1_3_10_Predicting_Customer_Spending.ipynb

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        "# <span style=\"color:blue\">Predicting Customer Spending Using Linear Regression</span>\n",
        "\n",
        "## <span style=\"color:green\">Project Overview</span>\n",
        "\n",
        "This project aims to predict customer spending using Linear Regression on the **Online Retail dataset**. We will go through various steps including data loading, cleaning, exploratory data analysis (EDA), feature selection, and model training using different regression techniques. Each step is critical to understanding how machine learning models can be applied to real-world data.\n",
        "\n",
        "## <span style=\"color:green\">Step 1: Importing Libraries and Loading Data</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Import necessary libraries such as pandas, matplotlib, seaborn, and scikit-learn.\n",
        "- Load the dataset from a given URL and display its first few rows to understand its structure.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Understanding the dataset structure helps in identifying the features and target variable for our prediction model.\n",
        "- Pandas is used for data manipulation and analysis.\n",
        "- Matplotlib and Seaborn are used for data visualization.\n",
        "\n",
        "## <span style=\"color:green\">Step 2: Data Cleaning and Preparation</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Clean the dataset by handling missing values and creating new features.\n",
        "- Calculate the total price for each transaction and extract date features like month and day of the week.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Cleaning data ensures that our model is trained on accurate and relevant information.\n",
        "- New features derived from existing ones can provide additional insights for the model.\n",
        "\n",
        "## <span style=\"color:green\">Step 3: Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Visualize the data to understand the distribution and relationships between features.\n",
        "- Use histograms, line plots, box plots, and scatter plots for a comprehensive analysis.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- EDA helps in identifying patterns, trends, and outliers in the data.\n",
        "- Visualizations are crucial for communicating findings and justifying model choices.\n",
        "\n",
        "## <span style=\"color:green\">Step 4: Feature Selection and Data Splitting</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Select relevant features for the prediction model.\n",
        "- Split the data into training and testing sets to evaluate model performance.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Feature selection is important for building a model that generalizes well to new data.\n",
        "- Data splitting helps in assessing the model's performance on unseen data.\n",
        "\n",
        "## <span style=\"color:green\">Step 5: Model Training and Evaluation - Linear Regression</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Train a Linear Regression model on the training data.\n",
        "- Evaluate the model using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Linear Regression is a simple yet powerful algorithm for predicting continuous values.\n",
        "- Evaluation metrics help in quantifying the model's accuracy and performance.\n",
        "\n",
        "## <span style=\"color:green\">Step 6: Predictions on New Data</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Use the trained model to make predictions on new data.\n",
        "- Save the predictions for future reference and analysis.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Making predictions on new data demonstrates the model's practical application.\n",
        "- Saving predictions ensures reproducibility and aids in further analysis.\n",
        "\n",
        "## <span style=\"color:green\">Step 7: Model Training and Evaluation - Ridge Regression</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Train a Ridge Regression model to handle multicollinearity and improve prediction accuracy.\n",
        "- Evaluate the model using the same metrics as before and compare with Linear Regression.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Ridge Regression adds regularization to reduce overfitting and handle correlated features.\n",
        "- Comparison with Linear Regression helps in selecting the best model for the task.\n",
        "\n",
        "## <span style=\"color:green\">Step 8: Model Training and Evaluation - Polynomial Regression</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Train a Polynomial Regression model to capture non-linear relationships in the data.\n",
        "- Evaluate the model using the same metrics and compare with previous models.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Polynomial Regression transforms features to a higher degree to model non-linear patterns.\n",
        "- Comparing different models provides insights into the best approach for the given problem.\n",
        "\n",
        "## <span style=\"color:green\">Step 9: Save Results and Plots</span>\n",
        "\n",
        "### <span style=\"color:orange\">Objective:</span>\n",
        "- Save evaluation metrics and predictions for each model in CSV format.\n",
        "- Save all plots as PDF files for documentation and presentation.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- Saving results ensures that your work is well-documented and can be easily shared.\n",
        "- Organized and well-documented projects are crucial for reproducibility and future reference.\n",
        "\n",
        "---\n",
        "\n",
        "By following these steps, you will gain a comprehensive understanding of how to build, train, and evaluate machine learning models for predicting customer spending. Each step builds upon the previous one, leading to a complete and practical machine learning project."
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YQHlqVyVu0HI"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "O8Q6hs-6u0Ol"
      },
      "execution_count": null,
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      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 1: Importing Libraries and Loading Data</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Import necessary libraries such as pandas, matplotlib, seaborn, and scikit-learn.\n",
        "- Load the dataset from a given URL and display its first few rows to understand its structure.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Understanding Libraries:</span>\n",
        "- **Pandas:** Used for data manipulation and analysis. It provides data structures and functions needed to manipulate structured data seamlessly.\n",
        "- **Matplotlib and Seaborn:** These are used for data visualization. Matplotlib provides low-level plotting capabilities while Seaborn provides a high-level interface for drawing attractive and informative statistical graphics.\n",
        "- **Scikit-learn:** A machine learning library in Python that provides simple and efficient tools for data mining and data analysis.\n",
        "\n",
        "### <span style=\"color:orange\">Loading Data:</span>\n",
        "- **Dataset URL:** The dataset is usually loaded from an online source or a local file path. In our case, we will use a URL to load the dataset directly into a pandas DataFrame.\n",
        "- **Initial Data Inspection:** Displaying the first few rows of the dataset helps in understanding the structure and types of data we are dealing with. This is done using the `head()` function in pandas.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **DataFrame:** A two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).\n",
        "- **Head Function:** The `head(n)` function returns the first `n` rows of the DataFrame. By default, it returns the first 5 rows.\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Import Libraries:**\n",
        "   - `import pandas as pd`\n",
        "   - `import matplotlib.pyplot as plt`\n",
        "   - `import seaborn as sns`\n",
        "   - `from sklearn.model_selection import train_test_split`\n",
        "   - `from sklearn.linear_model import LinearRegression`\n",
        "   - `from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score`\n",
        "2. **Load Dataset:**\n",
        "   - `url = \"your_dataset_url\"`\n",
        "   - `data = pd.read_csv(url)`\n",
        "3. **Display First Few Rows:**\n",
        "   - `data.head()`\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 1, you will have a clear understanding of the dataset's structure and the necessary libraries required for further data manipulation and analysis."
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas for data manipulation and analysis"
      ],
      "metadata": {
        "id": "SyT2z13W-HyB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "rJjhYso_-4BK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for statistical data visualization"
      ],
      "metadata": {
        "id": "D9q2hgd0-4Ee"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Import train_test_split for splitting data"
      ],
      "metadata": {
        "id": "VLaMAr1J-4HH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  # Import LinearRegression for building the regression model"
      ],
      "metadata": {
        "id": "LRTsFPqc-4IV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score  # Import metrics for evaluating the model\n"
      ],
      "metadata": {
        "id": "l6szOoN_-4Sz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m1pLc6EM-4Vk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "1mVOVcEc-4YS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx'  # URL to dataset"
      ],
      "metadata": {
        "id": "ZANeJRfQ-4c7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel(url)  # Load the dataset into a pandas DataFrame"
      ],
      "metadata": {
        "id": "cUK0VnEy-4g0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wYWh4GHD-4l_"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset"
      ],
      "metadata": {
        "id": "3qvEv_3z-4oh"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Print statement\n",
        "print(data.head())  # Display the first few rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4Iagkuw-4sV",
        "outputId": "728190bd-7022-434d-c6f7-68c51d702868"
      },
      "execution_count": null,
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          "output_type": "stream",
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          "text": [
            "First few rows of the dataset:\n",
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
            "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
            "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
            "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
            "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GLmY1b9k8dGq"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jA_Oo9y18dMB"
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      "source": [
        "# <span style=\"color:blue\">Step 2: Data Inspection and Cleaning</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Inspect the dataset for missing values and data types.\n",
        "- Clean the dataset by handling missing values and creating new features.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Data Inspection:</span>\n",
        "- **Missing Values:** It's crucial to identify and handle missing values to ensure data quality. The `isnull().sum()` function helps to find the total number of missing values in each column.\n",
        "- **Summary Statistics:** Provides a quick overview of the dataset using `describe()`, which includes count, mean, standard deviation, minimum, and maximum values.\n",
        "- **Data Types:** Checking data types using `dtypes` is essential for understanding what type of data each column holds (e.g., integer, float, object).\n",
        "\n",
        "### <span style=\"color:orange\">Data Cleaning:</span>\n",
        "- **Dropping Missing Values:** Rows with critical missing values in columns like 'CustomerID' and 'Description' are removed using the `dropna()` function.\n",
        "- **Creating New Features:**\n",
        "  - **TotalPrice:** Calculated by multiplying 'Quantity' and 'UnitPrice'.\n",
        "  - **Date Features:** Extract useful information from 'InvoiceDate' like the month and day of the week using the `pd.to_datetime()` function and its `.dt` accessor.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Handling Missing Values:** Dropping or filling missing values ensures that the dataset is ready for analysis and modeling.\n",
        "- **Feature Engineering:** Creating new features from existing data can provide additional insights and improve model performance.\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Check for Missing Values:**\n",
        "   - `data.isnull().sum()`\n",
        "2. **Summary Statistics:**\n",
        "   - `data.describe()`\n",
        "3. **Data Types:**\n",
        "   - `data.dtypes`\n",
        "4. **Drop Rows with Missing Values:**\n",
        "   - `data_cleaned = data.dropna(subset=['CustomerID', 'Description'])`\n",
        "5. **Calculate Total Price:**\n",
        "   - `data_cleaned['TotalPrice'] = data_cleaned['Quantity'] * data_cleaned['UnitPrice']`\n",
        "6. **Extract Date Features:**\n",
        "   - `data_cleaned['InvoiceDate'] = pd.to_datetime(data_cleaned['InvoiceDate'])`\n",
        "   - `data_cleaned['Month'] = data_cleaned['InvoiceDate'].dt.month`\n",
        "   - `data_cleaned['DayOfWeek'] = data_cleaned['InvoiceDate'].dt.dayofweek`\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 2, you will have a cleaned dataset with essential features ready for exploratory data analysis and further modeling."
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "18NNpxYg8dQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in each column:\")  # Print statement\n",
        "print(data.isnull().sum())  # Display missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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          "name": "stdout",
          "text": [
            "\n",
            "Missing values in each column:\n",
            "InvoiceNo           0\n",
            "StockCode           0\n",
            "Description      1454\n",
            "Quantity            0\n",
            "InvoiceDate         0\n",
            "UnitPrice           0\n",
            "CustomerID     135080\n",
            "Country             0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ph5K8Vt78dWv"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nMwB3USW8p_T"
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    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics of the dataset"
      ],
      "metadata": {
        "id": "RhHR8PZd6haR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics of the dataset:\")  # Print statement\n",
        "print(data.describe())  # Display summary statistics"
      ],
      "metadata": {
        "id": "-0knJVzM6hdn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ba947f5-f808-4f86-ed20-52a0d9c9536e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics of the dataset:\n",
            "            Quantity                    InvoiceDate      UnitPrice  \\\n",
            "count  541909.000000                         541909  541909.000000   \n",
            "mean        9.552250  2011-07-04 13:34:57.156386048       4.611114   \n",
            "min    -80995.000000            2010-12-01 08:26:00  -11062.060000   \n",
            "25%         1.000000            2011-03-28 11:34:00       1.250000   \n",
            "50%         3.000000            2011-07-19 17:17:00       2.080000   \n",
            "75%        10.000000            2011-10-19 11:27:00       4.130000   \n",
            "max     80995.000000            2011-12-09 12:50:00   38970.000000   \n",
            "std       218.081158                            NaN      96.759853   \n",
            "\n",
            "          CustomerID  \n",
            "count  406829.000000  \n",
            "mean    15287.690570  \n",
            "min     12346.000000  \n",
            "25%     13953.000000  \n",
            "50%     15152.000000  \n",
            "75%     16791.000000  \n",
            "max     18287.000000  \n",
            "std      1713.600303  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "usyvDkvj6hgg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-r_TT4lK6hjY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data types of each column"
      ],
      "metadata": {
        "id": "6X9qWaL-6hpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nData types of each column:\")  # Print statement\n",
