt2rYjQaUSqVdOzYEYAWLVrw\n0UcfceHCBaKjoxkzZgwAubm55ruaWrUy3fnl5eVFTk5OqWOWt6yUOAJCbs3b0djaY2NXg+zbhhuy\nUuJwqultsV8NN28yk2Owc6qJV0Dwbet9yctOw6DPJSs1Fic3HxLCTd/warj7kpkSg629E+6+9Yk4\na+pazstOJTn6Mp7+TfEObI6bb32GNWoPgK2DM4+9/DH718wlMfJ8hV2H+9UzdJh5btS+neu5evm0\neVtSYgzOLu7YO1je/efm7o1en0tJSQlKpRKFQoFCqUSprNaj4aKcXU+Kpm/LXuZlR60DTvY1iEm5\nNXTl5eJJk9qN2HvhNwCOXztFYmYyzf2bkZyVglKh5Hz0JYpLill3eAuTBrxMDTtHcvIfjJsAEsPC\nSLw5VFyrfXuc/APM2+zc3DBkZ99VL5VTYCCFOh35yckYi4pIPnmCpuMfnCQ0JiWKXs37mJcdtA7U\nsHMiNjXGolyQVz1mj57PjFVTOBd11ry+Xf2HOPbHUbLzTPPW9pzbyZie4yoneCuojklVtf4UCQwM\n5OLFixbrjEYj4eHhaDQaSkpKzOsVCgUajYbu3buzcuVKVq5cyfbt22nbti1guoW8MiVGnsPRxRPP\nuk0BaNr5cWKvHKOo8Nbtx1nJMehzswho3h2Aeq16o8tMJjstntjLYXgHtcDJ3de8f+S5/QBEXThI\ng7b9UGtsUdtoadi2L5HnDlCQn0O+LpPajU2Jk43WEc+6jclMimb39++xbu4I1s0bxbp5o8jLSuWn\nT1+r0gnVn7Vo050rF4+TeCMKgF2/rKJdx9BS5Xzr1MPF1YND+34E4GTYLuwdnCzmYwlxPPwUPq5e\ntAwIAWBM9+EcuHSYfMOtJEGj0jB75FSCvEzJRB13P+q4+xGeEElgrbrMHPYOjloHALo37cSN9MQH\nJqH6s4zLl3EOCkLrbppY7t2pM6nnz93VMZzq+uP/yCMoVKaPU9dGjcxzrB4Ep8JP4uXqRYh/cwCG\ndRnFkcuH0BdaJp7Thr3PR1sWWCRUADEp0bSp1xZbjS0AHRt1JjIxonKCtwIbheKOX1VFte6p6tSp\nEx9++CEHDhwwD9etWLGC1q1bU1JSwqlTp3jkkUc4c+YMQUFBNG3alI8++oj8/Hy0Wi1z5szhrbfe\nskrsxUUGDqydz0MDXkJtoyUn7QaHNi7G3smNPuNms3XZiwAcWLeQTo+/Tsteo8nXZXJw/UIA8rLT\nCNv2KT1Hv4dSqSLtRjjH9nwOQPTFQ7j51GPAq59ixMj1c/uJu2KazLh/9Rza9HuO1g+PAwWEn95N\n/NWTVrkG5c21picjn3mXTxdNoqSkmDr+jRjxtOkW+OvhF/lxw+dMnPwpCoWCCW8s5LsvZvLfbSuo\n4eTKhNcXoFKpOXfqNzauXoqhQE9WZhrTJw3GxdWTSdO+sHLtKo+npycHDhwwL+/fv5+ioiJ69erF\njRs3rBhZ5SooNPD2f95j2tBJ2NnYEZMax9TVs/F0dufLCUt5fMFoYtPimbl2Ph+OeR+1SgMYmb9l\nKTGpccSkxlHHvTarJ36DQgE5+TomfT/N2tWqMIbsbK5v20bDUaNRKJXk3rhB7E+mO/wc/fyo3bs3\nl1esQOPgSNPxt+YBNX1uPMaSEn7/9t/EH/wN/xqP0vzV10ChwJCVRcSWzdaqUrkzFBUwY9VU3hz0\nDnY2dsSlxTJn3fu4O3mw5LlPeGrxMJrWCSbIux4vPfIqLz1y6xEcM9dMZUvYRup41OU/E9dSYiwh\nLSeVuevft2KNKpaKqpMs3SmF0Wisfo8svU1KSgozZswgMTERo9FIs2bNmDx5Mh988AFqtZqUlBQS\nEhL48MMPadiwIatWrWLTpk2oVCp69+7NCy+8wPLly3F1dWX06NFcvXqVWbNmsXLlyr8974op/Sqp\nhtVb4NAN1g6hWujWuvQDSkXZmr7e4Z8LCb526G7tEKqFtwp3WjuEauPwwsr9Av7GtFZ3XHbp7NP/\nXKgSVOueKgAPDw8++6zsBzz26tWLHj16WKwbNWoUo0aNslj36qu3vg00aNDgHxMqIYQQQlSs6thT\nVe2TKiGEEEI8eCSpqkLmz59v7RCEEEIIcY8kqRJCCCGEKAc2xur3gAJJqoQQQghR5UhPlRBCCCFE\nOZCkSgghhBCiHKiq4fPJJakSQgghRJUjPVVCCCGEEOXAphr++p8kVUIIIYSocmT4TwghhBCiHJRn\nUjV37lzOnTuHQqFgypQphISEmLcdOXKExYsXo1Kp6Nq1Ky+//PI9n6f6pYFCCCGEeOCpjMo7fv2d\n48ePEx0dzbp165gzZw5z5syx2D579myWL1/OmjVrOHz4MOHh4fccsyRVQgghhKhyVCjv+PV3jh49\nSu/evQEICgoiKysLnU4HQGxsLM7Oznh7e6NUKunWrRtHjx6955glqRJCCCFElVNeSVVqaiqurq7m\n5Zo1a5KSkgJASkoKNWvWLHPbvZA5VfdIKfnoHXG4nGHtEKqFpq93sHYI1calZff+LfL/k5TJ/tYO\noVqoqS6ydgjiL2gqKEUxGo0VclyQpEoIIYQQVZCqnB6p4OnpSWpqqnk5OTkZDw+PMrclJSXh6el5\nz+eS7hYhhBBCVDnKu/jf3+nUqRM7duwA4NKlS3h6euLo6AiAn58fOp2OuLg4ioqK2LdvH506dbrn\nmKWnSgghhBBVTnn1VLVq1YqmTZsyfPhwFAoFM2bMYPPmzdSoUYM+ffowc+ZMJk2aBMAjjzxCQEDA\nPZ9LkiohhBBCVDnlOXf5rbfeslhu1KiR+b/btm3LunXryuU8klQJIYQQosopr56qyiRJlRBCCCGq\nHDUaa4dw1ySpEkIIIUSVo5SeKiGEEEKI+ydJlRBCCCFEOZA5VUIIIYQQ5UB6qoQQQgghyoFKYWPt\nEO6aJFVVQP02oTTuNBCFUokuI5mjm5eRl51WqpxCqaJV6NM07TKYjfPHlFnmTgS26EH7gS8TtvVT\nrp/dZ15vV6MmnYa+iZO7D4X6PI5t/5zkqEv3XC9rOHJxHz8eXEVxSRF+Hv68MOAt7LWOf1n+zNUw\nPlw7jWWv/YCHi5fFtqUb3icnL4vpYxdXdNiVrl391rw14BXsbe1IyEhk2uo5JGVZ/ohom6AWvDng\nZRy1jugNehZsWcapyLMADOv0OCM6D0GlUhGflsDMdfNJzEy2RlWsTq1WM3/+fCZNmoSfnx/x8fHW\nDqnS+IR0oEGPQSiVKrKT4ji76UuKCvJLlXMPbEKTfqNQ22rJz0jlzKYv0WenU7tVV5o9NgZ9Tqa5\n7PWjO4kK23lrZ4WCLhPeJyc5nrObvqyMalWokMC2PNPvTbS29iRnJLBs03ukZVu+dxrXacGzj07C\n3taBgkI93/z8EZeiTgPwcJvBDOw0CqVSSVLGDZZvfr/U/g+K6thTJT9TY2VuvvVp3nsUu76dytYl\nE8hMjKJV33Fllu3x1HSKDPr7Ol+zrk9QN7gL2aml//B3Gvom8VdPsvnDZzj+81c06tD/vs5V2VKz\nkvj+10/418g5LHp5BR4uXqzb991fli8o1LN27zc42tUote3M1TAib1ytyHCtxs5Gy4djPmDGunk8\nNnc4+y8d5r0n/2VRxlZjw5Jxc5m94SMGzBvB5zu+ZdHTswBo4d+Mp3uMZMzHL9J/7ggik6J4a+Cr\n1qhKlbB161Z0Op21w6h0ds5uBPcfy7EVC9m75C3yM1Jo/PCwUuVUGltaD3+Vc1u+Zu/iSSReOU3I\noGfM2xMunWDfkrfML4uECvBv3xtbR+cKr09lsNVoeXv4ApZveZ8Jiwdy4soBXh40zaKMWqVh2lNL\n+H7HMl5aOpgfdn3G28PmA1Dftykje09g2rcv8OKSx4lODOfpvm9YoyqVQonqjl9VRbVKquLi4mjZ\nsiVPPfUUo0eP5sknn2TXrl3WDuu+6HOz+G3tAvJzMgBIirqEi2fdMsue37uWc3tWlbktpOcIBk78\nksFvf0fbx55HoSj7nzYx8jz7Vn5AYUGexXp7Z3fcfOtx5eh2UxyR5/ltzfx7rZZVnPrjCE0DWuLu\nXAuA7i37cez3A39ZftP+/9A5uA9aG3uL9QWFelbv/ooh3cZUaLzW0q5+a+LS4rkcZ0oaN4f9RMeG\n7bC3vXUdNCoN762dx+9xfwAQdvUk7k5uONnVIE2XweQf3ic7PweAY1dPEuBZp/IrUkXMmjWLmTNn\nWjuMSufVpDWpEZfIzzL1mEef2o9PcPtS5dyDmpKbnkzWjSgAYk7tx7NeCCob7T+ew7aGCwEdQok4\n/N9yjd1aQoLakZgeR8SNKwDsOvUjLep1wO62v0FqlZpPfpzFhciTAPwefQY3Z08ctDXIys3gw7Xv\nkpFj+gHgS1GnqeMZVPkVqSQKVHf8qiqq3fBfQEAAK1euBCAzM5PHH3+cLl26oNX+8xu0KsrNTCb3\ntmET34atSb35QfZnqbFXylwf2KIHdYM788tnEykq1NNj9HQatH+EP8J+Kn2Mvzi2q1cAuowkWoU+\njV+jduTrMjj501ekJ0TeQ62sIyEtjlquPublWq7eZOdmosvPKdUbFZMUyYXrp5j17KfsOrnNYtvm\nAyvpHNIbD5dalRJ3ZfP3qEPsbT2V+YZ8MnOzqOPux5V4U6Kl0+ey7+JBc5khD/XnZMRZsvNzyM7P\nMe9vq7Hh0Tah7L2t7P83YWFh1g7BKhzcvclNSzIv56UlYevojEbrQKE+17ze0d2LvPRbf+OKDQUY\n8nJwcDO9v5y969LxuWlonVxJi7rCpZ9/MA8hNnv0Ka7u2YRSXf0eAlkWX/e6JKbHmZf1hnxy8jLx\ndqtNZMIf5nVHL+01l2ndoBNxKVHk6nPI1eeQnHnj1raGnbgad6HyKlDJlIqqkyzdqWrVU/VnLi4u\neHh4MGPGDKZPn86rr1nwkaQAACAASURBVL5KYWEhkydPNvdkHTp0CIDevXvz1VdfMXjwYCZOnMg3\n33zD0KFDzT+imJiYyDPPPMNTTz3FmDFjiI2NrfT6BLbogW+DNpzdXXZv1F/xa9ye8JO7KCzIw1hS\nwrUTO6jTtONdHcPGzpH/a+/O46qo+geOf+7lbgiCASoKKIgmImBqinuaJO6aiUvikqZZqT3prydw\nydyXTHOpnqesLMUlMR8lyzJzqRTEJRR3WQRkkX2Hu/7+QC9eQaVktfN+vXi9nJkzM+ccz537nXPO\nzG3QuDm3Yy+yb91rxJw7wnPj5iGR1p0motYUI5eVTmyUyxRIkFCsMR0yNRgMfHlgPRP7z0BmZnpf\nEZcSzfmo0wzqOqpa8lwTVAolaq3aZF2RphjzcnoOXmjXh6OLQxjV/UUWf7vaZNvsIW9wbMkBLFUW\nfPmAHlThyWUmV6DXaozLep0Wg16PmUJ5Xzoluvvam06rQaZQkZeWRPLlM4R9s4ZjGwORK+vhMWg8\nAA1beSE3t+DW+ZNVX5hqopSrUGuLTdaptcWoFOblpne2b8Wrg/6Pj/+3tMy2Ps8MouPTPQj65dMq\nyWttIHqqqllCQgJZWVnodDqsra1ZsmQJ//vf/1AoFGzbto2UlBQmTJjATz/9hF6vx93dnalTp9K7\nd2/69etHcHAwvXv3Jicnh/Xr1zNy5EgGDhzIwYMH2bRpE6tWraqSfLfuMhi3roMBOPvT18RfOklr\n70G06TGcnzfPpSgv8y8dT6GyoG3PETzduT9QMqG9OD8bcytb+k1ZBkBa/DX+CH7whGtNUT5FeVnE\nXy65675++ic6DpyClZ0D2berP8CsqJ9O/Y+fw/cBIDMzw9rSxrhNrVVjwFDmgvXr2QM4NGyGWzNP\nk/UGg4GvfthQbrD1JClUF6GQmT5VY65QUaAuO8H4UMQRDkUcoXOrjnw1YxMjVk8gPTcDgLUhn7D+\nwH+Z0HsMm99Yz7iPplVL/oWa49ylHy5d+wFg0Gkpzs02bpPK5Eik0jLzPrXqYszua29mcgVadRE5\nSTfJjLtuXH/92D66THoXqUxO24HjCN/6ZD0kUqQuRCEzDTqVchWF5Xz23Jq1492xq9n43WIiY06b\nbBvoPYphPfyZt3kqWXl/74GlusBMonx0olqmzn1zxMTEMH78eAwGA0qlklWrVrFr1y68vLwAiIyM\nxNu7ZFy/cePGKBQKsrJKnizx8vJCIpFga2uLu7s7ADY2NuTm5hIZGWnstfL29ubjjz+usjJcDf3e\nZGjOtYMPrbsO5qfP3qXwzhfWX1GQk0H85bByh/v2rZteoWPkZd1GrjQHiQQMBqAkyDDo9X85P9XJ\nt/NwfDsPB+BQ+D4u3zxv3JacnkADS1ss7nv67/TVE8QkXuPsNT8Acgqymb/5Tca98BpxKdGsD14M\ngFanpUhdyLv/mcqq6Z9XU4mqXkzKTfq372tctlRZYFWvPnGppcGzfYNGuDu58euF4wCcun6G5Kzb\ntHP24HZ2KlKJlPM3L6LT69j1x17mDH2T+uaW5Bb+8yZs/5PEhpY+mefs7YOtSxvjNgtbe4pyMtEW\nmc7XzEtNxMGri3FZpjRHbm5BfloyKmsb9FoN6vyS+XkSqRl6nY4GDi0wt7Kh+2sLgZIgTGomQ2lh\nRdg3H1R1MatMQmosPb18jcv1lJZYmluRmHbTJJ2zfSsCxn7A6l3vcin2nMm2vh2GMqjraAI/m0JG\nrukTu08aSR0c/qtzQdW9c6ru2rVrF3J56Zi74U5QAKBWq5HeGcIyMyv9D7r33waDAYlEYtxPo9EY\n96lq5la2tO83kR8/nf23AiqA+MuheDznx40zh9BpimnVuT86jYboc4crfIys5FgKcjNo9awv18MP\n0tyjB+rCPHIzkv5WnmpCx9bd2X30axLT4mlq58QPoXvo5tGnTLp3X15usjxr/TgWTPyQhg3s6dWu\nn3H9pdg/2XPsmyfulQqnbpxhyVNzae/ixbmY80zoPYZjF/+g8J4eBrmZnKUvz2P8+niikmNoZudI\nMztHbiRF84xL6dN/eUX59G7bncSMZBFQ/cMkXz5Dax+/krlVaUm49hjIrYgTZdKlRV/kmZemYdO8\nNRk3r+LaYyApV86VXKu8h1O/kQOnt6/HYNDj0rUfKVf/JOPmVX5cMtV4DKcOvbB1aVPnX6lwITqc\nt156H/fmz3Dp5p8M6+FP+JXjZaYo/GvkEj7dv7xMQGVj1YgJ/Wbyf5+Of+IDKqBWDetVVJ0Lqh7F\n09OTsLAwBg0aRFJSElKpFCsrqwrvN3jwYMLDw/Hw8KiG3IJr++eRK1X4TF5iXKfX6whZ/ya2jk/T\n3sefX7a8h8qyAb5TS5/G6zd1JQa9jp+/mEf8pZM0aNyMwTPWA5CbnsyJ79aXez6fSYuxeKoRFtYN\nsbJzwKvPaOMQ5LHty+n+0tt4POdHUV4Wx7avqPU9VfeysbJj8sBZrP12ITq9Dhf7lowcMAOAG7eu\nsPvIVwT6V82Qbl1SrFHzzjfvMX/kHMwV5sSlJTBv+1IaWdvx3+kf8eIqf+LTb/H+zpV8MGERMjM5\nYGDl3o+IS0sgLi2BZnZObH97MxIJ5BbmMefr+Y8875OoUaNGHDtW+oTp0aNH0Wq19O3bl8TExIfs\nWfcV5WRyYf+XdPafjURqRnZiDBcOBwPQwNEVNx8/QresRK/VcGbnRjyHTkKmUJKfnsK54P8AcP3I\nXjyHvkKff30ABj0Zcde59OOTOz9PrS1m9c4Apg8NRKkwJyk9no+C38PGqhGLX/mEGetH0trJC2f7\nVkzq/xaT+r9l3HfNrkDat+qGSlmPxZNL51Hp9DpmrB9ZE8WpcnWxp0piuLdbp5ZLSEhg1qxZfPfd\ndybrAwIC8PX1pU+fPmi1WhYuXEhcXBwajYY5c+bQqVMnnn/+eUJCQrCwsGDEiBFs2LABR0dH47/l\ncjnz5s1DrVYjl8tZvnw5jRs/+Omvb+YOquriPhHatv1PTWehTpgYXvb9PkL5Lq5/ciYuV6V9gWNr\nOgt1wudcquks1Bkhy/+s1vNFLNxU4bTtFs2owpxUXJ3qqXJ0dCwTUAGsXFnagyOTyVi2bFmZNL/+\nWvqI6r3HuPffmzdvrqysCoIgCILwGOpiT1WdCqoEQRAEQfhnkIjf/hMEQRAEQXh8EkndC1HqXo4F\nQRAEQXjyieE/QRAEQRCESiCCKkEQBEEQhMcnJqoLgiAIgiBUBqmYqC4IgiAIgvD4RE+VIAiCIAjC\n4xPDf4IgCIIgCJVBBFWCIAiCIAiPzyCCKkEQBEEQhEogFUGVIAiCIAjC45PKazoHf5kIqgRBEARB\nqH1ET5UgmDp0aUxNZ6FO+Nyid01noc5IDXSu6SzUCcNW7KjpLNQJIYHjajoLwgOIOVWCIAiCIAiV\nQSKt6Rz8ZSKoEgRBEASh1jGI4T9BEARBEIRKYFa1IYpGoyEgIIDExETMzMxYsWIFTk5O5aadPXs2\nCoWClStXPvSYda9vTRAEQRCEJ55BIq3w39/x/fffY2VlxY4dO5g+fToffvhhuen++OMP4uLiKnRM\nEVQJgiAIglDrGKRmFf77O06ePMkLL7wAQLdu3Th79myZNGq1mk8//ZTXX3+9QscUw3+CIAiCINQ+\n0qrt90lLS8PGxubOqaRIJBLUajUKhcKY5r///S9jx47F0tKyQscUQZUgCIIgCLXO3x3WK8/u3bvZ\nvXu3ybqIiAjT8xkMJsuxsbFERkYyc+ZMwsLCKnQeEVQJgiAIglD7VGJPlZ+fH35+fibrAgICSE1N\nxc3NDY1Gg8FgMOmlOnr0KImJiYwaNYq8vDwyMjL4/PPPmTp16gPPI4IqQRAEQRBqHb1Z1b5SoXv3\n7hw8eJCePXty5MgRvL29TbZPmjSJSZMmARAWFsbevXsfGlCBmKguCIIgCEItZJBKK/z3dwwcOBC9\nXs/YsWMJCgpizpw5AHz22WecO3fubx1T9FQJgiAIglD7VPFE9bvvprrftGnTyqzz9vYu05NVHhFU\n1SLOXr3w7DMaqVRGVspNTuz5CE1xQZl09i286DhgCnKlOXmZtzmxZx0FOekAtOk2jFadByCRSLgd\ne5Gw/Z+g12mRK83xHvomto4tkUikxJw/TsQv2wBo0Lg5nYe+jsqiAQaDjohfgoi7eKJay/53NW/R\ngT4DZqBQmJOdlcIPe5aRm5NqkqaRfUv6Df0/6llYU5CfzU/7PiA1JQoA24bODHppLub1GlBYmM2B\n4GWkp8YyxG8h9g6tjcdQqiy4dTOSvTvmVWv5qpKtpxeOffogkUopSEkh6rs96IqLy6STSKU08/Wl\naY+enFm1EnVOjnGbY9++2Hl6gURCfmIi0fv+h66oqDqLUSWaenXl6T7DkUrNyElJ4M89/0VbXFgm\nnV0Ld9wHjEOmVFGYmca5Pf+lKCcDpw698Bg8gaLcLGPamJM/Exv6c+nOEgk9py8i9/Yt/tzz3+oo\nVo2SyWSsXLmSOXPm4OjoyK1bt2o6S9WqqVcXWvYZhlQqIzclnog9n5fbpmxbuOM+4GXMlEoKM9OI\n2PM5RTkZOHboSdvB4ym+p03FnjxEbOghFJZWeA2bjGVjBzBAZMjXpN2IrM7iVQmDVFLTWfjLRFBV\nS1hYN6TzkOkc2PQW+dmpdBwwhfb9JnAq5D8m6WRyJT3HvMvhLe+RkRiFW9chdBk+g1+/WYSdU2vc\nug3l+02z0BTl89zLgbh1Hcql37+jfb+J6HUa9n30OjK5iiEzN3I7NpKkG3/y3MtzOXvwK+Ivh2LT\npAW+01aTHH0edWFeDdVGxcjlKoaOXsy3X88mJfEaHbuOxHfYOwRv/bdJuqGjF3Hs5/9w/fJvtHTr\nwZBRC/ly4wQkEikjxi3n2M//5dqlY7Rt1492zw7h1x83ErJ7kckx/Cas4cK5H6qzeFVKYW2Ny5Ah\nnP94E+rsbJoPGECzfv2ICQkpk7a1/3jybiWUWW/r5UUD15ac37QRvU7H02PG4vBcb+J+OlgdRagy\n5ta2eA6ZyPFN8yjMTqftgHG06TeaCyFbTNKZyZV0HDOT0C2ryE6MxaWrL17DJ3PqmzUAJF0Mf2iw\n5Oztg9LSmtzb/4zgYt++fYSHh9d0NmqEytqWtkMm8NumBRRlp9NmwMu49fMjMuQbk3RmciUdxrxJ\n2JYPyEmMxblrPzyHv0L4NyUvpUy+eJqIPZ+VOb7H4AnkZ6RwOugj6jd2pMuUQH5dMwedum7f4FTm\n03/V5bFzHBQUxKhRo/D392fkyJGcOHGCK1euEBMT89D9fvrpp4duDwgI4MiRIybrHtb1lpCQwIgR\nIwB4++23KbrvbvnIkSMEBAQ89Jw1ycm9C0lRf5KfXdLLcuPMzzT37FEmnb1rO/IykslIjLqT7hBN\nWrZHpjCnuUcPbl74DU1Rfsm204eMx4i7eII/DweBwYBWXUhGcgwNGjVHIjUj4nAQ8ZdDAchIikan\nVWPZoFF1FPuxNHftSFZGIimJ1wA4f+YALi07o1DUM6Zp2LgFKnNLrl/+DYAbV37HwvIpbBs2x6GZ\nJ3q9jmuXjgFwMeJnfv1xY5nztHi6C2YyOTeu/FENpaoeNm3cyY6KQp2dDcDt02ew8fAsN23CkV9J\nOHy4zPrC27eJ3r8PvVYLBgM5MdGYN7Sr0nxXB3v3jqRFXaQwu6T39+aZozT1LHvtsXNtS37GbbIT\nYwGIO3OURi29MFOoHnkOZf0GuHT1JeqPHys177XZkiVLeP/992s6GzXC3r0DaVEXKbrTpuLPHKVJ\nOW3K1tWdgoxUcu60qfgzx2jY0vORbcqupQfxZ0quY7kpCWTfisHOtW3lFqImSCUV/6slHqunKiEh\ngW+//Zbg4GDkcjmxsbHMnz8fb29vPDw8cHFxeeB+Bw4cwNfX93FO/0Dr1q2rkuNWJSs7B3LTk43L\nuelJmFs+hUJliboozzRdRpJxWasuorggFyvbJljZOZBwufRdGrkZSVg3dAQgOfq8cb1caU6jZm24\neHwPBr2O2PPHjduc2nRBXZhH1u2KvZK/JtnYOpGVUXqXr1EXUliYzVO2DqQkXS9JY9eMrIxEk/2y\nMhKxbdgcS6uGZGclM+ileTg08yAjPYFDIWvJzkwySd+j7xSO/Phx1ReoGqns7CjKSDcuF2Wko7C0\nxEylKjN8lxcfX+4xCpJL26uZUomthyep58q+kbiusbBrQn56inG5ID0FpaU1cpWF8YYFwNLOnoKM\n28ZlnboYdUEuFraNAbBu0pxur85HZfUU6bFXuHhgm3G4x2PQeK4d3oNUJq+mUtW80NDQms5CjbGw\na0JBemlbKUi/fadN1UNTVDrFo6RNlba9kjaVZ2xTVk2a0/XVeSitGpARe5VLB4LutCkDknt6dbTq\nYuM+dZle9g/rqcrLy6O4uBiNRgOAs7MzCxYsYOfOnaxdu5bz58+zf/9+Ro0axZgxY1iwYAEAixcv\n5tSpU2zatIm8vDxmzZrFxIkT8ff358qVK488b3JyMpMnT2b8+PFMmDCB+Psu+s8//zz5+flcvXqV\n4cOHM3HiRH799Vfj9hUrVjB27FhGjBjB7t27ycnJwdfX1/jir/3795c7ea0qmcmV6LRq47Jep8Wg\n1yNTKMtJpzFZp9OqkSlUyBSmx9Bp1Mjkpnc4UjMZPUf/m/jLYaTFl9a1nZMbL/17C95DX+fEd+vR\n67SVWbwqIVOo0GpN5wBpNcXIFealaeRKtPfUiTGNXIVKZYmT8zOcDdvLZx+9TEriNQaPXGCStplL\nByRIiI/9s+oKUgPM5PKSHqY7DDodBr0es3ve0VJRrUaNpmNAIEXp6aT+zSdmahMzuQL9PZ+xu59F\ns3I/i6ZtS6fVIFOoyEtLIvnyGcK+WcOxjYHIlfXwGDQegIatvJCbW3Dr/MmqL4xQKzy4TanuS1f+\n9d1MoSQ/LZmUy2c49c2HHN84D5nSHPdB/gCk3ojEpXt/kEiob++EXQt3pPInIGCXSiv+V0s8Vk+V\nm5sbXl5e9O3bl+eee45evXrRr18/evbsia+vL15eXly+fJnNmzdjZWXFuHHjuHr1KlOmTCEoKIgZ\nM2bw8ccf07NnT/z8/Lhx4wbLli3jq6++AmDt2rV8+eWXZc67fv16Ro4cycCBAzl48CCbNm1i5syZ\nZdJ98sknzJgxAx8fHxYuXAhAcXExDg4OBAYGUlRUhI+PD35+frRu3Zpz587RoUMHDh8+zKuvvvo4\nVVMhrbsMxq3rYAD0Oh2FuZnGbVKZHIlUiua+MXGtugiz++5uZXIlGnURWnUxZrLSL0UzhRKtunQi\npEyhove4eRRkpxG6b5PJMdLir7Bn9SSesneh78T3Ofz1+2QmP3wIt6Zp1IXIZKZfdDK5CvU9Zdao\ni5DJFOWmKS7K53bSdZISLgEQ/sdOuj03AblchUZTUu/u7V7g0vlDVVyS6mHfpQv2XboCJUGUOi/X\nuE0ikyGRStGp1Q/a/YGuf7sLiUxGc9/+tPQbxfVdOystz9XFuUs/XLr2A8Cg01Kcm23cdvezqC3z\nWTT9vEHJl6dWXURO0k0y464b118/to8uk95FKpPTduA4wreurcLSCLWBc5cXcO5a8rtyep3uL7Qp\n0+u7mVyBTl1ETlKcSZu6cSwE70nvAHAxZCuewybR++3V5CTe5Pa182gLyz7kVNcYJLVnWK+iHnui\n+urVq4mKiuK3335j8+bN7Nixg6ZNmxq3W1tb88YbbwAQFRVFVlaWyf7nzp0jIyOD/fv3A1BYWPqF\nOHv2bPr06WNcvjunKjIy0vg+CW9vbz7+uPyhmaioKDp06GBMd/z4cZRKJdnZ2YwZMwa5XE5mZkkg\nM2zYMH744Qc8PDxISEjA07P8+SWV6Wro91wN/R6A1t6DaOziYdxmZduUgpx0k+EGgJzUBJy9ehmX\n5cp6KMwtyU27RXZqPPVtm5ocI+t2SS+eRCql97j5ZKXc5PQPnxvTKMwtcXj6WWIijgKQmRxDavxV\n7Ft41fqgKj01jjaefY3LSqUFKvP6ZKbF35PmJg1sHEz2e8rWgbTbsRgMBpQqC+N6g14PgN6gN65z\nbd2NU7/vqKoiVKvk0FCS7wzBNPb2xsq5dHje3NYWdU7OX3pyz6pFCzR5eRTevo1Bq+X26XDaTi37\nKHJdEBta+mSes7cPti5tjNssbO0pyslEW2T6JZWXmoiDVxfjskxpjtzcgvy0ZFTWNui1GtT5JYGr\nRGqGXqejgUMLzK1s6P5ayU2emVyB1EyG0sKKsG8+qOpiCtUoNrTkyTyA5t4+2Lq4GbdZ2DYut03l\npybS1Kt0rlVpm0op06akUikGnQ4AdX4OZ7ZvMO7XZUogOSnlD9vXJXXx6b/H6jMzGAwUFxfj6urK\npEmT2L17NykpKSQmlsxhUavVLF68mHXr1rFt2zbatWtX5hhyuZwFCxawdetWtm7dSnBw8CPPK5FI\njEN1Go0G6QO6/gwGA5I7ka7+zhfmqVOnCA0NNZ7v7ivpe/XqZdx2byBXXeIuh2Lv2g4ru5IAwL3H\ni8REHCuTLjn6PJYNGtGoufuddMNJuHIKraaYmxd+w6VdL1SWDZBIpbTpNozY8yXHcOs6FI260CSg\ngpI7qM5DX8e+hRcAKgtr7Jxa1/qACiAu+gxWDexxbF6S907dRxN15YSxlwkgPTWWgvws3L1K7hg9\n2w8kJyuZzPR4bkadxrK+Lc4tOwPQrtMwEuIuGId06lk0wMKiARnpdf/idL/My5exdnVFZVcysbxJ\n9x6knY94xF6mrJo74zxwIJI7bz1+ys3NZJ5VXZV8+Qx2rh5Y2DUBwLXHQG5FlH3FSFr0Rcwb2GHT\nvLUxXcqVc+g0xTh7v0C7F6cikZqBRIJL136kXP2TjJtX+XHJVH5e8QY/r3iDyO+/4db5kyKgesKl\nXD6DnWtbY5tq0WMgtyLKDv+mRV/CvIEdTzV/+k66Ady+06aae/vg9eIUY5tyvtOmADyGTCgZ/gNs\nXdqgsrIhI/ZqNZWuCkn/wl8t8Vg9VcHBwYSHh7Nq1SokEgm5ubno9XocHR3R6XTk5+djZmZGw4YN\nSUpKIjIyEo1Gg1KpRHtnPke7du345ZdfaN++PTdu3OC3337jlVdeeeh5PT09CQsLY/DgwYSHh+Ph\n4VFuOhcXFyIjI+nZs6fxxxAzMzOxt7dHLpdz+PBhdDqd8VepO3XqxIYNG/jwww8fp1r+lsKcdML2\nf0Jv/wVIpVIyEqM4dbjkdQq2jk/T3sefX7a8h06r5vjOVXQe+joyhYrc9CT+CC6ZmJ9+6wYXf/uO\n/tNWA5B040+uhh0A4OnOA5ApVAx7u/QVDTcv/M6fv2zjaNAyOvZ/BbnSHIlEypWTISYT22srrVbN\n/l0L6TdkNnKFOZnpCRzYswxLKztGT1rHFxtK5rCEfPs+/YcH0KPvFPLzMtj/bcnrEjSaIr4Lmovv\nsH8jk8nJzkrmwJ5lxuPXt25EQUEW3Pcjm08CdU4OMfv303qcPxKplPzEROK/L3nCz9LREScfHy5v\n2YLcwpK29/wsQ9tXp2LQ67n05Rfc+u04zvUH0W7mLJBIUGdnE7X3u5oqUqUpysnkwv4v6ew/G4nU\njOzEGC4cLrnZa+DoipuPH6FbVqLXajizcyOeQychUyjJT0/hXHDJ5+v6kb14Dn2FPv/6AAx6MuKu\nc+nHoJosVo1q1KgRx46V3iQePXoUrVZL3759jTfhT7KSNrWFZ/3/hVRqRnZiLNcO7wGggWMLnvYZ\nyaktq9FrNZzb+TGeQydiplCRn55CRHDJazmuH/kfnkMn0ftfqzAYDGTGXefyjyW96DGhh2jv9zrO\nXV5AU5jPme3rn4jrlqEOTlSXGO7/Wea/QKfTsWbNGsLDw6lXrx5arZZp06aRnp7Oxo0bWbFiBfv2\n7eP69eu4ubnRsmVLgoOD2bp1KyNGjKBfv37MmjWLwMBA0tPT0ev1zJs3D09PTwICAvD19S0z/BcW\nFkZKSgrz5s1DrVYjl8tZvnw5Go2GWbNm8d133/H8888TEhJCbGwsgYGBNG7cGCcnJwoKCpg3bx6v\nvPIKKpUKHx8fzp49i6WlJcuXL+f06dOsWrWqzC9Zl+ebuYP+brX9oyRKsh6dSOA5nqvpLNQZqYbY\nms5CnTBsxZMxbF3VQgLH1XQW6ozBy7dV6/n+OJzz6ER3dO9rVYU5qbjHCqqeNBs2bMDBwYGXXnrp\nkWlFUFUxIqiqGBFUVZwIqipGBFUVI4Kqiqv2oOrXvxBUPV87girxRvU7pk2bhkql4s0336zprAiC\nIAjCP15dnKgugqo7Pvus7Kv/BUEQBEGoIXVvSpUIqgRBEARBqH0MIqgSBEEQBEGoBGZi+E8QBEEQ\nBOHxiZ4qQRAEQRCExyeG/wRBEARBECqDCKoEQRAEQRAqQd2bUiWCKkEQBEEQaiHRUyUIgiAIgvD4\nJGY1nYO/TgRVgiAIgiDUPqKnShAEQRAEoRKIoEoQBEEQBKESiInq/xxmdTGErgGDmm6q6SzUCdNv\nTq3pLNQZNjJtTWehTggJHFfTWagThqwIquks1BmG5duq9XySOvg1K4IqQRAEQRBqHYmZoaaz8JeJ\noEoQBEEQhNpHDP8JgiAIgiA8PjH8JwiCIAiCUAkkEjH8JwiCIAiC8PjE8J8gCIIgCMLjE8N/giAI\ngiAIlUAqFcN/giAIgiAIj030VAmCIAiCIFQCiZhTJQiCIAiC8PjE03+CIAiCIAiVoKp7qjQaDQEB\nASQmJmJmZsaKFStwcnIySbNu3TrCwsIwGAz4+PgwderDf1KsDo5YCoIgCILwpJNKKv73d3z//fdY\nWVmxY8cOpk+fzocffmiy/dq1a4SFhbFz50527NjBd999R2pq6kOPKXqqarHmXj3x6DMaqdSMrJQ4\nQvesR1NcUCZd4xZedBgwGZlSRX7mbU7uWU9hTjoASgtruo+ag6VNY/Z/+JpxH5nSnM5DX8fGsRUS\niYSb53/j/C91H+UJdgAAHnRJREFU/4dFrVs2oNGz9kikEooyikj49SZ6tb5MOqsW1jTqZI/ETIqu\nUMutY/EUZxTRwM2Gpj0c0RRojGkzLqSSfiGtOotR5Tq4PsuMwf/CXGFOSmYyy3YvIjX7tkkaz+bt\nmDnkbSyUFhRpitgQspaImHMAjO8ziQEdB2MwGLh5O4Y1e1eRkZdeE0WpUl4tOjF5wGxUynrczkxi\n/Z73SM8xrac2zZ5hyqA51FNaUKwpYvOBNVyMPQtAv2dHMKz7OKRSKSmZiWz8blGZ/euypl5daNln\nGFKpjNyUeCL2fI62uLBMOtsW7rgPeBkzpZLCzDQi9nxOUU4Gjh160nbweIpzs4xpY08eIjb0EApL\nK7yGTcaysQMYIDLka9JuRFZn8WqMTCZj5cqVzJkzB0dHR27dulXTWaoR0iru9jl58iTDhw8HoFu3\nbsydO9dke/369SkuLkatVqPT6ZBKpZibmz/0mKKnqpaqZ92QZ4e8xpEtiwhZ9zr5mSm06ze+TDoz\nuZIeY94hdO9GQtZO59aVcLyHvwmAwtySF6auICvlZpn9nuk3Ab1Oy/cfvcGPm97Gud1z2Ld8psrL\nVZXklnKa9nIk9vsorm2/jDpXjb1303LTOfR24uYP0VzffpnsqCwcn29m3J4TncX17ZeNf09aQKWS\nq1g8bjkrg5cw9oOX+P3ycd4ZEWiSRm4mZ+WkD/nPjxsZ96Efm3/+D4teXgZAp1beDOo0lKmbJjLu\nQz/i0uKYMfitmihKlVLKVbwzZhUb9y5i+tphhF85xpvD55ukkZnJmT9+HV//tJ43PhrBtkOf8M7o\nlQC0cmjLyz7Tmf/la7y+7kVuJt9gUv9/1URRqoTK2pa2QyZwassajq57h4LMNNz6+ZVJZyZX0mHM\nm0Ts3czRte+QcuUcnsNfMW5Pvniao+v+bfyLDT0EgMfgCeRnpHB07Tuc2b6e9qNex0yhqrby1aR9\n+/aRl5dX09mocVXdU5WWloaNjU3JuaRSJBIJarXauL1Jkyb079+fPn360KdPH8aMGYOlpeXD81yR\nEwcFBTFq1Cj8/f0ZOXIkJ06cKDddQEAAR44cqWh5ykhISGDEiBEPPJa3t3eF9n377bcpKioy2X7k\nyBECAgL+dt6qm6O7N8lRERRkl3Q13jhziGae3cuks3f1Ii8jmczEKACizhzCvuUzyBTmYDBwbNsy\nEi6Hldkv/uJJzh/eDgYDWnUhmckxWDdqViZdXWLlYk1eQh6avJJepsxL6Vi1bFAmnUFvIP7nm2hy\nS9LlJeSibPDPuFgDdGzZicT0W1y7dRWAA+H76dyqC/WU9YxpZGYyVu9ZxtmoMwBExPxJQ+tGWKos\naWHfkisJl8kvygfg7I1wWti7Vn9BqpiXa2eSMxKISrwCwKEz/+OZll0xV5jW06b/LeFC9GkALt08\nh611IyxU9cnOz+SDnQFk5pYE5Rdjz9Ks0ZNTT/buHUiLukhRdkkPZfyZozTxLHuNtnV1pyAjlZzE\n2DvpjtGwpecjAyS7lh7EnzkGQG5KAtm3YrBzbVu5haillixZwvvvv1/T2ahxEknF/x5l9+7djBo1\nyuTvjz/+MEljMJhOjI+Pj+fQoUP88ssvHDp0iJ07d5Ke/vAe+UcO/yUkJPDtt98SHByMXC4nNjaW\n+fPn061bt0eXooasW7euprPw2KzsmpKXnmxczktPwtzyKRQqC9R3vswA6ts5kJtRmk6rLkJdkEt9\n2yZkJkWjLsrHvP5TZY6fEn3e+G+Z0pyGzdpw+fh3VVSa6qFooEKdXWxcVmcXI68nR6o0Q1+sM67X\nFmjJK8gtWZDAU2425MRkG7erGprjMrwlcgs5+Yn5JP2RUO4QYl3l1LAZtzISjMuF6kKyC7JxsHXi\neuJV47pjkaU3NV3duhGXepO8ojzORZ9mVI8xNLRuREZuOr08+hB+vWzgXtc52DUn+Z56KlIXkluQ\nRRNbJ6KTrhrXnbz4qzFNx6e7k5AaS35RLvlFudzOSizd1ro71xIuVF8BqpiFXRMK0kuHMgvSb6O0\ntEauqoemqHSagqWdPQUZKcZlnboYdUEeFraNAbBq0pyur85DadWAjNirXDoQdGcI0YDknhcVadXF\nxn2edKGhoTWdhVrh7/ZAlcfPzw8/P9Oe1ICAAFJTU3Fzc0Oj0WAwGFAoFMbtFy5coF27dsYhv9at\nW3Pt2jW6du364Dw/KiN5eXkUFxej0ZTc1Ts7O7Nt2zYuXbrE6NGjGTNmDKtWrTKmDwsLY8qUKQwc\nOJBLly4B8PXXXzN69GhGjx7NZ599BkBycjKTJ09m/PjxTJgwgfj4+IrWzSP3ff7558nPz+fq1asM\nHz6ciRMn8uuvpRe+FStWMHbsWEaMGMHu3bvJycnB19fXGKXu37+fFStWVDg/VcFMrkSnLe2G1Ou0\nGPT6Mnd3svvSAWi1amQV7CaXmsnoMfodEi6HkRZ/9fEzXoOkMikGXWnwY9AbMBgMSGXlN3Nbr4a0\nmeyJRVNLkk+WzFlQZxWTE5PNzQPRXN91BTOFlCY9HKsl/9VFJVdRrDFtM8WaIswf0GZc7Vsya8hs\nVu9ZDsC1W1f58cz3BAfs54eFh3nGpT3f/LqlqrNd7ZRyFWptsck6tbYYlaL8ORXO9q14ddD/8fH/\nlpbZ1ueZQXR8ugdBv3xaJXmtCWZyBXpt6dzDB12jSq5lGpN1Oq0aM4WS/LRkUi6f4dQ3H3J84zxk\nSnPcB/kDkHojEpfu/UEiob69E3Yt3JHK5VVfMKHWqMyeqvJ0796dgwcPAiWjWfePhjVr1ozIyEj0\nej0ajYZr166VeTrwfo/sqXJzc8PLy4u+ffvy3HPP0atXL/r168fSpUtZtGgRbm5u/Pvf/zZOpJNI\nJHzxxRfs3LmTvXv3Ur9+ffbu3UtwcDBQEi3279+fTz/9lJEjRzJw4EAOHjzIpk2bmDlzpsm5165d\ny5dfflkmT+vXr3/kvgCffPIJM2bMwMfHh4ULFwJQXFyMg4MDgYGBFBUV4ePjg5+fH61bt+bcuXN0\n6NCBw4cP8+qrrz6qaird010G8XTXwQAYdFqK7pm8KZXJkUilaNWmw5padRFmMoXJOplciVZddrLo\n/WQKFb3GzaUgO41T+z6phBJUP1tPO2w8G5Ys6A3k3DPBXGImQSKRoNeU38uUfj6V9POpWLd6CteX\nnuba9ssUJOdTkFzaE3j7TAouQ1x5kqaJFqqLUMpN24xKoaKwnAnGHs29WDJuBSuDl3IuumQosId7\nL7q69WDIEl9yCrKZ8PxkFo5dwv99+WTNqypSF6KQKU3WKeUqCsv5bLk1a8e7Y1ez8bvFRMacNtk2\n0HsUw3r4M2/zVLLq+GR+5y4v4Nz1BQD0Oh3FuaU9vA++RhVjJjMNhszkCnTqInKS4siMu25cf+NY\nCN6T3gHgYshWPIdNovfbq8lJvMnta+fRFpZ9UEd4cj3gfrjSDBw4kBMnTjB27FgUCgUrV5bMh/zs\ns8/o1KkT7du3p3v37rz88ssAjBw5EkfHh99kV+jpv9WrVxMVFcVvv/3G5s2b2bFjB9HR0bi5uRm3\n39WxY0cAGjduTEREBJcvX6Zdu3bIZCWn6tChA1euXCEyMpI5c+YAJXOlPv744zLnnT17Nn369DEu\n340iK7IvQFRUFB06dDCmO378OEqlkuzsbMaMGYNcLiczMxOAYcOG8cMPP+Dh4UFCQgKenp4VqZpK\ndS30ANdCDwDQynsgjV08jNusbJtSkJOO5p6hP4Cc1ASae/U0LsuV9VCYW5KTlsjDSKRSeo2bS1ZK\nHGd/2FyJpahe6RfSjBPJbTzssGhaOolQYa1Ek69Br9aZ7KN8SonMQkF+QskQYPb1TJr2ckT5lApd\nkRa91oCuSAuARCrBoK97L6B7mLjUWPq2e8G4bKGyoL65FfFpcSbpXO1bstR/JQuD5hIR+6dxfedW\nXQi7epKcgpIv1MMRPzPh+Vd40iSkxtLTy9e4XE9piaW5FYlppg9+ONu3ImDsB6ze9S6XYs+ZbOvb\nYSiDuo4m8LMpZOQ+/FHsuiA29JBxInlzbx9sXdyM2yxsG1OUk4m2yDTwyU9NpKlXaQ+ATGmO3NyC\n/LQUVNY26LUa1Pkln0WpVIpBV/J5VefncGb7BuN+XaYEkpNS8RENoe6r6heq33031f2mTZtm/Pes\nWbOYNWtWhY/5yDjQYDBQXFyMq6srkyZNYvfu3aSkpBiDkfIyee++EonEZPKXRqMxzrK/u/7uuoqq\n6L53zw+g15f0Vpw6dYrQ0FC2bt3K1q1bjeOnvXr1Mm67N5CrKQmXQ2ns2o76dg4AuPUYzs2I42XS\npURfwKJBIxo2d7+Tbhi3roSj0xSXSXuv1l2HoFEX1umA6n45MdlYOtZH0aCkd8HumUZkXS/bTs1U\nMpx8miOrVxLo17O3QCKVoM4uxsbDDoc+TiWfDEnJEGHuzZzqLEaVO3PjNPZP2ePl3A6A0T3HceLy\n7xRpTHsY5o9exJq9q0wCKoC41Js827ITSnlJPXdz60F0clT1ZL4aXYgOp1GDJrg3L3kqdlgPf8Kv\nHKf4vnr618glfLp/eZmAysaqERP6zeT9r958IgKq+6VcPoOda1ss7JoA0KLHQG5FnCyTLi36EuYN\n7Hiq+dN30g3g9pVz6DTFNPf2wevFKUikZiCR4Ny1HylXS9qbx5AJJcN/gK1LG1RWNmTE1u0pCsJf\nU9VP/1WFR/ZUBQcHEx4ezqpVq5BIJOTm5qLX6+ncuTMRERG0a9eOuXPnMmXKlHL3b9OmDRs3bkSr\nLbnzj4iI4LXXXsPT05OwsDAGDx5MeHg4Hh4e5e5fnoru6+LiQmRkJD179iQsrGQibWZmJvb29sjl\ncg4fPoxOp0OtVqNQKOjUqRMbNmwo8wKwmlCYk0H4/k95zn8eEqkZmYlRnD78XwBsHVvh5ePPkS0L\n0WnV/L5zNZ2GTkemUJKbnsTJ4I8AcHDrRPsBk5HJlagsGzD47U8pzEnn8BfzadW5PzKFisFvl87x\niLvwe51+V5U2X0Pi8XiaD2iBRAqFqYUk/VYy0di8UT0aezchNiSKgqR8bp9OxmVYS5BIMOgMxP8c\ni16j5/bpZBx6OfH02DZggPzkfJJOPEmDfyXzghYGzWP28HcxV5iTkB7Psl2LsLNqyLpXNzF+7Wja\nNvPEtUlL3hg4kzcGlg6tv79jHntDg2nWsDnfvL0TvUFPem4ay79dVIMlqhpqbTGrdwYwfWggSoU5\nSenxfBT8HjZWjVj8yifMWD+S1k5eONu3YlL/t5jUv3T4c82uQNq36oZKWY/Fk0s/Yzq9jhnrR9ZE\ncSpdUU4mF/Zv4Vn/fyGVmpGdGMu1w3sAaODYgqd9RnJqy2r0Wg3ndn6M59CJmClU5KenEBFcci27\nfuR/eA6dRO9/rcJgMJAZd53LP+4AICb0EO39Xse5ywtoCvM5s309GJ6sXuPyNGrUiGPHjhmXjx49\nilarpW/fviQmPnwE4kkjrYM//icx3P8M4X10Oh1r1qwhPDycevXqodVqmTZtGk2aNDE+8vnMM8/w\n7rvvEhAQgK+vL3369OHIkSP89NNPrFy5kqCgIEJCQjAYDAwZMgR/f39SUlKYN28earUauVzO8uXL\n0Wg0zJo1i++++87kWHd5e3sTFhb2yH2ff/55QkJCiI2NJTAwkMaNG+Pk5ERBQQHz5s3jlVdeQaVS\n4ePjw9mzZ7G0tGT58uWcPn2aVatWsXv37kdWXNDcIY9X8/8QXg6LazoLdcL0mw//6QOhlI1MW9NZ\nqBNeo+I3qv9kQ1bU3RvJ6vaIcKHSxRdmPzrRHU7m1lWYk4p7ZFD1T7JhwwYcHBx46aWXHplWBFUV\nI4KqihFBVcWJoKpiRFBVMSKoqrjqDhcS/kJQ5VhLgirxMzV3TJs2DZVKxZtvvlnTWREEQRCEfzxZ\nbZosVUEiqLrj7vuzBEEQBEGoedIqf/6v8omgShAEQRCEWqcuTlQXQZUgCIIgCLWOCKoEQRAEQRAq\nQRW/UL1KiKBKEARBEIRaRyJ6qgRBEARBEB6fTFL3+qpEUCUIgiAIQq0jnv4TBEEQBEGoBFLRUyUI\ngiAIgvD4RE+VIAiCIAhCJRAT1QVBEARBECqBTGJW01n4y8QPKguCIAiCIFSCujcLTBAEQRAEoRYS\nQZUgCIIgCEIlEEGVIAiCIAhCJRBBlSAIgiAIQiUQQZUgCIIgCEIlEEGVIAiCIAhCJRBBlSAIgiAI\nQiUQQVUdEBQUxKhRo/D392fkyJGcOHGCK1euEBMTU9NZq1QJCQm0b9+e8ePH4+/vz8SJEzl58uRf\nPs4ff/zBmDFjGD16NMOHD2f79u3GbSEhIfj6+rJp0yY++ugj4/rPP/+ct956y7j8yy+/MHv27L90\n3rCwMGbNmvWX81vd4uLimD59Oi+99BIvvvgiS5YsoaioqKazVWvc3w5HjRrFoUOHajpbVervXmN+\n+umnh24PCAjgyJEjJuu8vb0fmD4hIYERI0YA8Pbbb5dpl0eOHCEgIOCh56xs5dVNecor619xb9mf\nhHr7pxJvVK/lEhIS+PbbbwkODkYulxMbG8v8+fPx9vbGw8MDFxeXms5ipXJxcWHr1q1A6Zf/2rVr\ncXNzq9D+t27dYsmSJXzxxRc4ODigVquZM2cOcrkcPz8/Tpw4wTvvvEOTJk1YsWKFcb8zZ86Qmppq\nsvywi1hdpdfrmTlzJgEBAXTt2hWAL7/8kgULFvDBBx/UcO5qj3vbYVZWFi+++CI9e/ZEpVLVcM4q\n39+9xiQkJHDgwAF8fX2rJF/r1q2rkuP+FQ+qm27dutV01h6oNtTbP5kIqmq5vLw8iouL0Wg0yOVy\nnJ2dWbBgAZMnT8bGxgZbW1sKCwtZt24dMpmMxo0bs2LFCr7//nuOHz/O7du3WbduHb/88gshISFI\npVJ8fHyYPHlyTRftkZo1a8b06dPZvn075ubmnD9/nuLiYsaOHYuvry9+fn4cPHgQiUTC/v37uXjx\nInK5nPHjx+Pg4ACAQqEgMDCQ1157jaZNm3L8+HEiIyNZsGABsbGxqNVq5HI56enpODs7ExMTg4uL\nC6dPn2b06NHk5eUxd+5csrOz0el0zJ8/Hzc3N06fPs3atWuRyWQ0adKEJUuWmOR9586dXLhwgWXL\nltVE1T3Q77//jrOzszGgAnjllVfo378/U6dOxcnJiejoaDIzM1mxYgXu7u4EBQWVaTsbN24kNzeX\nmJgY4uLimDt3Ls8991wNlqzqNGjQgIYNG7Jw4UIUCgVZWVmsXbuW9957j/j4eNRqNbNmzaJHjx74\n+PgwatQoDh48SPPmzWnbtq3x3x9++CHJycnMnTsXjUaDRCJh2bJlODk51Wj5KnKNiY2NZdu2bUil\nUlq1asWSJUtYvHgx58+fZ9OmTUyaNKncz8nDlFcX9/7W2/PPP09ISAgJCQm8++67WFtb06xZM+P2\nFStWVOiaEBgYWKl1s23bNi5dusSiRYuQSCS0b9+ed999Fyjprd62bRtJSUmsWbMGd3d3vv76a374\n4QcA+vbty7Rp0x5Z9rpeb/9kYvivlnNzc8PLy4u+ffsSEBDADz/8gKurKz179mT27Nl4eXmxcOFC\n1q1bx7Zt27C2tiYkJASApKQkgoKCUKvVHDx4kB07dhAUFMTPP/9MYmJiDZesYjw8PLh48SIODg7s\n2LGD7du3s379eqysrGjdujXnzp0D4PDhwwwePJjo6Gjc3d1NjtG0aVMyMzPp2rWrsd46d+6Ml5cX\n58+f59q1a7i6utKhQwdOnz5NQUGBMcj6+uuv6dmzJ19//TXvv/8+q1atAmDp0qV88sknfPPNN9ja\n2nLw4EHj+c6ePcvPP//M+++/X231VFHl1Y9EIqFVq1ZotVq0Wi1btmzhrbfe4uOPPyY+Pv6BbSc5\nOZnPP/+cefPmsWvXrpooTrVISEggKysLnU6HtbU1Gzdu5MCBAygUCrZt28bGjRuNQbVer8fd3Z09\ne/Zw9uxZHBwcCA4O5syZM+Tk5LB+/XpGjhzJ1q1befnll9m0aVMNl65i15jCwkI2b97Mzp07iY6O\n5urVq0yZMoXOnTszY8aMB35OANauXcv48eONf3dVtC4++eQT4zmk0pKvrOLi4gpfEyq7brRaLUuX\nLmXRokXs3LmT9PR0bt26BZR8lr744gsmTJjA3r17iY+PZ+/evQQFBREUFMSPP/5IXFxchcpel+vt\nn0z0VNUBq1evJioqit9++43NmzezY8cOmjZtCpQMTUgkEpo0aQKUjLuHh4fj7u6Op6cnEomECxcu\ncPPmTSZMmABAfn4+t27dMh6jNsvPz6devXpkZ2czZswY5HI5mZmZAAwbNowffvgBDw8PEhISjOXV\n6XTlHuv+O8G7dWVlZUXHjh3x9PTkq6++wsHBgY4dOwJw7tw5MjIy2L9/PwCFhYWkpaVx8+ZNZs6c\nCUBBQQFPPfUUjRs35vbt28yZM4dvv/0WuVxeVdXytz2ofgwGA1Kp1Dis8cwzz7BmzZoHth2ADh06\nAGBvb09ubm41laB6xMTEMH78eAwGA0qlklWrVrFr1y68vLwAiIyMNA4PN27c2NiDBeDl5YVEIsHW\n1tYYwNrY2JCbm0tkZCRz5swBStrfxx9/XAOlK+th1xgAa2tr3njjDQCioqKMZb2rvM/JXbNnz6ZP\nnz7G5bv1VtG6iIqKMrY1b29vjh8/jlKprPA14XGVVzfR0dHGnrjVq1cb0969bjRu3JiIiAguX75M\nu3btkMlKvmo7dOjAlStXKlT2ul5v/1QiqKrlDAYDarUaV1dXXF1dGT9+PAMGDDBul0gk3Pub2He7\nhAHjl7pcLqd3794sXry4ejNfCSIjIwEIDQ1l69atyOVy2rdvD0CvXr1Yv349oaGhxotPixYtiIyM\n5NlnnzUe49atWzRs2LDcoOrDDz/E0tKSmTNn4uzsTFRUFKdPnzZewORyOQsWLDCeEyA7O5tGjRoZ\n59zcFRYWRkJCAl27dmX37t3GL6HapEWLFuzYscNkncFg4MaNG7i4uKDX643rJRLJA9tOaGio8Yvi\nSXTvnKq7du3aZRIo3/u5U6vVxt4AMzMz4/p7/20wGEw+rxqNxrhPTXrUNUatVrN48WL27dtHw4YN\nee2118oco7zPyaNUtC7u1htgbJ+nTp2q8DXhcTyobu4GI/d72P/3veV8nHZQF+rtn6zmP9HCQwUH\nB7NgwQLjhyg3Nxe9Xo+jo6NxOEIikRiHZE6dOoWHh4fJMdq2bUtYWBiFhYUYDAaWLl1aJ572iouL\nY8uWLYwYMQJ7e3vkcjmHDx9Gp9MZ50J16tSJDRs2MGTIEADGjh1LUFAQcXFxQMlFZ+XKlUycOLHM\n8Vu3bk1cXBxJSUm0aNECqVSKjY0Nv//+uzGoateuHb/88gsAN27c4KuvvsLa2tq4DLB161auXLkC\nlNyJLl26lB9//JHr169XbQX9Dd27dychIYFjx44Z123ZsoWOHTvSoEEDzpw5A5T0PLi6utbZtlPV\nPD09CQsLA0qG2aVSKVZWVn9pv/Dw8DKf1ZrwqGtMfn4+ZmZmNGzYkKSkJCIjI41f5lqtFij/c/Io\nFa0LFxcX483V3fSZmZkVviY8jgfVTefOnYmIiABg7ty5REVFlbt/mzZt+PPPP41D6xEREbRp0+ax\n2kFdqLd/sif3VvMJMWLECKKjo/Hz86NevXpotVrmz59Peno6S5cuxcLCgiVLljBnzhxkMhlOTk4M\nGjTI2A0PJXOKJkyYwLhx4zAzM8PHx6fWPsV0d9hFrVaj0+l477338PLyIigoCH9/f3x8fOjduzfv\nv/8+y5cvZ8CAAZw/f57mzZsDJWVds2YN77zzjvEuc+jQoQwfPrzMuSQSCa1btza5k+zQoQPffvut\ncfKwv78/gYGBvPzyy+j1eubNmwfAsmXLCAwMRC6X06hRI0aPHm2ck6BUKlm0aBHz5s1jx44dJnev\nNU0qlfLFF1+wcOFC1q9fj8FgwMPDg/nz57N48WKKi4t57bXXSEpK4oMPPqhTbac6DRo0iFOnTjF+\n/Hg0Gk2Fe4FnzZrFvHnzjMPDy5cvr+KcPtqjrjErVqyge/fuvPTSS7i5ufHqq6+yYsUKtm7dyqVL\nl1i+fDmzZs0q93PyMOXVhUajKZPu9ddfJzAwkG+++QYnJyc0Gg3dunXj888/r9A1oSrqpkmTJsY5\nk8888wyurq7l7u/o6Mjo0aPx9/fHYDDg5+eHg4NDhctenrpQb/9kEsO93yiCUMds2LABBwcHXnrp\npZrOSp0XEBCAr6+v6P4X6jRxTfh7RL1VDtFTJdRZ06ZNQ6VS8eabb9Z0VgRBqAXENeHvEfVWeURP\nlSAIgiAIQiUQE9UFQRAEQRAqgQiqBEEQBEEQKoEIqgRBEARBECqBCKoEQRAEQRAqgQiqBEEQBEEQ\nKsH/AzbMylKWneKdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad649006d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 41088 entries, 1 to 41088\n",