        "print(data.dtypes)  # Display data types"
      ],
      "metadata": {
        "id": "jwxs_oxH6hsI",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data types of each column:\n",
            "InvoiceNo              object\n",
            "StockCode              object\n",
            "Description            object\n",
            "Quantity                int64\n",
            "InvoiceDate    datetime64[ns]\n",
            "UnitPrice             float64\n",
            "CustomerID            float64\n",
            "Country                object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CJz8mNr06hvB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N3kC2stm6h0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of the dataset"
      ],
      "metadata": {
        "id": "XyhcBZHb6h1G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nShape of the dataset:\")  # Print statement\n",
        "print(data.shape)  # Display the shape of the dataset"
      ],
      "metadata": {
        "id": "tIchAHdp6h3K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "381aa1a5-c83b-49e9-b02e-ec586bdb5181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape of the dataset:\n",
            "(541909, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rC12PDLh6RsR"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o4zl6MND6Rwp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing CustomerID and Description"
      ],
      "metadata": {
        "id": "aQiJguTL6R0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned = data.dropna(subset=['CustomerID', 'Description'])  # Drop rows with missing CustomerID and Description\n"
      ],
      "metadata": {
        "id": "Dmn8Ayjg6R2w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9gYV2R-k6R5Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate total price for each transaction"
      ],
      "metadata": {
        "id": "uzsQ26yR6SB5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned['TotalPrice'] = data_cleaned['Quantity'] * data_cleaned['UnitPrice']  # Calculate total price"
      ],
      "metadata": {
        "id": "vPz0vg0H6SFB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ecc4360-c693-4779-bf9d-82956a8a3de0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-d517e9f9ea15>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cleaned['TotalPrice'] = data_cleaned['Quantity'] * data_cleaned['UnitPrice']  # Calculate total price\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "St_iFfQg6SHy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract date features"
      ],
      "metadata": {
        "id": "HbSLb1r26SJY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned['InvoiceDate'] = pd.to_datetime(data_cleaned['InvoiceDate'])  # Convert InvoiceDate to datetime"
      ],
      "metadata": {
        "id": "kRpPUt786SKh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76424215-f3c7-42e2-b3bb-46a04fc5a4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-cc65c2d8c5ef>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cleaned['InvoiceDate'] = pd.to_datetime(data_cleaned['InvoiceDate'])  # Convert InvoiceDate to datetime\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned['Month'] = data_cleaned['InvoiceDate'].dt.month  # Extract month from InvoiceDate"
      ],
      "metadata": {
        "id": "uRbhJ-xJ6SOi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfdfb494-837b-4cc4-e189-239e2836c1e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-1980ba999cf4>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cleaned['Month'] = data_cleaned['InvoiceDate'].dt.month  # Extract month from InvoiceDate\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_cleaned['DayOfWeek'] = data_cleaned['InvoiceDate'].dt.dayofweek  # Extract day of the week from InvoiceDate"
      ],
      "metadata": {
        "id": "Li5iUqNu6SPp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80b1c02c-8254-4a82-92ee-316a595c3842"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-d31a79b071f4>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cleaned['DayOfWeek'] = data_cleaned['InvoiceDate'].dt.dayofweek  # Extract day of the week from InvoiceDate\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-EvhPhs-6ST6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UNjf-zEI6SV_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the cleaned data"
      ],
      "metadata": {
        "id": "ji24qvMe6SXv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFirst few rows of the cleaned dataset:\")  # Print statement\n",
        "print(data_cleaned.head())  # Display the first few rows of the cleaned dataset"
      ],
      "metadata": {
        "id": "N5y7BQ4c6SYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afad91ca-a8e5-47b2-9731-763747d22250"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First few rows of the cleaned dataset:\n",
            "  InvoiceNo StockCode                          Description  Quantity  \\\n",
            "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
            "1    536365     71053                  WHITE METAL LANTERN         6   \n",
            "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
            "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
            "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
            "\n",
            "          InvoiceDate  UnitPrice  CustomerID         Country  TotalPrice  \\\n",
            "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom       15.30   \n",
            "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom       22.00   \n",
            "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom       20.34   \n",
            "\n",
            "   Month  DayOfWeek  \n",
            "0     12          2  \n",
            "1     12          2  \n",
            "2     12          2  \n",
            "3     12          2  \n",
            "4     12          2  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PvBpag8s6Sbi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XzeTiXJr6SeK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of the cleaned dataset"
      ],
      "metadata": {
        "id": "MpIkRW0Z6Sgj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nShape of the cleaned dataset:\")  # Print statement\n",
        "print(data_cleaned.shape)  # Display the shape of the cleaned dataset"
      ],
      "metadata": {
        "id": "7tdJrWRv6Skx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e4ffc78-f04a-467c-8955-56fe72b4fa9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape of the cleaned dataset:\n",
            "(406829, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IHN_3-Qy6Sp5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pO2R9wWf6Tpp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 3: Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Conduct EDA to understand the distribution and relationships within the data.\n",
        "- Visualize the data using various plots to identify patterns and insights.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Distribution of Total Price:</span>\n",
        "- **Histogram:** A histogram is used to display the distribution of a single numerical variable. It helps in understanding the frequency of different ranges of values within the data.\n",
        "- **Example:** Plotting the distribution of 'TotalPrice' can reveal if the data is skewed, has outliers, or follows a normal distribution.\n",
        "\n",
        "### <span style=\"color:orange\">Total Price Over Time:</span>\n",
        "- **Line Plot:** A line plot is useful for visualizing trends over time. It can help identify seasonal patterns, trends, and anomalies.\n",
        "- **Example:** Plotting 'TotalPrice' over 'InvoiceDate' can show sales trends over time.\n",
        "\n",
        "### <span style=\"color:orange\">Boxplots by Month and Day of the Week:</span>\n",
        "- **Boxplot:** A boxplot provides a summary of a set of data values, showing the median, quartiles, and potential outliers. It is useful for comparing distributions across different categories.\n",
        "- **Example:** Boxplots of 'TotalPrice' by 'Month' and 'DayOfWeek' can highlight how sales vary across different times.\n",
        "\n",
        "### <span style=\"color:orange\">Scatter Plot of Quantity vs. Total Price:</span>\n",
        "- **Scatter Plot:** A scatter plot displays values for typically two variables for a set of data. It helps in identifying correlations between the two variables.\n",
        "- **Example:** A scatter plot of 'Quantity' vs. 'TotalPrice' can reveal the relationship between the quantity of items purchased and the total price.\n",
        "\n",
        "### <span style=\"color:orange\">Total Price by Country:</span>\n",
        "- **Line Plot:** Summarizes and visualizes data across different categories, such as countries, to show the distribution and comparison of totals.\n",
        "- **Example:** A line plot of 'TotalPrice' by 'Country' can show which countries contribute most to sales.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Visualizing Data:** Helps in understanding the underlying patterns, trends, and relationships within the data.\n",
        "- **Identifying Outliers:** Visualizations can highlight outliers or anomalies in the data.\n",
        "- **Comparing Categories:** Boxplots and line plots are useful for comparing data across different categories.\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Distribution of Total Price:**\n",
        "   - Use a histogram to plot `data_cleaned['TotalPrice']`.\n",
        "2. **Total Price Over Time:**\n",
        "   - Use a line plot to plot total price over `InvoiceDate`.\n",
        "3. **Boxplot by Month:**\n",
        "   - Create a boxplot of `TotalPrice` by `Month`.\n",
        "4. **Boxplot by Day of the Week:**\n",
        "   - Create a boxplot of `TotalPrice` by `DayOfWeek`.\n",
        "5. **Scatter Plot of Quantity vs. Total Price:**\n",
        "   - Create a scatter plot of `Quantity` vs. `TotalPrice`.\n",
        "6. **Total Price by Country:**\n",
        "   - Use a line plot to visualize `TotalPrice` by `Country`.\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 3, you will have a better understanding of your data, including distributions, trends, and relationships, which are crucial for building an effective predictive model."
      ],
      "metadata": {
        "id": "_F22FtSBvN2g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of Total Price"
      ],
      "metadata": {
        "id": "naNmrDQC6TtC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.histplot(data_cleaned['TotalPrice'], bins=50, color='skyblue')  # Plot the distribution with skyblue color\n",
        "plt.title('Distribution of Total Price')  # Set the title\n",
        "plt.xlabel('Total Price')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.savefig('/content/total_price_distribution.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "id": "Swe-T9WG6TzI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "0743aeaf-9db5-4db3-c186-9e99601f4ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1uEnfOlU6T2Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wZQMblmg6T5A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Total Price over time"
      ],
      "metadata": {
        "id": "aExVOxC_6T-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size\n",
        "data_cleaned.groupby('InvoiceDate')['TotalPrice'].sum().plot(color='green')  # Plot the total price over time with green color\n",
        "plt.title('Total Price Over Time')  # Set the title\n",
        "plt.xlabel('Date')  # Set the x-axis label\n",
        "plt.ylabel('Total Price')  # Set the y-axis label\n",
        "plt.savefig('/content/total_price_over_time.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "id": "U85BEOe36UBD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "947e7f8d-b76a-454d-d27e-3c81822eeeb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wMGg9scF6UI4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jU7ZSblA6ULa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot of Total Price by Month"
      ],
      "metadata": {
        "id": "RxmnzbMF6UOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.boxplot(x='Month', y='TotalPrice', data=data_cleaned, palette='pastel')  # Plot the boxplot with pastel palette\n",
        "plt.title('Total Price by Month')  # Set the title\n",
        "plt.xlabel('Month')  # Set the x-axis label\n",
        "plt.ylabel('Total Price')  # Set the y-axis label\n",
        "plt.savefig('/content/total_price_by_month.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "id": "o3cvnrzJ6URR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "2f11077c-a3ac-47da-8383-48b80325b2b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-5a0b310790b0>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='Month', y='TotalPrice', data=data_cleaned, palette='pastel')  # Plot the boxplot with pastel palette\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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Kzc31UIfnR8ADAAAAgHLsdrskKSIiotLxsulldbUJAQ8AAAAAyrHZbJKk/Pz8SsfLppfV1SYEPAAAAAAoJyYmRsHBwcrIyKhwnZ3D4dCqVasUEhKimJgYD3V4fgQ8AAAAACjHarUqOTlZOTk5WrBggfLy8lRSUqK8vDwtWLBAOTk56t+/f618Hp7FMAzD002gcna7XUFBQSouLq6Vh38BAAAAM6vsOXghISHq379/jT8iwd1swHPwAAAAAKAScXFx6tSpk3Jzc2W322Wz2RQTE1Mrj9yVIeABAAAAwHlYrVa1bdvW0224rfZGTwAAAABAlRDwAAAAAMAkCHgAAAAAYBIEPAAAAAAwCQIeAAAAAJgEAQ8AAAAATIKABwAAAAAmQcADAAAAAJMg4AEAAACASRDwAAAAAMAkCHgAAAAAYBIEPAAAAAAwiToV8L744gvdeuutioyMlMVi0ZIlS1zGU1JSZLFYXF69e/d2qSksLNTQoUNls9nUuHFjpaam6tixYy4133//va6//nr5+fkpKipKs2bNqtDL4sWL1b59e/n5+alTp0765JNPXMYNw9C0adMUEREhf39/JSYmavfu3dWzIgAAAACgEnUq4B0/flxxcXGaO3fueWt69+6t/Px85+v99993GR86dKi2b9+ujIwMLVu2TF988YUeeOAB57jdblevXr3UsmVLbd68WS+++KJmzJihN954w1mzfv16DRkyRKmpqfruu+80YMAADRgwQNu2bXPWzJo1S3PmzNH8+fO1YcMGBQQEKCkpSSUlJdW4RgAAAADg/1gMwzA83cTvYbFY9NFHH2nAgAHOaSkpKSoqKqpwZK/Mjh07FBsbq02bNqlbt26SpBUrVqhv3776+eefFRkZqXnz5unxxx9XQUGBfHx8JElTpkzRkiVLtHPnTknSoEGDdPz4cS1btsw572uuuUZdunTR/PnzZRiGIiMjNWnSJD3yyCOSpOLiYoWFhSktLU2DBw92axntdruCgoJUXFwsm81W1VUEAAAAwCTczQZ16gieO9auXavQ0FC1a9dODz30kA4fPuwcy8zMVOPGjZ3hTpISExNltVq1YcMGZ80NN9zgDHeSlJSUpF27dunIkSPOmsTERJfPTUpKUmZmpiQpLy9PBQUFLjVBQUGKj4931lTm1KlTstvtLi8AAAAAcJepAl7v3r31j3/8Q6tXr9YLL7ygdevWqU+fPiotLZUkFRQUKDQ01OU93t7eCg4OVkFBgbMmLCzMpabs54vVlB8v/77Kaiozc+ZMBQUFOV9RUVFVWn4AAAAA9Zu3pxuoTuVPfezUqZM6d+6smJgYrV27Vj179vRgZ+6ZOnWqJk6c6PzZbrcT8gAAAAC4zVRH8M7VunVrNW3aVHv27JEkhYeH69ChQy41Z8+eVWFhocLDw501Bw8edKkp+/liNeXHy7+vsprK+Pr6ymazubwAAAAAwF2mDng///yzDh8+rIiICElSQkKCioqKtHnzZmfN559/LofDofj4eGfNF198oTNnzjhrMjIy1K5dOzVp0sRZs3r1apfPysjIUEJCgiSpVatWCg8Pd6mx2+3asGGDswYAAAAAqludCnjHjh1TVlaWsrKyJP12M5OsrCzt27dPx44d06OPPqpvvvlGe/fu1erVq5WcnKw2bdooKSlJktShQwf17t1b999/vzZu3Kivv/5ao0eP1uDBgxUZGSlJuvvuu+Xj46PU1FRt375dixYt0uzZs11OnRw3bpxWrFihl156STt37tSMGTP07bffavTo0ZJ+u8Pn+PHj9cwzz2jp0qXaunWrhg0bpsjISJe7fgIAAABAdapTj0lYu3atevToUWH68OHDNW/ePA0YMEDfffedioqKFBkZqV69eulPf/qTy81OCgsLNXr0aH388ceyWq0aOHCg5syZo8DAQGfN999/r1GjRmnTpk1q2rSpxowZo8mTJ7t85uLFi/XEE09o7969atu2rWbNmqW+ffs6xw3D0PTp0/XGG2+oqKhI3bt31+uvv67LL7/c7eXlMQkAAAAAJPezQZ0KePUNAQ8AAACAVI+fgwcAAAAA9RUBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZRpwLeF198oVtvvVWRkZGyWCxasmSJy7hhGJo2bZoiIiLk7++vxMRE7d6926WmsLBQQ4cOlc1mU+PGjZWamqpjx4651Hz//fe6/vrr5efnp6ioKM2aNatCL4sXL1b79u3l5+enTp066ZNPPqlyLwAAAABQnepUwDt+/Lji4uI0d+7cSsdnzZqlOXPmaP78+dqwYYMCAgKUlJSkkpISZ83QoUO1fft2ZWRkaNmyZfriiy/0wAMPOMftdrt69eqlli1bavPmzXrxxRc1Y8YMvfHGG86a9evXa8iQIUpNTdV3332nAQMGaMCAAdq2bVuVegEAAACA6mQxDMPwdBO/h8Vi0UcffaQBAwZI+u2IWWRkpCZNmqRHHnlEklRcXKywsDClpaVp8ODB2rFjh2JjY7Vp0yZ169ZNkrRixQr17dtXP//8syIjIzVv3jw9/vjjKigokI+PjyRpypQpWrJkiXbu3ClJGjRokI4fP65ly5Y5+7nmmmvUpUsXzZ8/361e3GG32xUUFKTi4mLZbLZqWW8AAAAA6h53s0GdOoJ3IXl5eSooKFBiYqJzWlBQkOLj45WZmSlJyszMVOPGjZ3hTpISExNltVq1YcMGZ80NN9zgDHeSlJSUpF27dunIkSPOmvKfU1ZT9jnu9FKZU6dOyW63u7wAAAAAwF2mCXgFBQWSpLCwMJfpYWFhzrGCggKFhoa6jHt7eys4ONilprJ5lP+M89WUH79YL5WZOXOmgoKCnK+oqKiLLDUAAAAA/B/TBDwzmDp1qoqLi52v/fv3e7olAAAAAHWIaQJeeHi4JOngwYMu0w8ePOgcCw8P16FDh1zGz549q8LCQpeayuZR/jPOV1N+/GK9VMbX11c2m83lBQAAAADuMk3Aa9WqlcLDw7V69WrnNLvdrg0bNighIUGSlJCQoKKiIm3evNlZ8/nnn8vhcCg+Pt5Z88UXX+jMmTPOmoyMDLVr105NmjRx1pT/nLKass9xpxcAAAAAqG51KuAdO3ZMWVlZysrKkvTbzUyysrK0b98+WSwWjR8/Xs8884yWLl2qrVu3atiwYYqMjHTeabNDhw7q3bu37r//fm3cuFFff/21Ro8ercGDBysyMlKSdPfdd8vHx0epqanavn27Fi1apNmzZ2vixInOPsaNG6cVK1bopZde0s6dOzVjxgx9++23Gj16tCS51QsAAAAAVDujDlmzZo0hqcJr+PDhhmEYhsPhMJ588kkjLCzM8PX1NXr27Gns2rXLZR6HDx82hgwZYgQGBho2m80YMWKEcfToUZea7Oxso3v37oavr69x2WWXGc8//3yFXj744APj8ssvN3x8fIyOHTsay5cvdxl3p5eLKS4uNiQZxcXFVXofAAAAAHNxNxvU2efg1Qc8Bw8AAACAVA+fgwcAAAAA9R0BDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmIS3pxtA7eRwOJSbmyu73S6bzaaYmBhZrewPAAAAAGozAh4qyM7OVnp6ugoLC53TgoODlZycrLi4OA92BgAAAOBCCHhwkZ2drbS0NMXGxmrYsGGKiIhQfn6+MjIylJaWppSUFEIeAAAAUEtxzh2cHA6H0tPTFRsbq9TUVEVHR8vX11fR0dFKTU1VbGysli5dKofD4elWAQAAAFSCgAen3NxcFRYW6pZbbqlwvZ3ValViYqIOHz6s3NxcD3UIAAAA4EIIeHCy2+2SpIiIiErHy6aX1QEAAACoXQh4cLLZbJKk/Pz8SsfLppfVAQAAAKhdCHhwiomJUXBwsDIyMipcZ+dwOLRq1SqFhIQoJibGQx0CAAAAuBACHpysVquSk5OVk5OjBQsWKC8vTyUlJcrLy9OCBQuUk5Oj/v378zw8AAAAoJayGIZheLoJVM5utysoKEjFxcU1elpkZc/BCwkJUf/+/XlEAgAAAOAB7mYDnoOHCuLi4tSpUyfl5ubKbrfLZrMpJiaGI3cAAABALUfAQ6WsVqvatm3r6TYAAAAAVAGHZAAAAADAJAh4AAAAAGASBDwAAAAAMAkCHgAAAACYBAEPAAAAAEyCgAcAAAAAJkHAAwAAAACTIOABAAAAgEkQ8AAAAADAJAh4AAAAAGASBDwAAAAAMAkCHgAAAACYBAEPAAAAAEyCgAcAAAAAJkHAAwAAAACTIOABAAAAgEkQ8AAAAADAJAh4AAAAAGASBDwAAAAAMAkCHgAAAACYBAEPAAAAAEyCgAcAAAAAJkHAAwAAAACTMFXAmzFjhiwWi8urffv2zvGSkhKNGjVKISEhCgwM1MCBA3Xw4EGXeezbt0/9+vVTw4YNFRoaqkcffVRnz551qVm7dq2uuuoq+fr6qk2bNkpLS6vQy9y5cxUdHS0/Pz/Fx8dr48aNl2SZAQAAAKCMqQKeJHXs2FH5+fnO11dffeUcmzBhgj7++GMtXrxY69at04EDB3T77bc7x0tLS9WvXz+dPn1a69ev11tvvaW0tDRNmzbNWZOXl6d+/fqpR48eysrK0vjx43Xfffdp5cqVzppFixZp4sSJmj59urZs2aK4uDglJSXp0KFDNbMSAAAAANRLFsMwDE83UV1mzJihJUuWKCsrq8JYcXGxmjVrpvfee0933HGHJGnnzp3q0KGDMjMzdc011+jTTz/VH//4Rx04cEBhYWGSpPnz52vy5Mn65Zdf5OPjo8mTJ2v58uXatm2bc96DBw9WUVGRVqxYIUmKj4/XH/7wB7322muSJIfDoaioKI0ZM0ZTpkw5b/+nTp3SqVOnnD/b7XZFRUWpuLhYNpvtv14/AAAAAOomu92uoKCgi2YD0x3B2717tyIjI9W6dWsNHTpU+/btkyRt3rxZZ86cUWJiorO2ffv2atGihTIzMyVJmZmZ6tSpkzPcSVJSUpLsdru2b9/urCk/j7KasnmcPn1amzdvdqmxWq1KTEx01pzPzJkzFRQU5HxFRUX9F2sCAAAAQH1jqoAXHx+vtLQ0rVixQvPmzVNeXp6uv/56HT16VAUFBfLx8VHjxo1d3hMWFqaCggJJUkFBgUu4KxsvG7tQjd1u18mTJ/Xrr7+qtLS00pqyeZzP1KlTVVxc7Hzt37+/yusAAAAAQP3l7ekGqlOfPn2cf+7cubPi4+PVsmVLffDBB/L39/dgZ+7x9fWVr6+vp9sAAAAAUEeZ6gjeuRo3bqzLL79ce/bsUXh4uE6fPq2ioiKXmoMHDyo8PFySFB4eXuGummU/X6zGZrPJ399fTZs2lZeXV6U1ZfMAAAAAgEvB1AHv2LFjys3NVUREhLp27aoGDRpo9erVzvFdu3Zp3759SkhIkCQlJCRo69atLne7zMjIkM1mU2xsrLOm/DzKasrm4ePjo65du7rUOBwOrV692lkDAAAAAJeCqQLeI488onXr1mnv3r1av369brvtNnl5eWnIkCEKCgpSamqqJk6cqDVr1mjz5s0aMWKEEhISdM0110iSevXqpdjYWN17773Kzs7WypUr9cQTT2jUqFHOUycffPBB/fjjj3rssce0c+dOvf766/rggw80YcIEZx8TJ07U3/72N7311lvasWOHHnroIR0/flwjRozwyHoBAAAAUD+Y6hq8n3/+WUOGDNHhw4fVrFkzde/eXd98842aNWsmSfrLX/4iq9WqgQMH6tSpU0pKStLrr7/ufL+Xl5eWLVumhx56SAkJCQoICNDw4cP19NNPO2tatWql5cuXa8KECZo9e7aaN2+uN998U0lJSc6aQYMG6ZdfftG0adNUUFCgLl26aMWKFRVuvAIAAAAA1clUz8EzG3efdQEAAADA3Ortc/AAAAAAoL4y1SmaAABUJ4fDodzcXNntdtlsNsXExMhqZd8oAKD2IuABAFCJ7Oxspaenq7Cw0DktODhYycnJiouL82BnAACcHwEPAIBzZGdnKy0tTbGxsRo2bJgiIiKUn5+vjIwMpaWlKSUlhZAHAKiVOM8EAIByHA6H0tPTFRsbq9TUVEVHR8vX11fR0dFKTU1VbGysli5dKofD4elWAQCogIAHAEA5ubm5Kiws1C233FLhejur1arExEQdPnxYubm5HuoQAIDzI+ABAFCO3W6XJEVERFQ6Xja9rA4AgNqEgAcAQDllzxbKz8+vdLxsOs8nBQDURgQ8AADKiYmJUXBwsDIyMipcZ+dwOLRq1SqFhIQoJibGQx0CAHB+BDwAAMqxWq1KTk5WTk6OFixYoLy8PJWUlCgvL08LFixQTk6O+vfvz/PwAAC1ksUwDMPTTaBydrtdQUFBKi4u5lQgAKhhlT0HLyQkRP379+cRCQCAGuduNuA5eAAAVCIuLk6dOnVSbm6u7Ha7bDabYmJiOHIHAKjVCHgAAJyH1WpV27ZtPd0GAABuYzckAAAAAJgEAQ8AAAAATIKABwAAAAAmQcADAAAAAJMg4AEAAACASRDwAAAAAMAkCHgAAAAAYBIEPAAAAAAwid8d8E6fPq1du3bp7Nmz1dkPAAAAAOB3qnLAO3HihFJTU9WwYUN17NhR+/btkySNGTNGzz//fLU3CAAAAABwT5UD3tSpU5Wdna21a9fKz8/POT0xMVGLFi2q1uYAAAAAAO7zruoblixZokWLFumaa66RxWJxTu/YsaNyc3OrtTkAAAAAgPuqfATvl19+UWhoaIXpx48fdwl8AAAAAICaVeWA161bNy1fvtz5c1moe/PNN5WQkFB9nQEAAAAAqqTKp2g+99xz6tOnj3JycnT27FnNnj1bOTk5Wr9+vdatW3cpegQAAAAAuKHKR/C6d++urKwsnT17Vp06ddJnn32m0NBQZWZmqmvXrpeiRwAAAACAGyyGYRiebgKVs9vtCgoKUnFxsWw2m6fbAQAAAOAh7maDKh/B++STT7Ry5coK01euXKlPP/20qrMDAAAAAFSTKge8KVOmqLS0tMJ0wzA0ZcqUamkKAAAAAFB1VQ54u3fvVmxsbIXp7du31549e6qlKQAAAABA1VU54AUFBenHH3+sMH3Pnj0KCAiolqYAAAAAAFVX5YCXnJys8ePHKzc31zltz549mjRpkvr371+tzQEAAAAA3FflgDdr1iwFBASoffv2atWqlVq1aqUOHTooJCREf/7zny9FjwAAAAAAN1T5QedBQUFav369MjIylJ2dLX9/f3Xu3Fk33HDDpegPAAAAAOAmnoNXi/EcPAAAAACS+9nArSN4c+bM0QMPPCA/Pz/NmTPngrVjx46tWqcAAAAAgGrh1hG8Vq1a6dtvv1VISIhatWp1/plZLJXeYRO/D0fwAAAAAEjVfAQvLy+v0j8DAMzL4XAoNzdXdrtdNptNMTExslqrfG8uAABQg6p0k5UzZ86offv2WrZsmTp06HCpegIAeFh2drbS09NVWFjonBYcHKzk5GTFxcV5sDMANYkdPUDdU6WA16BBA5WUlFyqXlCL8AsdqL/fg+zsbKWlpSk2NlbDhg1TRESE8vPzlZGRobS0NKWkpBDygHqAHT1A3VTlu2g+99xz+uGHH/Tmm2/K27vKT1lAFXjqGjx+oQP193vgcDj0zDPPKCIiQqmpqS6B1uFwaMGCBSooKNDjjz9eL8IuUF+V39Fzyy23uOzoycnJqVc7eurrzj7UPtV6DV55mzZt0urVq/XZZ5+pU6dOCggIcBn/8MMPq94tao2yX+gdOnTQFVdcoTNnzqhBgwb69ddf2XOPeqM+fw9yc3NVWFioYcOGVdiAsVqtSkxM1OzZs5Wbm6u2bdt6qMuaw4Yd6iOHw6H09HTFxsa67OiJjo5WamqqFixYoKVLl6pTp06m/z7U1519qNuqHPAaN26sgQMHXope4GFlv9CDg4O1c+dOORwO55jValVwcHC9+YWO+qv892DHjh0qf5KDxWIx/ffAbrdLkiIiIiodL5teVmdmbNhBqp8hv/yOHofDoS+++EK//vqrmjZtqu7du9ebHT1lO/vat2+v5s2b6+TJk/L399eZM2dMv7PvXPXxe1De6dOnlZ6e7vweJCcny8fHx9NtnVeVA97ChQsvRR+oBcp+oVfG4XDo8OHDzjoz/0KX6t4XGdWn/PcgICBAbdq0ka+vr06dOqU9e/aY/ntQdspHfn6+mjdvrq+++splwy4/P9+lzqzKH8Xt0aOHfHx8dPr0ae3YsaNebdjV9426+hryy3bgbN68WbNnz3bZ0ZWenq7u3bu71JlR2c6+gIAA7dixo8J4YGCgqXf2lZedna0lS5boyJEjzmlNmjTRgAEDTP09KPPmm29q27Ztzp937dqlr7/+WldccYXuu+8+D3Z2fm4HPIfDoRdffFFLly7V6dOn1bNnT02fPl3+/v6Xsj/UoPJf3Oqoq6vq4hf5UqivIbeoqEiS5O3trZMnTyo7O9s5ZrVa5e3trbNnzzrrzCYmJkbBwcF6++23VVhYWGHDLjg4WCEhIYqJifFgl5dW2YZd8+bN9Z///Ec5OTnOsaCgIDVv3rxebNhlZ2fro48+cvm73rhxY9122231YqMuOztbCxculJeXl8v04uJiLVy4UCNGjDDteijbgfPll19WGDMMwzndzDt6LrTTW5KOHTumY8eOmXZnX5my78G5jhw5YvrvgVRxm7C8bdu26c0336yV24Zu/8v07LPP6n//938VGBioyy67TLNnz9aoUaMuZW+oYe4+49DMz0J054tcH7z55pt67LHH9PXXXzsD7mOPPVYvln/v3r2SpLNnz7qcpiz9tuF/9uxZlzqzsVqtioyM1OHDh3XuPbgMw9Dhw4cVERFh6mBTtmG3f/9+FRcXu4wVFxdr//79Onz4sHJzcz3U4aVXtlF37o6MoqIiLVy40GXHhxk5HA4tXrxYklRaWuoyVvbz4sWLK/yOMIuWLVtWa11dVHa2RnXV1UUOh0Pvv//+BWvef/99034PTp8+fd5twjLbtm3T6dOna6gj97n9L/Q//vEPvf7661q5cqWWLFmijz/+WO+++65p/6fWRwcOHKjWurqmLn+Rq1N9D7nu/k4z6+++s2fPuvU9KAu6ZuTu0VmzHsV1OBx65513LljzzjvvmPY7IEl79uzRsWPHLlhz7Ngx7dmzp4Y6qllr166t1rq6aMuWLdVaVxf98MMPF308WklJiX744Yca6qhm/etf/6rWuprkdsDbt2+f+vbt6/w5MTFRFovFtBv79dGhQ4eqta6uSU9Pr9a6uoiQqwpHbP7burpm9erV1VpXF13otKzfU1fX7Ny5U2fOnLlgzZkzZ7Rz584a6qjm7dq1q1rr6ppVq1ZVa11d5O4RejMfyd+wYUO11tU13377bbXW1SS3A97Zs2fl5+fnMq1BgwYX/Uegvps7d66io6Pl5+en+Ph4bdy40dMtnZe7G+1m3bj/8ccfq7WuLnL3JkpmvtnSwYMHq7WurmHPPetg6dKl1VpXF11sR1dV6+qa+r49IFU8Nfe/rauLtm7dWq11dU1dPqPH7ZusGIahlJQU+fr6OqeVlJTowQcfdHkWHs/B+z+LFi3SxIkTNX/+fMXHx+uVV15RUlKSdu3apdDQUE+3V0F9/2VWdnfA6qqriyq7U9h/U1cX1ffrLk6ePFmtdXVRfV8HBQUF1VpXF9X3HT2AJLdPxTfzKft1ldtH8IYPH67Q0FAFBQU5X/fcc48iIyNdpuH/vPzyy7r//vs1YsQIxcbGav78+WrYsKH+/ve/e7o1uGHQoEF6+umnNWjQIE+3AnhMz5499fjjj6tnz56ebgUA4EHt27fXuHHj1L59e0+34hHBwcG69957FRwc7OlWLsrtI3hmPiXrUjh9+rQ2b96sqVOnOqdZrVYlJiYqMzOz0vecOnVKp06dcv58oefL/Pzzz1Xae1pSUlKt10t+8MEHFxyPjIyscErvhYSHh6t58+ZV6qEq6+D3LP+XX36pH374odI9tHVt+aWqrwOr1aro6GgFBwersLBQe/fudTkNoT6sg4u50Doww/J7eXkpLy9PxcXFKiwslJeXl8sR/Prwd6BRo0YKCQlxLvvhw4d19OhR57in10FN/B4IDQ2Vn5+fSkpKdOjQIX4PnKO+r4P6vvxS/VkHO3fuPO+1t9W5Dmrr8hcWFurtt9+udKw2/B0oz2Kcex9sVIsDBw7osssu0/r165WQkOCc/thjj2ndunWVXpA6Y8YMPfXUUxWmFxcXV3jWzKuvvmqqC3tjYmI0ZsyYKr3HTOugvi+/xDqo78svsQ6kqq+D+r78EutAMtc6qO/LL7EO6vvyS+dfB3a7XUFBQZVmg/IIeJfI7wl4lR3Bi4qKqvR/4qXYU7F+/Xq353fttddecLy27a2q78svsQ6kml0H9X35JdaBVDeP4NWl5ZdYB1L1r4P6vvwS60Dy3Dqo78svnX8duBvw3D5FE1XTtGlTeXl5VTi97+DBgwoPD6/0Pb6+vi43sbmQ5s2b/1eHbitTlb/Id911V7V+9u9R3eugvi+/xDqQ6tY6qO/LL7EO6vvyS6wDiX8P+TvAOqjvy38ut2+ygqrx8fFR165dXZ4V5XA4tHr1apcjerXJww8/XK11dc2f//znaq2riwYMGFCtdXVRSEhItdbVNbNmzarWurqovq+D8s+8rY66uiguLq5a6+qa/v37V2tdXXTllVdWa11d9Pzzz1drXV3z3HPPVWtdTSLgXUITJ07U3/72N7311lvasWOHHnroIR0/flwjRozwdGuVatOmjQIDAy9YExgYqDZt2tRQRzXL29tbPXr0uGBNjx495O1t3gPf3bt3l9V64V8LVqtV3bt3r6GOat7FvgNVratrfHx8dMUVV1yw5oorrpCPj08NdVTz6vs64BloUrNmzaq1rq650Klfv6euLvrDH/5QrXV1kZ+fn6Kioi5YExUVVaVTD+uShg0bXnRnbkhIiBo2bFhDHbnPrS3VqjzM1Mx7c6pq0KBB+uWXXzRt2jQVFBSoS5cuWrFihcLCwjzdWqWsVqvuvPPOC94x9c4777xoAKjLkpOTJUlr1qypMNajRw/nuFl5e3vrxhtvrHT5y9x4442mDrnubrSbdeNe+m2D5UIPcDbzBk2Z++67T2+++Wal6+GKK67Qfffd54GuaobFYqnWurqobdu2WrVqlVt1ZuTuY6/M/His+v48zDKTJk3SSy+9pP3791cYi4qK0qRJkzzQVc158skn9ac//anSZ9+GhIToySef9EBXF+fWVpq7p2NZLBbTPgT79xo9erRGjx7t6TbcFhcXpxEjRig9PV2FhYXO6cHBwUpOTjbt6SjlJScnq1+/fvrqq6/066+/qmnTpurevbupQ015ZSF23bp1LrdDt1qtuvHGG00fcqOiorR792636szI4XAoPT3deYOn8o9rsdlsCgoK0tKlS9WpUydT7+yRfgt5p0+fVnp6uvN3QXJysqnDvfTb2RwZGRkKCwvTyZMnK/wd8PPz06FDh0x7Nof0W3ALDAzUsWPH5O3t7fIg57KfAwMDTRvwYmJiFBwcrICAANntdhUXFzvHGjdurEaNGunEiROKiYnxYJeXVtnRydDQUB06dKjCeNl0Mx/FLDNp0iSVlJTonXfe0eHDhxUSEqJ77rnHtEfuzvXkk0/qxIkTeuONN1RUVKTGjRvrgQceqJVH7sq4tcVafiMP5hcXF6dOnTopNzdXdrtdNptNMTExpt+YK8/b21s33XSTp9vwmPocchs1auT8s8ViUfkbDZf/uXydmeTm5qqwsFCFhYXq2LGjbrnlFkVERCg/P18ZGRnavn27s86sG7fl+fj46M477/R0GzWqLNwcPHhQsbGx6tChgxo0aKAzZ85ox44dysnJMXW4kVzPaDn3SGXZz2Y+o8VqtSo5OVlpaWmKjY1VYmKi8+/Azp07lZOTo5SUFNMuv/RbyA0MDNShQ4ec34GTJ0/K39/f+V0IDAw0dcgtz8/Pz9RnLlxMw4YNNX78eE+34Tbzb63hd7Farab+xxsXV19Dbvngdu5TZMr/bNaAV7anvn379kpNTXVuwEVHRys1NVVvvPGGdu7c6bJHH+ZSPtzs3r1bOTk5zrEGDRpIMne4KVN2RsuSJUt05MgR5/RGjRrVizNa4uLilJKSovT0dOeOHem309JSUlJMv/zlWSwW9ejRw2VnF1Cb/a6Ad/z4ca1bt0779u2rcJH12LFjq6UxAPCE8teUnO/UrHPrzOTYsWOSftu4O3cD3mq1qnPnztq5c6ezDuZU38NNmfp+Rkt9Xv7c3FwdO3ZM/fr1U2ZmpmbPnu0cCwkJUb9+/bR8+fJ6czYD6pYqB7zvvvtOffv21YkTJ3T8+HEFBwfr119/VcOGDRUaGkrAA1Cnlb/25NixYxU2bgMDA0197UlAQIAk6fvvv1d8fLzLhpzD4dDWrVtd6mBe9Xnjvrz6fkZLfV3+smtPb7jhBvXs2bPC9+D06dNavny5yzWqQG1R5YA3YcIE3XrrrZo/f76CgoL0zTffqEGDBrrnnns0bty4S9EjANSYc689ufnmm+vVtSeNGzeWJO3YsUMLFixQYmKi87SkVatWaceOHS51MLf6unEPlN08JT8/X9HR0RW+B/n5+S51QG1S5YCXlZWlv/71r7JarfLy8tKpU6fUunVrzZo1S8OHD9ftt99+KfoEgBpTn689KX8EMz8/3+W0pODgYEVFRZn6CCYASP/3uzAjI8PlemTpt7MZVq1apZCQEH4XolaqcsBr0KCB8y95aGio9u3bpw4dOigoKKjSZ2QAQF1UX09PK38Es0OHDurRo4fLHRR37Nhh6iOYACC5/i6s7GwGs5/NgbrNYpx7m7iL6NWrl1JSUnT33Xfr/vvv1/fff6+xY8fq7bff1pEjR7Rhw4ZL1Wu9Y7fbFRQUpOLiYk4BAFCjsrOzKzwPMyQkRP379zf1EUwAKI/fhahN3M0GVQ543377rY4ePaoePXro0KFDGjZsmNavX6+2bdtqwYIF6tKly3/bO/4/Ah4AT3I4HPXuCCYAnIvfhagtLlnAQ80h4AEAAACQ3M8GVd79cPPNN6uoqKjSD7z55purOjsAAAAAQDWpcsBbu3ZthYebS1JJSYm+/PLLamkKAAAAAFB1bt9F8/vvv3f+OScnRwUFBc6fS0tLtWLFCl122WXV2x0AAAAAwG1uB7wuXbrIYrHIYrFUeiqmv7+/Xn311WptDgAAAADgPrcDXl5engzDUOvWrbVx40Y1a9bMOebj46PQ0FB5eXldkiYBAAAAABfndsBr2bKlpN9uFQsAAAAAqH3cDnjl5ebm6pVXXtGOHTskSbGxsRo3bpxiYmKqtTkAAAAAgPuqfBfNlStXKjY2Vhs3blTnzp3VuXNnbdiwQR07dlRGRsal6BEAAAAA4IYqP+j8yiuvVFJSkp5//nmX6VOmTNFnn32mLVu2VGuD9RkPOgcAAAAgXcIHne/YsUOpqakVpo8cOVI5OTlVnR0AAAAAoJpUOeA1a9ZMWVlZFaZnZWUpNDS0OnoCAAAAAPwObt9k5emnn9Yjjzyi+++/Xw888IB+/PFHXXvttZKkr7/+Wi+88IImTpx4yRoFAAAAAFyY29fgeXl5KT8/X82aNdMrr7yil156SQcOHJAkRUZG6tFHH9XYsWNlsVguacP1CdfgAQAAAJDczwZuBzyr1aqCggKX0zCPHj0qSWrUqNF/2S4qQ8ADAAAAILmfDar0HLxzj84R7AAAAACg9qhSwLv88ssvegpmYWHhf9UQAAAAAOD3qVLAe+qppxQUFHSpegEAAAAA/BeqFPAGDx7MoxAAAAAAoJZy+zl43B0TAAAAAGo3twOemzfbBAAAAAB4iNunaDocjkvZBwAAAADgv+T2ETwAAAAAQO1GwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgElV60DkAAPWJw+FQbm6u7Ha7bDabYmJiZLWybxQAUHsR8AAAqER2drbS09NVWFjonBYcHKzk5GTFxcV5sDMAAM6PgAcAwDmys7OVlpam2NhYDRs2TBEREcrPz1dGRobS0tKUkpJCyAMA1EqcZwIAQDkOh0Pp6emKjY1VamqqoqOj5evrq+joaKWmpio2NlZLly6Vw+HwdKsAAFRAwAMAoJzc3FwVFhbqlltuqXC9ndVqVWJiog4fPqzc3FwPdQgAwPkR8AAAKMdut0uSIiIiKh0vm15WBwBAbULAAwCgHJvNJknKz8+vdLxselkdAAC1CQEPAIByYmJiFBwcrIyMjArX2TkcDq1atUohISGKiYnxUIcAAJwfAQ8AgHKsVquSk5OVk5OjBQsWKC8vTyUlJcrLy9OCBQuUk5Oj/v378zw8AECtZDEMw/B0E6ic3W5XUFCQiouLORUIAGpYZc/BCwkJUf/+/XlEAgCgxrmbDXgOHgAAlYiLi1OnTp2Um5sru90um82mmJgYjtwBAGo1Ah4AAOdhtVrVtm1bT7cBAIDb2A0JAAAAACZBwAMAAAAAkyDgAQAAAIBJmCrgRUdHy2KxuLyef/55l5rvv/9e119/vfz8/BQVFaVZs2ZVmM/ixYvVvn17+fn5qVOnTvrkk09cxg3D0LRp0xQRESF/f38lJiZq9+7dLjWFhYUaOnSobDabGjdurNTUVB07dqz6FxoAAAAA/j9TBTxJevrpp5Wfn+98jRkzxjlmt9vVq1cvtWzZUps3b9aLL76oGTNm6I033nDWrF+/XkOGDFFqaqq+++47DRgwQAMGDNC2bducNbNmzdKcOXM0f/58bdiwQQEBAUpKSlJJSYmzZujQodq+fbsyMjK0bNkyffHFF3rggQdqZiUAAAAAqJdM9Ry86OhojR8/XuPHj690fN68eXr88cdVUFAgHx8fSdKUKVO0ZMkS7dy5U5I0aNAgHT9+XMuWLXO+75prrlGXLl00f/58GYahyMhITZo0SY888ogkqbi4WGFhYUpLS9PgwYO1Y8cOxcbGatOmTerWrZskacWKFerbt69+/vlnRUZGurU8PAcPAAAAgOR+NjDdEbznn39eISEhuvLKK/Xiiy/q7NmzzrHMzEzdcMMNznAnSUlJSdq1a5eOHDnirElMTHSZZ1JSkjIzMyVJeXl5KigocKkJCgpSfHy8syYzM1ONGzd2hjtJSkxMlNVq1YYNG87b+6lTp2S3211eAAAAAOAuUz0Hb+zYsbrqqqsUHBys9evXa+rUqcrPz9fLL78sSSooKFCrVq1c3hMWFuYca9KkiQoKCpzTytcUFBQ468q/73w1oaGhLuPe3t4KDg521lRm5syZeuqpp6q62AAAAAAgqQ4cwZsyZUqFG6ec+yo7vXLixIm66aab1LlzZz344IN66aWX9Oqrr+rUqVMeXgr3TJ06VcXFxc7X/v37Pd0SAAAAgDqk1h/BmzRpklJSUi5Y07p160qnx8fH6+zZs9q7d6/atWun8PBwHTx40KWm7Ofw8HDnfyurKT9eNi0iIsKlpkuXLs6aQ4cOuczj7NmzKiwsdL6/Mr6+vvL19b3gsgIAAADA+dT6I3jNmjVT+/btL/gqf01deVlZWbJarc7TJRMSEvTFF1/ozJkzzpqMjAy1a9dOTZo0cdasXr3aZT4ZGRlKSEiQJLVq1Urh4eEuNXa7XRs2bHDWJCQkqKioSJs3b3bWfP7553I4HIqPj6+GtQIAAAAAFdX6gOeuzMxMvfLKK8rOztaPP/6od999VxMmTNA999zjDG933323fHx8lJqaqu3bt2vRokWaPXu2Jk6c6JzPuHHjtGLFCr300kvauXOnZsyYoW+//VajR4+WJFksFo0fP17PPPOMli5dqq1bt2rYsGGKjIzUgAEDJEkdOnRQ7969df/992vjxo36+uuvNXr0aA0ePNjtO2gCAAAAQFWZ5jEJW7Zs0cMPP6ydO3fq1KlTatWqle69915NnDjR5bTH77//XqNGjdKmTZvUtGlTjRkzRpMnT3aZ1+LFi/XEE09o7969atu2rWbNmqW+ffs6xw3D0PTp0/XGG2+oqKhI3bt31+uvv67LL7/cWVNYWKjRo0fr448/ltVq1cCBAzVnzhwFBga6vUw8JgEAAACA5H42ME3AMyMCHgAAAACpHj8HDwAAAADqKwIeAAAAAJgEAQ8AAAAATIKABwAAAAAmQcADAAAAAJMg4AEAAACASRDwAAAAAMAkCHgAAAAAYBIEPAAAAAAwCQIeAAAAAJgEAQ8AAAAATIKABwAAAAAmQcADAAAAAJMg4AEAAACASRDwAAAAAMAkCHgAAAAAYBIEPAAAAAAwCQIeAAAAAJgEAQ8AAAAATIKABwAAAAAmQcADAAAAAJMg4AEAAACASXh7ugHUTg6HQ7m5ubLb7bLZbIqJiZHVyv4AAAAAoDYj4KGC7Oxspaenq7Cw0DktODhYycnJiouL82BnAAAAAC6EgAcX2dnZSktLU2xsrIYNG6aIiAjl5+crIyNDaWlpSklJIeQBAAAAtRTn3MHJ4XAoPT1dsbGxSk1NVXR0tHx9fRUdHa3U1FTFxsZq6dKlcjgcnm4VAAAAQCUIeHDKzc1VYWGhbrnllgrX21mtViUmJurw4cPKzc31UIcAAAAALoSABye73S5JioiIqHS8bHpZHQAAAIDahYAHJ5vNJknKz8+vdLxselkdAAAAgNqFgAenmJgYBQcHKyMjo8J1dg6HQ6tWrVJISIhiYmI81CEAAACACyHgwclqtSo5OVk5OTlasGCB8vLyVFJSory8PC1YsEA5OTnq378/z8MDAAAAaimLYRiGp5tA5ex2u4KCglRcXFyjp0VW9hy8kJAQ9e/fn0ckAAAAAB7gbjbgOXioIC4uTp06dVJubq7sdrtsNptiYmI4cgcAAADUcgQ8VMpqtapt27aebgMAAABAFXBIBgAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMos4EvGeffVbXXnutGjZsqMaNG1das2/fPvXr108NGzZUaGioHn30UZ09e9alZu3atbrqqqvk6+urNm3aKC0trcJ85s6dq+joaPn5+Sk+Pl4bN250GS8pKdGoUaMUEhKiwMBADRw4UAcPHqxyLwAAAABQnepMwDt9+rTuvPNOPfTQQ5WOl5aWql+/fjp9+rTWr1+vt956S2lpaZo2bZqzJi8vT/369VOPHj2UlZWl8ePH67777tPKlSudNYsWLdLEiRM1ffp0bdmyRXFxcUpKStKhQ4ecNRMmTNDHH3+sxYsXa926dTpw4IBuv/32KvUCAAAAANXNYhiG4ekmqiItLU3jx49XUVGRy/RPP/1Uf/zjH3XgwAGFhYVJkubPn6/Jkyfrl19+kY+PjyZPnqzly5dr27ZtzvcNHjxYRUVFWrFihSQpPj5ef/jDH/Taa69JkhwOh6KiojRmzBhNmTJFxcXFatasmd577z3dcccdkqSdO3eqQ4cOyszM1DXXXONWL+6w2+0KCgpScXGxbDbbf7XeAAAAANRd7maDOnME72IyMzPVqVMnZ6CSpKSkJNntdm3fvt1Zk5iY6PK+pKQkZWZmSvrtKOHmzZtdaqxWqxITE501mzdv1pkzZ1xq2rdvrxYtWjhr3OmlMqdOnZLdbnd5AQAAAIC7TBPwCgoKXAKVJOfPBQUFF6yx2+06efKkfv31V5WWllZaU34ePj4+Fa4DPLfmYr1UZubMmQoKCnK+oqKi3Fl0AAAAAJDk4YA3ZcoUWSyWC7527tzpyRZr1NSpU1VcXOx87d+/39MtAQAAAKhDvD354ZMmTVJKSsoFa1q3bu3WvMLDwyvc7bLszpbh4eHO/557t8uDBw/KZrPJ399fXl5e8vLyqrSm/DxOnz6toqIil6N459ZcrJfK+Pr6ytfX163lBQAAAIBzefQIXrNmzdS+ffsLvty9IUlCQoK2bt3qcrfLjIwM2Ww2xcbGOmtWr17t8r6MjAwlJCRIknx8fNS1a1eXGofDodWrVztrunbtqgYNGrjU7Nq1S/v27XPWuNMLAAAAAFQ3jx7Bq4p9+/apsLBQ+/btU2lpqbKysiRJbdq0UWBgoHr16qXY2Fjde++9mjVrlgoKCvTEE09o1KhRzqNiDz74oF577TU99thjGjlypD7//HN98MEHWr58ufNzJk6cqOHDh6tbt266+uqr9corr+j48eMaMWKEJCkoKEipqamaOHGigoODZbPZNGbMGCUkJOiaa66RJLd6AQAAAIBqZ9QRw4cPNyRVeK1Zs8ZZs3fvXqNPnz6Gv7+/0bRpU2PSpEnGmTNnXOazZs0ao0uXLoaPj4/RunVrY+HChRU+69VXXzVatGhh+Pj4GFdffbXxzTffuIyfPHnSePjhh40mTZoYDRs2NG677TYjPz/fpcadXi6muLjYkGQUFxdX6X0AAAAAzMXdbFDnnoNXn/AcPAAAAABSPXwOHgAAAADUdwQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMos4EvGeffVbXXnutGjZsqMaNG1daY7FYKrz++c9/utSsXbtWV111lXx9fdWmTRulpaVVmM/cuXMVHR0tPz8/xcfHa+PGjS7jJSUlGjVqlEJCQhQYGKiBAwfq4MGDLjX79u1Tv3791LBhQ4WGhurRRx/V2bNn/6t1AAAAAAAXUmcC3unTp3XnnXfqoYceumDdwoULlZ+f73wNGDDAOZaXl6d+/fqpR48eysrK0vjx43Xfffdp5cqVzppFixZp4sSJmj59urZs2aK4uDglJSXp0KFDzpoJEybo448/1uLFi7Vu3TodOHBAt99+u3O8tLRU/fr10+nTp7V+/Xq99dZbSktL07Rp06pvhQAAAADAOSyGYRiebqIq0tLSNH78eBUVFVUYs1gs+uijj1xCXXmTJ0/W8uXLtW3bNue0wYMHq6ioSCtWrJAkxcfH6w9/+INee+01SZLD4VBUVJTGjBmjKVOmqLi4WM2aNdN7772nO+64Q5K0c+dOdejQQZmZmbrmmmv06aef6o9//KMOHDigsLAwSdL8+fM1efJk/fLLL/Lx8XFrWe12u4KCglRcXCybzebuKgIAAABgMu5mgzpzBM9do0aNUtOmTXX11Vfr73//u8rn18zMTCUmJrrUJyUlKTMzU9JvRwk3b97sUmO1WpWYmOis2bx5s86cOeNS0759e7Vo0cJZk5mZqU6dOjnDXdnn2O12bd++/by9nzp1Sna73eUFAAAAAO7y9nQD1enpp5/WzTffrIYNG+qzzz7Tww8/rGPHjmns2LGSpIKCApfQJUlhYWGy2+06efKkjhw5otLS0kprdu7c6ZyHj49PhesAw8LCVFBQcMHPKRs7n5kzZ+qpp56q+oIDAAAAgDx8BG/KlCmV3hil/KssWLnjySef1HXXXacrr7xSkydP1mOPPaYXX3zxEi5B9Zo6daqKi4udr/3793u6JQAAAAB1iEeP4E2aNEkpKSkXrGnduvXvnn98fLz+9Kc/6dSpU/L19VV4eHiFu10ePHhQNptN/v7+8vLykpeXV6U14eHhkqTw8HCdPn1aRUVFLkfxzq05986bZfMsq6mMr6+vfH19f/fyAgAAAKjfPHoEr1mzZmrfvv0FX+7ekKQyWVlZatKkiTM0JSQkaPXq1S41GRkZSkhIkCT5+Pioa9euLjUOh0OrV6921nTt2lUNGjRwqdm1a5f27dvnrElISNDWrVtd7ryZkZEhm82m2NjY3708AAAAAHAhdeYavH379qmwsFD79u1TaWmpsrKyJElt2rRRYGCgPv74Yx08eFDXXHON/Pz8lJGRoeeee06PPPKIcx4PPvigXnvtNT322GMaOXKkPv/8c33wwQdavny5s2bixIkaPny4unXrpquvvlqvvPKKjh8/rhEjRkiSgoKClJqaqokTJyo4OFg2m01jxoxRQkKCrrnmGklSr169FBsbq3vvvVezZs1SQUGBnnjiCY0aNYojdAAAAAAumToT8KZNm6a33nrL+fOVV14pSVqzZo1uuukmNWjQQHPnztWECRNkGIbatGmjl19+Wffff7/zPa1atdLy5cs1YcIEzZ49W82bN9ebb76ppKQkZ82gQYP0yy+/aNq0aSooKFCXLl20YsUKl5um/OUvf5HVatXAgQN16tQpJSUl6fXXX3eOe3l5admyZXrooYeUkJCggIAADR8+XE8//fSlXEUAAAAA6rk69xy8+oTn4AEAAACQ6vFz8AAAAACgviLgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCS8Pd0AAAAAANRWDodDubm5stvtstlsiomJkdVae4+TEfAAAAAAoBLZ2dlKT09XYWGhc1pwcLCSk5MVFxfnwc7Oj4AHAAAAAOfIzs5WWlqaYmNjNWzYMEVERCg/P18ZGRlKS0tTSkpKrQx5tffYIgAAAAB4gMPhUHp6umJjY5Wamqro6Gj5+voqOjpaqampio2N1dKlS+VwODzdagUEPAAAAAAoJzc3V4WFhbrlllsqXG9ntVqVmJiow4cPKzc310Mdnh8BDwAAAADKsdvtkqSIiIhKx8uml9XVJgQ8AAAAACjHZrNJkvLz8ysdL5teVlebEPAAAAAAoJyYmBgFBwcrIyOjwnV2DodDq1atUkhIiGJiYjzU4fkR8AAAAACgHKvVquTkZOXk5GjBggXKy8tTSUmJ8vLytGDBAuXk5Kh///618nl4FsMwDE83gcrZ7XYFBQWpuLi4Vh7+BQAAAMyssufghYSEqH///jX+iAR3swHPwQMAAACASsTFxalTp07Kzc2V3W6XzWZTTExMrTxyV4aABwAAAADnYbVa1bZtW0+34bbaGz0BAAAAAFVCwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEt6ebgDnZxiGJMlut3u4EwAAAACeVJYJyjLC+RDwarGjR49KkqKiojzcCQAAAIDa4OjRowoKCjrvuMW4WASExzgcDh04cECNGjWSxWKp8c+32+2KiorS/v37ZbPZavzzPa2+L7/EOpBYB/V9+SXWQX1ffol1UN+XX2IdSKyD2rD8hmHo6NGjioyMlNV6/ivtOIJXi1mtVjVv3tzTbchms9XLL3KZ+r78EutAYh3U9+WXWAf1ffkl1kF9X36JdSCxDjy9/Bc6cleGm6wAAAAAgEkQ8AAAAADAJAh4OC9fX19Nnz5dvr6+nm7FI+r78kusA4l1UN+XX2Id1Pfll1gH9X35JdaBxDqoS8vPTVYAAAAAwCQ4ggcAAAAAJkHAAwAAAACTIOABAAAAgEkQ8AAAAADAJAh4qOCLL77QrbfeqsjISFksFi1ZssTTLdWomTNn6g9/+IMaNWqk0NBQDRgwQLt27fJ0WzVq3rx56ty5s/NhngkJCfr000893ZbHPP/887JYLBo/frynW6kxM2bMkMVicXm1b9/e023VqP/85z+65557FBISIn9/f3Xq1Enffvutp9uqMdHR0RX+DlgsFo0aNcrTrdWI0tJSPfnkk2rVqpX8/f0VExOjP/3pT6pv96Y7evSoxo8fr5YtW8rf31/XXnutNm3a5Om2LpmLbQMZhqFp06YpIiJC/v7+SkxM1O7duz3T7CVwseX/8MMP1atXL4WEhMhisSgrK8sjfV5KF1oHZ86c0eTJk9WpUycFBAQoMjJSw4YN04EDBzzXcCUIeKjg+PHjiouL09y5cz3dikesW7dOo0aN0jfffKOMjAydOXNGvXr10vHjxz3dWo1p3ry5nn/+eW3evFnffvutbr75ZiUnJ2v79u2ebq3Gbdq0SX/961/VuXNnT7dS4zp27Kj8/Hzn66uvvvJ0SzXmyJEjuu6669SgQQN9+umnysnJ0UsvvaQmTZp4urUas2nTJpf//xkZGZKkO++808Od1YwXXnhB8+bN02uvvaYdO3bohRde0KxZs/Tqq696urUadd999ykjI0Nvv/22tm7dql69eikxMVH/+c9/PN3aJXGxbaBZs2Zpzpw5mj9/vjZs2KCAgAAlJSWppKSkhju9NC62/MePH1f37t31wgsv1HBnNedC6+DEiRPasmWLnnzySW3ZskUffvihdu3apf79+3ug0wswgAuQZHz00UeebsOjDh06ZEgy1q1b5+lWPKpJkybGm2++6ek2atTRo0eNtm3bGhkZGcaNN95ojBs3ztMt1Zjp06cbcXFxnm7DYyZPnmx0797d023UKuPGjTNiYmIMh8Ph6VZqRL9+/YyRI0e6TLv99tuNoUOHeqijmnfixAnDy8vLWLZsmcv0q666ynj88cc91FXNOXcbyOFwGOHh4caLL77onFZUVGT4+voa77//vgc6vLQutA2Yl5dnSDK+++67Gu2pprmzHbxx40ZDkvHTTz/VTFNu4AgecBHFxcWSpODgYA934hmlpaX65z//qePHjyshIcHT7dSoUaNGqV+/fkpMTPR0Kx6xe/duRUZGqnXr1ho6dKj27dvn6ZZqzNKlS9WtWzfdeeedCg0N1ZVXXqm//e1vnm7LY06fPq133nlHI0eOlMVi8XQ7NeLaa6/V6tWr9cMPP0iSsrOz9dVXX6lPnz4e7qzmnD17VqWlpfLz83OZ7u/vX6+O6JfJy8tTQUGBy78JQUFBio+PV2Zmpgc7gycVFxfLYrGocePGnm7FydvTDQC1mcPh0Pjx43Xdddfpiiuu8HQ7NWrr1q1KSEhQSUmJAgMD9dFHHyk2NtbTbdWYf/7zn9qyZYuprzW5kPj4eKWlpaldu3bKz8/XU089peuvv17btm1To0aNPN3eJffjjz9q3rx5mjhxov73f/9XmzZt0tixY+Xj46Phw4d7ur0at2TJEhUVFSklJcXTrdSYKVOmyG63q3379vLy8lJpaameffZZDR061NOt1ZhGjRopISFBf/rTn9ShQweFhYXp/fffV2Zmptq0aePp9mpcQUGBJCksLMxlelhYmHMM9UtJSYkmT56sIUOGyGazebodJwIecAGjRo3Stm3b6uWeynbt2ikrK0vFxcX617/+peHDh2vdunX1IuTt379f48aNU0ZGRoU91/VF+aMUnTt3Vnx8vFq2bKkPPvhAqampHuysZjgcDnXr1k3PPfecJOnKK6/Utm3bNH/+/HoZ8BYsWKA+ffooMjLS063UmA8++EDvvvuu3nvvPXXs2FFZWVkaP368IiMj69XfgbffflsjR47UZZddJi8vL1111VUaMmSINm/e7OnWAI86c+aM7rrrLhmGoXnz5nm6HRecogmcx+jRo7Vs2TKtWbNGzZs393Q7Nc7Hx0dt2rRR165dNXPmTMXFxWn27NmebqtGbN68WYcOHdJVV10lb29veXt7a926dZozZ468vb1VWlrq6RZrXOPGjXX55Zdrz549nm6lRkRERFTYmdGhQ4d6dZpqmZ9++kmrVq3Sfffd5+lWatSjjz6qKVOmaPDgwerUqZPuvfdeTZgwQTNnzvR0azUqJiZG69at07Fjx7R//35t3LhRZ86cUevWrT3dWo0LDw+XJB08eNBl+sGDB51jqB/Kwt1PP/2kjIyMWnX0TiLgARUYhqHRo0fro48+0ueff65WrVp5uqVaweFw6NSpU55uo0b07NlTW7duVVZWlvPVrVs3DR06VFlZWfLy8vJ0izXu2LFjys3NVUREhKdbqRHXXXddhcej/PDDD2rZsqWHOvKchQsXKjQ0VP369fN0KzXqxIkTslpdN5O8vLzkcDg81JFnBQQEKCIiQkeOHNHKlSuVnJzs6ZZqXKtWrRQeHq7Vq1c7p9ntdm3YsKHeXaNen5WFu927d2vVqlUKCQnxdEsVcIomKjh27JjLXvq8vDxlZWUpODhYLVq08GBnNWPUqFF67733lJ6erkaNGjnPqw8KCpK/v7+Hu6sZU6dOVZ8+fdSiRQsdPXpU7733ntauXauVK1d6urUa0ahRowrXXAYEBCgkJKTeXIv5yCOP6NZbb1XLli114MABTZ8+XV5eXhoyZIinW6sREyZM0LXXXqvnnntOd911lzZu3Kg33nhDb7zxhqdbq1EOh0MLFy7U8OHD5e1dvzYZbr31Vj377LNq0aKFOnbsqO+++04vv/yyRo4c6enWatTKlStlGIbatWunPXv26NFHH1X79u01YsQIT7d2SVxsG2j8+PF65pln1LZtW7Vq1UpPPvmkIiMjNWDAAM81XY0utvyFhYXat2+f87lvZTvCwsPDTXMU80LrICIiQnfccYe2bNmiZcuWqbS01LmdGBwcLB8fH0+17crDd/FELbRmzRpDUoXX8OHDPd1ajahs2SUZCxcu9HRrNWbkyJFGy5YtDR8fH6NZs2ZGz549jc8++8zTbXlUfXtMwqBBg4yIiAjDx8fHuOyyy4xBgwYZe/bs8XRbNerjjz82rrjiCsPX19do37698cYbb3i6pRq3cuVKQ5Kxa9cuT7dS4+x2uzFu3DijRYsWhp+fn9G6dWvj8ccfN06dOuXp1mrUokWLjNatWxs+Pj5GeHi4MWrUKKOoqMjTbV0yF9sGcjgcxpNPPmmEhYUZvr6+Rs+ePU31/bjY8i9cuLDS8enTp3u07+p0oXVQ9niIyl5r1qzxdOtOFsMwjEsZIAEAAAAANYNr8AAAAADAJAh4AAAAAGASBDwAAAAAMAkCHgAAAACYBAEPAAAAAEyCgAcAAAAAJkHAAwAAAACTIOABAAAAgEkQ8AAAMCmLxaIlS5Z4ug0AQA0i4AEAUM1SUlJksVj04IMPVhgbNWqULBaLUlJSqu3zZsyYoS5dulTb/AAAdRcBDwCASyAqKkr//Oc/dfLkSee0kpISvffee2rRooUHOwMAmBkBDwCAS+Cqq65SVFSUPvzwQ+e0Dz/8UC1atNCVV17pnHbq1CmNHTtWoaGh8vPzU/fu3bVp0ybn+Nq1a2WxWLR69Wp169ZNDRs21LXXXqtdu3ZJktLS0vTUU08pOztbFotFFotFaWlpzvf/+uuvuu2229SwYUO1bdtWS5cuvfQLDwDwGAIeAACXyMiRI7Vw4ULnz3//+981YsQIl5rHHntM//73v/XWW29py5YtatOmjZKSklRYWOhS9/jjj+ull17St99+K29vb40cOVKSNGjQIE2aNEkdO3ZUfn6+8vPzNWjQIOf7nnrqKd111136/vvv1bdvXw0dOrTCvAEA5kHAAwDgErnnnnv01Vdf6aefftJPP/2kr7/+Wvfcc49z/Pjx45o3b55efPFF9enTR7Gxsfrb3/4mf39/LViwwGVezz77rG688UbFxsZqypQpWr9+vUpKSuTv76/AwEB5e3srPDxc4eHh8vf3d74vJSVFQ4YMUZs2bfTcc8/p2LFj2rhxY42tAwBAzfL2dAMAAJhVs2bN1K9fP6WlpckwDPXr109NmzZ1jufm5urMmTO67rrrnNMaNGigq6++Wjt27HCZV+fOnZ1/joiIkCQdOnTootfzlX9fQECAbDabDh069F8tFwCg9iLgAQBwCY0cOVKjR4+WJM2dO/d3z6dBgwbOP1ssFkmSw+Go0vvK3uvO+wAAdROnaAIAcAn17t1bp0+f1pkzZ5SUlOQyFhMTIx8fH3399dfOaWfOnNGmTZsUGxvr9mf4+PiotLS02noGANRdHMEDAOAS8vLycp5u6eXl5TIWEBCghx56SI8++qiCg4PVokULzZo1SydOnFBqaqrbnxEdHa28vDxlZWWpefPmatSokXx9fat1OQAAdQMBDwCAS8xms5137Pnnn5fD4dC9996ro0ePqlu3blq5cqWaNGni9vwHDhyoDz/8UD169FBRUZEWLlxYrQ9SBwDUHRbDMAxPNwEAAAAA+O9xDR4AAAAAmAQBDwAAAABMgoAHAAAAACZBwAMAAAAAkyDgAQAAAIBJEPAAAAAAwCQIeAAAAABgEgQ8AAAAADAJAh4AAAAAmAQBDwAAAABMgoAHAAAAACbx/wDt2BvTcfIVKwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u5OAyril6UUD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k9wR11m66UWg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot of Total Price by Day of the Week"
      ],
      "metadata": {
        "id": "a0KQ4nUOBCIG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.boxplot(x='DayOfWeek', y='TotalPrice', data=data_cleaned, palette='muted')  # Plot the boxplot with muted palette\n",
        "plt.title('Total Price by Day of the Week')  # Set the title\n",
        "plt.xlabel('Day of the Week')  # Set the x-axis label\n",
        "plt.ylabel('Total Price')  # Set the y-axis label\n",
        "plt.savefig('/content/total_price_by_day_of_week.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "qiF8tS23BCLB",
        "outputId": "e65cadff-cc8c-4e62-9c77-c5b30c351dac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-7dc2af63a934>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='DayOfWeek', y='TotalPrice', data=data_cleaned, palette='muted')  # Plot the boxplot with muted palette\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iXFRxRm3BCMS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "thACZ_4HBCOs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of Quantity vs Total Price"
      ],
      "metadata": {
        "id": "7ohswESmBCSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.scatterplot(x='Quantity', y='TotalPrice', data=data_cleaned, color='red')  # Plot the scatter plot with red color\n",
        "plt.title('Quantity vs Total Price')  # Set the title\n",
        "plt.xlabel('Quantity')  # Set the x-axis label\n",
        "plt.ylabel('Total Price')  # Set the y-axis label\n",
        "plt.savefig('/content/quantity_vs_total_price.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "y612jRshBCVq",
        "outputId": "1fd49ffe-daf4-46b5-94b1-440289b28a61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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PP/xwWXcHAAALl2gCAErMI488on379mnkyJGqW7euXnnllbLuUqn46KOPVL16dd1///1l3RUAAFwwgwcAgJvmzp2rLVu26IUXXtCAAQPO+qh9AADKCgEPAAA3RUZGKj09XfHx8fr000+tRd8BACgvCHgAAAAAYBOsgwcAAAAANkHAAwAAAACb4Cma5Vh+fr727t2rGjVqyOFwlHV3AAAAAJQRY4wOHTqk8PBweXicfZ6OgFeO7d27VxEREWXdDQAAAADlRGpqqurWrXvWdgJeOVbwdLbU1FT5+/uXcW8AAAAAlJWcnBxFRESc9wnOBLxyrOCyTH9/fwIeAAAAgPPeusVDVgAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANlGlrDsAAAAAAOVKZqaUni5lZ0uBgVJIiBQUVNa9cgszeAAAAABQIDVV6tZNatZMattWiopyvk5NLeueuYWABwAAAACSc+bu8celxYtd9y9e7NyfmVk2/SoGAh4AAAAASM7LMs8MdwUWL3a2l3MEPAAAAACQnPfcXUx7OUDAAwAAAABJCgi4uPZygIAHAAAAAJIUGirFxRXdFhfnbC/nCHgAAAAAIDmXQvj448IhLy7Oub8CLJXAOngAAAAAUCAiQpo586918AICnDN3FSDcSQQ8AAAAAHAVFFRhAt2ZuEQTAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJipUwFu+fLnuvvtuhYeHy+Fw6KuvvnJp79WrlxwOh8t25513utRkZGSoR48e8vf3V2BgoHr37q3Dhw+71GzYsEE33nijfHx8FBERoYkTJxbqy5w5cxQVFSUfHx+1aNFCCxYscGk3xmjMmDGqU6eOfH19FRsbqx07dpTMQAAAAABAESpUwDty5IiuvvpqTZ48+aw1d955p/bt22dtn3/+uUt7jx49tHnzZiUkJGjevHlavny5nnjiCas9JydHcXFxql+/vtauXavXXntN48aN04cffmjVrFixQt27d1fv3r21bt06derUSZ06ddKmTZusmokTJ+qdd97R1KlTtWrVKlWrVk3x8fE6fvx4CY4IAAAAAPzFYYwxZd2JC+FwOPTll1+qU6dO1r5evXopKyur0Mxega1bt6p58+Zas2aNrr32WknSwoUL1aFDB/3+++8KDw/XlClT9PzzzystLU1eXl6SpJEjR+qrr77Stm3bJEldu3bVkSNHNG/ePOvYbdu2VcuWLTV16lQZYxQeHq5hw4Zp+PDhkqTs7GyFhoZq+vTp6tatm1vnmJOTo4CAAGVnZ8vf37+4QwQAAADAJtzNBhVqBs8dS5cuVUhIiJo2baqnnnpKf/75p9WWlJSkwMBAK9xJUmxsrDw8PLRq1Sqr5qabbrLCnSTFx8dr+/btyszMtGpiY2NdPjc+Pl5JSUmSpN27dystLc2lJiAgQNHR0VZNUXJzc5WTk+OyAQAAAIC7bBXw7rzzTn3yySdKTEzUq6++qmXLlql9+/bKy8uTJKWlpSkkJMTlPVWqVFHNmjWVlpZm1YSGhrrUFLw+X83p7ae/r6iaoowfP14BAQHWFhERUazzBwAAAFC5VSnrDpSk0y99bNGiha666ipdfvnlWrp0qW6//fYy7Jl7Ro0apaFDh1qvc3JyCHkAAAAA3GarGbwzNWzYULVr19avv/4qSQoLC9P+/ftdak6dOqWMjAyFhYVZNenp6S41Ba/PV3N6++nvK6qmKN7e3vL393fZAAAAAMBdtg54v//+u/7880/VqVNHkhQTE6OsrCytXbvWqlmyZIny8/MVHR1t1SxfvlwnT560ahISEtS0aVMFBQVZNYmJiS6flZCQoJiYGElSgwYNFBYW5lKTk5OjVatWWTUAAAAAUNIqVMA7fPiwkpOTlZycLMn5MJPk5GSlpKTo8OHDeuaZZ7Ry5Urt2bNHiYmJuvfee9WoUSPFx8dLkpo1a6Y777xTffr00erVq/Xjjz9qwIAB6tatm8LDwyVJDz74oLy8vNS7d29t3rxZs2bN0qRJk1wunRw0aJAWLlyoN954Q9u2bdO4ceP0008/acCAAZKcT/gcPHiw/vGPf+jrr7/Wxo0b9cgjjyg8PNzlqZ8AAAAAUJIq1DIJS5cu1a233lpof8+ePTVlyhR16tRJ69atU1ZWlsLDwxUXF6e///3vLg87ycjI0IABAzR37lx5eHioc+fOeuedd1S9enWrZsOGDerfv7/WrFmj2rVr6+mnn9aIESNcPnPOnDkaPXq09uzZo8aNG2vixInq0KGD1W6M0dixY/Xhhx8qKytLN9xwg95//301adLE7fNlmQQAAAAAkvvZoEIFvMqGgAcAAABAqsTr4AEAAABAZUXAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE1UqIC3fPly3X333QoPD5fD4dBXX33l0m6M0ZgxY1SnTh35+voqNjZWO3bscKnJyMhQjx495O/vr8DAQPXu3VuHDx92qdmwYYNuvPFG+fj4KCIiQhMnTizUlzlz5igqKko+Pj5q0aKFFixYUOy+AAAAAEBJqlAB78iRI7r66qs1efLkItsnTpyod955R1OnTtWqVatUrVo1xcfH6/jx41ZNjx49tHnzZiUkJGjevHlavny5nnjiCas9JydHcXFxql+/vtauXavXXntN48aN04cffmjVrFixQt27d1fv3r21bt06derUSZ06ddKmTZuK1RcAAAAAKEkOY4wp605cCIfDoS+//FKdOnWS5JwxCw8P17BhwzR8+HBJUnZ2tkJDQzV9+nR169ZNW7duVfPmzbVmzRpde+21kqSFCxeqQ4cO+v333xUeHq4pU6bo+eefV1pamry8vCRJI0eO1FdffaVt27ZJkrp27aojR45o3rx5Vn/atm2rli1baurUqW71xR05OTkKCAhQdna2/P39S2TcAAAAAFQ87maDCjWDdy67d+9WWlqaYmNjrX0BAQGKjo5WUlKSJCkpKUmBgYFWuJOk2NhYeXh4aNWqVVbNTTfdZIU7SYqPj9f27duVmZlp1Zz+OQU1BZ/jTl+Kkpubq5ycHJcNAAAAANxlm4CXlpYmSQoNDXXZHxoaarWlpaUpJCTEpb1KlSqqWbOmS01Rxzj9M85Wc3r7+fpSlPHjxysgIMDaIiIiznPWAAAAAPAX2wQ8Oxg1apSys7OtLTU1tay7BAAAAKACsU3ACwsLkySlp6e77E9PT7fawsLCtH//fpf2U6dOKSMjw6WmqGOc/hlnqzm9/Xx9KYq3t7f8/f1dNgAAAABwl20CXoMGDRQWFqbExERrX05OjlatWqWYmBhJUkxMjLKysrR27VqrZsmSJcrPz1d0dLRVs3z5cp08edKqSUhIUNOmTRUUFGTVnP45BTUFn+NOXwAAAACgpFWogHf48GElJycrOTlZkvNhJsnJyUpJSZHD4dDgwYP1j3/8Q19//bU2btyoRx55ROHh4daTNps1a6Y777xTffr00erVq/Xjjz9qwIAB6tatm8LDwyVJDz74oLy8vNS7d29t3rxZs2bN0qRJkzR06FCrH4MGDdLChQv1xhtvaNu2bRo3bpx++uknDRgwQJLc6gsAAAAAlDhTgXz33XdGUqGtZ8+exhhj8vPzzQsvvGBCQ0ONt7e3uf3228327dtdjvHnn3+a7t27m+rVqxt/f3/z6KOPmkOHDrnUrF+/3txwww3G29vbXHbZZWbChAmF+jJ79mzTpEkT4+XlZa644gozf/58l3Z3+nI+2dnZRpLJzs4u1vsAAAAA2Iu72aDCroNXGbAOHgAAAACpEq6DBwAAAACVHQEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyiSll3AAAAAADOKjNTSk+XsrOlwEApJEQKCirrXpVbzOABAAAAKJ9SU6Vu3aRmzaS2baWoKOfr1NSy7lm5RcADAAAAUP5kZkqPPy4tXuy6f/Fi5/7MzLLpVzlHwAMAAABQ/qSnFw53BRYvdrajEAIeAAAAgPInO/vi2ispAh4AAACA8icg4OLaKykCHgAAAIDyJzRUiosrui0uztmOQgh4AAAAAMqfoCDp448Lh7y4OOd+lkooEuvgAQAAACifIiKkmTP/WgcvIMA5c0e4OysCHgAAAIDyKyiIQFcMXKIJAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABswlYBb9y4cXI4HC5bVFSU1X78+HH1799ftWrVUvXq1dW5c2elp6e7HCMlJUUdO3aUn5+fQkJC9Mwzz+jUqVMuNUuXLtU111wjb29vNWrUSNOnTy/Ul8mTJysyMlI+Pj6Kjo7W6tWrS+WcAQAAAKCArQKeJF1xxRXat2+ftf3www9W25AhQzR37lzNmTNHy5Yt0969e3X//fdb7Xl5eerYsaNOnDihFStWaMaMGZo+fbrGjBlj1ezevVsdO3bUrbfequTkZA0ePFiPP/64Fi1aZNXMmjVLQ4cO1dixY/Xzzz/r6quvVnx8vPbv339pBgEAAABApeQwxpiy7kRJGTdunL766islJycXasvOzlZwcLD+/e9/64EHHpAkbdu2Tc2aNVNSUpLatm2rb775RnfddZf27t2r0NBQSdLUqVM1YsQIHThwQF5eXhoxYoTmz5+vTZs2Wcfu1q2bsrKytHDhQklSdHS0rrvuOr333nuSpPz8fEVEROjpp5/WyJEjz9r/3Nxc5ebmWq9zcnIUERGh7Oxs+fv7X/T4AAAAAKiYcnJyFBAQcN5sYLsZvB07dig8PFwNGzZUjx49lJKSIklau3atTp48qdjYWKs2KipK9erVU1JSkiQpKSlJLVq0sMKdJMXHxysnJ0ebN2+2ak4/RkFNwTFOnDihtWvXutR4eHgoNjbWqjmb8ePHKyAgwNoiIiIuYiQAAAAAVDa2CnjR0dGaPn26Fi5cqClTpmj37t268cYbdejQIaWlpcnLy0uBgYEu7wkNDVVaWpokKS0tzSXcFbQXtJ2rJicnR8eOHdPBgweVl5dXZE3BMc5m1KhRys7OtrbU1NRijwEAAACAyqtKWXegJLVv3976/VVXXaXo6GjVr19fs2fPlq+vbxn2zD3e3t7y9vYu624AAAAAqKBsNYN3psDAQDVp0kS//vqrwsLCdOLECWVlZbnUpKenKywsTJIUFhZW6KmaBa/PV+Pv7y9fX1/Vrl1bnp6eRdYUHAMAAAAASoOtA97hw4e1c+dO1alTR61bt1bVqlWVmJhotW/fvl0pKSmKiYmRJMXExGjjxo0uT7tMSEiQv7+/mjdvbtWcfoyCmoJjeHl5qXXr1i41+fn5SkxMtGoAAAAAoDTYKuANHz5cy5Yt0549e7RixQrdd9998vT0VPfu3RUQEKDevXtr6NCh+u6777R27Vo9+uijiomJUdu2bSVJcXFxat68uR5++GGtX79eixYt0ujRo9W/f3/r0sm+fftq165devbZZ7Vt2za9//77mj17toYMGWL1Y+jQofroo480Y8YMbd26VU899ZSOHDmiRx99tEzGBQAAAEDlYKt78H7//Xd1795df/75p4KDg3XDDTdo5cqVCg4OliS99dZb8vDwUOfOnZWbm6v4+Hi9//771vs9PT01b948PfXUU4qJiVG1atXUs2dPvfTSS1ZNgwYNNH/+fA0ZMkSTJk1S3bp19fHHHys+Pt6q6dq1qw4cOKAxY8YoLS1NLVu21MKFCws9eAUAAAAASpKt1sGzG3fXugAAAABgb5V2HTwAAAAAqKwIeAAAAABgE7a6Bw8AAFvJzJTS06XsbCkwUAoJkYKCyrpXAIByjBk8AADKo9RUqVs3qVkzqW1bKSrK+To1tax7BgAoxwh4AACUN5mZ0uOPS4sXu+5fvNi5PzOzbPoFACj3CHgAAJQ36emFw12BxYud7QAAFIGABwBAeZOdfXHtAIBKi4AHAEB5ExBwce0AgEqLgAcAQHkTGirFxRXdFhfnbAcAoAgEPAAAypugIOnjjwuHvLg4536WSgAAnAXr4AEAUB5FREgzZ/61Dl5AgHPmjnAHADgHAh4AAOVVUBCBDgBQLFyiCQAAAAA2QcADAAAAAJsg4AEAAACATRDwAAAAAMAmCHgAAAAAYBMEPAAAAACwCQIeAAAAANgEAQ8AAAAAbIKABwAAAAA2ccEB78SJE9q+fbtOnTpVkv0BAAAAAFygYge8o0ePqnfv3vLz89MVV1yhlJQUSdLTTz+tCRMmlHgHAQAAAADuKXbAGzVqlNavX6+lS5fKx8fH2h8bG6tZs2aVaOcAAAAAAO6rUtw3fPXVV5o1a5batm0rh8Nh7b/iiiu0c+fOEu0cAAAAAMB9xZ7BO3DggEJCQgrtP3LkiEvgAwAAAABcWsUOeNdee63mz59vvS4IdR9//LFiYmJKrmcAAAAAgGIp9iWar7zyitq3b68tW7bo1KlTmjRpkrZs2aIVK1Zo2bJlpdFHAAAAAIAbij2Dd8MNNyg5OVmnTp1SixYttHjxYoWEhCgpKUmtW7cujT4CAAAAANzgMMaYsu4EipaTk6OAgABlZ2fL39+/rLsDAAAAoIy4mw2KPYO3YMECLVq0qND+RYsW6Ztvvinu4QAAAAAAJaTYAW/kyJHKy8srtN8Yo5EjR5ZIpwAAAAAAxVfsgLdjxw41b9680P6oqCj9+uuvJdIpAAAAAEDxFTvgBQQEaNeuXYX2//rrr6pWrVqJdAoAAAAAUHzFDnj33nuvBg8erJ07d1r7fv31Vw0bNkz33HNPiXYOAAAAAOC+Yge8iRMnqlq1aoqKilKDBg3UoEEDNWvWTLVq1dLrr79eGn0EAAAAALih2AudBwQEaMWKFUpISND69evl6+urq666SjfddFNp9A8AAAAA4CbWwSvHWAcPAAAAgOR+NnBrBu+dd97RE088IR8fH73zzjvnrB04cGDxegoAAAAAKBFuzeA1aNBAP/30k2rVqqUGDRqc/WAOR5FP2MSFYQYPAAAAgFTCM3i7d+8u8vcAAAAAgPKjWE/RPHnypC6//HJt3bq1tPoDAAAKZGZK27ZJq1ZJ27c7XwMAcA7Feopm1apVdfz48dLqCwAAKJCaKj3+uLR48V/74uKkf/5TqlZNSk+XsrOlwEApJEQKCiqzrgIAyo9ir4PXv39/vfrqqzp16lRp9AcAAGRmFg53kvTjj9Kvv0rduknNmklt20pRUc7Xqal/1e3bJ23YIH3/vbRxo/M1AKBSKPY6eGvWrFFiYqIWL16sFi1aqFq1ai7t//3vf0uscwAAVErp6YXDnSQNHiz94x9SYuJf+6pVk667TtqxQ/r9d6lGDSkpSRoyRDpyxFkTGytNnSpdfvkl6T4AoOwUO+AFBgaqc+fOpdEXAAAgOS+9LFCtmjPYtW0rhYVJL7/s2vb559KkSa77b7/dub97d2fI+/ZbqW9f6ZNPpDp1LtlpAAAuvWIHvGnTppVGPwAAQIGAACkyUvriC6lKlb/utatWTWreXLrvPmfg8/Jy7p8wwXmJppeXc/bu7bclT09p2TLpyy+dr7/9VjpwgIAHADbn9j14+fn5evXVV3X99dfruuuu08iRI3Xs2LHS7BsAAJVTaKjzEs2RI6WWLaUOHaSFC52zcR98IN1/vzOorVwp3Xab9Pzz0mWXSY895tz3+efO+/X27XO+njVLevFFKT/fGQB5IicA2JbbAe/ll1/Wc889p+rVq+uyyy7TpEmT1L9//9LsGwAAldPhw1K/fs4w9vLL0tq1zvvurr5auvFGqXVracQI6aabpDVrpOuvl/7+d2n+fGegmzTJeVnn8ePO973zjvPyzlatpHbtin4wCwDAFhzGGONOYePGjTV8+HA9+eSTkqRvv/1WHTt21LFjx+ThUeyHccIN7q5WDwCwmQ0bpGeecT4Y5dAh6eBBKSND8vV1hr6PPpL69JFuvdVZX6OG5OEhHTsmLVrkDIXz5knGSHff7ayZO9cZ6gru5zt+XKpVyxn2uGwTAMo9d7OB28ksJSVFHTp0sF7HxsbK4XBo7969F9dTAADg6tQp6f/+zxnaJKlqValRI6lxY+mJJ6SlS/8Kad9957xMc9QoKTjYOZsnSf7+Us2azss3q1VzHvPzz50zfHffLXXp4nxfr17M5AGAjbgd8E6dOiUfHx+XfVWrVtXJkydLvFN2MnnyZEVGRsrHx0fR0dFavXp1WXcJACA570Hbtk1atars70nLzJR27pQ2bXKucxcR4dxvzF+bh4fzISq+vpK3t/T009Jdd/11z11SkjRo0F8Lnvv5OcNeQXtUlPPSzdOXWJCc9/o9/vilP//yNP4AYCNuP0XTGKNevXrJ29vb2nf8+HH17dvXZS081sH7y6xZszR06FBNnTpV0dHRevvttxUfH6/t27crJCSkrLsHAJVXamrhhcTj4qSPP/4rXF0qv/8unTghHT3qDG/bt0tt2jifnPnUU86nXxaIjZWmTHE+ZfOrr5wPYCkIbIMHOy/NfOUV5zIJBf8Am5goORxFh7sCixc7194rCIelrTyNPwDYjNszeD179lRISIgCAgKs7aGHHlJ4eLjLPvzlzTffVJ8+ffToo4+qefPmmjp1qvz8/PSvf/2rrLsGAJVXZmbhcCGVzUxWSooz2D35pLRnj/PBKlde6Xxa5pnhTnK+fuopZ/vpV9UkJjov2ZScwXDQINfz+PZb5+ecy+lr75Wm8jT+AGBDbs/gsf5d8Zw4cUJr167VqFGjrH0eHh6KjY1VUlJSke/Jzc1Vbm6u9TonJ6fU+wkAlU56euFwUeBSzmRlZkq7d0v/+IczgE2YICUkOO+rO368cLgr8O23zjB25jPSjh93/lqzpvTmm87ZvdMdPnzu/lyqf6QtL+MPADbF4y9LycGDB5WXl6fQ0FCX/aGhoUpLSyvyPePHj3eZDY3gMhUAKHnnm6m6VDNZ+/ZJ1av/FeQK/lHv6FH3+nhmYPPxcV6aKTlnBN9+27W9alXnZZBFiYtzrr13KZSX8QcAmyLglSOjRo1Sdna2taXyVDMAKHnnm6m6FDNZqanSrl1/hTrJ+dRLyXn5ZWDgud8fGPhXveQMdvv2SaNHS3v3St27O49zevsvvzgXST8z5BXc+3apZs3Kw/gDgI0R8EpJ7dq15enpqfT0dJf96enpCgsLK/I93t7e8vf3d9kAACUsNLRsZ7IK7kGTXEPa3r3Ozw8IcG6xsUW/PzbW2V7w0LO4OOeDV3x8nA8o+eCDwuFu9GjpjjukyEhp5kxp61bn0zW3bnW+vpRXjJT1+AOAzRHwSomXl5dat26txNOeWJafn6/ExETFxMSUYc8AoJILCnLOWJXVTFbBPWgrVzovsywIco8/Lr31lvOJmn5+ztB2ZsiLjXUufu7n57yUc8MG6cMPpbw8qVUr53snTpRWr5a+/15at855nKuvlurW/ev8o6Kk6Gjnr5f6freyHn8AsDmHMWfepY2SMmvWLPXs2VMffPCB2rRpo7fffluzZ8/Wtm3bCt2bVxR3V6sHAFyAzExn2MrOds6IhYaWXrg4/bN8fKQ5c6SPPpI++USqV08aONB5L15IiHPNugYNnPfnFdyPl5X118xewdJEOTnO5Q+McS6JULWqlJEh1ahRuudSUi7l+AOADbibDdx6iubXX3/t9gffc889btfaXdeuXXXgwAGNGTNGaWlpatmypRYuXOhWuAMAlLKgoEsTKIpa8+32252zVY884ly/7p13nCHt0CEpN9e5Nl6dOtKxY84gV6PGX8sibN/uXPS8XbvCn3X