            "Data columns (total 7 columns):\n",
            "Store            41088 non-null float64\n",
            "DayOfWeek        41088 non-null float64\n",
            "Date             41088 non-null datetime64[ns]\n",
            "Open             41088 non-null float64\n",
            "Promo            41088 non-null int64\n",
            "StateHoliday     41088 non-null float64\n",
            "SchoolHoliday    41088 non-null int64\n",
            "dtypes: datetime64[ns](1), float64(4), int64(2)\n",
            "memory usage: 2.5 MB\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py:418: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
            "  \"matplotlib is currently using a non-GUI backend, \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z2UpPT1d6enqt6+zfv187duzwywJbml27clRQkK8VK1YpJWWmFi+eX+s65FUTmdlDXvaR\nmT3kFTgNlvDWrVuVnJwsSYqPj9eZM2dUVlZW4zrz5s3Tr3/9a/+ssIXJydmhAQMGSZK6dIlTaelZ\nlZeTV33IzB7yso/M7CGvwHE2dIWioiIlJCRY25GRkSosLFRoaKgkad26derXr59iY2MbdcCIiLZy\nOoObuFz73O6wJl/WFOfOnVXnzn2s23O7o+TxnJfbHSPJfl5S88zMV3lJvs8s0Hl548t8rsWM2ceM\n2cOMBU6DJXwtj8djfX369GmtW7dOq1at0okTJxr1/SUl5+we8roUFpbW2Ha7w2pd5u16TVVRUaWz\nZyus26usrNapU+UKDS3V2bNnbOclNc/MfJWXVH9mLtfFZj9j16prxnyFGbPP15kxY8yYXXUVfoOn\no6Ojo1VUVGRtnzx5Um63W5K0bds2nTp1Sj/96U/1y1/+Unl5ecrIyPDRklumqKgoFRcXW9tFRUWK\nioqSdPkUD3nVVl9mzFhtzJh9ZGYPeQVOgyWclJSkTZs2SZLy8vIUHR1tnYr+7ne/qw0bNujtt9/W\n8uXLlZCQoNTUVP+uuJnr1++b2rLlPUnSvn17FRUVpbZtQyRJgwcnk5cX9WXGjNXGjNlHZvaQV+A0\neDq6b9++SkhI0MiRI+VwOJSWlqZ169YpLCxMQ4cODcQaW5RevXqra9fumjBhnBwOh6ZMeUYbNqxX\nSEioBg4cbHp5zVJ9mQ0b9oDp5TU7zJh9ZGYPeQVOoz4Tfvrpp2tsd+vWrdZ1OnbsqNWrV/tmVS3c\nk09OrLF9++131LoOedVEZvaQl31kZg95BQa/MQsAAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAA\nDKGEAQAwhBIGAMAQShgAAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAADKGEAQAwhBIGAMAQShgA\nAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAADKGEAQAwhBIG\nAMAQShgAAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAADKGEAQAwxGl6Af42bt77DV4n5KYbPgYA\nQDN0Q7fPqylDal02bt77Xi8HACDQOB0NAIAhlDAAAIZQwgAAGEIJAwBgCCUMAIAhlDAAAIZQwgAA\nGEIJAwBgSKN+WUdGRoZ2794th8Oh1NRUJSYmWvu2bdumhQsXKigoSHFxcUpPT1dQ0Fe725cuXaC8\nvFw5HA5NnjxV3bsnWPvIyzsys4e87CMze8grMBpMbfv27Tpy5Iiys7OVnp6u9PT0GvufffZZLV26\nVG+99ZbKy8v197//3W+LbQl27cpRQUG+VqxYpZSUmVq8eH6N/eRVG5nZQ172kZk95BU4Db4T3rp1\nq5KTkyVJ8fHxOnPmjMrKyhQaGipJWrdunfV1ZGSkSkpK/Ljc5i8nZ4cGDBgkSerSJU6lpWdVXl6m\nkBDyqguZ2UNe9pGZPeQVOA2+Ey4qKlJERIS1HRkZqcLCQmv7yj/EyZMn9eGHH2rgwIF+WGbLUVxc\nrPbt21vb7dtHqLi42Nomr9rIzB7yso/M7CGvwLH9Bxw8Hk+ty4qLizVhwgSlpaXVKGxvIiLayukM\ntntYn3K7w/x2223auNSuXRvrGE5nkCIjQ2oc005eUuAz85bPtZf5MkNfZ8aMMWPXYsbsYcYCp8ES\njo6OVlFRkbV98uRJud1ua7usrEyPPfaYfvWrX+mee+5p8IAlJeeauFTfKSws9dtth4SE69ChAusY\nx4+fUFBQG2u7TRuHrbykwGd2bT5ud1ity3yZYX2Zud1hLW7GvOXlS8yYfb7OjBljxuyqq/AbPB2d\nlJSkTZs2SZLy8vIUHR1tnYqQpHnz5mns2LG69957fbTUlq1fv29qy5b3JEn79u1VVFSU2rYNsfaT\nV21kZg952Udm9pBX4DT4Trhv375KSEjQyJEj5XA4lJaWpnXr1iksLEz33HOP/vSnP+nIkSNau3at\nJOkHP/iBRowY4feFN1e9evVW167dNWHCODkcDk2Z8ow2bFivkJBQ3X13f/Lyor7M7r9/KJldgxmz\nj8zsIa/AadRnwk8//XSN7W7dullf5+bm+nZFN4Ann5xYY/v22++wviYv7+rKrE2bNmTmBTNmH5nZ\nQ16BwX9dDQCAIZQwAACGUMIAABhCCQMAYIjtX9aBG9+4ee/Xuz/kJsYGAHyBZ1PU8GrKkFqXjZv3\nvtfLAQDXh9PRAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkD\nAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDC\nAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGU\nMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhlDCAAAYQgkDAGAIJQwAgCGUMAAAhjgbc6WMjAzt3r1b\nDodDqampSkxMtPZ99NFHWrhwoYKDg3Xvvffqqaee8ttiW4qlSxcoLy9XDodDkydPVffuCdY+8vKO\nzOwhL/vIzB7yCowG3wlv375dR44cUXZ2ttLT05Wenl5j/5w5c7Rs2TJlZWXpww8/1P79+/222JZg\n164cFRTka8WKVUpJmanFi+fX2E9etZGZPeRlH5nZQ16B02AJb926VcnJyZKk+Ph4nTlzRmVlZZKk\n/Px8hYeHKyYmRkFBQRo4cKC2bt3q3xU3czk5OzRgwCBJUpcucSotPavy8st5HTtWQF5e1JcZM1Yb\nM2YfmdlDXoHT4OnooqIiJST85zREZGSkCgsLFRoaqsLCQkVGRtbYl5+fX+/tRUS0ldMZfB1Lbpqe\nPXsqLy9PkhS9UEpISFBubq7Pj3Pu3Fl17txHbneYJMntjpLHc15ud4zy8yts5yWZyezqvCRzmX3y\nyectZsauduW++ENLn7HxR9/RZz9/o8Zln11zncdat5fb/SOfHdPXmQUyrz8NHy935ekal12blyQV\ntmqvB3+/0ifHZMYCp1GfCV/N4/Fc1wFLSs5d1/c31ebNl1+pud1hKiwslSTr/32poqJKZ89WWLdd\nWVmtU6fKFRpaqtOnm3bfTWR2JS/JbGZNYWrGrrg6L39o6TOW9PLiWpd5y8yXGfo6s+aY1x3yXWbM\nmO/V9cK8wdPR0dHRKioqsrZPnjwpt9vtdd+JEycUHR19vWtt0aKiolRcXGxtFxUVKSoq6v/vc5OX\nF/VlxozVxozZR2b2kFfgNFjCSUlJ2rRpkyQpLy9P0dHRCg0NlSR17NhRZWVlKigoUHV1tTZv3qyk\npCT/rriZ69fvm9qy5T1J0r59exUVFaW2bUMkSTExHcjLi/oyY8ZqY8bsIzN7yCtwHJ5GnF+eP3++\ndu7cKYfDobS0NH366acKCwvT0KFDtWPHDs2ff/kn577zne9o/Pjx9d5WIN/+e+PvU4WS9N//vUy7\nd++Sw+HQlCnP6PPP9ykkJFQDBw7W4cN7beUlfbUzGzbsAWbMC2bMPl9mRl7MWFOO702jStiX+Iew\n/wM7X+XMyKtpx7eLzOxlRl7MWFOO7w2/MQsAAEMoYQAADKGEAQAwhBIGAMAQShgAAEMoYQAADKGE\nAQAwhBIGAMCQgP+yDgAAcBnvhAEAMIQSBgDAEEoYAABDKGEAAAyhhAEAMIQSBgDAkBumhDMzM/XI\nI49o1KhRGjZsmD766CPt3btXhw4dMr00vygoKFCfPn00evRojRo1SmPHjtXWrVtt386HH36okSNH\nasSIEXrwwQf15ptvWvvWr1+v++67T8uXL9fixYuty1955RVNnjzZ2n733Xc1ZcoUW8f9+OOPNWnS\nJNvrNeXo0aOaMGGCHn74Yf34xz/W7Nmzdf78edPLanaunctHHnlE//d//2d6WQHR1OegTZs21bs/\nJSVFmzdvrnHZ3XffXef1CwoK9NBDD0mSfv3rX9ea082bNyslJaXeY/qLt4y88Xaf7bg6g+aen9Nn\nt2RQQUGB3n77ba1du1Yul0uHDx/WjBkzdPfdd6tnz56Ki4szvUS/iIuL0+rVqyX9pyQWLlyobt26\nNer7jx07ptmzZ2vlypWKjY1VZWWlpk6dKpfLpeHDh+ujjz7Sb37zG8XExGju3LnW9+Xk5KiwsLDG\ndn1D3dJdunRJEydOVEpKivr37y9JevXVVzVz5ky9+OKLhlfX/Fw9l6dPn9aPf/xjDRgwQDfddJPh\nlflPU5+DCgoK9Oc//1n33XefX9a1aNEiv9xuU9SV0be+9S3TS6tTIPK7IUq4rKxMFy5cUFVVlVwu\nl7p06aKZM2dq3LhxioyM1M0336yKigotWrRITqdTt9xyi+bOnav//d//1QcffKCTJ09q0aJFevfd\nd7V+/XoFBQUpOTlZ48aNM33XGq1z586aMGGC3nzzTbVp00Z79uzRhQsX9Oijj+q+++7T8OHDtXHj\nRjkcDr3zzjvKy8uTy+XS6NGjFRsbK0lq1aqVpk2bpieeeEIdOnTQBx98oNzcXM2cOVOHDx9WZWWl\nXC6XiouL1aVLFx06dEhxcXHauXOnRowYobKyMqWmpurMmTO6ePGiZsyYoW7dumnnzp1auHChnE6n\nYmJiNHv27Bprf+utt/Svf/1L6enpJqJr0D/+8Q916dLFKmBJ+tnPfqbvfve7euyxx9SpUycdPHhQ\nJSUlmjt3rnr06KHMzMxas7Rs2TKVlpbq0KFDOnr0qFJTUzVw4ECD98z/2rdvL7fbrbS0NLVq1Uqn\nT5/WwoUL9eyzzyo/P1+VlZWaNGmS7rnnHiUnJ+uRRx7Rxo0bdeuttyohIcH6esGCBfryyy+Vmpqq\nqqoqORwOpaenq1OnTqbvoqTGPQcdPnxYa9asUVBQkG6//XbNnj1bs2bN0p49e7R8+XL913/9l9fH\nT328ZeJwOKz9Q4YM0fr161VQUKBnnnlG4eHh6ty5s7V/7ty5jXqumDZtml8yWrNmjT799FM9//zz\ncjgc6tOnj5555hlJl8+WrVmzRsePH9f8+fPVo0cPvf7669qwYYMk6dvf/rYef/zxBjNo7vndEKej\nu3XrpsTERH37299WSkqKNmzYoPj4eA0YMEBTpkxRYmKi0tLStGjRIq1Zs0bh4eFav369JOn48ePK\nzMxUZWWlNm7cqKysLGVmZuqvf/2rvvjiC8P3zJ6ePXsqLy9PsbGxysrK0ptvvqklS5aoXbt26tq1\nq3bt2iVJeu+99/SDH/xABw8eVI8ePWrcRocOHVRSUqL+/ftb+fXr10+JiYnas2ePPvvsM8XHx6tv\n377auXOnzp07Z5Xy66+/rgEDBuj111/Xc889pxdeeEGSNGfOHL388st64403dPPNN2vjxo3W8T75\n5BP99a9/1XPPPRewnOzylpPD4dDtt9+u6upqVVdX67XXXtPkyZP10ksvKT8/v85Z+vLLL/XKK69o\n+vTpys7ONnF3AqqgoECnT5/WxYsXFR4ermXLlunPf/6zWrVqpTVr1mjZsmXWi7JLly6pR48e+sMf\n/qBPPvlEsbGxWrt2rXJycnT27FktWbJEw4YN0+rVq/WTn/xEy5cvN3zv/qMxz0EVFRX63e9+p7fe\neksHDx7Uvn37NH78ePXr10+//OUv63z8SNLChQs1evRo639XNDaTl19+2TpGUNDlp/0LFy40+rnC\nXxlVV1drzpw5ev755/XWW2+puLhYx44dk3T5MbZy5UqNGTNGf/zjH5Wfn68//vGPyszMVGZmpv7y\nl7/o6NGjjcqgOed3Q7wTlqTf/va3OnDggP7+97/rd7/7nbKystShQwdJl0+JORwOxcTESLr8ecCO\nHTvUo0cP9erVSw6HQ//617905MgRjRkzRpJUXl6uY8eOWbfREpSXl6tt27Y6c+aMRo4cKZfLpZKS\nEknSj370I23YsEE9e/ZUQUGBdb8vXrzo9baufSV5JbN27drpzjvvVK9evbRq1SrFxsbqzjvvlCTt\n2rVLp06d0jvvvCNJqqioUFFRkY4cOaKJEydKks6dO6eIiAjdcsstOnnypKZOnaq3335bLpfLX7Fc\nt7py8ng8CgoKsk6nff3rX9f8+fPrnCVJ6tu3ryTpa1/7mkpLSwN0DwLr0KFDGj16tDwej1q3bq0X\nXnhB2dnZSkxMlCTl5uZaH19Dzos2AAAFjklEQVTccsst1jtkSUpMTJTD4dDNN99svfCJjIxUaWmp\ncnNzNXXqVEmX5/Gll14ycO/qVt9zkCSFh4frF7/4hSTpwIED1n2+wtvj54opU6Zo8ODB1vaV/Bqb\nyYEDB6zZu/vuu/XBBx+odevWjX6u8BVvGR08eNB6x//b3/7Wuu6V55VbbrlFu3fv1r///W/17t1b\nTufl2urbt6/27t3bqAyac343RAl7PB5VVlYqPj5e8fHxGj16tL73ve9Z+x0Oh67+FdlXTj1Isp78\nXS6XBg0apFmzZgV28T6Um5srSdq2bZtWr14tl8ulPn36SJLuvfdeLVmyRNu2bbOG8bbbblNubq7u\nuusu6zaOHTsmt9vttYQXLFig0NBQTZw4UV26dNGBAwe0c+dOa6BdLpdmzpxpHVOSzpw5o+joaOsz\nwis+/vhjFRQUqH///vr9739vPTk1R7fddpuysrJqXObxeLR//37FxcXp0qVL1uUOh6POWdq2bZv1\nBHIju/oz4Suys7NrvNC6+vFYWVlpvbsIDg62Lr/6a4/HU+NxXFVVZX1Pc9DQc1BlZaVmzZql//mf\n/5Hb7dYTTzxR6za8PX4a0thMruQnyZrX7du3N/q5whfqyuhKeV2rvn9/6T/393rmojnk13ym+Dqs\nXbtWM2fOtMIsLS3VpUuX1LFjR+s0mMPhsE4Jbt++XT179qxxGwkJCfr4449VUVEhj8ejOXPmtKif\nfj169Khee+01PfTQQ/ra174ml8ul9957TxcvXrQ+y/3GN76hpUuX6oc//KEk6dFHH1VmZqaOHj0q\n6fIQzps3T2PHjq11+127dtXRo0d1/Phx3XbbbQoKClJkZKT+8Y9/WCXcu3dvvfvuu5Kk/fv3a9Wq\nVQoPD7e2JWn16tXau3evpMuvZOfMmaO//OUv+vzzz/0b0HVISkpSQUGB/va3v1mXvfbaa7rzzjvV\nvn175eTkSLr8TiY+Pr7Fz5K/9erVSx9//LGkyx8HBQUFqV27dra+b8eOHbUewyY19BxUXl6u4OBg\nud1uHT9+XLm5udaTfnV1tSTvj5+GNDaTuLg460X6leuXlJQ0+rnCF+rKqF+/ftq9e7ckKTU1VQcO\nHPD6/d27d9c///lP6yOg3bt3q3v37tc1F80hvxviZflDDz2kgwcPavjw4Wrbtq2qq6s1Y8YMFRcX\na86cOQoJCdHs2bM1depUOZ1OderUSffff7912ke6/FnomDFj9NOf/lTBwcFKTk5u9j/NeeW0X2Vl\npS5evKhnn31WiYmJyszM1KhRo5ScnKxBgwbpueeeU0ZGhr73ve9pz549uvXWWyVdvs/z58/Xb37z\nG+tV6gMPPKAHH3yw1rEcDoe6du1a45Vo37599fbbb1s/HDNq1ChNmzZNP/nJT3Tp0iVNnz5dkpSe\nnq5p06bJ5XIpOjpaI0aMsD4zad26tZ5//nlNnz5dWVlZNV79NhdBQUFauXKl0tLStGTJEnk8HvXs\n2VMzZszQrFmzdOHCBT3xxBM6fvy4XnzxxRY5S4F0//33a/v27Ro9erSqqqoaffZp0qRJmj59uvXx\nRUZGhp9X2ngNPQfNnTtXSUlJevjhh9WtWzf9/Oc/19y5c7V69Wp9+umnysjI0KRJk7w+furjLZOq\nqqpa13vyySc1bdo0vfHGG+rUqZOqqqr0rW99S6+88kqjniv8mVFMTIz1MyFf//rXFR8f7/X7O3bs\nqBEjRmjUqFHyeDwaPny4YmNjG52BN80hP/6U4VfI0qVLFRsbq4cfftj0Um4YKSkpuu+++3x62g4w\njeeK62MnvxvinTAa9vjjj+umm27SU089ZXopAJoxniuuj938eCcMAIAhN8QPZgEA0BJRwgAAGEIJ\nAwBgCCUMAIAhlDAAAIZQwgAAGPL/AJIgo+EYph/JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad57ef2f28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jv2zTjpIs1SE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h2>Plan for Modeling</h2>\n",
        "\n",
        "<h3>Regression</h3>\n",
        "<p>The first model we will construct will be a regression model. Our target for this data will be the amount of sales made. We will take the test data set, which has already been cleaned and standardized and construct a regression model for this data. After the model is constructed using sklearn's LinearRegression package, we will evaluate its performance by testing it on the training set and possibly a subset of the training set. Once this is completed, we will evaluate our model for accuracy. If the model appears reasonably accurate (have yet to determine a value for this but likely > 90% accuracy), we will stop there for this model. However, if the model is not accurate, we will begin to explore strategies for improving the accuracy of this model. Additional cleaning, or manipulation of the data may be necessary. </p>\n",
        "\n",
        "<h3>Clustering</h3>\n",
        "<p>The next model we will use is clustering. Our strategy for this data will be very similar to our strategy for regression. The data has already been cleaned and standardized, so we will just apply our model and then evaluate its effectiveness. If the model appears inaccurate we will take measures to improve the model. One main difference when using Clustering is determining how many clusters will optimize the models accuracy. For right now, we will use the strategy which was used in the DataCamp clustering exercise.</p>\n",
        "\n",
        "<h3>Association Rule Analysis</h3>\n",
        "<p>The last model we will construct is an association rule analysis model. With this model, we will seek to search for any specific fields that lead to a different specific results. For example, is it possible that a certain holiday leads to a greater number of purhcases. One such method to go about this uses the a priori algorithm. However, since some of our data is numeric, we will instead use the generalized rule induction method. This method works on booleans, categorical data, and numeric data so it is most suited to our data set. </p>"
      ]
    },
    {
      "metadata": {
        "id": "8IDwk96ps1SF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h3>End of Deliverable 1</h3>"
      ]
    },
    {
      "metadata": {
        "id": "SCKjkx4Ts1SG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h3>Deliverable 2 starts here</h3>\n",
        "<h2>Data Preparation for Modeling (three models)</h2>\n",
        "<p>Use the examples in your previous HW notebooks to prepare the data for specific modeling tasks.</p>\n",
        "<p>Use this section to create your code and document your findings.</p>"
      ]
    },
    {
      "metadata": {
        "id": "hrax21C9s1SI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h2>Modeling</h2>\n",
        "<p>Use three:  clustering, classification, regression and association rule analysis to model your data.</p>\n",
        "<p>The ultimate goal is to develop a prediction model with good accuracy for the test data.</p>\n",
        "<p>As you model, you will continue preprocessing as different models require different forms of data for processing.</p>\n",
        "<p>Place code and discussion of results in this section.</p>"
      ]
    },
    {
      "metadata": {
        "id": "YjOvn7UeKcBT",
        "colab_type": "code",
        "outputId": "8a55c131-7d2a-4958-f1e0-6ca2d43992a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "cell_type": "code",
      "source": [
        "# Regression Model\n",
        "# IMPORTANT NOTE: Please reimport datasets before running each model. Not doing so \n",
        "# will result in an error due to the way the data gets modified before each model.\n",
        "\n",
        "from sklearn import linear_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# Initialize model\n",
        "regr = linear_model.LinearRegression()\n",
        "\n",
        "# Split training data\n",
        "_ = train.pop('Date')\n",
        "y = train.pop('Sales')\n",
        "X = train\n",
        "\n",
        "# Divide data into training and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .2, random_state= 40)\n",
        "\n",
        "# Fit data\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "# Predict values\n",
        "prediction = regr.predict(X_test)\n",
        "\n",
        "# Evaluate model\n",
        "\n",
        "print('Coefficients: \\n', regr.coef_)\n",
        "\n",
        "print('Coefficient of multiple determination: %.2f' % r2_score(y_test, prediction))\n",
        "\n",
        "plt.scatter(y_test, prediction)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coefficients: \n",
            " [ -170.60959912  -422.35680909 44730.52045387  1548.27016809\n",
            "  1281.5045516  -1111.74560559    68.30040701]\n",
            "Coefficient of multiple determination: 0.85\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a5Zb83uLXFb2xRLLJalKL0sTOB3u8fnxwsg0ezo/HHiy+MQcsnBUecripHQvu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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad5d95eb00>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "v-UZtJxo1fVL",
        "colab_type": "code",
        "outputId": "d4db2918-b104-44ce-c298-3f2f140b4e9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "# Clustering \n",
        "# IMPORTANT NOTE: Please reimport datasets before running each model. Not doing so \n",
        "# will result in an error due to the way the data gets modified before each model.\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "_ = train.pop('Date')\n",
        "y = train.pop('Sales')\n",
        "X = train\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= .2, random_state= 40)\n",
        "\n",
        "ks = range(1, 10)\n",
        "inertias = []\n",
        "\n",
        "for k in ks:\n",
        "    # Create a KMeans instance with k clusters: model\n",
        "    model = KMeans(n_clusters=k)\n",
        "    \n",
        "    # Fit model to samples\n",
        "    model.fit(X_train)\n",
        "    \n",
        "    # Append the inertia to the list of inertias\n",
        "    inertias.append(model.inertia_)\n",
        "    \n",
        "plt.plot(ks, inertias, '-o')\n",
        "plt.xlabel('number of clusters, k')\n",
        "plt.ylabel('inertia')\n",
        "plt.xticks(ks)\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yp5iqmgajyxERESemMOCkzCYTN6XE0dTcwuYd+UaXIyIiTkxhwImNSOyCr7cnH+7Ip7Gp\nxehyRETESSkMODEfiyejkmOoqmng073FRpcjIiJOSmHAyd14TVfMJhPvfZaHXYcQiYjIZVAYcHLh\nwT4MjrdytKSaA3kVRpcjIiJOSGHABdx86hCijZ/pNkMREbl0CgMuoHdsED2jA/niYCklFXVGlyMi\nIk5GYcAFmEwmxgyJww68r0OIRETkEikMuIiU+EhCAixs3VVI3ckmo8sREREnojDgIjw9zNx4TVfq\nG5r5eFeh0eWIiIgTURhwIaMGxmLxNPN+Vh4tLbrNUERELo7CgAsJ8PUidUAXSivr2XGw1OhyRETE\nSSgMuJgbU/Q0QxERuTQKAy4mNsKfAT3DOJBXwZGiE0aXIyIiTkBhwAWNGaLZARERuXgKAy5oQM8w\nosP9yPyymIrqk0aXIyIinZzCgAsymUyMSYmjucXOpux8o8sREZFOTmHARQ0f0AV/H0827cinsanZ\n6HJERKQTUxhwUd5eHowaGEt1XSOf7Ck2uhwREenEFAZc2A2DY/Ewm3gvKw+7XYcQiYjIuSkMuLCw\nIB9SEiI5Zqth75Fyo8sREZFOSmHAxY355hCiz3SboYiInJvCgIvrFRNE79ggdn5VRvHxWqPLERGR\nTkhhwA3cPKQboEOIRETk3BQG3MDgvhGEBXmzNaeQmvpGo8sREZFORmHADXiYzdx4TVcaGlv4eGeh\n0eWIiEgnozDgJq5LjsHby4MPPs+juaXF6HJERKQTURhwE/4+XoxI7EJZ1UmyD5QaXY6IiHQiCgNu\n5CbdZigiIuegMOBGuoT5kdQ7nNz8Sr4uqDK6HBER6SQUBtzMzUNOzQ7oNkMRETlFYcDN9OseSqzV\nn6x9JRyvqje6HBER6QQcGgbq6+u56aabePPNNyksLGT69OlMnTqVe++9l4aGBgDWrFnDhAkTmDhx\nIq+//joAjY2NzJkzhylTpjBt2jTy8lr/Frtv3z4mT57M5MmTefTRR9u+ztKlS7ntttuYOHEiW7Zs\ncWRLTs9kMjEmJY7mFjubduQbXY6IiHQCDg0Df//73wkODgZg0aJFTJ06lRUrVtC9e3dWr15NbW0t\nixcv5pVXXmH58uUsW7aMiooK1q5dS1BQEK+++iozZszgmWeeAeCJJ54gPT2dlStXUl1dzZYtW8jL\ny2PdunWsWLGCJUuWsGDBApqbmx3ZltMb1j+KAF8vNu/I52Sj/luJiLg7h4WBr776itzcXK6//noA\nMjMzufHGGwEYPXo0GRkZ7Ny5k8TERAIDA/Hx8WHw4MFkZ2eTkZHBmDFjAEhNTSU7O5uGhgby8/NJ\nSko6Y4zMzEzS0tKwWCyEhYURGxtLbm6uo9pyCRYvD0YPiqWmvomM3UVGlyMiIgbzdNTATz75JI88\n8ghvv/02AHV1dVgsFgDCw8Ox2WyUlpYSFhbW9p6wsLCzrpvNZkwmE6WlpQQFBbW99psxQkJCzjlG\nfHz8BesLDfXD09Oj3foFsFoD23U8R7ptTDzvZh7hwx35TLgpHrPZdMbnnamX7+IqvbhKH6BeOitX\n6cVV+oCO68UhYeDtt99m4MCBxMXFnfPzdrv9iq9f6hj/rby8fZ/gZ7UGYrOdaNcxHW1IQhQZe4rY\n8tkRBvQKb7vujL2cj6v04ip9gHrprFylF1fpA9q/lwsFC4eEgc2bN5OXl8fmzZspKirCYrHg5+dH\nfX09Pj4+FBcXExkZSWRkJKWl356GV1JSwsCBA4mMjMRms5GQkEBjYyN2ux2r1UpFRUXba08f49Ch\nQ2ddl+9285A4MvYUsTEr74wwICIi7sUhewaeffZZ3njjDV577TUmTpzIzJkzSU1NZcOGDQBs3LiR\ntLQ0kpOTycnJoaqqipqaGrKzs0lJSWHEiBGsX78egE2bNjF06FC8vLzo1asXWVlZZ4wxbNgwNm/e\nTENDA8XFxZSUlNCnTx9HtOVyuncJpG9cCLu/Pk5BaY3R5YiIiEEctmfgv91999088MADrFq1ipiY\nGMaPH4+Xlxdz5szhzjvvxGQyMWvWLAIDAxk7dizbt29nypQpWCwWFi5cCEB6ejrz5s2jpaWF5ORk\nUlNTAZg0aRLTpk3DZDIxf/58zGYdn3CxxqTEcSCvgvez8rjj/yUYXY6IiBjAZL/YRXYX095rSs66\nTtXSYmfukgyqahr406wRBPh6OW0v5+IqvbhKH6BeOitX6cVV+oCO3TOgv0K7ObPZxE3XdKWhqYUt\nX+gQIhERd6QwIKQlx+Bj8eDD7HyamluMLkdERDqYwoDg6+3JyKRoyk+cJGt/idHliIhIB1MYEABu\nuqYrAC+v28eP/rCGeS9mkvllscFViYhIR+iwuwmkcztU2LpJpbGpdZngmK2GJWv2ADC0f5RhdYmI\niONpZkAAeCfj8HmuH+nQOkREpOMpDAgABaXnPp65sEyHEYmIuDqFAQEgJsLvnNejw/07uBIREelo\nCgMCwLjhPc55fURil44tREREOpzCgACtmwR//cOr6WoNwMNsIizIG4DNO/KpqW80uDoREXEk3U0g\nbYb2j2Jo/6i2IzBf35zLu58c5fm3d/PbScl46JkPIiIuST/d5bwmXNeb5N7h7DlczqoPc40uR0RE\nHERhQM7LbDZx1w+vJjbCn/ezjvHRzgKjSxIREQe47DBw5IjuP3cHvt6e3H1bEv4+nizfsJ8DeRVG\nlyQiIu3sosJAc3MzW7Zs4e233+btt9/mtdde4+c//7mja5NOIjLEl5m3JgKw+K0cSivrDK5IRETa\n00VtIPzDH/5AZWUl+/fvZ/DgwezcuZO7777b0bVJJ9KveyhTb7qK5RsPsGh1DunTB+Nj0f5TERFX\ncFEzA0VFRbz44ov07NmTRYsWsWLFCnJychxdm3Qyowd3ZfSgWI7Zqlm6di8tdrvRJYmISDu4pD0D\nTU1NnDx5ktjYWHJztbvcHU256SoSuoWQfcDGvz8+ZHQ5IiLSDi4qDAwbNowXXniBm266iVtvvZW7\n7rqLlpYWR9cmnZCnh5mZtyZiDfHhP9sP8+lePeZYRMTZXdSi7z333ENzczMeHh4MGjSIsrIyRowY\n4ejapJMK8PXinglJPLH8c156Zy9RoX507xJodFkiInKZLjgzsGXLFgBWr17NW2+9xerVq/n666+p\nrKxk3bp1HVKgdE6x1gDu+uHVNDa1sOiNXVRWnzS6JBERuUwXDAP79+8H4PPPPz/nL3FvA/tEMOH6\n3pSfOMnf3syhsanZ6JJEROQyXHCZ4K677gJg5MiRjBs37ozPvfrqq46rSpzGLUO7kW+rJmNPMcvW\n7+fOcf0wmUxGlyUiIpfggmFg79697N69m5deeom6um8PmmlqamLx4sVMmTLF4QVK52YymfjZLQkU\nHa9j++4iuloD+H9DuxldloiIXIILhgGLxUJZWRknTpw4Y1nAZDJx//33O7w4cQ5enh7cPSGR/3nl\nM17flEtMhB9JvSOMLktERC7SBcNA79696d27N9deey2DBw/uqJrECYUEeHP3hCQW/iubJWv28ND0\nFGIi/I0uS0RELsJFnTPw7LPPOroOcQE9o4P4+dgE6k42s2j1LqrrGo0uSURELsJFnTMQExPD9OnT\nSU5OxsvLq+36vffe67DCxDkN69+FfFsN72Qc4e9v7+Z3k5Lx9NCTskVEOrOL+indtWtXhg4dio+P\nDx4eHm2/RM7l1ut6MeiqCPYeKWfVBzq2WkSks7uomYHZs2dTXl7OsWPHSExMpKWlBbNZf9uTczOb\nTPzy+/35339+zgfZx4iN9Of6gbFGlyUiIudxUX+iv/POO9x+++08+OCDADz++OOsXr3aoYWJc/P1\n9uSeCUkE+Hrxr40H2H+03OiSRETkPC4qDLz00kv8+9//JjQ0FIAHHniAVatWObQwcX7WEF9m3ToA\ngMVv7cZWUfcd7xARESNcVBgIDAzE19e37d99fHzO2Egocj7x3UL5yc19qa5rZNEbu6g72WR0SSIi\n8l8uKgyEhoby1ltvcfLkSfbs2cPTTz9NWFiYo2sTF3H9wFhuHNyVfFsNS9d+SYvdbnRJIiJymosK\nA4899hg5OTnU1NTw8MMPc/LkSf74xz86ujZxIZNv6kO/7qHsOFjKWx99bXQ5IiJymou6myAoKIh5\n8+Y5uhZxYR5mM78ZP4A//iOLdzKOEBvhz7CruxhdloiIcJFhYO3atSxdupTKykrsp03xbt682VF1\niQsK8PXinglJPLE8i5ff3UdUmB89o4OMLktExO1dVBh47rnn+OMf/0hMTIyj6xEXFxPhz69/OIC/\nrt7Jojd2Me+nQwgN9Da6LBERt3ZRewa6d+/OkCFDiI2NPeOXyOVI6h3OxOv7UFndwN/e3EVDY7PR\nJYmIuLWLmhkYNGgQf/7zn7n22mvPOIZ4+PDhDitMXNv3ro0jv7SabTlFvPLuPn71g/6YTCajyxIR\ncUsXFQa2b9+OyWTiiy++OOO6woBcLpPJxB3fS6DoeC2ffFlMrNWfccN7GF2WiIhbuuAywTe3DzY3\nN9PU1HTGr+ZmTe3KlfHyNDP71kRCA715c8vXfHGw1OiSRETc0gVnBm677TYAfvvb33ZIMeJ+ggO8\nuWdCEgv++TlL/rOHh6ZfQ1drgNFliYi4lQuGgYSEBACuvfbaDilG3FP3LoHc+f3+/P3t3SxavYtH\nfppCoJ/F6LJERNzGRe0ZuBx1dXXMnTuXsrIyTp48ycyZM0lISOD++++nubkZq9XK008/jcViYc2a\nNSxbtgyz2cykSZOYOHEijY2NzJ07l4KCAjw8PFiwYAFxcXHs27eP+fPnAxAfH89jjz0GwNKlS1m/\nfj0mk4nZs2czatQoR7UmDjAkIZL8ET1Ys+0wf397N7+/fSCeHnpMtohIR3DYT9tNmzYxYMAA/vnP\nf/Lss8+ycOFCFi1axNSpU1mxYgXdu3dn9erV1NbWsnjxYl555RWWL1/OsmXLqKioYO3atQQFBfHq\nq68yY8YMnnnmGQCeeOIJ0tPTWblyJdXV1WzZsoW8vDzWrVvHihUrWLJkCQsWLNCeBif0w5E9uSbe\nyr6jFax4/6DR5YiIuA2HhYGxY8fyq1/9CoDCwkKioqLIzMzkxhtvBGD06NFkZGSwc+dOEhMTCQwM\nxMfHh8GDB5OdnU1GRgZjxowBIDU1lezsbBoaGsjPzycpKemMMTIzM0lLS8NisRAWFkZsbCy5ubmO\nak0cxGwy8ctx/YmLDGDzjnw+zD5mdEkiIm7B4fOwkydP5r777iM9PZ26ujoslta14PDwcGw2G6Wl\npWc8ATEsLOys62azGZPJRGlpKUFB3x5f+11jiPPxtnhw94REAv28WPHeQfYePm50SSIiLs9hewa+\nsXLlSvbu3csf/vCHM55rYD/PY2wv5fqljnG60FA/PD09vvN1l8JqDWzX8YxkZC9WayAP/2IoD/19\nG8+v2cMz944iOsL/isZzBa7SB6iXzspVenGVPqDjenFYGNi9ezfh4eFER0fTr18/mpub8ff3p76+\nHh8fH4qLi4mMjCQyMpLS0m/vLy8pKWHgwIFERkZis9lISEigsbERu92O1WqloqKi7bWnj3Ho0KGz\nrl9IeXltu/ZrtQZis51o1zGN0hl6sQZYmH5zPC+/u49H/287D9+Rgq/3pX+7doZe2oOr9AHqpbNy\nlV5cpQ9o/14uFCwctkyQlZXFSy+9BEBpaSm1tbWkpqayYcMGADZu3EhaWhrJycnk5ORQVVVFTU0N\n2dnZpKSkMGLECNavXw+0bkYcOnQoXl5e9OrVi6ysrDPGGDZsGJs3b6ahoYHi4mJKSkro06ePo1qT\nDpKWHMOYlDgKy2pZsmYPLS3fPeMjIiKXzmEzA5MnT+ahhx5i6tSp1NfXM2/ePAYMGMADDzzAqlWr\niImJYfz48Xh5eTFnzhzuvPNOTCYTs2bNIjAwkLFjx7J9+3amTJmCxWJh4cKFAKSnpzNv3jxaWlpI\nTk4mNTUVgEmTJjFt2jRMJhPz58/HbNZtaa5g0g29KSyrYddXZbyx5SsmjlbIExFpbyb7xSywu6D2\nnkbS1JTj1NY38vg/Pqf4eC2//H4/UgdEX/R7O1svl8tV+gD10lm5Si+u0ge4yDKBSHvx8/HingmJ\n+Hp78sq7+/mqoNLokkREXIrCgDiF6HB/fjP+appbWvjbGzkcr6o3uiQREZehMCBOY0DPcG6/4Soq\naxp47s0cTjbqlEkRkfagMCDIAQLOAAAgAElEQVROZUxKV0YmRXOk6AQvr9t7UWdKiIjIhSkMiFMx\nmUxMvzmePl2D+XRvCWszjhhdkoiI01MYEKfj5Wlm9q2JhAd589ZHX5N9QEdPi4hcCYUBcUpB/hbu\nnpCExcvMC//5krySaqNLEhFxWgoD4rS6RQXyq+/352RjM4tW76KqtsHokkREnJLCgDi1a+IjGT+y\nJ2VV9fx/b+bQ1NxidEkiIk5HYUCc3g9G9CAlIZIDxyr558YDusNAROQSKQyI0zOZTNw5rh/dogL4\naGcBH3x+zOiSREScisKAuARvLw/umZBEkL+FlR/ksufwcaNLEhFxGg57aqFIRwsL8mH2jxN5akU2\nz63eRViQDyUVdcSE+zFueA+G9o8yukQRkU5JMwPiUvrEBjMyKZqGphaKjtfS0mLnmK2GJWv2kPll\nsdHliYh0SgoD4nJyj537qYbv6LRCEZFzUhgQl1NQWnvO64VlNR1ciYiIc1AYEJcTE+F3zuuBfl66\n7VBE5BwUBsTljBve45zXK6obePndfTQ26WAiEZHT6W4CcTnf3DXwTsYRCstqiA73Z9TAGLbmFLJ1\nVyEFpTXMujWR0EBvgysVEekcFAbEJQ3tH8XQ/lFYrYHYbCcASEuKZtn6fWTsKeZ/ln3G7FsT6R0b\nbHClIiLG0zKBuA2Llwe//H5/br+hD1U1DTy5IpuPdxYYXZaIiOEUBsStmEwmvndtN34/aSDeXh68\n/O4+/rXxgB5wJCJuTWFA3NLVPcN45KcpxFr9+SD7GH9e9YUegSwibkthQNxWZKgfD02/hmv6Wtl3\ntILHX8niaPEJo8sSEelwCgPi1nwsnvzm1gGMT+tJWVU9/7v8cz7dq2OLRcS9KAyI2zObTPxwRE/u\nnpCI2Wzi+X/v4fXNubS06IAiEXEPCgMipwy6yspDd6QQFerLu58c5a+rd1Fb32h0WSIiDqcwIHKa\n2Ah/HvlpCgN6hZHzdRmPL8uioFTPNBAR16YwIPJf/Hy8+O1tydwyrBvF5XX88R9ZfHGw1OiyREQc\nRmFA5BzMZhMTr+/Dr394NS0tdp57Yxf/2XaIFj3oSERckMKAyAUM7R/Fg9OuISzIm7c+PsTf39pN\nfUOT0WWJiLQrhQGR79C9SyCP/GwI8XEhfH7AxhPLP6ekos7oskRE2o3CgMhFCPKzMGfyQG4c3JV8\nWw2Pv/IZew4fN7osEZF2oTAgcpE8Pcz85Oa+/OyWBE42NvPnVV+w8dOj2LWPQEScnMKAyCW6LjmG\n+6cOJsjfwsoPc1m6di8Njc1GlyUictkUBkQuQ5/YYOb9dAi9YoLI2FPEwn9lc7yq3uiyREQui8KA\nyGUKDfTmgamDGJkYzeGiE/zPK59xIK/C6LJERC6ZwoDIFfDy9ODnYxOYetNVVNc18fSrO9i8I9/o\nskRELonCgMgVMplM3JQSx5zJA/H19uQfG/bzjw37aWpuMbo0EZGLojAg0k76dQ9l3k9TiIsMYPOO\nfJ5+dQeVNQ1GlyUi8p0UBkTaUUSIL+nTrmFIQiQHj1XyP698xqHCKqPLEhG5IIUBkXbmbfFgxo+u\nZsKoXlScOMnCf2WTsbvI6LJERM5LYUDEAUwmE+OG9+DeiUl4eph5Ye2XrPzgIM0t2kcgIp2PwoCI\nAyX1juDhO64hOtyPjZ/l8exrO6muazS6LBGRMzg0DDz11FPcfvvtTJgwgY0bN1JYWMj06dOZOnUq\n9957Lw0NrZur1qxZw4QJE5g4cSKvv/46AI2NjcyZM4cpU6Ywbdo08vLyANi3bx+TJ09m8uTJPPro\no21fa+nSpdx2221MnDiRLVu2OLItkUsSHe7PQ9NTSO4dzp7D5Ty+7DOO2aqNLktEpI3DwsAnn3zC\nwYMHWbVqFUuXLuV///d/WbRoEVOnTmXFihV0796d1atXU1tby+LFi3nllVdYvnw5y5Yto6KigrVr\n1xIUFMSrr77KjBkzeOaZZwB44oknSE9PZ+XKlVRXV7Nlyxby8vJYt24dK1asYMmSJSxYsIDmZh0P\nK52Hn48nd9+WxPdTu2OrqOeJf3zO5/tLjC5LRARwYBgYMmQIf/3rXwEICgqirq6OzMxMbrzxRgBG\njx5NRkYGO3fuJDExkcDAQHx8fBg8eDDZ2dlkZGQwZswYAFJTU8nOzqahoYH8/HySkpLOGCMzM5O0\ntDQsFgthYWHExsaSm5vrqNZELovZZOLH1/Vm5vgBACx+azdvffQ1LXrQkYgYzGFhwMPDAz8/PwBW\nr17NddddR11dHRaLBYDw8HBsNhulpaWEhYW1vS8sLOys62azGZPJRGlpKUFBQW2v/a4xRDqjlIRI\nHpp+DRHBPvxn+2H+9kYOdSebjC5LRNyYp6O/wPvvv8/q1at56aWXuPnmm9uun++xr5dy/VLHOF1o\nqB+enh7f+bpLYbUGtut4RlIvjmW1BvLXHuE8tfwzvjhYysIV2Tz886HEWAMu+B5XoV46J1fpxVX6\ngI7rxaFh4OOPP+b5559n6dKlBAYG4ufnR319PT4+PhQXFxMZGUlkZCSlpaVt7ykpKWHgwIFERkZi\ns9lISEigsbERu92O1WqlouLbB8GcPsahQ4fOun4h5eW17dqr1RqIzXaiXcc0inrpOLNvHcDrm75i\n42d5/PYvW5jxo6tJ7BV+1us6ex+XQr10Tq7Si6v0Ae3fy4WChcOWCU6cOMFTTz3FkiVLCAkJAVrX\n/jds2ADAxo0bSUtLIzk5mZycHKqqqqipqSE7O5uUlBRGjBjB+vXrAdi0aRNDhw7Fy8uLXr16kZWV\ndcYYw4YNY/PmzTQ0NFBcXExJSQl9+vRxVGsi7cbDbGbyjVdx57h+NDa18OxrO1n3yZGLmt0SEWkv\nDpsZWLduHeXl5fz2t79tu7Zw4UIefvhhVq1aRUxMDOPHj8fLy4s5c+Zw5513YjKZmDVrFoGBgYwd\nO5bt27czZcoULBYLCxcuBCA9PZ158+bR0tJCcnIyqampAEyaNIlp06ZhMpmYP38+ZrOOUBDnMSIx\nmpgIf/72Zg6rN3/F0eIT/HxsP7y92ncpS0TkXEx2N/0rSHtPI2lqqnNytl4qq0+y+K3d5OZX0i0y\ngNkTEokI9nW6Pi5EvXROrtKLq/QBHbtM4PANhCJy8YIDvPnDlEH8670DfLSzgEeWfkqQvxdlVSeJ\nCfdj3PAeDO0fZXSZIuJiFAZEOhkvTzM/uyWBFrudrbsKsVW0HqB1zFbDkjV7ABQIRKRdaWFdpJM6\nfJ5HH/9766FzXhcRuVwKAyKdVEHpuW9/LTpey4J/fs5HOwt0WJGItAstE4h0UjERfhyz1Zx13dvL\ng4PHKjl4rJIV7x/gmr6RjEzsQnz3UMwmkwGVioizUxgQ6aTGDe/RtkfgdD+7JYHesUFs313EtpxC\nMvYUkbGniPAgb1IHRDMisQuRoX4GVCwizkphQKST+maT4DsZRygsqyE63J9xw7u3Xf/hiJ78ILUH\nB49VsjWnkM/2lfCf7Yf5z/bD9O0azIjEaFISIvH11m9zEbkw/ZQQ6cSG9o9iaP+o895vbDKZ6BsX\nQt+4EH5yU18+P1DCtpwi9h4p58CxSv71/gFS4iMZkRhNfLcQLSOIyDkpDIi4CG+LB6kDokkdEE1p\nRR3b97QuI2zfXcT23UWEB/kwIrELqYnRRIb4Gl2uiHQiCgMiLigixLdtGeFAXgXbcor4bF8Ja7Yd\nZs22w/SNC2FEYhdS4rWMICIKAyIuzWQyEd8tlPhuoUwdcxWf77exLaeQfUcrOJBXwYr3DpISb2VE\nYjR9tYwg4rYUBkTchI/FkxGJ0YxIPLWMsLuIrTmFbNtdxLbdRUQE+5A6oAsjEqOxahlBxK0oDIi4\noYgQX344siffH9GDg3kVbM0pJGufrW0ZIT4u5NTdCFZ8LPoxIeLq9LtcxI2ZT1tG+MmYpjOWEfbn\nVfCv9w6QkmBlZGI0V8VpGUHEVSkMiAhw5jKC7dQywracQrblFLEtp3UZYURiNKkDumgZQcTFKAyI\nyFmsIb78aGRPfvBfywj/3nqIf289REK3U8sI8ZF4WzyMLldErpDCgIic138vI2Tt+3YZYd/RCv75\n3gGGxEcyIrELfeNCMGkZQcQpKQyIyEXxsXgyMimakUnRlFTUsf3UEsLWnEK25hRiDfFhxIDWZYQI\nLSOIOBWFARG5ZJEhvoxP68UPR/bkwNFTywj7S3h76yHe1jKCiNNRGBCRy2Y2mUjoHkpC91B+MqYv\nWftbn41wxjJCQiQjE6M5XlXPuk+OUFBWS0y4H+OG92h76JKIGEthQETaha+3J2lJMaQlxVBSXnvq\nboQitu4qZOuuwjNee8xW0/Z4ZgUCEeOZjS5ARFxPZKgf49N68eRvhvOHKYPwO8/zD17blEvR8Vrs\ndnsHVygip9PMgIg4jNlkol/3UOobms/5+fITJ0n/v08I9re0PYo5vlsIMRH+OuBIpAMpDIiIw8VE\n+HHMVnPW9dAAC1fFhbD/aAWf7Svhs30lAAT4enFV12Di40KI7xZKXGQAZrPCgYijKAyIiMONG96j\nbY/A6SbdcBVD+0dht9spKa9jf14F+089UXHHwVJ2HCwFwNfbg6u6npo5iAuhe5dAPD20yinSXhQG\nRMThvtkk+E7GEQrLaogO92fc8O5t100mE1FhfkSF+XFdcgwApZV1HMhrDQb7j1aw66sydn1VBoDF\ny0yf2OC2cNArJggvT93CKHK5FAZEpEMM7R/F0P5RWK2B2GwnvvP1EcG+RAT7kjogGoCK6pOtwSCv\nggNHK/jycDlfHi4HwNPDRK/oIPp2CyU+LoQ+scE630DkEigMiIhTCAnw5tp+UVzbr3U24URtAwfy\nKttmDw7mV3LgWCVrAQ+zie5dAok/tSnxqq4h+Pnox53I+eh3h4g4pUA/C9fEW7km3gpAbX0Tufnf\n7jk4XHSCrwuqeDfzKCYTxEUGEB8XeuquhWAC/SwGdyDSeSgMiIhL8PPxJKl3BEm9IwCob2jiq4Kq\ntnDwdUEVR4ureS8rD4DYCH/6dgtpmz0ICfA2snwRQykMiIhL8rF4cnWPMK7uEQZAY1MzXxdUte45\nyKsgN7+S/OwaNmXnAxAV6kt8t2/uWAglPNjHyPJFOpTCgIi4BS9Pj7bHMQM0NbdwpOhEWzg4eKyC\nj3YW8tHO1qOTw4N8vg0H3UKIDPFte0Rz5pfFvJNxWM9ZEJehMCAibsnTw0zv2GB6xwYzdlh3Wlrs\n5JVUnzrroJwDeRVs313E9t1FAAQHWIiPC8HiaWZrTlHbOHrOgrgChQEREcB86g6E7l0CuXlIHC12\nOwWlNW3nHOzPq+DTvSXnff9bH33N1T3DCPD16sCqRdqHwoCIyDmYTSa6WgPoag3ghsFdsdvtFJfX\n8dALn3Cu5yqVVNRxz18/JsjPi+hwf2Ii/IkO9yM6wp+YcH9CAixtywwinY3CgIjIRTCZTHQJ8yM2\nwv+cz1kI9PWiZ0wQhWU1bYcjnc7X24Po8NaA0BoU/IkJ9yMi2FfPXRDDKQyIiFyC8z1nYeqYvm17\nBhoamyk6XktBWQ2Fpaf+WVbLkVNnH5zOy9NMlzC/1pAQ7n9qJqH1aGY9f0E6isKAiMgl+K7nLABY\nvDzoFhVIt6jAM97b1NyCraKOgtJaCstqKCyraf34eA15JdVnvNZsMmEN9SWmbSbBr21mwceiH93S\nvvQdJSJyiS71OQvf8PQwn/oD3R+wtl1vsds5XlV/Zkgoq6WwtIYdx2vbnt74jfAg77ZxYiL82vYo\naPOiXC6FARERg5lNprYHMyX1Dm+7brfbqaptpLC05r+WHGrYfeg4uw8dP2OcbzYvRkd8uzfhuzYv\n6swEAYUBEZFOy2QyEexvIdjfQkL30DM+V1vfSGHZf+9LOP/mxS5hrbMIMafNKHxdUMX//efLttfp\nzAT3pTAgIuKE/Hy82g5NOt1/b178ZsnhaPEJDhVWnWe0M63dflhhwM0oDIiIuJBL2byYsaf4nGPk\nl9Zwz18/pkuYH1FhvkSF+tElzI/I0NaPvS0eHdGKdCCFARERN3CuzYt5JdXnPDPBx+KBn48nXxdU\nkZtfedbnQwO9iQr1PRUQ/NpCgzXEV7dDOimFARERN3W+MxN++v8SGNo/iqbmFkor6yk+Xkvx8VqK\nyutaPy6vZd/RCvYdPXNvgskE1mBfIsN86RLaelbCNzML4UE+OlypE3NoGDhw4AAzZ87kZz/7GdOm\nTaOwsJD777+f5uZmrFYrTz/9NBaLhTVr1rBs2TLMZjOTJk1i4sSJNDY2MnfuXAoKCvDw8GDBggXE\nxcWxb98+5s+fD0B8fDyPPfYYAEuXLmX9+vWYTCZmz57NqFGjHNmaiIjT+64zEzw9Wg9E6hLmd9Z7\nTzY2Yyuvo+hUOCg+Xnfqn7Xs/vo4uznzTgdPDxORoX5Ehfq2hoRTMwtRYX4E++uoZqM5LAzU1tby\n+OOPM3z48LZrixYtYurUqdxyyy38+c9/ZvXq1YwfP57FixezevVqvLy8uO222xgzZgybNm0iKCiI\nZ555hq1bt/LMM8/w7LPP8sQTT5Cenk5SUhJz5sxhy5Yt9OrVi3Xr1rFy5Uqqq6uZOnUqI0eOxMND\n61oiIhdyuWcmeHt50DUygK6RAWd9rra+qS0YFJ82m1B0vI6C0rOXJbwtHq0h4dRsQpewbz/W2Qkd\nw2FhwGKx8MILL/DCCy+0XcvMzGz7m/zo0aN56aWX6NmzJ4mJiQQGtm52GTx4MNnZ2WRkZDB+/HgA\nUlNTSU9Pp6Ghgfz8fJKSktrGyMjIwGazkZaWhsViISwsjNjYWHJzc4mPj3dUeyIich5+Pp70jA6i\nZ3TQGdftdjsnahtPBYNaSr6ZWTheR1FZLUeLq88ay9/H87S9Cd/MKrQuP5x+EqPOS7gyDgsDnp6e\neHqeOXxdXR0WiwWA8PBwbDYbpaWlhIWFtb0mLCzsrOtmsxmTyURpaSlBQd9+c30zRkhIyDnHuFAY\nCA31w9OzfWcOrNbA736Rk1AvnY+r9AHqpbPqiF4igd49ws+63tJip6yynoLSagps1RSU1pBva/34\ncNEJvio4+7bIsCBvYqwBmEwmcnK/PaXxm/MSgoJ8uG5QV0e243Ad9f1l2AZC+7meAXqJ1y91jNOV\nl9d+52suxaVOsXVm6qXzcZU+QL10Vp2ll5gQH2JCfOCqiLZrzS3fbGSsO7WRsZaS463LDnu+KuN8\nP/H/unIHWz7PwxriS0SwDxEhvliDfQgL8nGKux7a+//JhYJFh4YBPz8/6uvr8fHxobi4mMjISCIj\nIykt/TbRlZSUMHDgQCIjI7HZbCQkJNDY2IjdbsdqtVJR8e3u1dPHOHTo0FnXRUTE+XmYza1LA6F+\n0PvMWYXGpmZ+88wWWs6RCBqaWvh0b8lZ102m1tsjI4Jbw0HEqbDwTWgICfB2uzsfOjQMpKamsmHD\nBn70ox+xceNG0tLSSE5O5uGHH6aqqgoPDw+ys7NJT0+nurqa9evXk5aWxqZNmxg6dCheXl706tWL\nrKwsUlJS2LhxI9OnT6dHjx68/PLL3H333ZSXl1NSUkKfPn06sjURETGAl6cHMRH+5zwvoavVn3tv\nS6a0sg5bRT2llXWUVtZTWlGHrbKeg3kVHMg7e0wPs4nwYJ9zBAVfIkJ8CPT1crm7HxwWBnbv3s2T\nTz5Jfn4+np6ebNiwgT/96U/MnTuXVatWERMTw/jx4/Hy8mLOnDnceeedmEwmZs2aRWBgIGPHjmX7\n9u1MmTIFi8XCwoULAUhPT2fevHm0tLSQnJxMamoqAJMmTWLatGmYTCbmz5+P2dz5p4BEROTKne+8\nhHHDexAe7EN4sA/x3c5+X2NTC8er6rG1hYT6M4LDnsPlQPlZ7/P28mhddjht6eGb0BAR7Iufj/Md\n4WOyX8wCuwtq77WxzrLe1h7US+fjKn2AeumsnL2X1rsJzn1ewuWqb2hqDQmnZhNKK+uxnfpnaWUd\ndSebz/k+fx/PtlkE66l/RgT7Yg3xITzIB4vXhTevO+rOiE6zZ0BERMQRLve8hAvxsXjS1RpAV+vZ\nZynY7XZq6psoOy0g2Crr2mYXCspqOFJ87jqC/S1nB4VgH8JDfMk9VsHStXvbXttRT5JUGBAREblE\nJpOJAF8vAny96N7l7L9x2+12qmoasJ22R+H02YVDBSf4Kv/iniIJradEKgyIiIg4EZPJRHCAN8EB\n3vT5r8dMQ+vtkuUnTlJaceaMwvmeJFlYdvYGyfakMCAiItLBPMzm1n0Fwb4kENp2/XxPkmx92qTj\naMu9iIhIJzFueI/zXO/u0K+rmQEREZFO4rueJOkoCgMiIiKdiCPujPguWiYQERFxcwoDIiIibk5h\nQERExM0pDIiIiLg5hQERERE3pzAgIiLi5hQGRERE3JzCgIiIiJtTGBAREXFzJrvdbje6CBERETGO\nZgZERETcnMKAiIiIm1MYEBERcXMKAyIiIm5OYUBERMTNKQyIiIi4OU+jC3AFBw4cYObMmfzsZz9j\n2rRpRpdzRZ566ik+//xzmpqa+PWvf83NN99sdEmXrK6ujrlz51JWVsbJkyeZOXMmo0ePNrqsK1Jf\nX8/3v/99Zs6cyY9//GOjy7ksmZmZ3HvvvVx11VUA9O3bl0ceecTgqi7fmjVrWLp0KZ6entxzzz1c\nf/31Rpd0yV5//XXWrFnT9u+7d+9mx44dBlZ0+WpqanjggQeorKyksbGRWbNmkZaWZnRZl6WlpYVH\nH32UgwcP4uXlxfz58+ndu7dDv6bCwBWqra3l8ccfZ/jw4UaXcsU++eQTDh48yKpVqygvL+fWW291\nyjCwadMmBgwYwK9+9Svy8/P5xS9+4fRh4O9//zvBwcFGl3HFrr32WhYtWmR0GVesvLycxYsX88Yb\nb1BbW8tzzz3nlGFg4sSJTJw4EYBPP/2Ud9991+CKLt9bb71Fz549mTNnDsXFxfz0pz9l/fr1Rpd1\nWT744ANOnDjBypUrOXr0KE888QRLlixx6NdUGLhCFouFF154gRdeeMHoUq7YkCFDSEpKAiAoKIi6\nujqam5vx8PAwuLJLM3bs2LaPCwsLiYqKMrCaK/fVV1+Rm5vrlH/YuKqMjAyGDx9OQEAAAQEBPP74\n40aXdMUWL17Mn/70J6PLuGyhoaHs378fgKqqKkJDQw2u6PIdPny47Wdxt27dKCgocPjPYu0ZuEKe\nnp74+PgYXUa78PDwwM/PD4DVq1dz3XXXOV0QON3kyZO57777SE9PN7qUK/Lkk08yd+5co8toF7m5\nucyYMYMpU6awbds2o8u5bMeOHaO+vp4ZM2YwdepUMjIyjC7piuzatYvo6GisVqvRpVy2cePGUVBQ\nwJgxY5g2bRoPPPCA0SVdtr59+7J161aam5v5+uuvycvLo7y83KFfUzMDcpb333+f1atX89JLLxld\nyhVZuXIle/fu5Q9/+ANr1qzBZDIZXdIle/vttxk4cCBxcXFGl3LFevTowezZs7nlllvIy8vjjjvu\nYOPGjVgsFqNLuywVFRX87W9/o6CggDvuuINNmzY55fcYtIb/W2+91egyrsi///1vYmJiePHFF9m3\nbx/p6em8+eabRpd1WUaNGkV2djY/+clPiI+Pp1evXjj6yQEKA3KGjz/+mOeff56lS5cSGBhodDmX\nZffu3YSHhxMdHU2/fv1obm7m+PHjhIeHG13aJdu8eTN5eXls3ryZoqIiLBYLXbp0ITU11ejSLllU\nVFTbEk63bt2IiIiguLjYKYNOeHg4gwYNwtPTk27duuHv7++032PQurnz4YcfNrqMK5Kdnc3IkSMB\nSEhIoKSkxCmXOb/xu9/9ru3jm266yeHfW1omkDYnTpzgqaeeYsmSJYSEhBhdzmXLyspqm9UoLS2l\ntrbWadcPn332Wd544w1ee+01Jk6cyMyZM50yCEDr7vsXX3wRAJvNRllZmdPu5xg5ciSffPIJLS0t\nlJeXO/X3WHFxMf7+/k47Q/ON7t27s3PnTgDy8/Px9/d32iCwb98+HnzwQQA++ugj+vfvj9ns2D+u\nNTNwhXbv3s2TTz5Jfn4+np6ebNiwgeeee84p/zBdt24d5eXl/Pa3v2279uSTTxITE2NgVZdu8uTJ\nPPTQQ0ydOpX6+nrmzZvn8N9I8t1uuOEG7rvvPj744AMaGxuZP3++0/4BFBUVxfe+9z0mTZoEwMMP\nP+y032M2m42wsDCjy7hit99+O+np6UybNo2mpibmz59vdEmXrW/fvtjtdm677Ta8vb07ZGOnHmEs\nIiLi5pwzyoqIiEi7URgQERFxcwoDIiIibk5hQERExM0pDIiIiLg5hQERERE3pzAg4samT5/O9u3b\nHfo1jhw5ws0333zR933Hx8fT1NR0yV8nOzubvLy8S37fxXrzzTe57777HDa+iJEUBkTEoXbs2EH/\n/v0dfgjMm2++6dAwIOLKdAKhiBPIzMzk//7v/+jSpQu5ubl4enqydOlSysrKmDp1Kh999BEAzz33\nHE1NTfzud79j0KBB/OY3v+HDDz+ksbGRGTNm8Nprr3Ho0CHmz5/fdo77hx9+yNKlSykuLmbmzJmM\nGzeOyspKHn30UY4fP051dTU///nP+cEPfsBzzz3HsWPHKCgo4IEHHmDAgAFtNR46dIhHH30Uu91O\nU1MTc+bMwWq18vzzz1NVVcX8+fPPCAT19fU8+OCDFBYWAvD73/+ea6+9tu3zp/cCrScYvvzyy5w8\neZJ58+bh5eVFfX09s2bNorGxkfXr17Nr1y4efPBBunfvzmOPPUZdXR21tbX8/ve/JzU1lblz52Kx\nWDh06BB/+tOfWL58OZ988gkWi4WoqCiefPLJizoVcdu2bfzlL3/h5ZdfdtpneIicTmFAxEl88cUX\nbNy4kfDwcKZPn87WrVvp16/feV9fW1vLgAEDuOuuu5g+fToffvghL7zwAm+++SYrVqxoCwPNzc28\n9NJLHDlyhClTpnDLLXzm7lsAAAQTSURBVLfw7LPPkpaWxoQJE6itreVHP/oRI0aMAFof3/vPf/7z\nrCf0/fGPf2x7//79+5k5cyYffPABd911F9u3bz9rZuDFF1+kS5cu/OUvf+Hw4cMsXrz4jDBwPq+9\n9ho33HADd911F2VlZXz88ceMHz+ef/zjH/zmN79h+PDh3HXXXfziF79g2LBh2Gw2br/9djZu3Nj2\n32X58uVUVlbyr3/9i6ysLDw8PFi37v9v7w5CEuuiAI7/LXNjYESQEBRkqyjEJFoFGUjRJqIgFCNo\nEQju3ERpFuGmHAKlTREELVoVETWtpUCkVQUiRBAVYlRSVARa9i3kE5sam9nM4Hh+y/fuu+/czXuH\nex7vfOfm5ubL329Ho1F8Ph9LS0uSCIh/hiQDQhQInU6X7VxWU1PD3d3dl9cYjUYg8y/9lpYWALRa\nLQ8PD9kx/7/k6+rqAEgkEoTDYY6Pj9nc3ARAqVRyeXkJgF6v/7RV7+HhIfPz80Cm7v/4+Egikfhp\nbEdHR1gsFiDT3nhubu7L9QB0dXUxNjZGLBbDZDLR29v7YUw4HObp6YmFhYVs/Le3twAYDAYANBoN\n7e3t2Gw2zGYzPT09aLXavPe+urpidHSUxcVFqqqqfileIQqBJANCFIjPOrD9+FJOpVLvjuVe87MO\nbrnj397eUCgUqFQqPB4Pzc3N78YGg0HKysq+nCffsdxz6XQ67/lcyWQSgNbWVra3twmFQmxsbLC1\ntcW3b9/ejVWpVAQCgU8b8OSWAfx+P6enpwSDQWw2G4FAIO9uy9nZGR0dHSwvL/9y8iJEIZAPCIUo\nYOXl5dzf3/P8/Mzr6ysHBwe/PUcoFAIyNf/S0lIqKysxGo3s7u4Cmdr+1NTUl1/46/V69vf3AYhE\nIlRUVORt62swGNjb2wMypYfh4eEPa4vH4wCcnJxkdxlWV1eJx+N0dnbi9XqzbWsVCgWpVArgXfyJ\nRAKv1/vh/hcXF6ysrKDT6RgZGcFsNhONRvOusa2tjenpaWKxWHbXRIh/gewMCFHANBoNfX199Pf3\nU1tbS2Nj42/PoVQqsdvtnJ+f43K5UCgUOBwOXC4XFouFZDLJ4OAgSmX+x4Xb7cbj8bC2tsbLywuz\ns7N5xw8NDeF2u7FaraTT6XetswG6u7tZX1/HarXS1NREQ0MDAPX19TidTtRqNel0GqfTCWTKHR6P\nh/HxcSYmJpicnGRnZ4dkMondbv9w/+rqaiKRCAMDA6jVajQaDQ6Hg+vra2ZmZvD7/Z/GXVJSgs/n\nw2q1YjAYsuUVIQqZtDAWQogfuN1uZmZm/nYYQvwxUiYQQogcyWQSk8n0t8MQ4o+SnQEhhBCiyMnO\ngBBCCFHkJBkQQgghipwkA0IIIUSRk2RACCGEKHKSDAghhBBF7j8ty3sWuMZEkQAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fad5d95ea90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VkJaqu-wO5aK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#AA \n",