55aV/PiXlUo0/AFQybs3geXi4dyWnw+FQXl7eRXcKTszgAUAFl5kpdetW9LIAt9/uXLvu5Zedr59/3hneHnnEOatXcMlieLgz5AUESH/84QyL334rtWhxac8FAFCmSnQGLz8/v8Q6BgBApXGuNd8SE52zdwWSk51r4Q0eLN10kzRrlnONvNPu5dbttzv3849+AICzcHuhcwAAUEznW9OtenXn/Xg+Ps6Hrtx+uzPU3XST876808Od5Hzt6el8GAsAAEW4oIB35MgRLVu2TCkpKTpx4oRL28CBA0ukYwAAVHjnW9MtP1/q0uWv13FxzuUMcnLOPvO3eLF06lTJ9REAYCvFDnjr1q1Thw4ddPToUR05ckQ1a9bUwYMH5efnp5CQEAIeAAAFCtZ8KyqsxcVJzZo516I780mSP/xw7uOevkA6AACnKfY6eEOGDNHdd9+tzMxM+fr6auXKlfrtt9/UunVrvf7666XRRwAAKqbzrflWp07Ra9Kd7x477sEDAJxFsWfwkpOT9cEHH8jDw0Oenp7Kzc1Vw4YNNXHiRPXs2VP3339/afQTAICKKSJCmjmzeGu+BQc7FzX/9tvCbbGxznYAAIpQ7Bm8qlWrWssmhISEKCUlRZIUEBCg1NTUku0dAAB2EBRU9Ezd2dSpI02d6gxzp4uNlT74wNkOAEARij2D16pVK61Zs0aNGzfWzTffrDFjxujgwYP69NNPdeWVV5ZGHwEAqHwuv1z65BPpwIG/Zv6Cgwl3AIBzcmuh89P99NNPOnTokG699Vbt379fjzzyiFasWKHGjRvrn//8p1q2bFlKXa18WOgcAAAAgOR+Nih2wMOlQ8ADAAAAILmfDYp9D95tt92mrKysIj/wtttuK+7hAAAAAAAlpNgBb+nSpYUWN5ek48eP6/vvvy+RTgEAAAAAis/th6xs2LDB+v2WLVuUlpZmvc7Ly9PChQt12WWXlWzvAAAAAABuczvgtWzZUg6HQw6Ho8hLMX19ffXuu++WaOcAAAAAAO5zO+Dt3r1bxhg1bNhQq1evVvBpi6x6eXkpJCREnp6epdJJAAAAAMD5uR3w6tevL0nKz88vtc4AAAAAAC5csRc6l6SdO3fq7bff1tatWyVJzZs316BBg3T55ZeXaOcAAAAAAO4r9lM0Fy1apObNm2v16tW66qqrdNVVV2nVqlW64oorlJCQUBp9BAAAAAC4odgLnbdq1Urx8fGaMGGCy/6RI0dq8eLF+vnnn0u0g5UZC50DAAAAkEpxofOtW7eqd+/ehfY/9thj2rJlS3EPBwAAAAAoIcUOeMHBwUpOTi60Pzk5WSEhISXRJwAAAADABXD7ISsvvfSShg8frj59+uiJJ57Qrl271K5dO0nSjz/+qFdffVVDhw4ttY4CAAAAAM7N7XvwPD09tW/fPgUHB+vtt9/WG2+8ob1790qSwsPD9cwzz2jgwIFyOByl2uHKhHvwAAAAAEjuZwO3A56Hh4fS0tJcLsM8dOiQJKlGjRoX2V0UhYAHAAAAQHI/GxRrHbwzZ+cIdgAAAABQfhQr4DVp0uS8l2BmZGRcVIcAAAAAABemWAHvxRdfVEBAQGn1BQAAAABwEYoV8Lp168ZSCAAAAABQTrm9Dh5PxwQAAACA8s3tgOfmwzYBAAAAAGXE7Us08/PzS7MfAAAAAICL5PYMHgAAAACgfCPgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2ITbyyQAAIBLLDNTSk+XsrOlwEApJEQKCirrXgEAyjFm8AAAKI9SU6Vu3aRmzaS2baWoKOfr1NSy7hkAoBwj4AEAUN5kZkqPPy4tXuy6f/Fi5/7MzLLpFwCg3CPgAQBQ3qSnFw53BRYvdrYDAFAEAh4AAOVNdvbFtQMAKi0CHgAA5U1AwMW1AwAqLQIeAADlTWioFBdXdFtcnLMdAIAiEPAAAChvgoKkjz8uHPLi4pz7WSoBAHAWrIMHAEB5FBEhzZz51zp4AQHOmTvCHQDgHAh4AACUV0FBBDoAQLFwiSYAAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAm7BVwIuMjJTD4XDZJkyY4FKzYcMG3XjjjfLx8VFERIQmTpxY6Dhz5sxRVFSUfHx81KJFCy1YsMCl3RijMWPGqE6dOvL19VVsbKx27NjhUpORkaEePXrI399fgYGB6t27tw4fPlzyJw0AAAAA/5+tAp4kvfTSS9q3b5+1Pf3001ZbTk6O4uLiVL9+fa1du1avvfaaxo0bpw8//NCqWbFihbp3767evXtr3bp16tSpkzp16qRNmzZZNRMnTtQ777yjqVOnatWqVapWrZri4+N1/Phxq6ZHjx7avHmzEhISNG/ePC1fvlxPPPHEpRkEAAAAAJWSwxhjyroTJSUyMlKDBw/W4MGDi2yfMmWKnn/+eaWlpcnLy0uSNHLkSH311Vfatm2bJKlr1646cuSI5s2bZ72vbdu2atmypaZOnSpjjMLDwzVs2DANHz5ckpSdna3Q0FBNnz5d3bp109atW9W8eXOtWbNG1157rSRp4cKF6tChg37//XeFh4e7dT45OTkKCAhQdna2/P39L3RYAAAAAFRw7mYD283gTZgwQbVq1VKrVq302muv6dSpU1ZbUlKSbrrpJivcSVJ8fLy2b9+uzMxMqyY2NtblmPHx8UpKSpIk7d69W2lpaS41AQEBio6OtmqSkpIUGBhohTtJio2NlYeHh1atWnXWvufm5ionJ8dlAwAAAAB32Wqh84EDB+qaa65RzZo1tWLFCo0aNUr79u3Tm2++KUlKS0tTgwYNXN4TGhpqtQUFBSktLc3ad3pNWlqaVXf6+85WExIS4tJepUoV1axZ06opyvjx4/Xiiy8W97QBAAAAQFIFmMEbOXJkoQennLkVXF45dOhQ3XLLLbrqqqvUt29fvfHGG3r33XeVm5tbxmfhnlGjRik7O9vaUlNTy7pLAAAAACqQcj+DN2zYMPXq1eucNQ0bNixyf3R0tE6dOqU9e/aoadOmCgsLU3p6uktNweuwsDDr16JqTm8v2FenTh2XmpYtW1o1+/fvdznGqVOnlJGRYb2/KN7e3vL29j7nuQIAAADA2ZT7Gbzg4GBFRUWdczv9nrrTJScny8PDw7pcMiYmRsuXL9fJkyetmoSEBDVt2lRBQUFWTWJiostxEhISFBMTI0lq0KCBwsLCXGpycnK0atUqqyYmJkZZWVlau3atVbNkyRLl5+crOjq6BEYFAAAAAAor9wHPXUlJSXr77be1fv167dq1S5999pmGDBmihx56yApvDz74oLy8vNS7d29t3rxZs2bN0qRJkzR06FDrOIMGDdLChQv1xhtvaNu2bRo3bpx++uknDRgwQJLkcDg0ePBg/eMf/9DXX3+tjRs36pFHHlF4eLg6deokSWrWrJnuvPNO9enTR6tXr9aPP/6oAQMGqFu3bm4/QRMAAAAAiss2yyT8/PPP6tevn7Zt26bc3Fw1aNBADz/8sIYOHepy2eOGDRvUv39/rVmzRrVr19bTTz+tESNGuBxrzpw5Gj16tPbs2aPGjRtr4sSJ6tChg9VujNHYsWP14YcfKisrSzfccIPef/99NWnSxKrJyMjQgAEDNHfuXHl4eKhz58565513VL16dbfPiWUSAAAAAEjuZwPbBDw7IuABAAAAkCrxOngAAAAAUFkR8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANlGlrDsAFCkzU0pPl7KzpcBAKSRECgoq614BAAAA5RozeCh/UlOlbt2kZs2ktm2lqCjn69TUsu4ZAAAAUK4R8FC+ZGZKjz8uLV7sun/xYuf+zMyy6RcAAABQARDwUL6kpxcOdwUWL3a2AwAAACgSAQ/lS3b2xbUDAAAAlRgBD+VLQMDFtQMAAACVGAEP5UtoqBQXV3RbXJyzHQAAAECRCHgoX4KCpI8/Lhzy4uKc+1kqAQAAADgr1sFD+RMRIc2c+dc6eAEBzpk7wh0AAABwTgQ8lE9BQQQ6AAAAoJi4RBMAAAAAbIKABwAAAAA2QcADAAAAAJsg4AEAAACATRDwAAAAAMAmCHgAAAAAYBMEPAAAAACwCQIeAAAAANgEAQ8AAAAAbIKABwAAAAA2QcADAAAAAJsg4AEAAACATRDwAAAAAMAmCHgAAAAAYBMEPAAAAACwCQIeAAAAANgEAQ8AAAAAbIKABwAAAAA2UWEC3ssvv6x27drJz89PgYGBRdakpKSoY8eO8vPzU0hIiJ555hmdOnXKpWbp0qW65ppr5O3trUaNGmn69OmFjjN58mRFRkbKx8dH0dHRWr16tUv78ePH1b9/f9WqVUvVq1dX586dlZ6eXuy+AAAAAEBJqjAB78SJE+rSpYueeuqpItvz8vLUsWNHnThxQitWrNCMGTM0ffp0jRkzxqrZvXu3OnbsqFtvvVXJyckaPHiwHn/8cS1atMiqmTVrloYOHaqxY8fq559/1tVXX634+Hjt37/fqhkyZIjmzp2rOXPmaNmyZdq7d6/uv//+YvUFAAAAAEqawxhjyroTxTF9+nQNHjxYWVlZLvu/+eYb3XXXXdq7d69CQ0MlSVOnTtWIESN04MABeXl5acSIEZo/f742bdpkva9bt27KysrSwoULJUnR0dG67rrr9N5770mS8vPzFRERoaefflojR45Udna2goOD9e9//1sPPPCAJGnbtm1q1qyZkpKS1LZtW7f64o6cnBwFBAQoOztb/v7+FzVuAAAAACoud7NBhZnBO5+kpCS1aNHCClSSFB8fr5ycHG3evNmqiY2NdXlffHy8kpKSJDlnCdeuXetS4+HhodjYWKtm7dq1OnnypEtNVFSU6tWrZ9W405ei5ObmKicnx2UDAAAAAHfZJuClpaW5BCpJ1uu0tLRz1uTk5OjYsWM6ePCg8vLyiqw5/RheXl6F7gM8s+Z8fSnK+PHjFRAQYG0RERHunDoAAAAASCrjgDdy5Eg5HI5zbtu2bSvLLl5So0aNUnZ2trWlpqaWdZcAAAAAVCBVyvLDhw0bpl69ep2zpmHDhm4dKywsrNDTLguebBkWFmb9eubTLtPT0+Xv7y9fX195enrK09OzyJrTj3HixAllZWW5zOKdWXO+vhTF29tb3t7ebp0vAAAAAJypTGfwgoODFRUVdc7N3QeSxMTEaOPGjS5Pu0xISJC/v7+aN29u1SQmJrq8LyEhQTExMZIkLy8vtW7d2qUmPz9fiYmJVk3r1q1VtWpVl5rt27crJSXFqnGnLwAAAABQ0sp0Bq84UlJSlJGRoZSUFOXl5Sk5OVmS1KhRI1WvXl1xcXFq3ry5Hn74YU2cOFFpaWkaPXq0+vfvb82K9e3bV++9956effZZPfbYY1qyZIlmz56t+fPnW58zdOhQ9ezZU9dee63atGmjt99+W0eOHNGjjz4qSQoICFDv3r01dOhQ1axZU/7+/nr66acVExOjtm3bSpJbfQEAAACAEmcqiJ49expJhbbvvvvOqtmzZ49p37698fX1NbVr1zbDhg0zJ0+edDnOd999Z1q2bGm8vLxMw4YNzbRp0wp91rvvvmvq1atnvLy8TJs2bczKlStd2o8dO2b69etngoKCjJ+fn7nvvvvMvn37XGrc6cv5ZGdnG0kmOzu7WO8DAAAAYC/uZoMKtw5eZcI6eAAAAACkSrgOHgAAAABUdgQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsosIEvJdfflnt2rWTn5+fAgMDi6xxOByFtpkzZ7rULF26VNdcc428vb3VqFEjTZ8+vdBxJk+erMjISPn4+Cg6OlqrV692aT9+/Lj69++vWrVqqXr16urcubPS09NdalJSUtSxY0f5+fkpJCREzzzzjE6dOnVRYwAAAAAA51JhAt6JEyfUpUsXPfXUU+esmzZtmvbt22dtnTp1stp2796tjh076tZbb1VycrIGDx6sxx9/XIsWLbJqZs2apaFDh2rs2LH6+eefdfXVVys+Pl779++3aoYMGaK5c+dqzpw5WrZsmfbu3av777/fas/Ly1PHjh114sQJrVixQjNmzND06dM1ZsyYkhsQAAAAADiDwxhjyroTxTF9+nQNHjxYWVlZhdocDoe+/PJLl1B3uhEjRmj+/PnatGmTta9bt27KysrSwoULJUnR0dG67rrr9N5770mS8vPzFRERoaefflojR45Udna2goOD9e9//1sPPPCAJGnbtm1q1qyZkpKS1LZtW33zzTe66667tHfvXoWGhkqSpk6dqhEjRujAgQPy8vJy61xzcnIUEBCg7Oxs+fv7uztEAAAAAGzG3WxQYWbw3NW/f3/Vrl1bbdq00b/+9S+dnl+TkpIUGxvrUh8fH6+kpCRJzlnCtWvXutR4eHgoNjbWqlm7dq1OnjzpUhMVFaV69epZNUlJSWrRooUV7go+JycnR5s3bz5r33Nzc5WTk+OyAQAAAIC7qpR1B0rSSy+9pNtuu01+fn5avHix+vXrp8OHD2vgwIGSpLS0NJfQJUmhoaHKycnRsWPHlJmZqby8vCJrtm3bZh3Dy8ur0H2AoaGhSktLO+fnFLSdzfjx4/Xiiy8W/8QBAAAAQGU8gzdy5MgiH4xy+lYQrNzxwgsv6Prrr1erVq00YsQIPfvss3rttddK8QxK1qhRo5SdnW1tqampZd0lAAAAABVImc7gDRs2TL169TpnTcOGDS/4+NHR0fr73/+u3NxceXt7KywsrNDTLtPT0+Xv7y9fX195enrK09OzyJqwsDBJUlhYmE6cOKGsrCyXWbwza8588mbBMQtqiuLt7S1vb+8LPl8AAAAAlVuZzuAFBwcrKirqnJu7DyQpSnJysoKCgqzQFBMTo8TERJeahIQExcTESJK8vLzUunVrl5r8/HwlJiZaNa1bt1bVqlVdarZv366UlBSrJiYmRhs3bnR58mZCQoL8/f3VvHnzCz4fAAAAADiXCnMPXkpKijIyMpSSkqK8vDwlJydLkho1aqTq1atr7ty5Sk9PV9u2beXj46OEhAS98sorGj58uHWMvn376r333tOzzz6rxx57TEuWLNHs2bM1f/58q2bo0KHq2bOnrr32WrVp00Zvv/22jhw5okcffVSSFBAQoN69e2vo0KGqWbOm/P399fTTTysmJkZt27aVJMXFxal58+Z6+OGHNXHiRKWlpWn06NHq378/M3QAAAAASk2FCXhjxozRjBkzrNetWrWSJH333Xe65ZZbVLVqVU2ePFlDhgyRMUaNGjXSm2++qT59+ljvadCggebPn68hQ4Zo0qRJqlu3rj7++GPFx8dbNV27dtWBAwc0ZswYpaWlqWXLllq4cKHLQ1PeeusteXh4qHPnzsrNzVV8fLzef/99q93T01Pz5s3TU089pZiYGFWrVk09e/bUSy+9VJpDBAAAAKCSq3Dr4FUmrIMHAAAAQKrE6+ABAAAAQGVFwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCaqlHUHUAFkZkrp6VJ2thQYKIWESEFBZd0rAAAAAGdgBg/nlpoqdesmNWsmtW0rRUU5X6emlnXPAAAAAJyBgIezy8yUHn9cWrzYdf/ixc79mZll0y8AAAAARSLg4ezS0wuHuwKLFzvbAQAAAJQbBDycXXb2xbUDAAAAuKQIeDi7gICLawcAAABwSRHwcHahoVJcXNFtcXHOdgAAAADlBgEPZxcUJH38ceGQFxfn3M9SCQAAAEC5wjp4OLeICGnmzL/WwQsIcM7cEe4AAACAcoeAh/MLCiLQAQAAABUAl2gCAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyCgAcAAAAANkHAAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBDwAAAAAsAkCHgAAAADYBAEPAAAAAGyiSll3AGdnjJEk5eTklHFPAAAAAJSlgkxQkBHOhoBXjh06dEiSFBERUcY9AQAAAFAeHDp0SAEBAWdtd5jzRUCUmfz8fO3du1c1atSQw+Eo6+64yMnJUUREhFJTU+Xv71/W3bEVxrb0MLalh7EtPYxt6WFsSw9jW3oY29JT3sfWGKNDhw4pPDxcHh5nv9OOGbxyzMPDQ3Xr1i3rbpyTv79/ufwBsAPGtvQwtqWHsS09jG3pYWxLD2Nbehjb0lOex/ZcM3cFeMgKAAAAANgEAQ8AAAAAbIKAhwvi7e2tsWPHytvbu6y7YjuMbelhbEsPY1t6GNvSw9iWHsa29DC2pccuY8tDVgAAAADAJpjBAwAAAACbIOABAAAAgE0Q8AAAAADAJgh4AAAAAGATBLxK7pdfftG9996r2rVry9/fXzfccIO+++47l5qUlBR17NhRfn5+CgkJ0TPPPKNTp0651CxdulTXXHONvL291ahRI02fPr3QZ02ePFmRkZHy8fFRdHS0Vq9e7dJ+/Phx9e/fX7Vq1VL16tXVuXNnpaenl/g5X0rz589XdHS0fH19FRQUpE6dOrm0M7YXJzc3Vy1btpTD4VBycrJL24YNG3TjjTfKx8dHERERmjhxYqH3z5kzR1FRUfLx8VGLFi20YMECl3ZjjMaMGaM6derI19dXsbGx2rFjh0tNRkaGevToIX9/fwUGBqp37946fPhwiZ/rpbBnzx717t1bDRo0kK+vry6//HKNHTtWJ06ccKljbC+t8/18Vybjx4/Xddddpxo1aigkJESdOnXS9u3bXWrc+b67VN+9FdmECRPkcDg0ePBgax9je+H++OMPPfTQQ6pVq5Z8fX3VokUL/fTTT1Z7SX0nlsT3c0WSl5enF154weW/W3//+991+jMkK+XYGlRqjRs3Nh06dDDr1683v/zyi+nXr5/x8/Mz+/btM8YYc+rUKXPllVea2NhYs27dOrNgwQJTu3ZtM2rUKOsYu3btMn5+fmbo0KFmy5Yt5t133zWenp5m4cKFVs3MmTONl5eX+de//mU2b95s+vTpYwIDA016erpV07dvXxMREWESExPNTz/9ZNq2bWvatWt36QajhH3xxRcmKCjITJkyxWzfvt1s3rzZzJo1y2pnbC/ewIEDTfv27Y0ks27dOmt/dna2CQ0NNT169DCbNm0yn3/+ufH19TUffPCBVfPjjz8aT09PM3HiRLNlyxYzevRoU7VqVbNx40arZsKECSYgIMB89dVXZv369eaee+4xDRo0MMeOHbNq7rzzTnP11VeblStXmu+//940atTIdO/e/ZKcf0n75ptvTK9evcyiRYvMzp07zf/+9z8TEhJihg0bZtUwtpeWOz/flUl8fLyZNm2a2bRpk0lOTjYdOnQw9erVM4cPH7Zqzvd9dym/eyuq1atXm8jISHPVVVeZQYMGWfsZ2wuTkZFh6tevb3r16mVWrVpldu3aZRYtWmR+/fVXq6YkvhNL6vu5Inn55ZdNrVq1zLx588zu3bvNnDlzTPXq1c2kSZOsmso4tgS8SuzAgQNGklm+fLm1Lycnx0gyCQkJxhhjFixYYDw8PExaWppVM2XKFOPv729yc3ONMcY8++yz5oorrnA5dteuXU18fLz1uk2bNqZ///7W67y8PBMeHm7Gjx9vjDEmKyvLVK1a1cyZM8eq2bp1q5FkkpKSSvCsL42TJ0+ayy67zHz88cdnrWFsL86CBQtMVFSU2bx5c6GA9/7775ugoCBrHI0xZsSIEaZp06bW67/97W+mY8eOLseMjo42Tz75pDHGmPz8fBMWFmZee+01qz0rK8t4e3ubzz//3BhjzJYtW4wks2bNGqvmm2++MQ6Hw/zxxx8ler5lZeLEiaZBgwbWa8b20jrfz3dlt3//fiPJLFu2zBjj3vfdpfruragOHTpkGjdubBISEszNN99sBTzG9sKNGDHC3HDDDWdtL6nvxJL4fq5oOnbsaB577DGXfffff7/p0aOHMabyji2XaFZitWrVUtOmTfXJJ5/oyJEjOnXqlD744AOFhISodevWkqSkpCS1aNFCoaGh1vvi4+OVk5OjzZs3WzWxsbEux46Pj1dSUpIk6cSJE1q7dq1LjYeHh2JjY62atWvX6uTJky41UVFRqlevnlVTkfz888/6448/5OHhoVatWqlOnTpq3769Nm3aZNUwthcuPT1dffr00aeffio/P79C7UlJSbrpppvk5eVl7YuPj9f27duVmZlp1ZxrbHfv3q20tDSXmoCAAEVHR1s1SUlJCgwM1LXXXmvVxMbGysPDQ6tWrSq5Ey5D2dnZqlmzpvWasb103Pn5ruyys7Mlyfo76s733aX67q2o+vfvr44dOxY6f8b2wn399de69tpr1aVLF4WEhKhVq1b66KOPrPaS+k4sie/niqZdu3ZKTEzUL7/8Iklav369fvjhB7Vv315S5R1bAl4l5nA49O2332rdunWqUaOGfHx89Oabb2rhwoUKCgqSJKWlpbl8UUuyXqelpZ2zJicnR8eOHdPBgweVl5dXZM3px/Dy8lJgYOBZayqSXbt2SZLGjRun0aNHa968eQoKCtItt9yijIwMSYzthTLGqFevXurbt6/Ll/HpLmZsT28//X1nqwkJCXFpr1KlimrWrFkhx/ZMv/76q9599109+eST1j7G9tJx5+e7MsvPz9fgwYN1/fXX68orr5Tk3vfdpfrurYhmzpypn3/+WePHjy/UxtheuF27dmnKlClq3LixFi1apKeeekoDBw7UjBkzJJXcd2JJfD9XNCNHjlS3bt0UFRWlqlWrqlWrVho8eLB69OghqfKOLQHPhkaOHCmHw3HObdu2bTLGqH///goJCdH333+v1atXq1OnTrr77ru1b9++sj6Ncsndsc3Pz5ckPf/88+rcubNat26tadOmyeFwaM6cOWV8FuWTu2P77rvv6tChQxo1alRZd7nCcHdsT/fHH3/ozjvvVJcuXdSnT58y6jlwdv3799emTZs0c+bMsu6KLaSmpmrQoEH67LPP5OPjU9bdsZX8/Hxdc801euWVV9SqVSs98cQT6tOnj6ZOnVrWXavwZs+erc8++0z//ve/9fPPP2vGjBl6/fXXrfBcWVUp6w6g5A0bNky9evU6Z03Dhg21ZMkSzZs3T5mZmfL395ckvf/++0pISNCMGTM0cuRIhYWFFXpyVcETs8LCwqxfz3yKVnp6uvz9/eXr6ytPT095enoWWXP6MU6cOKGsrCyXfx08vaY8cHdsCwJy8+bNrf3e3t5q2LChUlJSJImxPUNx/t4mJSXJ29vbpe3aa69Vjx49NGPGjLOOm3T+sT29vWBfnTp1XGpatmxp1ezfv9/lGKdOnVJGRkaFHNsCe/fu1a233qp27drpww8/dKljbC+d2rVrn/fnu7IaMGCA5s2bp+XLl6tu3brWfne+7y7Vd29Fs3btWu3fv1/XXHONtS8vL0/Lly/Xe++9p0WLFjG2F6hOnTou/z8gSc2aNdN//vMfSSX3nVgS388VzTPPPGPN4klSixYt9Ntvv2n8+PHq2bNnpR1bZvBsKDg4WFFRUefcvLy8dPToUUnOa9tP5+HhYc1AxcTEaOPGjS5/8RMSEuTv7299WcXExCgxMdHlGAkJCYqJiZEkeXl5qXXr1i41+fn5SkxMtGpat26tqlWrutRs375dKSkpVk154O7Ytm7dWt7e3i6P7z558qT27Nmj+vXrS2Jsz+Tu2L7zzjtav369kpOTlZycbD2CeNasWXr55ZclOcdt+fLlOnnypHX8hIQENW3a1Lr8+Hxj26BBA4WFhbnU5OTkaNWqVVZNTEyMsrKytHbtWqtmyZIlys/PV3R0dCmM0oVxd2wl58zdLbfcYs06n/n9wNheOu78fFc2xhgNGDBAX375pZYsWaIGDRq4tLvzfXepvnsrmttvv10bN260vluTk5Otfzgr+D1je2Guv/76Qst5/PLLL9b/D5TUd2JJfD9XNEePHi303ylPT0/r/2Mr7dhe8se6oNw4cOCAqVWrlrn//vtNcnKy2b59uxk+fLipWrWqSU5ONsb89cjjuLg4k5ycbBYuXGiCg4OLfOTxM888Y7Zu3WomT55c5COPvb29zfTp082WLVvME088YQIDA12etNW3b19Tr149s2TJEvPTTz+ZmJgYExMTc+kGpIQNGjTIXHbZZWbRokVm27Ztpnfv3iYkJMRkZGQYYxjbkrJ79+5CT9HMysoyoaGh5uGHHzabNm0yM2fONH5+foUeZ1ylShXz+uuvm61bt5qxY8cW+Sj/wMBA87///c9s2LDB3HvvvUU+WrlVq1Zm1apV5ocffjCNGzeusI/y//33302jRo3M7bffbn7//Xezb98+ayvA2F5a7vx8VyZPPfWUCQgIMEuXLnX5+3n06FGr5nzfd5fyu7eiO/0pmsYwthdq9erVpkqVKubll182O3bsMJ999pnx8/Mz//d//2fVlMR3Ykl9P1ckPXv2NJdddpm1TMJ///tfU7t2bfPss89aNZVxbAl4ldyaNWtMXFycqVmzpqlRo4Zp27atWbBggUvNnj17TPv27Y2vr6+pXbu2GTZsmDl58qRLzXfffWdatmxpvLy8TMOGDc20adMKfda7775r6tWrZ7y8vEybNm3MypUrXdqPHTtm+vXrZ4KCgoyfn5+57777XP7HsqI5ceKEGTZsmAkJCTE1atQwsbGxZtOmTS41jO3FKyrgGWPM+vXrzQ033GC8vb3NZZddZiZMmFDovbNnzzZNmjQxXl5e5oorrjDz5893ac/PzzcvvPCCCQ0NNd7e3ub2228327dvd6n5888/Tffu3U316tWNv7+/efTRR82hQ4dK/DwvhWnTphlJRW6nY2wvrfP9fFcmZ/v7efr3ojvfd5fqu7eiOzPgMbYXbu7cuebKK6803t7eJioqynz44Ycu7SX1nVgS388VSU5Ojhk0aJCpV6+e8fHxMQ0bNjTPP/+8y3IGlXFsHcacttQ7AAAAAKDC4h48AAAAALAJAh4AAAAA2AQBDwAAAABsgoAHAAAAADZBwAMAAAAAmyDgAQAAAIBNEPAAAAAAwCYIeAAAAABgEwQ8AABsaOnSpXI4HMrKyirrrgAALiECHgAAFyA1NVWPPfaYwsPD5eXlpfr162vQoEH6888/L3lfbrnlFg0ePNhlX7t27bRv3z4FBARIkqZPn67AwMBL3jcAwKVFwAMAoJh27dqla6+9Vjt27NDnn3+uX3/9VVOnTlViYqJiYmKUkZFR1l2Ul5eXwsLC5HA4yrorAIBLiIAHAEAx9e/fX15eXlq8eLFuvvlm1atXT+3bt9e3336rP/74Q88//7wkyeFw6KuvvnJ5b2BgoKZPn269HjFihJo0aSI/Pz81bNhQL7zwgk6ePGm1jxs3Ti1bttSnn36qyMhIBQQEqFu3bjp06JAkqVevXlq2bJkmTZokh8Mhh8OhPXv2uFyiuXTpUj366KPKzs62asaNG6eXXnpJV155ZaHza9mypV544YWSHzgAQKkj4AEAUAwZGRlatGiR+vXrJ19fX5e2sLAw9ejRQ7NmzZIxxq3j1ahRQ9OnT9eWLVs0adIkffTRR3rrrbdcanbu3KmvvvpK8+bN07x587Rs2TJNmDBBkjRp0iTFxMSoT58+2rdvn/bt26eIiAiX97dr105vv/22/P39rZrhw4frscce09atW7VmzRqrdt26ddqwYYMeffTRCxkeAEAZq1LWHQAAoCLZsWOHjDFq1qxZke3NmjVTZmamDhw44NbxRo8ebf0+MjJSw4cP18yZM/Xss89a+/Pz8zV9+nTVqFFDkvTwww8rMTFRL7/8sgICAuTl5SU/Pz+FhYUV+RleXl4KCAiQw+Fwqalevbri4+M1bdo0XXfddZKkadOm6eabb1bDhg3d6j8AoHxhBg8AgAtwvhk6Ly8vt44za9YsXX/99QoLC1P16tU1evRopaSkuNRERkZa4U6S6tSpo/379xe/00Xo06ePPv/8cx0/flwnTpzQv//9bz322GMlcmwAwKVHwAMAoBgaNWokh8OhrVu3Ftm+detWBQcHKzAwUA6Ho1AQPP3+uqSkJPXo0UMdOnTQvHnztG7dOj3//PM6ceKEy3uqVq3q8trhcCg/P79Ezufuu++Wt7e3vvzyS82dO1cnT57UAw88UCLHBgBcelyiCQBAMdSqVUt33HGH3n//fQ0ZMsTlPry0tDR99tln6t+/vyQpODhY+/bts9p37Niho0ePWq9XrFih+vXrWw9lkaTffvut2H3y8vJSXl7eBdVUqVJFPXv21LRp0+Tl5aVu3boVurcQAFBxMIMHAEAxvffee8rNzVV8fLyWL1+u1NRULVy4UHfccYeaNGmiMWPGSJJuu+02vffee1q3bp1++ukn9e3b12U2rnHjxkpJSdHMmTO1c+dOvfPOO/ryyy+L3Z/IyEitWrVKe/bs0cGDB4uc3YuMjNThw4eVmJiogwcPugTNxx9/XEuWLNHChQu5PBMAKjgCHgAAxdS4cWOtWbNGDRs21N/+9jfVr19f7du3V5MmTfTjjz+qevXqkqQ33nhDERERuvHGG/Xggw9q+PDh8vPzs45zzz33aMiQIRowYIBatmypFStWXNDyBMOHD5enp6eaN2+u4ODgQvfwSc4nafbt21ddu3ZVcHCwJk6c6HI+7dq1U1RUlKKjoy9gRAAA5YXDuPscZwAAcFZjx47Vm2++qYSEBLVt27asu1Msxhg1btxY/fr109ChQ8u6OwCAi8A9eAAAlIAXX3xRkZGRWrlypdq0aSMPj4pxkcyBAwc0c+ZMpaWlsfYdANgAM3gAAFRiDodDtWvX1qRJk/Tggw+WdXcAABeJGTwAACox/p0XAOylYlw/AgAAAAA4LwIeAAAAANgEAQ8AAAAAbIKABwAAAAA2QcADAAAAAJsg4AEAAACATRDwAAAAAMAmCHgAAAAAYBP/D5pLglXECaCDAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BQ0kaRkEBRsl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AT_n0SyFBRv7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line plot of Total Price by Country"
      ],
      "metadata": {
        "id": "iYPos_j9BRyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size\n",
        "data_cleaned.groupby('Country')['TotalPrice'].sum().sort_values(ascending=False).plot(kind='line', color='purple')  # Plot the line plot with purple color\n",
        "plt.title('Total Price by Country')  # Set the title\n",
        "plt.xlabel('Country')  # Set the x-axis label\n",
        "plt.ylabel('Total Price')  # Set the y-axis label\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "plt.savefig('/content/total_price_by_country.pdf')  # Save the figure as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "a73XZRcPBR1p",
        "outputId": "24b2a226-b151-4e3a-fece-71afacf56eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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yyy3Of99q165tDBw40OXftOxkbgu2Y8cO47777jPCw8ON4sWLG88++6xx8eLFbB8zYcIEQ5Lx5ptvXvO5szN79mzjzjvvNKKiooyAgACjXLlyxgMPPGCsXLnS5br9+/cb9913n1GsWDEjJCTEaNGihfH9999neb79+/cbsbGxRnBwsFGmTBnjxRdfNJYuXZrttmD16tXL8vjsfubz58836tatawQEBLhsEZbTc1zp66+/NiQZTzzxRO5+IAAAn2czjDysFmIRNptNc+fOdS7OMmvWLPXu3Vv/+c9/sswdK1q0qMqWLatXXnlFb775ptLT053nLl68qCJFimjJkiVq3769J78FAAC80nvvvaehQ4fq4MGDqlSpktnleJX58+erW7duWr16tW699VazywEAeIFCMaS8SZMmstvtOnHiRI7/ALZp00YZGRnav3+/czjinj17JLkuwAMAQGFlGIamTp2q2267jbCdjU8//VRVq1Z12UMcAFC4+UzgPn/+vPbt2+e8f+DAAW3ZskVRUVGqWbOmevfurb59+2rixIlq0qSJ/vrrL8XHx6thw4bq3LmzYmNj1bRpUz366KOaPHmyHA6HBg4cqPbt29/Qyq8AAFhdSkqKFixYoBUrVmj79u2aP3++2SV5lcwFWRcuXKj33nvvhlY7BwD4Fp8ZUr5y5Uq1a9cuy/F+/fopLi5O6enpeuONNzRjxgwdOXJEJUuWVKtWrTRmzBg1aNBAkvTnn39q0KBBWrJkicLCwtSxY0dNnDgxx+1YAAAoDA4ePKiYmBgVK1ZMzzzzjMaOHWt2SV7FZrOpaNGieuCBB/TRRx8pIMBn+hkAgBvkM4EbAAAAAABvUmj24QYAAAAAwJMsPebJ4XDozz//VHh4OPOlAAAAAABuZxiGzp07p/Lly8vP79o9bEsH7j///FPR0dFmlwEAAAAAKGQOHz6sihUrXvMaSwfu8PBwSZe/0YiICJOrAQAAAAD4uuTkZEVHRzvz6LVYOnBnDiOPiIggcAMAAAAAPCY305pNXTStSpUqstlsWb4GDhxoZlkAAAAAANwwUzvcGzZskN1ud97//fff1b59e/Xs2dPEqgAAAAAAuHGmBu5SpUq53B8/fryqVaum2267zaSKAAAAAAAoGF4zh/vSpUuaOXOmhg0bluNY+LS0NKWlpTnvJycne6o8AAAAAADyxNQ53FeaN2+ekpKS1L9//xyvGTdunCIjI51fbAkGAAAAAPBWNsMwDLOLkKS77rpLQUFB+u6773K8JrsOd3R0tM6ePcsq5QAAAAAAt0tOTlZkZGSucqhXDCk/dOiQli1bpjlz5lzzuuDgYAUHB3uoKgAAAAAA8s8rhpRPmzZNpUuXVufOnc0uBQAAAACAAmF64HY4HJo2bZr69eungACvaLgDAAAAAHDDTA/cy5YtU2Jioh599FGzSwEAAAAAoMCY3lK+88475SXrtgEAAAAAUGBM73ADAAAAAOCLCNwAAAAAALgBgRsAAAAAADcgcAMAAAAA4AYEbgAAAAAA3IDADQAAAACAGxC4PWDde+v0xd1faOecnWaXAgAAAADwEAK3Bxzfdlx7F+7VXzv/MrsUAAAAAICHELg9ICQyRJKUdjbN5EoAAAAAAJ5C4PaA4MhgSVLq2VSTKwEAAAAAeAqB2wPocAMAAABA4UPg9oDMDndaMoEbAAAAAAoLArcH0OEGAAAAgMKHwO0BzOEGAAAAgMKHwO0BdLgBAAAAoPAhcHsAHW4AAAAAKHwI3B4QHHE5cF86d0kOu8PkagAAAAAAnkDg9oDMIeXS5dANAAAAAPB9BG4PCAgJkH+QvySGlQMAAABAYUHg9hDnXtwsnAYAAAAAhQKB20Myh5XT4QYAAACAwoHA7SF0uAEAAACgcCFwewgdbgAAAAAoXAjcHkKHGwAAAAAKFwK3h9DhBgAAAIDChcDtIXS4AQAAAKBwIXB7SGbgpsMNAAAAAIUDgdtDMoeU0+EGAAAAgMKBwO0hziHlyQRuAAAAACgMCNweQocbAAAAAAoXAreHMIcbAAAAAAoXAreH0OEGAAAAgMKFwO0hdLgBAAAAoHAhcHtIcMT/Fk0zHIbJ1QAAAAAA3I3A7SGZQ8plSJfOXzK3GAAAAACA2xG4PSQgNEB+AZd/3AwrBwAAAADfR+D2EJvN9r+9uFk4DQAAAAB8HoHbgzKHldPhBgAAAADfR+D2IDrcAAAAAFB4ELg9iA43AAAAABQeBG4PosMNAAAAAIUHgduD6HADAAAAQOFB4PYgOtwAAAAAUHgQuD0oM3DT4QYAAAAA30fg9qDMIeV0uAEAAADA9xG4PYgh5QAAAABQeBC4PcjZ4U4mcAMAAACAryNwexBzuAEAAACg8CBwexBzuAEAAACg8CBwexAdbgAAAAAoPAjcHnTlHG7DMEyuBgAAAADgTqYH7iNHjujhhx9WiRIlFBoaqgYNGui3334zuyy3CI643OE27IbSU9JNrgYAAAAA4E4BZr74mTNn1KZNG7Vr104//PCDSpUqpb1796p48eJmluU2gWGBsvnbZNgNpZ5NVVDRILNLAgAAAAC4iamB+6233lJ0dLSmTZvmPBYTE2NiRe5ls9kUHBGs1DOplxdOq2B2RQAAAAAAdzF1SPmCBQvUvHlz9ezZU6VLl1aTJk306aef5nh9WlqakpOTXb6sJnMeNwunAQAAAIBvMzVwJyQk6MMPP1SNGjW0ePFiPf300xo8eLCmT5+e7fXjxo1TZGSk8ys6OtrDFd+4zJXK2RoMAAAAAHybqYHb4XCoadOmevPNN9WkSRM98cQTevzxx/XRRx9le/2oUaN09uxZ59fhw4c9XPGNo8MNAAAAAIWDqYG7XLlyqlu3rsuxOnXqKDExMdvrg4ODFRER4fJlNXS4AQAAAKBwMDVwt2nTRrt373Y5tmfPHlWuXNmkityPDjcAAAAAFA6mBu6hQ4dq3bp1evPNN7Vv3z598cUX+uSTTzRw4EAzy3IrOtwAAAAAUDiYGrhvuukmzZ07V19++aXq16+v119/XZMnT1bv3r3NLMutMgM3HW4AAAAA8G2m7sMtSXfffbfuvvtus8vwmMwh5XS4AQAAAMC3mdrhLowYUg4AAAAAhQOB28OcHe5kAjcAAAAA+DICt4cxhxsAAAAACgcCt4cxhxsAAAAACgcCt4fR4QYAAACAwoHA7WFXdrgNwzC5GgAAAACAuxC4PSyzw+3IcCjjYobJ1QAAAAAA3IXA7WFBYUGS7fJthpUDAAAAgO8icHuYzc+m4Aj24gYAAAAAX0fgNkHmPG463AAAAADguwjcJsicx02HGwAAAAB8F4HbBHS4AQAAAMD3EbhNQIcbAAAAAHwfgdsEdLgBAAAAwPcRuE1AhxsAAAAAfB+B2wSZgZsONwAAAAD4LgK3CTKHlNPhBgAAAADfReA2AUPKAQAAAMD3EbhN4OxwJxO4AQAAAMBXEbhNwBxuAAAAAPB9BG4TMIcbAAAAAHwfgdsEdLgBAAAAwPcRuE1AhxsAAAAAfB+B2wSZHW77JbsyUjNMrgYAAAAA4A4EbhMEhwc7bzOsHAAAAAB8E4HbBDY/m4LCgyQxrBwAAAAAfBWB2ySZ87jpcAMAAACAbyJwmyRzHjcdbgAAAADwTQRuk9DhBgAAAADfRuA2CR1uAAAAAPBtBG6T0OEGAAAAAN9G4DYJHW4AAAAA8G0EbpNkBm463AAAAADgmwjcJskcUk6HGwAAAAB8E4HbJAwpBwAAAADfRuA2ibPDnUzgBgAAAABfROA2CXO4AQAAAMC3EbhNwhxuAAAAAPBtBG6T0OEGAAAAAN9G4DYJHW4AAAAA8G0EbpNkdrgzUjNkv2Q3uRoAAAAAQEEjcJskOCLYeZth5QAAAADgewjcJvHz91NgWKAkhpUDAAAAgC8icJsocx43HW4AAAAA8D0EbhNlzuOmww0AAAAAvofAbSI63AAAAADguwjcJqLDDQAAAAC+i8BtIjrcAAAAAOC7CNwmosMNAAAAAL6LwG2izMBNhxsAAAAAfA+B20SZQ8rpcAMAAACA7yFwm4gh5QAAAADgu0wN3K+++qpsNpvLV+3atc0syaNYNA0AAAAAfFeA2QXUq1dPy5Ytc94PCDC9JI9xdriT6XADAAAAgK8xPd0GBASobNmyubo2LS1NaWn/C6fJycnuKssjmMMNAAAAAL7L9Dnce/fuVfny5VW1alX17t1biYmJOV47btw4RUZGOr+io6M9WGnBY5VyAAAAAPBdpgbuli1bKi4uTj/++KM+/PBDHThwQLfeeqvOnTuX7fWjRo3S2bNnnV+HDx/2cMUFiw43AAAAAPguU4eUd+zY0Xm7YcOGatmypSpXrqyvv/5ajz32WJbrg4ODFRwc7MkS3Sqzw51+IV32dLv8A/1NrggAAAAAUFBMH1J+pWLFiqlmzZrat2+f2aV4RHDE/z48YOE0AAAAAPAtXhW4z58/r/3796tcuXJml+IR/oH+CiwSKIlh5QAAAADga0wN3CNGjNCqVat08OBB/fLLL7r33nvl7++vXr16mVmWR2V2uVk4DQAAAAB8i6lzuP/44w/16tVLp06dUqlSpXTLLbdo3bp1KlWqlJlleVRwZLDOHztPhxsAAAAAfIypgfurr74y8+W9QuZK5XS4AQAAAMC3eNUc7sIoc6VyOtwAAAAA4FsI3Cajww0AAAAAvonAbTI63AAAAADgmwjcJssM3HS4AQAAAMC3ELhNljmknA43AAAAAPgWArfJGFIOAAAAAL6JwG0yFk0DAAAAAN9E4DaZs8OdTIcbAAAAAHwJgdtkzOEGAAAAAN9E4DYZq5QDAAAAgG8icJuMDjcAAAAA+CYCt8kyO9yXzl+Sw+4wuRoAAAAAQEEhcJsss8MtsXAaAAAAAPgSArfJ/IP8FRASIIlh5QAAAADgSwjcXoCF0wAAAADA9xC4vUBwxH/34qbDDQAAAAA+g8DtBTLncdPhBgAAAADfQeD2AplDyulwAwAAAIDvIHB7ATrcAAAAAOB7CNxegA43AAAAAPgeArcXYJVyAAAAAPA9BG4vkDmknA43AAAAAPgOArcXYEg5AAAAAPgeArcXYNE0AAAAAPA9BG4v4OxwJ9PhBgAAAABfQeD2AszhBgAAAADfQ+D2AqxSDgAAAAC+h8DtBehwAwAAAIDvIXB7Aecc7nNpMhyGydUAAAAAAAoCgdsLZHa4ZVwO3QAAAAAA6yNwe4GAkAD5B/lLYlg5AAAAAPgKAreXYOE0AAAAAPAtBG4vERzx33ncdLgBAAAAwCcQuL1E5jxuOtwAAAAA4BsI3F7CuVI5HW4AAAAA8AkEbi9BhxsAAAAAfAuB20vQ4QYAAAAA30Lg9hKsUg4AAAAAvoXA7SUyh5TT4QYAAAAA30Dg9hIMKQcAAAAA30Lg9hIsmgYAAAAAvoXA7SWcHe5kOtwAAAAA4AsI3F6COdwAAAAA4FsI3F6CVcoBAAAAwLcQuL0EHW4AAAAA8C0Ebi9x5RxuwzBMrgYAAAAAcKMI3F4is8NtOAxdOn/J5GoAAAAAADeKwO0lAkID5Bdw+e1gWDkAAAAAWB+B20vYbDYWTgMAAAAAH0Lg9iIsnAYAAAAAvoPA7UWCI+hwAwAAAICv8JrAPX78eNlsNj333HNml2Ia50rldLgBAAAAwPK8InBv2LBBH3/8sRo2bGh2KabKHFJOhxsAAAAArM/0wH3+/Hn17t1bn376qYoXL37Na9PS0pScnOzy5UvocAMAAACA7zA9cA8cOFCdO3dWbGzsda8dN26cIiMjnV/R0dEeqNBzWKUcAAAAAHyHqYH7q6++0qZNmzRu3LhcXT9q1CidPXvW+XX48GE3V+hZrFIOAAAAAL4jwKwXPnz4sIYMGaKlS5cqJCQkV48JDg5WcHCwmyszD0PKAQAAAMB3mBa4N27cqBMnTqhp06bOY3a7XatXr9b777+vtLQ0+fv7m1WeKVg0DQAAAAB8h2mB+29/+5u2b9/ucuyRRx5R7dq19cILLxS6sC3R4QYAAAAAX5LvwH3p0iUdOHBA1apVU0BA3p8mPDxc9evXdzkWFhamEiVKZDleWDjncCcTuAEAAADA6vK8aNqFCxf02GOPqUiRIqpXr54SExMlSYMGDdL48eMLvMDChFXKAQAAAMB35Dlwjxo1Slu3btXKlStdFjuLjY3VrFmzbqiYlStXavLkyTf0HFbGKuUAAAAA4DvyPBZ83rx5mjVrllq1aiWbzeY8Xq9ePe3fv79AiytsruxwG4bh8vMFAAAAAFhLnjvcf/31l0qXLp3leEpKCgHxBmV2uA27ofQL6SZXAwAAAAC4EXkO3M2bN9fChQud9zND9meffabWrVsXXGWFUGBYoGz+l3+eDCsHAAAAAGvL85DyN998Ux07dtSOHTuUkZGh9957Tzt27NAvv/yiVatWuaPGQsNmsyk4IlipZ1KVejZV4eXDzS4JAAAAAJBPee5w33LLLdqyZYsyMjLUoEEDLVmyRKVLl9batWvVrFkzd9RYqLBwGgAAAAD4hnztw12tWjV9+umnBV0LxNZgAAAAAOAr8tzhXrRokRYvXpzl+OLFi/XDDz8USFGFWXDE5cBNhxsAAAAArC3PgXvkyJGy2+1ZjhuGoZEjRxZIUYVZ5pByOtwAAAAAYG15Dtx79+5V3bp1sxyvXbu29u3bVyBFFWaZQ8rpcAMAAACAteU5cEdGRiohISHL8X379iksLKxAiirMmMMNAAAAAL4hz4H7nnvu0XPPPaf9+/c7j+3bt0/Dhw9X165dC7S4wohVygEAAADAN+Q5cE+YMEFhYWGqXbu2YmJiFBMTozp16qhEiRJ655133FFjocKQcgAAAADwDXneFiwyMlK//PKLli5dqq1btyo0NFQNGzZU27Zt3VFfocOiaQAAAADgG/K1D7fNZtOdd96pO++8s6DrKfTocAMAAACAb8hV4J4yZYqeeOIJhYSEaMqUKde8dvDgwQVSWGHlnMOdTOAGAAAAACvLVeB+99131bt3b4WEhOjdd9/N8TqbzUbgvkGsUg4AAAAAviFXgfvAgQPZ3kbBY5VyAAAAAPANeVqlPD09XdWqVdPOnTvdVU+hd2WH2zAMk6sBAAAAAORXngJ3YGCgUlMZ6uxOmR1uR7pDGakZJlcDAAAAAMivPO/DPXDgQL311lvKyCAMukNQ0SDJdvk2w8oBAAAAwLryvC3Yhg0bFB8fryVLlqhBgwYKCwtzOT9nzpwCK64wsvnZFBwRrLSzaUo9m6qiZYuaXRIAAAAAIB/yHLiLFSumHj16uKMW/FdIZIjSzqbR4QYAAAAAC8tz4J42bZo76sAV2BoMAAAAAKwv13O4HQ6H3nrrLbVp00Y33XSTRo4cqYsXL7qztkKLrcEAAAAAwPpyHbjHjh2rF198UUWLFlWFChX03nvvaeDAge6srdAKjqDDDQAAAABWl+vAPWPGDP3f//2fFi9erHnz5um7777Tv//9bzkcDnfWVyhlDimnww0AAAAA1pXrwJ2YmKhOnTo578fGxspms+nPP/90S2GFGXO4AQAAAMD6ch24MzIyFBIS4nIsMDBQ6enpBV5UYcccbgAAAACwvlyvUm4Yhvr376/g4GDnsdTUVD311FMue3GzD/eNY0g5AAAAAFhfrgN3v379shx7+OGHC7QYXJbZ4WZIOQAAAABYV64DN/tvew4dbgAAAACwvlzP4YbnOOdwJxO4AQAAAMCqCNxeiFXKAQAAAMD6CNxeiFXKAQAAAMD6CNxeiA43AAAAAFgfgdsLZXa47Wl2ZaRlmFwNAAAAACA/crVK+YIFC3L9hF27ds13MbgsKDzIeTvtbJoCSud6MXkAAAAAgJfIVZLr1q1brp7MZrPJbrffSD2Q5Ofvp6DwIF06d0mpZ1MVVjrM7JIAAAAAAHmUq8DtcDjcXQeuEhIZokvnLrFwGgAAAABYFHO4vRQLpwEAAACAteVrcnBKSopWrVqlxMREXbp0yeXc4MGDC6Swwo6twQAAAADA2vIcuDdv3qxOnTrpwoULSklJUVRUlE6ePKkiRYqodOnSBO4CEhxBhxsAAAAArCzPQ8qHDh2qLl266MyZMwoNDdW6det06NAhNWvWTO+88447aiyUMoeU0+EGAAAAAGvKc+DesmWLhg8fLj8/P/n7+ystLU3R0dGaMGGCXnzxRXfUWCgxhxsAAAAArC3PgTswMFB+fpcfVrp0aSUmJkqSIiMjdfjw4YKtrhBjDjcAAAAAWFue53A3adJEGzZsUI0aNXTbbbdp9OjROnnypD7//HPVr1/fHTUWSnS4AQAAAMDa8tzhfvPNN1WuXDlJ0tixY1W8eHE9/fTT+uuvv/Txxx8XeIGFFR1uAAAAALC2PHe4mzdv7rxdunRp/fjjjwVaEC5j0TQAAAAAsLY8d7jvuOMOJSUlZTmenJysO+64oyBqgq7ocCcTuAEAAADAivIcuFeuXKlLly5lOZ6amqqffvqpQIoCc7gBAAAAwOpyPaR827Ztzts7duzQsWPHnPftdrt+/PFHVahQoWCrK8SYww0AAAAA1pbrwN24cWPZbDbZbLZsh46Hhobqn//8Z55e/MMPP9SHH36ogwcPSpLq1aun0aNHq2PHjnl6Hl9EhxsAAAAArC3XgfvAgQMyDENVq1bV+vXrVapUKee5oKAglS5dWv7+/nl68YoVK2r8+PGqUaOGDMPQ9OnTdc8992jz5s2qV69enp7L12R2uDMuZsiebpd/YN5+tgAAAAAAc+U6cFeuXFmS5HA4CuzFu3Tp4nJ/7Nix+vDDD7Vu3bpsA3daWprS0v43xDo5ObnAavE2wRHBzttpZ9NUpGQRE6sBAAAAAORVnhdNk6T9+/dr0KBBio2NVWxsrAYPHqz9+/ffUCF2u11fffWVUlJS1Lp162yvGTdunCIjI51f0dHRN/Sa3swvwE+BYYGSGFYOAAAAAFaU58C9ePFi1a1bV+vXr1fDhg3VsGFD/frrr6pXr56WLl2a5wK2b9+uokWLKjg4WE899ZTmzp2runXrZnvtqFGjdPbsWefX4cOH8/x6VsLCaQAAAABgXbkeUp5p5MiRGjp0qMaPH5/l+AsvvKD27dvn6flq1aqlLVu26OzZs5o9e7b69eunVatWZRu6g4ODFRwcnM2z+KbgyGCd+/McHW4AAAAAsKA8d7h37typxx57LMvxRx99VDt27MhzAUFBQapevbqaNWumcePGqVGjRnrvvffy/Dy+iA43AAAAAFhXngN3qVKltGXLlizHt2zZotKlS99wQQ6Hw2VhtMKMrcEAAAAAwLpyPaT8tdde04gRI/T444/riSeeUEJCgm6++WZJ0po1a/TWW29p2LBheXrxUaNGqWPHjqpUqZLOnTunL774QitXrtTixYvz9l34qMyVyulwAwAAAID15DpwjxkzRk899ZRefvllhYeHa+LEiRo1apQkqXz58nr11Vc1ePDgPL34iRMn1LdvXx09elSRkZFq2LChFi9enOd54L6KDjcAAAAAWFeuA7dhGJIkm82moUOHaujQoTp37pwkKTw8PF8vPnXq1Hw9rrBgDjcAAAAAWFeeVim32Wwu9/MbtJE7dLgBAAAAwLryFLhr1qyZJXRf7fTp0zdUEP6HDjcAAAAAWFeeAveYMWMUGRnprlpwlcwON4EbAAAAAKwnT4H7wQcfLJCtv5A7mR1uhpQDAAAAgPXkeh/u6w0lR8FzdriT6XADAAAAgNXkOnBnrlIOz2EONwAAAABYV66HlDscDnfWgWywSjkAAAAAWFeuO9zwvMwOd3pKuhwZfOABAAAAAFZC4PZimR1uiXncAAAAAGA1BG4v5h/or4DQy6P+GVYOAAAAANZC4PZyLJwGAAAAANZE4PZyLJwGAAAAANZE4PZydLgBAAAAwJoI3F6ODjcAAAAAWBOB28vR4QYAAAAAayJwe7mgiCBJdLgBAAAAwGoI3F6ODjcAAAAAWBOB28sxhxsAAAAArInA7eXocAMAAACANRG4vVxmh5vADQAAAADWQuD2cpkdboaUAwAAAIC1ELi9nLPDnUyHGwAAAACshMDt5ZjDDQAAAADWROD2cqxSDgAAAADWROD2cpkd7kvnLslhd5hcDQAAAAAgtwjcXi6zwy1dDt0AAAAAAGsgcHu5gOAA+Qf7S2JYOQAAAABYCYHbAlg4DQAAAACsh8BtASycBgAAAADWQ+C2ADrcAAAAAGA9BG4LoMMNAAAAANZD4LYAOtwAAAAAYD0Ebgugww0AAAAA1kPgtoDgiMuBmw43AAAAAFgHgdsC6HADAAAAgPUQuC2AOdwAAAAAYD0EbgvI7HATuAEAAADAOgjcFpDZ4WZIOQAAAABYB4HbApwd7mQ63AAAAABgFQRuC2AONwAAAABYD4HbAlilHAAAAACsh8BtAc4Od3KaDIdhcjUAAAAAgNwgcFtAZodbhnTp/CVziwEAAAAA5AqB2wICQgLkF3j5rWJYOQAAAABYA4HbAmw2GwunAQAAAIDFELgtgoXTAAAAAMBaCNwWQYcbAAAAAKyFwG0RdLgBAAAAwFoI3BZBhxsAAAAArIXAbRF0uAEAAADAWgjcFhEccTlw0+EGAAAAAGswNXCPGzdON910k8LDw1W6dGl169ZNu3fvNrMkr0WHGwAAAACsxdTAvWrVKg0cOFDr1q3T0qVLlZ6erjvvvFMpKSlmluWVmMMNAAAAANYSYOaL//jjjy734+LiVLp0aW3cuFFt27bNcn1aWprS0v4XOJOTk91eo7fI7HATuAEAAADAGrxqDvfZs2clSVFRUdmeHzdunCIjI51f0dHRnizPVJkdboaUAwAAAIA1eE3gdjgceu6559SmTRvVr18/22tGjRqls2fPOr8OHz7s4SrN4+xwJ9PhBgAAAAArMHVI+ZUGDhyo33//XT///HOO1wQHBys4ONiDVXkP5nADAAAAgLV4ReB+9tln9f3332v16tWqWLGi2eV4JVYpBwAAAABrMXVIuWEYevbZZzV37lwtX75cMTExZpbj1Zwd7uQ0GYZhcjUAAAAAgOsxtcM9cOBAffHFF5o/f77Cw8N17NgxSVJkZKRCQ0PNLM3rZHa4Dbuh9JR0BRUNMrkiAAAAAMC1mNrh/vDDD3X27FndfvvtKleunPNr1qxZZpbllQKLBMrmb5PEsHIAAAAAsAJTO9wMjc49m82mkMgQXTx98fLCaRXMrggAAAAAcC1esy0Yro+F0wAAAADAOgjcFsLWYAAAAABgHQRuC6HDDQAAAADWQeC2EDrcAAAAAGAdBG4LocMNAAAAANZB4LaQzMBNhxsAAAAAvB+B20KCI+hwAwAAAIBVELgthDncAAAAAGAdBG4LYUg5AAAAAFgHgdtCMjvcDCkHAAAAAO9H4LYQOtwAAAAAYB0EbgtxzuFOJnADAAAAgLcjcFsI+3ADAAAAgHUQuC3kylXKDcMwuRoAAAAAwLUQuC0ks8PtyHAo42KGydUAAAAAAK6FwG0hQUWDZPOzSWJYOQAAAAB4OwK3hdhsNgVHsFI5AAAAAFgBgdtiWDgNAAAAAKyBwG0xVy6cBgAAAADwXgRui6HDDQAAAADWQOC2GDrcAAAAAGANBG6LocMNAAAAANZA4LaYzMBNhxsAAAAAvBuB22Iyh5TT4QYAAAAA70bgthj24QYAAAAAayBwWwxDygEAAADAGgjcFsOQcgAAAACwBgK3xdDhBgAAAABrIHBbjHMf7mQCNwAAAAB4MwK3xbAPNwAAAABYA4HbYpwdboaUAwAAAIBXI3BbTGaH237JrozUDJOrAQAAAADkhMBtMcHhwZLt8m2GlQMAAACA9yJwW4zNz3Y5dIth5QAAAADgzQjcFsTCaQAAAADg/QjcFsTCaQAAAADg/QjcFkSHGwAAAAC8H4HbguhwAwAAAID3I3BbEB1uAAAAAPB+BG4LygzcdLgBAAAAwHsRuC0oc0g5HW4AAAAA8F4Ebguiww0AAAAA3o/AbUHBEQRuAAAAAPB2BG4LYkg5AAAAAHg/ArcFMaQcAAAAALwfgduCnPtwJxO4AQAAAMBbEbgtiH24AQAAAMD7EbgtyNnhZkg5AAAAAHgtArcFZXa4M1IzZL9kN7kaAAAAAEB2CNwWlLktmMSwcgAAAADwVgRuC/Lz91NQ0SBJDCsHAAAAAG9lauBevXq1unTpovLly8tms2nevHlmlmMpLJwGAAAAAN7N1MCdkpKiRo0a6YMPPjCzDEti4TQAAAAA8G4BZr54x44d1bFjx1xfn5aWprS0/wXM5ORkd5RlCXS4AQAAAMC7WWoO97hx4xQZGen8io6ONrsk09DhBgAAAADvZqnAPWrUKJ09e9b5dfjwYbNLMg0dbgAAAADwbqYOKc+r4OBgBQcHX//CQiAzcNPhBgAAAADvZKkON/4nc0g5HW4AAAAA8E4Ebouiww0AAAAA3s3UIeXnz5/Xvn37nPcPHDigLVu2KCoqSpUqVTKxMu8XHEHgBgAAAABvZmrg/u2339SuXTvn/WHDhkmS+vXrp7i4OJOqsgaGlAMAAACAdzM1cN9+++0yDMPMEiyLIeUAAAAA4N2Yw21Rzn24kwncAAAAAOCNCNwWxT7cAAAAAODdCNwW5exwM6QcAAAAALwSgduiMjvc6RfSZU+3m1wNAAAAAOBqBG6LytwWTGIeNwAAAAB4IwK3RfkH+iuwSKAkhpUDAAAAgDcicFsYC6cBAAAAgPcicFsYC6cBAAAAgPcicFsYHW4AAAAA8F4Ebgujww0AAAAA3ovAbWF0uAEAAADAexG4LSwzcNPhBgAAAADvQ+C2sMwh5XS4AQAAAMD7ELgtjA43AAAAAHgvAreFsWgaAAAAAHgvAreFBUewaBoAAAAAeCsCt4UxpBwAAAAAvBeB28JYNA0AAAAAvBeB28KcHe5kOtwAAAAA4G0I3BbGomkAAAAA4L0I3BaW2eG+dP6SHHaHydUAAAAAAK5E4LawzA63xLByAAAAAPA2BG4L8w/yV0BIgCSGlQMAAACAtyFwW1zmsHJWKgcAAAAA70LgtjgWTgMAAAAA70Tgtjg63AAAAADgnQjcFkeHGwAAAAC8E4Hb4uhwAwAAAIB3InBbXGbgpsMNAAAAAN6FwG1xmUPK6XADAAAAgHchcFscHW4AAAAA8E4Ebotj0TQAAAAA8E4Ebotj0TQAAAAA8E4EbosLjmBIOQAAAAB4IwK3xbFoGgAAAAB4JwK3xWUOKb/w1wX98esfSjqYpPSL6SZXBQAAAAAIMLsA3JjQqFBJUsqJFE1tNdV5PDgiWGFlwlS0TFEVLVtUYWXCstwvWubyn4GhgWaVDwAAAAA+i8BtccWrFlfr4a11aPUhpRxP0fnj52VPsystOU1pyWk6vff0dZ/jynAeViYsSyAvWqaoipQsotASoQqJDJHNz+aB7wwAAAAArM1mGIZhdhH5lZycrMjISJ09e1YRERFml+MVDMNQWnKazh877wzgKcdTdP7YeeftK+/b0+x5en6bv02hUaEqUqKIM4SHlghVkZJFVKREkWxvhxYPlV8AsxcAAAAAWF9ecigdbh9js9kUEhmikMgQlaxV8prXZobzqwP5+ePnnYE98/7FUxd16fwlGXZDF/66oAt/XchTXSHFQpwB/eqwHhoVqtDioQopHuL6Z7EQgjoAAAAAyyJwF2JXhvMSNUtc9/qMtAxdPHVRF05duPznyQsut7M7lpp0efX01KTUy7f35a3GoPAgZwgPKfa/QJ4lnF/xZ+Z1/kH++fmxAAAAAECBIHAj1wKCAxRePlzh5cNz/RhHhkMXz1wRyE9dyBLOU0+n6uKZi5dD+ZnLty+duyRJunTuki6du6SziWfzXG9gkUAVKVlEJWqWUInaJVSydkmVqlNKJWuXVNFyRWWzMRcdAAAAgPswhxteyZHhUGrSf4P4mav+vCKYp55JzXI+7WzadZ8/OCJYJWuXvPxVp6TzdvFqxeUfSGccAAAAQPaYww3L8wvwu7z4WskieX6sw+5QWnKaUs+k6vyx8zq5+6RO7jqpkzsv/3lm/xmlJafpyPojOrL+SJbXjaoelSWIl6xdUsERwQX17QEAAAAoBOhwo9DJSMvQ6X2nL4fwK4L4yV0nlZ6SnuPjwsuHu4bwOiVVpmEZhZUK82D1AAAAAMyUlxxK4Ab+yzAMJf+RnG0QP3/0fI6PKxZTTBVbVVSFlhVUsWVFlW1cVgEhDB4BAAAAfBGBGyhgqUmpWYam/7XjL53eezrLtX6BfirbuKwzgFdoWUFR1aNYpA0AAADwAQRuwENSz6bqzw1/6o9f/9CRX4/oyK9HlHIiJct1oVGhqtCigiq0/O9XiwoqUiLv89MBAAAAmIvADZjEMAydPXTWGcD/WPeHjm46KnuaPcu1UdWjnAG8YsuKKtOojAKCGYoOAAAAeDMCN+BF7JfsOr7tuEsX/NSeU1mu8w/yV9kmZV0CuH/Q5S3KbDab9N8R6bm+LUm2nG/7B/krsEig/IP8Ge4OAAAA5BKBG/ByF89cvLwt2X8D+B+//qGLpy6aU4xNCgwNVGCRQAWEBiiwSKACQ7O/fb3zmbcDQgLk5+8nm7/txv70s8nmb7v8Jx8KAAAAwAtYLnB/8MEHevvtt3Xs2DE1atRI//znP9WiRYvrPo7ADV9hGIbOJJxxhu8jvx7Rqd2nZDgM5/nLN/J+2/n4bG5bSWb4zgziV3f2Xf6Ush7L67lrFnONU9d5rM3fJv9Af/kF+OX8FZjPcwF+/3vuQNfbV59z3r/Wuauex+Znc/7sMz8Eud59AAAAX2OpwD1r1iz17dtXH330kVq2bKnJkyfrm2++0e7du1W6dOlrPpbADeSfPd2u9AvpyriYofQL6Uq/6Ho7N+eyvf6/1xoOQw67Q4b9On86DBl2C34CgNz57wcY1w3mOX0IkpsPSq74U7r2Nc7Xvep2drXk+lo/1xEZubmd2+uct6/4cr5+Tl+2a5y7+vHX+sAph59rXm9f/bPK68/7mu/DtT7Uud7nZtf7cCyPHy7l5gMpAIBvsFTgbtmypW666Sa9//77kiSHw6Ho6GgNGjRII0eOdLk2LS1NaWlpzvvJycmKjo4mcAM+4FoBPcs5x1XdfCP7Ln5+z12zzmv9LzMXjzXshhwZjmy/7On2HM850nM4fuVj07Ne73zO9KzXZXvuv/evPuf8mQPIv5w+LLjy5tXBPKdzV1+WzblrHsvuea9x7OrnKogPY651Oyc3MgIpu+/ruseuOJ7bY9d97RwvuIHHerNr/V11w/nrlpOXn6U7nzuH58/N95ft61zv55SL187v43P9/61cnguOCFb3md2v/fomy0vgNnVJ5EuXLmnjxo0aNWqU85ifn59iY2O1du3aLNePGzdOY8aM8WSJADzE5meTv5+/FGh2JciO4bj8wYdhGM7bMuRyLNf3c7gmywchUrYfjtzQuatf76rXdtaUl2sdrh8K5XT7yhEdub5tv+pn7sj+vbj6y+Vnnd3X1e/jtaanZPdhVV6uLaifczaPy/L3NKcPxHI6fI3rc/o7ekOu+JkZVpzbAwAeEFoi1OwSCpSpgfvkyZOy2+0qU6aMy/EyZcpo165dWa4fNWqUhg0b5ryf2eEGALiXc7gsUMjl9IFSXj+Ecn1S1+fP6Vyezmf3nMZV93N77Ornut4HLzd6OyfXO53Lx+f2+3c5nttj13ntHE/fwOipG+XRwa75/fuc2/N5fP1rXuqhv4+5flxerndTbdk+/lrvST7P+do2uZb6boKDgxUcHGx2GQAAoJCy2S7PrwcAIDf8zHzxkiVLyt/fX8ePH3c5fvz4cZUtW9akqgAAAAAAuHGmBu6goCA1a9ZM8fHxzmMOh0Px8fFq3bq1iZUBAAAAAHBjTB9SPmzYMPXr10/NmzdXixYtNHnyZKWkpOiRRx4xuzQAAAAAAPLN9MD9wAMP6K+//tLo0aN17NgxNW7cWD/++GOWhdQAAAAAALAS0/fhvhF52f8MAAAAAIAblZccauocbgAAAAAAfBWBGwAAAAAANyBwAwAAAADgBgRuAAAAAADcgMANAAAAAIAbELgBAAAAAHADAjcAAAAAAG5A4AYAAAAAwA0I3AAAAAAAuAGBGwAAAAAANyBwAwAAAADgBgRuAAAAAADcIMDsAm6EYRiSpOTkZJMrAQAAAAAUBpn5MzOPXoulA/e5c+ckSdHR0SZXAgAAAAAoTM6dO6fIyMhrXmMzchPLvZTD4dCff/6p8PBw2Ww2s8vJUXJysqKjo3X48GFFRESYXQ4KCO+r7+E99U28r76H99Q38b76Ht5T38N7eplhGDp37pzKly8vP79rz9K2dIfbz89PFStWNLuMXIuIiCjUfzF9Fe+r7+E99U28r76H99Q38b76Ht5T38N7qut2tjOxaBoAAAAAAG5A4AYAAAAAwA0I3B4QHBysV155RcHBwWaXggLE++p7eE99E++r7+E99U28r76H99T38J7mnaUXTQMAAAAAwFvR4QYAAAAAwA0I3AAAAAAAuAGBGwAAAAAANyBwAwAAAADgBgRuAAAAAADcIMDsAnzZiRMndOLECTkcDpfjDRs2NKkiAAAAAICnELjdYOPGjerXr5927typzF3XbDabDMOQzWaT3W43uUIAkmS32xUXF6f4+PhsPxxbvny5SZUBAADAFxC43eDRRx9VzZo1NXXqVJUpU0Y2m83sklCAduzYocTERF26dMnleNeuXU2qCPk1ZMgQxcXFqXPnzqpfvz7/rfqQFStWqF27dtme++CDDzRw4EAPVwQAAAojm5HZgkWBCQ8P1+bNm1W9enWzS0EBSkhI0L333qvt27c7RyxIcoY0Ri5YT8mSJTVjxgx16tTJ7FJQwIoXL65ly5apWbNmLsffe+89vfzyy0pOTjapMuTFtm3bcn0t07Wso0mTJrn+gHPTpk1urgbucuHChWwbFPy3ak0pKSkaP358jqMCExISTKrM+9HhdoO//e1v2rp1K4HbxwwZMkQxMTGKj49XTEyM1q9fr1OnTmn48OF65513zC4P+RAUFMR/pz7q7bffVseOHbV69WrVrl1bkjRx4kS99tprWrhwocnVIbcaN27sMiXrWvjQ0zq6devmvJ2amqr/+7//U926ddW6dWtJ0rp16/Sf//xHzzzzjEkV4kb89ddfeuSRR/TDDz9ke57/Vq1pwIABWrVqlfr06aNy5coxKjAP6HC7wcmTJ9WvXz+1aNFC9evXV2BgoMt5hh5bU8mSJbV8+XI1bNhQkZGRWr9+vWrVqqXly5dr+PDh2rx5s9klIo8mTpyohIQEvf/++/zD4YMmTJigKVOm6Oeff9asWbP05ptvatGiRWrTpo3ZpSGXDh065Ly9efNmjRgxQs8//7wzmK1du1YTJ07UhAkTXEIcrGPAgAEqV66cXn/9dZfjr7zyig4fPqx//etfJlWG/Ordu7cOHTqkyZMn6/bbb9fcuXN1/PhxvfHGG5o4caI6d+5sdonIh2LFimnhwoX8G5oPdLjdYO3atVqzZk22n+yxaJp12e12hYeHS7ocvv/880/VqlVLlStX1u7du02uDvnx888/a8WKFfrhhx9Ur169LB+OzZkzx6TKUBD+/ve/69SpU2revLnsdrsWL16sVq1amV0W8qBy5crO2z179tSUKVNcpoA0bNhQ0dHRevnllwncFvXNN9/ot99+y3L84YcfVvPmzQncFrR8+XLNnz9fzZs3l5+fnypXrqz27dsrIiJC48aNI3BbVPHixRUVFWV2GZZE4HaDQYMG6eGHH9bLL7+sMmXKmF0OCkj9+vW1detWxcTEqGXLlpowYYKCgoL0ySefqGrVqmaXh3woVqyY7r33XrPLQAGZMmVKlmMVKlRQkSJF1LZtW61fv17r16+XJA0ePNjT5eEGbd++XTExMVmOx8TEaMeOHSZUhIIQGhqqNWvWqEaNGi7H16xZo5CQEJOqwo1ISUlR6dKlJV0OaX/99Zdq1qypBg0aMCffwl5//XWNHj1a06dPV5EiRcwux1IYUu4G4eHh2rJli6pVq2Z2KShAixcvVkpKirp37659+/bp7rvv1p49e1SiRAnNmjVLd9xxh9klAoVadmEsOzabjcVdLKhp06aqX7++PvvsMwUFBUmSLl26pAEDBuj333/nF3mLGj9+vMaMGaPHH39cLVq0kCT9+uuv+te//qWXX35ZI0eONLlC5NVNN92kN954Q3fddZe6du2qYsWKady4cZoyZYpmz56t/fv3m10i8qFJkybav3+/DMNQlSpVsowK5P/BOSNwu0G/fv106623asCAAWaXAjc7ffq0ihcvzvxfAHCz9evXq0uXLjIMw7nK8bZt22Sz2fTdd985wxqs5+uvv9Z7772nnTt3SpLq1KmjIUOG6P777ze5MuTHzJkzlZGRof79+2vjxo3q0KGDTp8+raCgIMXFxemBBx4wu0Tkw5gxY655/pVXXvFQJdZD4HaDsWPHavLkyercubMaNGiQ5RMghjIC3mP27Nn6+uuvs926hE9rrSk9PV21a9fW999/rzp16phdDgpQSkqK/v3vf2vXrl2SLgezhx56SGFhYSZXBiAnFy5c0K5du1SpUiWVLFnS7HIAjyNwu8G1hjUylNFaunfvrri4OEVERKh79+7XvJYFtqxnypQpeumll9S/f3998skneuSRR7R//35t2LBBAwcO1NixY80uEflUoUIFLVu2jMANWEBSUpJmz56thIQEjRgxQlFRUdq0aZPKlCmjChUqmF0eANwQFk1zgwMHDphdAgpIZGSkc7h4ZGSkydWgoP3f//2fPvnkE/Xq1UtxcXH6+9//rqpVq2r06NE6ffq02eXhBgwcOFBvvfWWPvvsMwUE8E+dr9i7d69WrFihEydOyOFwuJwbPXq0SVXhRmzbtk2xsbGKjIzUwYMHNWDAAEVFRWnOnDlKTEzUjBkzzC4RuTBs2DC9/vrrCgsL07Bhw6557aRJkzxUFW5UVFSU9uzZo5IlS153CiW/N+WM30LcLHMAAXN8rWnatGnZ3oZvSExM1M033yzp8kq5586dkyT16dNHrVq10vvvv29mebgBGzZsUHx8vJYsWaIGDRpkGXLMiBTr+fTTT/X000+rZMmSKlu2rMu/qzabjcBtUcOGDVP//v01YcIE59abktSpUyc99NBDJlaGvNi8ebPS09Odt3PC78PW8u677zr/u5w8ebK5xVgYgdtNZsyYobffflt79+6VJNWsWVPPP/+8+vTpY3JlADKVLVtWp0+fVuXKlVWpUiWtW7dOjRo10oEDB8RsG2srVqyYevToYXYZKEBvvPGGxo4dqxdeeMHsUlCANmzYoI8//jjL8QoVKujYsWMmVIT8WLFiRba3YW39+vXL9jbyhsDtBpMmTdLLL7+sZ599Vm3atJEk/fzzz3rqqad08uRJDR061OQKkVtNmjTJ9aexLLBlPXfccYcWLFigJk2a6JFHHtHQoUM1e/Zs/fbbb9edsw/vxogU33PmzBn17NnT7DJQwIKDg5WcnJzl+J49e1SqVCkTKgJwPampqVkWmo2IiDCpGu/HomluEBMTozFjxqhv374ux6dPn65XX32VOd4Wcr0tEK7EdgjW43A45HA4nHN8v/rqK/3yyy+qUaOGnnzySedevwDM99hjj+mmm27SU089ZXYpKEADBgzQqVOn9PXXXysqKkrbtm2Tv7+/unXrprZt2zKM1YJSUlI0fvx4xcfHZ7veAosHW1NKSopeeOEFff311zp16lSW83a73YSqrIHA7QYhISH6/fffVb16dZfje/fuVYMGDZSammpSZQBQeLDlm28ZN26cJk2axJabPubs2bO677779Ntvv+ncuXMqX768jh07ptatW2vRokVs+WZBvXr10qpVq9SnTx+VK1cuy0jBIUOGmFQZbsTAgQO1YsUKvf766+rTp48++OADHTlyRB9//LHGjx+v3r17m12i1yJwu0H9+vX10EMP6cUXX3Q5/sYbb2jWrFnavn27SZUBuNqZM2c0depU7dy5U5JUt25dPfLII4qKijK5MtwItnzzPWy56dt+/vlnbdu2TefPn1fTpk0VGxtrdknIp2LFimnhwoXOaZXwDZUqVdKMGTN0++23KyIiQps2bVL16tX1+eef68svv9SiRYvMLtFrEbjd4Ntvv9UDDzyg2NhY5/9s1qxZo/j4eH399de69957Ta4Q+WG32/Xuu+/m2DFjOwTrWb16tbp27aqIiAg1b95ckrRx40YlJSXpu+++U9u2bU2uEPlVu3ZtvfLKK+rVq5fCw8O1detWly3fWIEeANwjJiZGixYtUp06dcwuBQWoaNGi2rFjhypVqqSKFStqzpw5atGihQ4cOKAGDRro/PnzZpfotVg0zQ169OihX3/9Ve+++67mzZsnSapTp47Wr1+vJk2amFsc8m3MmDH67LPPNHz4cP3jH//QSy+9pIMHD2revHlsR2NRAwcO1P33368PP/xQ/v7+ki5/sPLMM89o4MCBjEaxMLZ8A7zXlClT9MQTTygkJERTpky55rVMFbCe119/XaNHj9b06dNVpEgRs8tBAalataoOHDigSpUqqXbt2vr666/VokULfffddypWrJjZ5Xk1OtxALlWrVk1TpkxR586dFR4eri1btjiPrVu3Tl988YXZJSKPQkNDtWXLFtWqVcvl+O7du9W4cWNdvHjRpMpwo6pWrapvv/1WTZo0UfPmzfX444/rySef1JIlS/Tggw8yIsWi/vjjDy1YsCDbUUaTJk0yqSrkVUxMjH777TeVKFGCqQI+qEmTJtq/f78Mw1CVKlWyrLfAGhrW9O6778rf31+DBw/WsmXL1KVLFxmGofT0dE2aNIm5+ddAh7uAZLelRU5YNt+ajh07pgYNGki6PKzm7NmzkqS7775bL7/8spmlIZ+aNm2qnTt3ZgncO3fuVKNGjUyqCgWBLd98T3x8vLp27aqqVatq165dql+/vg4ePCjDMNS0aVOzy0MebNmyRZGRkZLEzi0+qFu3bmaXADe4clvj2NhY7dq1Sxs3blT16tXVsGFDEyvzfnS4C4ifn1+u92tm2XxrqlWrlmbMmKGWLVvqlltu0d13362RI0dq1qxZGjRokE6cOGF2icijWbNm6e9//7sGDRqkVq1aSZLWrVunDz74QOPHj3eZf8Y/JtbClm++p0WLFurYsaPGjBnjnJdfunRp9e7dWx06dNDTTz9tdonIJX9/fx09elSlS5fWHXfcoTlz5jAkFfBi6enp6tChgz766CPVqFHD7HIsh8BdQFatWuW8ffDgQY0cOVL9+/dX69atJUlr167V9OnTNW7cOPXr18+sMnEDRo4cqYiICL344ouaNWuWHn74YVWpUkWJiYkaOnSoxo8fb3aJyCM/P79rnrfZbDIMQzabjQ/KAJNdOZWnePHi+vnnn1WvXj1t3bpV99xzjw4ePGh2icilyMhIrVu3TnXq1JGfn5+OHz+uUqVKmV0WgGsoVaqU84Nr5A2B2w3+9re/acCAAerVq5fL8S+++EKffPKJVq5caU5hKFDr1q1z/o+nS5cuZpeDfDh06FCur61cubIbK4E7pKamatu2bTpx4oQcDofLua5du5pUFfKrbNmyWrFiherUqaO6detq/Pjx6tq1q7Zu3ao2bdqwQq6F9OjRQ2vWrFGdOnW0atUq3XzzzTmOOlm+fLmHq8ONYlcX3zR06FAFBwfTYMoH5nC7wdq1a/XRRx9lOd68eXMNGDDAhIpwo9LT0/Xkk0/q5Zdfdi7w0qpVK+cwZFgTIdp3/fjjj+rbt69OnjyZ5RwjFqypVatW+vnnn1WnTh116tRJw4cP1/bt2zVnzhz+X2wxM2fO1PTp07V//36tWrVK9erVYzVrH8KuLr4pIyND//rXv7Rs2TI1a9ZMYWFhLudZuDJndLjdoFatWrrnnns0YcIEl+N///vfNX/+fO3evdukynAjIiMjtWXLlmuuqApr2rFjR7afwtMFta4aNWrozjvv1OjRo1WmTBmzy0EBSEhI0Pnz59WwYUOlpKRo+PDhzlFGkyZN4gM0i2rXrp3mzp3LHG4fwq4uvqldu3Y5nrPZbIxGuQYCtxssWrRIPXr0UPXq1dWyZUtJ0vr167V37159++236tSpk8kVIj/69eunxo0bu6zSCGtLSEjQvffeq+3btzvna0tyLoBIF9S6IiIitHnzZlWrVs3sUgCgUAkLC9POnTtVqVIllStXTgsXLlTTpk2VkJCgJk2aOHd5AQoLhpS7QadOnbRnzx59+OGH2rVrlySpS5cueuqppxQdHW1ydcivGjVq6LXXXtOaNWuyHUozePBgkypDfg0ZMkQxMTGKj49XTEyM1q9fr1OnTmn48OF65513zC4PN+C+++7TypUrCdyAFxo2bJhef/11hYWFadiwYde8lmGq1lOxYkUdPXpUlSpVUrVq1bRkyRI1bdpUGzZsUHBwsNnlAR5HhxvIpWsNJbfZbEpISPBgNSgIJUuW1PLly9WwYUNFRkZq/fr1qlWrlpYvX67hw4dr8+bNZpeIfLpw4YJ69uypUqVKqUGDBgoMDHQ5zwdk1lC8ePFcb7nJQkzWceUwcoap+h52dfFNKSkpGj9+vOLj47NdjJTfg3NG4HaDbdu2ZXvcZrMpJCRElSpV4hM+wAsUL15cmzZtUkxMjKpVq6bPPvtM7dq10/79+9WgQQNduHDB7BKRT1OnTtVTTz2lkJAQlShRwiW08QGZdUyfPj3X17LlJuCd1q5dq7Vr17Kri8X16tVLq1atUp8+fVSuXLksH4YOGTLEpMq8H4HbDfz8/Jx/Ca+eEypJgYGBeuCBB/Txxx8rJCTElBqRd6+99ppGjBiRZSXVixcv6u2332blTQu69dZbNXz4cHXr1k0PPfSQzpw5o3/84x/65JNPtHHjRv3+++9ml4h8Klu2rAYPHqyRI0ded791AOY5e/as7Ha7oqKiXI6fPn1aAQEBioiIMKkyAFcqVqyYFi5cqDZt2phdiuUQuN1g/vz5euGFF/T888+rRYsWki4vmjZx4kS98sorysjI0MiRI/XAAw8wT9RC/P39dfToUZUuXdrl+KlTp1S6dGkW2LKgxYsXKyUlRd27d9fevXvVpUsX7dmzRyVKlNBXX32lv/3tb2aXiHyKiorShg0bmMPtY+x2u+bNm6edO3dKkurVq6euXbvK39/f5MqQXx07dlSXLl30zDPPuBz/6KOPtGDBAi1atMikypBfM2bMuOb5vn37eqgSFKSYmBgtWrRIderUMbsUyyFwu0GLFi30+uuv66677nI5vnjxYr388stav3695s2bp+HDh2v//v0mVYm88vPz0/Hjx1WqVCmX48uXL9cDDzygv/76y6TKUJBOnz6dp3mj8E5Dhw5VqVKl9OKLL5pdCgrIvn371KlTJx05ckS1atWSJO3evVvR0dFauHAhH65YVFRUlNasWZPll/hdu3apTZs2OnXqlEmVIb+KFy/ucj89PV0XLlxQUFCQihQpwnoLFjVz5kzNnz9f06dPzzLaE9fGKuVusH379mz3A61cubK2b98uSWrcuLGOHj3q6dKQD5nhy2azqWbNmi5BzG636/z583rqqadMrBB51b179+teExAQoLJly6p9+/bMObMgu92uCRMmaPHixWrYsGGWRdNY+dh6Bg8erGrVqmndunXO4cenTp3Sww8/rMGDB2vhwoUmV4j8SEtLU0ZGRpbj6enpunjxogkV4UadOXMmy7G9e/fq6aef1vPPP29CRcivJk2auPzeu2/fPpUpU0ZVqlTJ8u/qpk2bPF2eZRC43aB27doaP368PvnkEwUFBUm6/A/H+PHjVbt2bUnSkSNHVKZMGTPLRC5NnjxZhmHo0Ucf1ZgxYxQZGek8FxQUpCpVqqh169YmVoi8uvI9zInD4dDevXv12WefacSIEXrttdc8UBkKyvbt29WkSRNJyjIXn9EL1rRq1SqXsC1JJUqU0Pjx45lTaGEtWrTQJ598on/+858uxz/66CM1a9bMpKpQ0GrUqKHx48fr4Ycfdm6ZC+/XrVs3s0vwCQwpd4NffvlFXbt2lZ+fnxo2bCjp8i9/drtd33//vVq1aqXPP/9cx44d45M+C1m1apXatGmjgAA+pypMvv/+ez3zzDNKTEw0uxSgUIuKitL333+vm2++2eX4mjVr1KVLF4apWtSaNWsUGxurm266ybluRnx8vDZs2KAlS5bo1ltvNblCFJQtW7aobdu2Sk5ONrsUwKMI3G5y7tw5/fvf/9aePXskSbVq1dJDDz2k8PBwkytDfm3atEmBgYFq0KCBpMuL402bNk1169bVq6++6hzNAN+SlJSkRx99VHPmzDG7FKBQ69u3rzZt2qSpU6c6FyT99ddf9fjjj6tZs2aKi4szt0Dk25YtWzRhwgRt3bpVoaGhatiwoUaNGqUaNWqYXRryYcGCBS73DcPQ0aNH9f777ys6Olo//PCDSZUB5iBwA7l00003aeTIkerRo4cSEhJUt25dde/eXRs2bFDnzp01efJks0sE8F8pKSkaP3684uPjdeLECTkcDpfz7MNtPUlJSerfv7++++4750ijjIwMde3aVXFxcbmaKgLA/a7eitFms6lUqVK64447NHHiRJUrV86kynAj7Ha73n33XX399ddKTEzUpUuXXM4zyihnjI11k71792rFihXZ/qLHfs3WtGfPHjVu3FiS9M033+i2227TF198oTVr1ujBBx8kcANeZMCAAVq1apX69OmjcuXKMW/bwhwOh95++20tWLBAly5dUrdu3dSvXz/ZbDbVqVNH1atXN7tE3KD9+/dr2rRpSkhI0OTJk1W6dGn98MMPqlSpkurVq2d2ecijq3/vhW8YM2aMPvvsMw0fPlz/+Mc/9NJLL+ngwYOaN28e2eY66HC7waeffqqnn35aJUuWVNmyZV1+0bPZbKziZ1ERERHauHGjatSoofbt2+vuu+/WkCFDlJiYqFq1arGaKuBFihUrpoULF7KYlg94/fXX9eqrryo2NlahoaFavHixevXqpX/9619ml4YCsGrVKnXs2FFt2rTR6tWrtXPnTlWtWlXjx4/Xb7/9ptmzZ5tdIgBJ1apV05QpU9S5c2eFh4dry5YtzmPr1q3TF198YXaJXovA7QaVK1fWM888oxdeeMHsUlCA7rjjDkVHRys2NlaPPfaYduzYoerVq2vVqlXq16+fDh48aHaJAP4rJiZGixYtyrK3L6ynRo0aGjFihJ588klJ0rJly9S5c2ddvHgxy9BVWE/r1q3Vs2dPDRs2TOHh4dq6dauqVq2q9evXq3v37vrjjz/MLhF5ZLfbFRcXl+OUnuXLl5tUGW5EWFiYdu7cqUqVKqlcuXJauHChmjZtqoSEBDVp0kRnz541u0Svxb9UbnDmzBn17NnT7DJQwCZPnqxNmzbp2Wef1UsvveQcxjh79uwsq+YCMNfrr7+u0aNH68KFC2aXghuUmJioTp06Oe/HxsbKZrPpzz//NLEqFJTt27fr3nvvzXK8dOnSOnnypAkV4UYNGTJEQ4YMkd1uV/369dWoUSOXL1hTxYoVdfToUUmXu91LliyRJG3YsEHBwcFmlub1mMPtBj179tSSJUv01FNPmV0KClDDhg21ffv2LMfffvtt+fv7m1ARgJxMnDhR+/fvV5kyZVSlShUFBga6nGdqj3VkZGQoJCTE5VhgYKDS09NNqggFqVixYjp69KhiYmJcjm/evFkVKlQwqSrciK+++kpff/21ywdlsL57771X8fHxatmypQYNGqSHH35YU6dOVWJiooYOHWp2eV6NwO0G1atX18svv6x169apQYMGWX7RGzx4sEmVwR2u/kUQgPm6detmdgkoIIZhqH///i4dlNTUVD311FMKCwtzHmPrPmt68MEH9cILL+ibb76RzWaTw+HQmjVrNGLECPXt29fs8pAPQUFBLGbog8aPH++8/cADD6hy5cr65ZdfVKNGDXXp0sXEyrwfc7jd4OpPaa9ks9nYjsai/Pz8rrnSsd1u92A1AFA4PPLII7m6btq0aW6uBO5w6dIlDRw4UHFxcbLb7QoICJDdbtdDDz2kuLg4RpBZ0MSJE5WQkKD333+fHSJ8yKlTp1SiRAlJ0uHDh/Xpp5/q4sWL6tKli9q2bWtydd6NwA3k0vz5813up6ena/PmzZo+fbrGjBmjxx57zKTKAGQnKSlJs2fP1v79+/X8888rKipKmzZtUpkyZRiqCniZxMRE/f777zp//ryaNGmiGjVqmF0S8qB79+4u95cvX66oqCjVq1cvy0hPRqNYy/bt29WlSxcdPnxYNWrU0FdffaUOHTooJSVFfn5+SklJ0ezZsxlZdg0EbuAGffHFF5o1a1aWQA7APNu2bVNsbKwiIyN18OBB7d69W1WrVtU//vEPJSYmasaMGWaXCOAqmb+S0hW1ntyORJEYjWI1HTt2VEBAgEaOHKnPP/9c33//ve666y59+umnkqRBgwZp48aNWrduncmVei8CdwEZNmyYXn/9dYWFhWnYsGHXvHbSpEkeqgqekJCQoIYNG+r8+fNmlwLgv2JjY9W0aVNNmDDBZauhX375RQ899BDb+AFeZOrUqXr33Xe1d+9eSZe3gnvuuec0YMAAkysDULJkSS1fvtz5u25ERIQ2bNigZs2aSZJ27dqlVq1aKSkpydxCvRiLphWQzZs3O1dM3bx5s8nVwFMuXryoKVOmMDwV8DIbNmzQxx9/nOV4hQoVdOzYMRMqApCd0aNHa9KkSRo0aJBat24tSVq7dq2GDh2qxMREvfbaayZXiLy64447NGfOHBUrVszleHJysrp168Y+3BZz+vRplS1bVpJUtGhRhYWFqXjx4s7zxYsX17lz58wqzxII3AVkxYoV2d6+Gn8hrat48eIuw9wMw9C5c+cUGhqqf//73yZWBuBqwcHBSk5OznJ8z549KlWqlAkVAcjOhx9+qE8//VS9evVyHuvatasaNmyoQYMGEbgtaOXKlbp06VKW46mpqfrpp59MqAg36uppHkz7yBsCdwF69913r7kP3blz59ShQwetWbPGg1WhoEyePNnlvp+fn0qVKqWWLVvqyJEj5hQFIFtdu3bVa6+9pq+//lrS5V8OEhMT9cILL6hHjx4mVwcgU3p6upo3b57leLNmzZSRkWFCRcivbdu2OW/v2LHDZTSR3W7Xjz/+yIhAi7pya8art2VMS0szszRLYA53AQoNDdXHH3+c7b6R58+f11133aVTp05p165dJlSHgnbu3Dl9+eWXmjp1qn777Te2BQO8yNmzZ3Xfffdpw4YNOn/+vMqXL69jx46pdevWWrRokcv+zQDMM2jQIAUGBmZZ32bEiBG6ePGiPvjgA5MqQ15duX1qdvEiNDRU//znP/Xoo496ujTcALZmvHEE7gI0e/Zs9enTR7NmzVLXrl2dx1NSUnTXXXfpxIkTWrVqlcqVK2dilbhRq1ev1tSpU/Xtt9+qfPny6t69u3r06KGbbrrJ7NIAXGXNmjXaunWrzp8/r6ZNmyo2NtbskgBcYdCgQZoxY4aio6PVqlUrSdKvv/6qxMRE9e3b12VLKRad9W6HDh2SYRiqWrWq1q9f7zJ9JygoSKVLl2ZfdRRKDCkvQPfdd5+SkpLUq1cvLVy4ULfffrtSUlLUoUMHHT9+nLBtYceOHVNcXJymTp2q5ORk3X///UpLS9O8efNUt25ds8sDcAWHw6G4uDjNmTNHBw8elM1mU0xMjMqWLSvDMJh7BniR33//XU2bNpUk7d+/X9LlVZFLliyp33//3Xkd/916v8qVK0u6/P9gAP9Dh9sNJkyYoLFjx2r+/PkaPXq0jhw5olWrVqlixYpml4Z86NKli1avXq3OnTurd+/e6tChg/z9/RUYGKitW7cSuAEvYhiGunTpokWLFqlRo0aqXbu2DMPQzp07tX37dnXt2lXz5s0zu0wA8CkLFixQx44dFRgYqAULFlzz2itHgQKFAYHbTUaOHKm3335bVapU0cqVKxUdHW12ScingIAADR48WE8//bRq1KjhPE7gBrzPtGnTNGTIEM2fP1/t2rVzObd8+XJ169ZN77//frZrbQAwX3JyspYvX67atWurdu3aZpeDXPLz89OxY8dUunRp+fn55XidzWZjzRsUOgwpL0Ddu3d3uR8YGKiSJUtqyJAhLsfnzJnjybJwg37++WdNnTpVzZo1U506ddSnTx89+OCDZpcFIBtffvmlXnzxxSxhW7q8N+zIkSP173//m8ANeIn7779fbdu21bPPPquLFy+qefPmOnjwoAzD0FdffcWuAhZx5TDynIaUHz58mG3eUCjl/BEU8iwyMtLlq1evXqpbt26W47CWVq1a6dNPP9XRo0f15JNP6quvvlL58uXlcDi0dOlS9lYHvMi2bdvUoUOHHM937NhRW7du9WBFAK5l9erVuvXWWyVJc+fOlWEYSkpK0pQpU/TGG2+YXB0K0unTp/Wvf/3L7DIAj2NIOZAPu3fv1tSpU/X5558rKSlJ7du3v+6cJQDuFxQUpEOHDuW4QOWff/6pmJgY9g0FvERoaKj27Nmj6Oho9e3bV+XLl9f48eOVmJiounXr6vz582aXiAKydetWNW3alCHlKHTocAP5UKtWLU2YMEF//PGHvvzyS7PLAfBfdrtdAQE5z5by9/dXRkaGBysCcC3R0dFau3atUlJS9OOPP+rOO++UJJ05c0YhISEmVwcAN4453MAN8Pf3V7du3dStWzezSwGgy6uU9+/fX8HBwdmep7MNeJfnnntOvXv3VtGiRVW5cmXdfvvtki4PNW/QoIG5xQFAAWBIOQDAZzzyyCO5um7atGlurgRAbv322286fPiw2rdvr6JFi0qSFi5cqGLFiqlNmzYmV4fcunrx4KslJSVp1apVDClHoUPgBgAAAHBD+MATyB6BGwAAAKaw2+2Ki4tTfHy8Tpw4kWVLqeXLl5tUGQAUDOZwF5C8rFDdtWtXN1YCAABgDUOGDFFcXJw6d+6s+vXry2azmV0SABQoOtwFxM/PdcF3m82mK3+0V/4DwtwVAAAAqWTJkpoxY4Y6depkdikA4BZsC1ZAHA6H82vJkiVq3LixfvjhByUlJSkpKUmLFi1S06ZN9eOPP5pdKgAAgFcICgpS9erVzS4DANyGDrcb1K9fXx999JFuueUWl+M//fSTnnjiCe3cudOkygAAALzHxIkTlZCQoPfff5/h5AB8EnO43WD//v0qVqxYluORkZE6ePCgx+sBAADwRj///LNWrFihH374QfXq1VNgYKDL+Tlz5phUGQAUDDrcbtC2bVuFhITo888/V5kyZSRJx48fV9++fZWamqpVq1aZXCEAAID5rreVFFtIAbA6Arcb7Nu3T/fee6/27Nmj6OhoSdLhw4dVo0YNzZs3j7lKAAAAAFAIELjdxDAMLV26VLt27ZIk1alTR7GxscxPAgAAhV7x4sWz/Z0oMjJSNWvW1IgRI9S+fXsTKgOAgkXgdrPU1FQFBwcTtAEAAP5r+vTp2R5PSkrSxo0bNWvWLM2ePVtdunTxcGUAULAI3G7gcDg0duxYffTRRzp+/Lj27NmjqlWr6uWXX1aVKlX02GOPmV0iAACA15o0aZJmz56tX375xexSAOCGsA+3G7zxxhuKi4vThAkTFBQU5Dxev359ffbZZyZWBgAA4P3uvvtu57Q8ALAyArcbzJgxQ5988ol69+4tf39/5/FGjRrxjwcAAMB1pKWluTQtAMCqCNxucOTIkWxXInc4HEpPTzehIgAAAOuYOnWqGjdubHYZAHDDAswuwBfVrVtXP/30kypXruxyfPbs2WrSpIlJVQEAAHiHYcOGZXv87Nmz2rRpk/bs2aPVq1d7uCoAKHgEbjcYPXq0+vXrpyNHjsjhcGjOnDnavXu3ZsyYoe+//97s8gAAAEy1efPmbI9HRESoffv2mjNnjmJiYjxcFQAUPFYpd5OffvpJr732mrZu3arz58+radOmGj16tO68806zSwMAAAAAeACBGwAAAAAAN2DRNDeoWrWqTp06leV4UlKSqlatakJFAAAAAABPI3C7wcGDB2W327McT0tL05EjR0yoCAAAAADgaSyaVoAWLFjgvL148WJFRkY679vtdsXHx6tKlSomVAYAAAAA8DTmcBcgP7/LAwZsNpuu/rEGBgaqSpUqmjhxou6++24zygMAAAAAeBCB2w1iYmK0YcMGlSxZ0uxSAAAAAAAmIXADAAAAAOAGzOEuIFOmTNETTzyhkJAQTZky5ZrXDh482ENVAQAAAADMQoe7gMTExOi3335TiRIlFBMTk+N1NptNCQkJHqwMAAAAAGAGAjcAAAAAAG7APtwAAAAAALgBc7jdwG63Ky4uTvHx8Tpx4oQcDofL+eXLl5tUGQAAAADAUwjcbjBkyBDFxcWpc+fOql+/vmw2m9klAQAAAAA8jDncblCyZEnNmDFDnTp1MrsUAAAAAIBJmMPtBkFBQapevbrZZQAAAAAATETgdoPhw4frvffeE4MHAAAAAKDwYki5G9x7771asWKFoqKiVK9ePQUGBrqcnzNnjkmVAQAAAAA8hUXT3KBYsWK69957zS4DAAAAAGAiOtwAAAAAALgBc7gBAAAAAHADhpQXoOLFi2e753ZkZKRq1qypESNGqH379iZUBgAAAADwNIaUF6Dp06dnezwpKUkbN27UrFmzNHv2bHXp0sXDlQEAAAAAPI3A7UGTJk3S7Nmz9csvv5hdCgAAAADAzQjcHrRnzx61atVKp0+fNrsUAAAAAICbsWiaB6WlpSkoKMjsMgAAAAAAHkDg9qCpU6eqcePGZpcBAAAAAPAAVikvQMOGDcv2+NmzZ7Vp0ybt2bNHq1ev9nBVAAAAAAAzELgL0ObNm7M9HhERofbt22vOnDmKiYnxcFUAAAAAADOwaBoAAAAAAG7AHG4AAAAAANyAwA0AAAAAgBsQuAEAAAAAcAMCNwAAAAAAbkDgBgAAAADADQjcAAAAAAC4AYEbAAAvdOzYMQ0aNEhVq1ZVcHCwoqOj1aVLF8XHx3u0DpvNpnnz5nn0NQEA8BUBZhcAAABcHTx4UG3atFGxYsX09ttvq0GDBkpPT9fixYs1cOBA7dq1y+wSXVy6dElBQUFmlwEAgNehww0AgJd55plnZLPZtH79evXo0UM1a9ZUvXr1NGzYMK1bt06SlJiYqHvuuUdFixZVRESE7r//fh0/ftz5HP3791e3bt1cnve5557T7bff7rx/++23a/Dgwfr73/+uqKgolS1bVq+++qrzfJUqVSRJ9957r2w2m/P+q6++qsaNG+uzzz5TTEyMQkJCNGPGDJUoUUJpaWkur9mtWzf16dOnwH42AABYCYEbAAAvcvr0af34448aOHCgwsLCspwvVqyYHA6H7rnnHp0+fVqrVq3S0qVLlZCQoAceeCDPrzd9+nSFhYXp119/1YQJE/Taa69p6dKlkqQNGzZIkqZNm6ajR48670vSvn379O2332rOnDnasmWLevbsKbvdrgULFjivOXHihBYuXKhHH300z3UBAOALGFIOAIAX2bdvnwzDUO3atXO8Jj4+Xtu3b9eBAwcUHR0tSZoxY4bq1aunDRs26Kabbsr16zVs2FCvvPKKJKlGjRp6//33FR8fr/bt26tUqVKSLof8smXLujzu0qVLmjFjhvMaSXrooYc0bdo09ezZU5I0c+ZMVapUyaWrDgBAYUKHGwAAL2IYxnWv2blzp6Kjo51hW5Lq1q2rYsWKaefOnXl6vYYNG7rcL1eunE6cOHHdx1WuXNklbEvS448/riVLlujIkSOSpLi4OPXv3182my1PNQEA4CvocAMA4EVq1Kghm812wwuj+fn5ZQnv6enpWa4LDAx0uW+z2eRwOK77/NkNd2/SpIkaNWqkGTNm6M4779R//vMfLVy4MI+VAwDgO+hwAwDgRaKionTXXXfpgw8+UEpKSpbzSUlJqlOnjg4fPqzDhw87j+/YsUNJSUmqW7euJKlUqVI6evSoy2O3bNmS53oCAwNlt9tzff2AAQMUFxenadOmKTY21qULDwBAYUPgBgDAy3zwwQey2+1q0aKFvv32W+3du1c7d+7UlClT1Lp1a8XGxqpBgwbq3bu3Nm3apPXr16tv37667bbb1Lx5c0nSHXfcod9++00zZszQ3r179corr+j333/Pcy1VqlRRfHy8jh07pjNnzlz3+oceekh//PGHPv30UxZLAwAUegRuAAC8TNWqVbVp0ya1a9dOw4cPV/369dW+fXvFx8frww8/lM1m0/z581W8eHG1bdtWsbGxqlq1qmbNmuV8jrvuuksvv/yy/v73v+umm27SuXPn1Ldv3zzXMnHiRC1dulTR0dFq0qTJda+PjIxUjx49VLRo0SzbkgEAUNjYjNyszgIAAJBLf/vb31SvXj1NmTLF7FIAADAVgRsAABSIM2fOaOXKlbrvvvu0Y8cO1apVy+ySAAAwFauUAwCAAtGkSROdOXNGb731FmEbAADR4QYAAAAAwC1YNA0AAAAAADcgcAMAAAAA4AYEbgAAAAAA3IDADQAAAACAGxC4AQAAAABwAwI3AAAAAABuQOAGAAAAAMANCNwAAAAAALjB/wMjRyfyLw5YlQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5iww_Ps7BR4j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gwM3hCvcBR7X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 4: Feature Selection and Data Splitting</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Select relevant features for the predictive model.\n",
        "- Split the data into training and testing sets to evaluate the model's performance.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Feature Selection:</span>\n",
        "- **Purpose:** To identify the most relevant variables (features) that will be used to predict the target variable.\n",
        "- **Example:** In this project, we selected `Month`, `DayOfWeek`, `Quantity`, and `UnitPrice` as the features (`X`) and `TotalPrice` as the target (`y`).\n",
        "\n",
        "### <span style=\"color:orange\">Data Splitting:</span>\n",
        "- **Purpose:** To divide the dataset into two parts:\n",
        "  - **Training Set:** Used to train the machine learning model.\n",
        "  - **Testing Set:** Used to evaluate the performance of the trained model.\n",
        "- **Ratio:** Typically, the data is split into 80% training and 20% testing, but this ratio can be adjusted based on the dataset size and specific requirements.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Preventing Overfitting:** By splitting the data, we ensure that the model is evaluated on unseen data, which helps in assessing its generalization capability.\n",
        "- **Evaluation Metrics:** Performance metrics calculated on the test set give an indication of how well the model is likely to perform on new data.\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Select Features and Target:**\n",
        "   - `X = data_cleaned[['Month', 'DayOfWeek', 'Quantity', 'UnitPrice']]`\n",
        "   - `y = data_cleaned['TotalPrice']`\n",
        "2. **Split the Data:**\n",
        "   - Use `train_test_split` from `sklearn.model_selection` to split `X` and `y` into training and testing sets.\n",
        "   - Example: `X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)`\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 4, you will have your data properly prepared for training and evaluating your predictive model. This ensures that you can accurately assess the model's performance and make necessary adjustments."