        "# IMPORTANT NOTE: Please reimport datasets before running each model. Not doing so \n",
        "# will result in an error due to the way the data gets modified before each model.\n",
        "\n",
        "#adding extra columns to dataframe for aprioi algo\n",
        "store.insert(10, \"DistanceBin\", 1115) \n",
        "store.insert(11, \"SalesBin\", 1115) \n",
        "store.insert(12, \"Assortment2\", 1115)\n",
        "\n",
        "#disbale warning\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "\n",
        "#creates list of first 21 sales to plug into AA model\n",
        "ind_mean_sales = [0] * 1115\n",
        "for k in range (0, 20):\n",
        "    for i in range (0, 1114):\n",
        "        ind_mean_sales[i] += train.Sales[i]\n",
        "\n",
        "#takes average of each store sales \n",
        "for i in range (0, 1114):\n",
        "    ind_mean_sales[i] += (ind_mean_sales[i] / 21)\n",
        "\n",
        "#poplating assortment2 column\n",
        "for i in range (0,1115):     \n",
        "    if store.Assortment[i] == 'a':\n",
        "        store.Assortment2[i] = 'AssortmentA'\n",
        "        \n",
        "    elif store.Assortment[i] == 'b':\n",
        "        store.Assortment2[i] = 'AssortmentB'\n",
        "    \n",
        "    elif store.Assortment[i] == 'c':\n",
        "        store.Assortment2[i] = 'AssortmentC' \n",
        "    else:\n",
        "        store.Assortment2[i] = 'AssortmentD'\n",
        "        \n",
        "#binning values for sales\n",
        "for i in range (0,1115):     \n",
        "    if ind_mean_sales[i] < 100000:\n",
        "        store.SalesBin[i] = 'LowProfit'\n",
        "        \n",
        "    elif ind_mean_sales[i]  < 300000:\n",
        "        store.SalesBin[i] = 'MediumProfit'\n",
        "       \n",
        "    else:\n",
        "        store.SalesBin[i] = 'HighProfit'\n",
        "\n",
        "#binning values for sales\n",
        "for i in range (0,1115):     \n",
        "    if store.CompetitionDistance[i] < 3850:\n",
        "        store.DistanceBin[i] = 'LowCompDistance'\n",
        "        \n",
        "    elif store.CompetitionDistance[i] < 10600:\n",
        "        store.DistanceBin[i] = 'MediumCompDistance'\n",
        "       \n",
        "    else:\n",
        "        store.DistanceBin[i] = 'HighCompDistance'\n",
        "\n",
        "    \n",
        "#turns columns intoo lists \n",
        "store_type = store.StoreType.tolist()\n",
        "store_assortment = store.Assortment2.tolist()\n",
        "store_distance = store.DistanceBin.tolist()\n",
        "store_sales = store.SalesBin.tolist()\n",
        "\n",
        "#combines sep lists into lists of nested lists for aprori algo\n",
        "records = list(it.chain.from_iterable(it.zip_longest(store_type,store_assortment, store_distance,store_sales)))\n",
        "\n",
        "#nests the tuple into list of nested list\n",
        "i=0\n",
        "new_list=[]\n",
        "while i<len(records):\n",
        "    new_list.append(records[i:i+4])\n",
        "    i+=4\n",
        "\n",
        "#populating aapriori algo  \n",
        "association_rules = apriori(new_list, min_support = 0.3, min_confidence = 0.3, min_lift = 1)\n",
        "association_results = list(association_rules)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tF41SEkgO_Qu",
        "colab_type": "code",
        "outputId": "050cc277-7814-4a22-9914-5a6ee53af81e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "#Prints ternary associations\n",
        "#NOTE single chars = STORE TYPE\n",
        "for item in association_results:\n",
        "\n",
        "    # first index of the inner list\n",
        "    # Contains base item and add item\n",
        "    pair = item[0] \n",
        "    items = [x for x in pair]\n",
        "    if (len(items) != 3):\n",
        "        continue\n",
        "    print(\"Rule: \" + items[0] + \" -> \" + items[1] + \" -> \" + items[2])\n",
        "\n",
        "    #second index of the inner list\n",
        "    print(\"Support: \" + str(item[1]))\n",
        "\n",
        "    #third index of the list located at 0th\n",
        "    #of the third index of the inner list\n",
        "\n",
        "    print(\"Confidence: \" + str(item[2][0][2]))\n",
        "    print(\"Lift: \" + str(item[2][0][3]))\n",
        "    print(\"=====================================\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rule: LowCompDistance -> AssortmentA -> MediumProfit\n",
            "Support: 0.3345291479820628\n",
            "Confidence: 0.689463955637708\n",
            "Lift: 1.1029444914433923\n",
            "=====================================\n",
            "Rule: AssortmentA -> a -> MediumProfit\n",
            "Support: 0.3040358744394619\n",
            "Confidence: 0.6266173752310537\n",
            "Lift: 1.1605953046223005\n",
            "=====================================\n",
            "Rule: LowCompDistance -> a -> MediumProfit\n",
            "Support: 0.3309417040358744\n",
            "Confidence: 0.5970873786407767\n",
            "Lift: 1.105901041834661\n",
            "=====================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "99f8lrOxs1SK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h2>Evaluation and Discussion of Results</h2>\n",
        " \n",
        "<h3>Regression </h3>\n",
        "<p>When implementing the linear regression model, the date field was removed because we felt it had little statistical relevance to our target. We then popped out the sales column and stored it. The rest of the data set was stored separately. The data was then split into a training and test set, with 80% of entries belonging to the training set and the rest belonging to the test set. The model was then fit on the training set. Then the test set was used to predict new points and see how they compared to their actual values. Our coefficient of multiple determination was .85. This means that 85% of the variance was accounted for in our model, with only 15% coming from factors not accounted for. While not quite hitting our initial benchmark of 90%, we felt that this was accurate enough for our model. With additional data, or adjustments of parameters, it is likely that this model could be improved.</p>\n",
        "\n",
        "<h3>Clustering</h3>\n",
        "<p>A similar process to regression was taken in terms of removing the date attribute and splitting the data. Then we looped through several values of clusters to see which the best one was. Typically the rule of thumb to avoid overfitting, or ignoring local behaviors is to use the elbow of an inertia graph. However, for cluster values 1-10, the inertia values were extremely high, in the magnitude of 100,00s. When we attempted to test clusters in the range of 10 to 20, the computation took too long do to all of the calculations it had to perform. There is likely much more work that needs to be done cleaning and standardizing the data to find useful clusters. In addition to that, due to the large sample size it was hard to visually distinguish any meaningful clusters. A possible remedy to this would be to select a random sample of the data and see if any meaningful clusters can be taken there.</p>\n",
        "\n",
        "<h3>Association Rule Analysis</h3>\n",
        "<p>The bulk of the work that had to be done to prepare the data for the association rule analysis was to bin the numerical data. All of the rules we found basically imply a 10% to 16% increase in the likelihood of those events occuring together versus them occuring on their own. The first rule states that a low distance of competition implies a store carrying AssortmentA which implies a Medium Profit. The second rule states that store type a implies carrying AssortmentA which also implies Medium Profit. The last rule states that store type a implies low competition distance which implies Medium Profit. </p>\n",
        "  \n",
        "  \n",
        "  \n",
        "  "
      ]
    },
    {
      "metadata": {
        "id": "2LmB8jYRs1SL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<h2>Sources</h2>\n",
        "<p>Document all sources here.</p>"
      ]
    },
    {
      "metadata": {
        "id": "yfoH-aprs1SM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Regression model based off of example in Scikit documentation https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html\n",
        "# Clustering and evaluation were based off of Datacamp Clustering Exercise\n",
        "#AA used implementing apyrori package, and special thanks to https://stackabuse.com/association-rule-mining-via-apriori-algorithm-in-python/"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}