      ],
      "metadata": {
        "id": "QTzpC47JvanR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecting features and target"
      ],
      "metadata": {
        "id": "oxi8gb9HBR-D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_cleaned[['Month', 'DayOfWeek', 'Quantity', 'UnitPrice']]  # Select features"
      ],
      "metadata": {
        "id": "r2HiWa9hBSBb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data_cleaned['TotalPrice']  # Select target"
      ],
      "metadata": {
        "id": "OOajJZPBBfmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8BemocyQBfpl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets"
      ],
      "metadata": {
        "id": "wqKrV4WeBfvm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split the data"
      ],
      "metadata": {
        "id": "_yCH5BkYBfyD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a9ikfN9TBf0Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6bIl9fOKvoH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 5: Model Training and Evaluation</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Train a Linear Regression model on the training data.\n",
        "- Evaluate the model's performance using the testing data.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Model Training:</span>\n",
        "- **Purpose:** To create a machine learning model that can predict the target variable (`TotalPrice`) based on the selected features.\n",
        "- **Algorithm:** In this step, we use **Linear Regression**.\n",
        "  - **Linear Regression:** A simple yet powerful algorithm that models the relationship between a dependent variable and one or more independent variables by fitting a linear equation.\n",
        "\n",
        "### <span style=\"color:orange\">Model Evaluation:</span>\n",
        "- **Purpose:** To assess the performance of the trained model on unseen data (testing set).\n",
        "- **Metrics:**\n",
        "  - **Mean Absolute Error (MAE):** Measures the average magnitude of the errors in a set of predictions, without considering their direction.\n",
        "  - **Mean Squared Error (MSE):** Measures the average of the squares of the errors, giving higher weight to larger errors.\n",
        "  - **R-squared (R²):** Indicates the proportion of the variance in the dependent variable that is predictable from the independent variables.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Training the Model:**\n",
        "  - Use the `fit` method of the Linear Regression model to train it on the training data (`X_train` and `y_train`).\n",
        "  - Example: `model.fit(X_train, y_train)`\n",
        "- **Making Predictions:**\n",
        "  - Use the `predict` method to make predictions on the testing data (`X_test`).\n",
        "  - Example: `y_pred = model.predict(X_test)`\n",
        "- **Evaluating Performance:**\n",
        "  - Calculate MAE, MSE, and R² using the true values (`y_test`) and the predicted values (`y_pred`).\n",
        "  - Example:\n",
        "    - `mae = mean_absolute_error(y_test, y_pred)`\n",
        "    - `mse = mean_squared_error(y_test, y_pred)`\n",
        "    - `r2 = r2_score(y_test, y_pred)`\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Initialize the Model:**\n",
        "   - `model = LinearRegression()`\n",
        "2. **Train the Model:**\n",
        "   - `model.fit(X_train, y_train)`\n",
        "3. **Make Predictions:**\n",
        "   - `y_pred = model.predict(X_test)`\n",
        "4. **Evaluate the Model:**\n",
        "   - Calculate MAE, MSE, and R² using `mean_absolute_error`, `mean_squared_error`, and `r2_score`.\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 5, you will have a trained Linear Regression model and a clear understanding of its performance based on various evaluation metrics. This forms the foundation for further optimization and model comparison."
      ],
      "metadata": {
        "id": "DVWtXe2Kvoh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the model"
      ],
      "metadata": {
        "id": "Mhz8QrHIBf3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()  # Initialize the Linear Regression model"
      ],
      "metadata": {
        "id": "Sk3BEYAnBf4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8yFT5QqWBtPV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model"
      ],
      "metadata": {
        "id": "Kfx6Qq8pBtVs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)  # Fit the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "S9tzZilABtYz",
        "outputId": "298440d2-cbbc-4261-a8f3-91bd40295002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mIJ4stK6BtfB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions"
      ],
      "metadata": {
        "id": "BMQj6mcKBtiY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)  # Predict on the test data"
      ],
      "metadata": {
        "id": "Feuyw3CFBtk-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hipGQmFfBtn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model"
      ],
      "metadata": {
        "id": "YAD4-jMVB6E8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae = mean_absolute_error(y_test, y_pred)  # Calculate Mean Absolute Error"
      ],
      "metadata": {
        "id": "GkadjD1oB6JL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)  # Calculate Mean Squared Error"
      ],
      "metadata": {
        "id": "-0N9xsGbB6L_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2 = r2_score(y_test, y_pred)  # Calculate R^2 Score"
      ],
      "metadata": {
        "id": "BPZkfOkaB6QF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UFSAiB1QB6Td"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean Absolute Error: {mae}\")  # Print Mean Absolute Error\n",
        "print(f\"Mean Squared Error: {mse}\")  # Print Mean Squared Error\n",
        "print(f\"R^2 Score: {r2}\")  # Print R^2 Score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxNZaUNOB6W4",
        "outputId": "0e6e8494-87ff-4158-ecb2-a1d047d27a08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 15.919036580640636\n",
            "Mean Squared Error: 6875.750154777151\n",
            "R^2 Score: -0.07706676196612916\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jUaLbGILCNhc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4WrUiyLCvxDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 6: Saving Evaluation Metrics and Predictions</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Save the evaluation metrics and predictions to CSV files for future reference and analysis.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Evaluation Metrics:</span>\n",
        "- **Purpose:** To store the evaluation metrics calculated in the previous step.\n",
        "- **Metrics:** We will save Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) values.\n",
        "- **Method:** Use `pandas.DataFrame` to create a DataFrame of the metrics and then save it using the `to_csv` method.\n",
        "\n",
        "### <span style=\"color:orange\">Predictions:</span>\n",
        "- **Purpose:** To store the predictions made by the model on new data.\n",
        "- **Method:** Use `pandas.DataFrame` to create a DataFrame of the predictions and then save it using the `to_csv` method.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Creating DataFrames:**\n",
        "  - Convert the evaluation metrics and predictions into DataFrames.\n",
        "  - Example for evaluation metrics:\n",
        "    ```python\n",
        "    evaluation_metrics = pd.DataFrame({\n",
        "        'Metric': ['Mean Absolute Error', 'Mean Squared Error', 'R² Score'],\n",
        "        'Value': [mae, mse, r2]\n",
        "    })\n",
        "    ```\n",
        "  - Example for predictions:\n",
        "    ```python\n",
        "    predictions_df = pd.DataFrame(predictions, columns=['PredictedTotalPrice'])\n",
        "    ```\n",
        "- **Saving to CSV:**\n",
        "  - Use the `to_csv` method to save the DataFrames to CSV files.\n",
        "  - Example:\n",
        "    ```python\n",
        "    evaluation_metrics.to_csv('/content/evaluation_metrics.csv', index=False)\n",
        "    predictions_df.to_csv('/content/predicted_customer_spending.csv', index=False)\n",
        "    ```\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Create Evaluation Metrics DataFrame:**\n",
        "   - `evaluation_metrics = pd.DataFrame({'Metric': ['Mean Absolute Error', 'Mean Squared Error', 'R² Score'], 'Value': [mae, mse, r2]})`\n",
        "2. **Save Evaluation Metrics to CSV:**\n",
        "   - `evaluation_metrics.to_csv('/content/evaluation_metrics.csv', index=False)`\n",
        "3. **Create Predictions DataFrame:**\n",
        "   - `predictions_df = pd.DataFrame(predictions, columns=['PredictedTotalPrice'])`\n",
        "4. **Save Predictions to CSV:**\n",
        "   - `predictions_df.to_csv('/content/predicted_customer_spending.csv', index=False)`\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 6, you will have successfully saved the evaluation metrics and predictions, allowing for easy retrieval and analysis in the future. This step ensures that the results of your model are well-documented and accessible."
      ],
      "metadata": {
        "id": "LTxnMX9OvxSJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save evaluation metrics to CSV"
      ],
      "metadata": {
        "id": "NdDW3NCuCNlQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation_metrics = pd.DataFrame({\n",
        "    'Metric': ['Mean Absolute Error', 'Mean Squared Error', 'R^2 Score'],  # Evaluation metrics\n",
        "    'Value': [mae, mse, r2]  # Corresponding values\n",
        "})"
      ],
      "metadata": {
        "id": "PYQkyRT9CNo6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q4BOrO9KCNrz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation_metrics.to_csv('/content/evaluation_metrics.csv', index=False)  # Save the evaluation metrics as a CSV"
      ],
      "metadata": {
        "id": "fuoEz9AXCNvK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nEvaluation metrics saved as CSV.\")  # Print statement"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IC6v7fLUCNzM",
        "outputId": "480459ae-6ef1-4605-83d1-5fff4a0c1f6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Evaluation metrics saved as CSV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vrWBYNRACZTc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting on new data (example)"
      ],
      "metadata": {
        "id": "stJN256kCZXV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = pd.DataFrame({\n",
        "    'Month': [7, 8],  # Example months\n",
        "    'DayOfWeek': [2, 3],  # Example days of the week\n",
        "    'Quantity': [10, 15],  # Example quantities\n",
        "    'UnitPrice': [2.5, 3.0]  # Example unit prices\n",
        "})"
      ],
      "metadata": {
        "id": "fsfAblfHCZbA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iGpVd49OCZef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(new_data)  # Predict on new data"
      ],
      "metadata": {
        "id": "Z_Y31vWwCZhd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9GT330BCCiG8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save predictions to CSV"
      ],
      "metadata": {
        "id": "1QGXhsuKCiKA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df = pd.DataFrame(predictions, columns=['PredictedTotalPrice'])  # Create DataFrame for predictions"
      ],
      "metadata": {
        "id": "IScp7f9WCiNU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_df.to_csv('/content/predicted_customer_spending.csv', index=False)  # Save the predictions as a CSV"
      ],
      "metadata": {
        "id": "H3NdTUbDCiQM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nPredictions saved as CSV.\")  # Print statement"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgpWCPKACiSv",
        "outputId": "9e841992-e2ac-4486-b9da-e0dfc4bfed84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Predictions saved as CSV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G9ux4Ns4CiVF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions_df.head())  # Display the first few rows of predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A27eHbZaCibP",
        "outputId": "b35af7e0-4cc7-43fa-e445-20e362409d64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   PredictedTotalPrice\n",
            "0            18.126293\n",
            "1            25.384895\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UR340SD0Cid8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qw72jARfCZkd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue\">Step 7: Feature Scaling with Standardization</span>\n",
        "\n",
        "## <span style=\"color:green\">Objective</span>\n",
        "- Standardize the numerical features to ensure they are on the same scale for better model performance.\n",
        "\n",
        "## <span style=\"color:green\">Notes</span>\n",
        "\n",
        "### <span style=\"color:orange\">Purpose of Feature Scaling:</span>\n",
        "- **Normalization:** Ensures that all features have the same scale (e.g., between 0 and 1).\n",
        "- **Standardization:** Transforms data to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "### <span style=\"color:orange\">Method Used:</span>\n",
        "- **StandardScaler:** Standardizes features by removing the mean and scaling to unit variance.\n",
        "\n",
        "### <span style=\"color:orange\">Key Points:</span>\n",
        "- **Fit and Transform:** Fit the `StandardScaler` on the training data and then transform both training and testing data.\n",
        "  - Example:\n",
        "    ```python\n",
        "    scaler = StandardScaler()\n",
        "    X_train_scaled = scaler.fit_transform(X_train)\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "    ```\n",
        "- **Benefits:** Helps models converge faster and can prevent features with larger ranges from dominating the prediction.\n",
        "\n",
        "### <span style=\"color:orange\">Example Workflow:</span>\n",
        "1. **Initialize the StandardScaler:**\n",
        "   - `scaler = StandardScaler()`\n",
        "2. **Fit and Transform Training Data:**\n",
        "   - `X_train_scaled = scaler.fit_transform(X_train)`\n",
        "3. **Transform Testing Data:**\n",
        "   - `X_test_scaled = scaler.transform(X_test)`\n",
        "\n",
        "---\n",
        "\n",
        "By completing Step 7, you will have standardized the numerical features in your dataset, preparing them for improved performance and stability in machine learning models like Linear Regression and Ridge Regression."
      ],
      "metadata": {
        "id": "sxzy3bFlv8sb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # Import StandardScaler from sklearn.preprocessing"
      ],
      "metadata": {
        "id": "sCV_JA3GFGEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gKf-8b4wFZCF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the scaler with description"
      ],
      "metadata": {
        "id": "lowoIcOuFZF0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initialize the StandardScaler"
      ],
      "metadata": {
        "id": "kPcrFsWyFZIs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ilmv2XmtFZME"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and transform the training data with description"
      ],
      "metadata": {
        "id": "bLNqcDZfFZPa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled = scaler.fit_transform(X_train)  # Fit and transform the training data"
      ],
      "metadata": {
        "id": "cod4gVOpFZSV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_SzyvgAWEtfU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform the test data with description"
      ],
      "metadata": {
        "id": "iNAWtCAZHOKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled = scaler.transform(X_test)  # Transform the test data"
      ],
      "metadata": {
        "id": "hzVRklWTHON3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TXE4YfqDHOQ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2zh7VfjWHOTm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Step 8: Ridge Regression</span>\n",
        "\n",
        "#### <span style=\"color:green\">Ridge Regression</span>\n",
        "- **Model Description**: Ridge Regression is a linear regression technique that includes L2 regularization. It helps prevent overfitting by adding a penalty term to the loss function, which is proportional to the square of the coefficients.\n",
        "  \n",
        "- **Initialization**: Import `Ridge` from `sklearn.linear_model` and initialize the Ridge Regression model (`ridge_model`) with an alpha value of 0.1. Alpha controls the regularization strength; higher values increase regularization.\n",
        "  \n",
        "- **Training**: Fit the Ridge model using scaled training data (`X_train_scaled`, `y_train`). During training, the model adjusts its coefficients to minimize the loss function, balancing between data fitting and regularization.\n",
        "  \n",
        "- **Prediction**: Predict the target variable (`y_pred_ridge`) using the trained Ridge model on the scaled test data (`X_test_scaled`). These predictions estimate total prices based on learned coefficients and test data features.\n",
        "  \n",
        "- **Evaluation Metrics**:\n",
        "  - **Mean Absolute Error (MAE)**: Computes the average absolute difference between predicted and actual total prices.\n",
        "  - **Mean Squared Error (MSE)**: Calculates the average squared difference between predicted and actual total prices, giving more weight to larger errors.\n",
        "  - **R-squared (R²) Score**: Indicates the proportion of variance in total prices explained by the model. A higher R² score indicates better model fit.\n",
        "  \n",
        "- **Output**: Print the MAE, MSE, and R² score to evaluate the Ridge Regression model's performance."
      ],
      "metadata": {
        "id": "_oY4G-ywx-ah"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge  # Import Ridge from sklearn.linear_model"
      ],
      "metadata": {
        "id": "156xll3fHOV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-sfKlCZIHfdz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Ridge Regression model with description"
      ],
      "metadata": {
        "id": "bBC_adN6HOY_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_model = Ridge(alpha=0.1)  # Initialize the Ridge Regression model with alpha regularization parameter (adjust alpha as needed)\n"
      ],
      "metadata": {
        "id": "I-D0GcwEHgMy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7dZN5yL8HgSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Ridge model with description"
      ],
      "metadata": {
        "id": "ZUlVX8oQHgT3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_model.fit(X_train_scaled, y_train)  # Train the Ridge model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Gslj6OOVHgYw",
        "outputId": "b2525a8e-ca67-4eed-8ada-a2990f77c5a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Ridge(alpha=0.1)"
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            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2qfI1i7tHgbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using the Ridge model with description"
      ],
      "metadata": {
        "id": "Zy6ynDSnHgc5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_ridge = ridge_model.predict(X_test_scaled)  # Predict using the Ridge model"
      ],
      "metadata": {
        "id": "HNthiAv7Hghc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Ridge model with description"
      ],
      "metadata": {
        "id": "fnrADG2zHtUk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_mae = mean_absolute_error(y_test, y_pred_ridge)  # Calculate Mean Absolute Error"
      ],
      "metadata": {
        "id": "yVHC5WCCHtW1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_mse = mean_squared_error(y_test, y_pred_ridge)  # Calculate Mean Squared Error"
      ],
      "metadata": {
        "id": "wLefepkCHtZd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_r2 = r2_score(y_test, y_pred_ridge)  # Calculate R^2 score"
      ],
      "metadata": {
        "id": "xk0daYwmHtcL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I90Z6MYAHte8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Ridge Regression Mean Absolute Error: {ridge_mae}\")  # Print Mean Absolute Error\n",
        "print(f\"Ridge Regression Mean Squared Error: {ridge_mse}\")  # Print Mean Squared Error\n",
        "print(f\"Ridge Regression R^2 Score: {ridge_r2}\")  # Print R^2 score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vdqK9uLHtiP",
        "outputId": "f3be6d15-8e7a-4789-ca52-8df1d8227385"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ridge Regression Mean Absolute Error: 15.919034908475119\n",
            "Ridge Regression Mean Squared Error: 6875.748343765254\n",
            "Ridge Regression R^2 Score: -0.07706647827625157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oQ8ea4S3H7CG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BnekvX1uH7O_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Step 9: Polynomial Regression</span>\n",
        "\n",
        "#### <span style=\"color:green\">Polynomial Regression</span>\n",
        "- <span style=\"color:green\">**Model Description**</span>: Polynomial Regression extends linear regression by incorporating higher-degree polynomial features. It captures nonlinear relationships between features and the target variable.\n",
        "\n",
        "- <span style=\"color:green\">**Initialization**</span>: Import `PolynomialFeatures` from `sklearn.preprocessing` and initialize `poly` with a degree of 2. The degree determines the highest power of features to be used in the polynomial transformation.\n",
        "\n",
        "- <span style=\"color:green\">**Feature Transformation**</span>:\n",
        "  - <span style=\"color:green\">**Training Set**</span>: Use `poly.fit_transform(X_train_scaled)` to transform the scaled training features (`X_train_scaled`) into polynomial features (`X_train_poly`).\n",
        "  - <span style=\"color:green\">**Test Set**</span>: Use `poly.transform(X_test_scaled)` to transform the scaled test features (`X_test_scaled`) into the same polynomial features (`X_test_poly`). This ensures consistency in feature representation across training and test datasets.\n",
        "\n",
        "- <span style=\"color:green\">**Model Training**</span>: Initialize a new `LinearRegression` model (`poly_model`) and train it using the polynomial features (`X_train_poly`) and corresponding target values (`y_train`). The model learns coefficients that best fit the polynomial relationships.\n",
        "\n",
        "- <span style=\"color:green\">**Prediction**</span>: Use the trained polynomial model (`poly_model`) to predict the target variable (`y_pred_poly`) for the test set (`X_test_poly`). These predictions estimate total prices based on polynomial relationships learned during training.\n",
        "\n",
        "- <span style=\"color:green\">**Evaluation Metrics**</span>:\n",
        "  - <span style=\"color:green\">**Mean Absolute Error (MAE)**</span>: Measures the average absolute difference between predicted and actual total prices.\n",
        "  - <span style=\"color:green\">**Mean Squared Error (MSE)**</span>: Computes the average squared difference between predicted and actual total prices, giving higher weight to larger errors.\n",
        "  - <span style=\"color:green\">**R-squared (R²) Score**</span>: Indicates the proportion of variance in total prices explained by the polynomial model. Higher R² scores indicate better model fit to the data.\n",
        "\n",
        "- <span style=\"color:green\">**Output**</span>: Print the MAE, MSE, and R² score to evaluate the Polynomial Regression model's performance in capturing nonlinearities and improving prediction accuracy over simple linear models."
      ],
      "metadata": {
        "id": "NM0V1G8iyL8b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures  # Import PolynomialFeatures from sklearn.preprocessing"
      ],
      "metadata": {
        "id": "eaW9BS9_H7Sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ArSfTS0-H7VZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize PolynomialFeatures with description"
      ],
      "metadata": {
        "id": "Sr-3QfLxH7Yg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly = PolynomialFeatures(degree=2)  # Initialize PolynomialFeatures with degree 2 (adjust degree as needed)"
      ],
      "metadata": {
        "id": "h57YpCTnH7an"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZHTL9wa9H7ch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform features to higher degree with description"
      ],
      "metadata": {
        "id": "nn2D8oM9H7fG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_poly = poly.fit_transform(X_train_scaled)  # Transform training features to higher degree"
      ],
      "metadata": {
        "id": "AqrIxZx_IW4Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_poly = poly.transform(X_test_scaled)  # Transform test features to higher degree"
      ],
      "metadata": {
        "id": "XqxCqp-cIW7X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AnKmJr3lIW-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a new Linear Regression model with description"
      ],
      "metadata": {
        "id": "yB_4fU2QIXA2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_model = LinearRegression()  # Initialize a new Linear Regression model"
      ],
      "metadata": {
        "id": "mb4u_XrLIXGk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ggDu7szKIXIl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model with polynomial features with description"
      ],
      "metadata": {
        "id": "VvUzj3PDIXKZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_model.fit(X_train_poly, y_train)  # Train the model with polynomial features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "-NDAd6Y8Ip3I",
        "outputId": "e5c93bf7-010b-4e4a-dadc-02e02d941dd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
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              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y1A7X1huIp6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using the polynomial model with description"
      ],
      "metadata": {
        "id": "NOsYNk-fIp-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_poly = poly_model.predict(X_test_poly)  # Predict using the polynomial model"
      ],
      "metadata": {
        "id": "QV3dre7BIqBY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2B0qk1ZDIqDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the polynomial model with description"
      ],
      "metadata": {
        "id": "Aau8J7ZoIqFa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_mae = mean_absolute_error(y_test, y_pred_poly)  # Calculate Mean Absolute Error"
      ],
      "metadata": {
        "id": "gg8NVwSIIqHr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_mse = mean_squared_error(y_test, y_pred_poly)  # Calculate Mean Squared Error"
      ],
      "metadata": {
        "id": "mVUSilQRIqLo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_r2 = r2_score(y_test, y_pred_poly)  # Calculate R^2 score"
      ],
      "metadata": {
        "id": "IytMILJHIqNh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6lcEti2SI--H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Polynomial Regression Mean Absolute Error: {poly_mae}\")  # Print Mean Absolute Error\n",
        "print(f\"Polynomial Regression Mean Squared Error: {poly_mse}\")  # Print Mean Squared Error\n",
        "print(f\"Polynomial Regression R^2 Score: {poly_r2}\")  # Print R^2 score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FMMbtGYI_Ay",
        "outputId": "0cd8ee07-2db2-411a-ce30-1e57e91d3756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polynomial Regression Mean Absolute Error: 3.9951353448494925e-12\n",
            "Polynomial Regression Mean Squared Error: 3.7740219173621446e-23\n",
            "Polynomial Regression R^2 Score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fNvCcDNPI_C_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BM64NuapI_G9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Step 10: Cross-Validation</span>\n",
        "\n",
        "#### <span style=\"color:green\">Cross-Validation Overview</span>\n",
        "- <span style=\"color:green\">**Cross-Validation Purpose**</span>: Cross-validation is a crucial technique in machine learning to evaluate the performance of a model and assess its ability to generalize to new data. It helps in detecting overfitting by simulating how the model would perform on unseen data.\n",
        "\n",
        "#### <span style=\"color:green\">Using Ridge Regression</span>\n",
        "- <span style=\"color:green\">**Ridge Regression Description**</span>: Ridge Regression is a linear regression technique that incorporates regularization using an L2 penalty term. This penalty helps in preventing overfitting by penalizing large coefficients.\n",
        "- <span style=\"color:green\">**Cross-Validation Setup**</span>:\n",
        "  - <span style=\"color:green\">**Importing Libraries**</span>:\n",
        "    - Import `cross_val_score` from `sklearn.model_selection` to perform cross-validation.\n",
        "  - <span style=\"color:green\">**Procedure**</span>:\n",
        "    - Initialize `ridge_model` with Ridge Regression and an alpha value (`alpha=0.1`).\n",
        "    - Use `cross_val_score` to perform 5-fold cross-validation (`cv=5`) on `X_train_scaled` and `y_train` with `ridge_model`.\n",
        "    - Specify `scoring='r2'` to evaluate using the R² score, which measures the proportion of variance in the dependent variable that is predictable from the independent variables.\n",
        "  - <span style=\"color:green\">**Evaluation**</span>:\n",
        "    - Print the cross-validated R² scores (`ridge_cv_scores`). These scores provide insights into how well the Ridge Regression model performs across different subsets of the training data.\n",
        "\n",
        "#### <span style=\"color:green\">Using Polynomial Regression</span>\n",
        "- <span style=\"color:green\">**Polynomial Regression Description**</span>: Polynomial Regression extends linear regression by adding polynomial terms to the model equation, allowing it to capture nonlinear relationships between the features and the target variable.\n",
        "- <span style=\"color:green\">**Cross-Validation Setup**</span>:\n",
        "  - <span style=\"color:green\">**Importing Libraries**</span>:\n",
        "    - Import `PolynomialFeatures` from `sklearn.preprocessing` to create polynomial features.\n",
        "  - <span style=\"color:green\">**Procedure**</span>:\n",
        "    - Initialize `poly` with `PolynomialFeatures` of degree 2 (`degree=2`) to transform `X_train_scaled` and `X_test_scaled` into polynomial features.\n",
        "    - Initialize `poly_model` with `LinearRegression` to fit a linear model on these polynomial features.\n",
        "    - Use `cross_val_score` to perform 5-fold cross-validation (`cv=5`) on `X_train_poly` (polynomial features) and `y_train` with `poly_model`.\n",
        "  - <span style=\"color:green\">**Evaluation**</span>:\n",
        "    - Print the cross-validated R² scores (`poly_cv_scores`). These scores indicate how well the Polynomial Regression model generalizes across different subsets of the training data.\n",
        "\n",
        "#### <span style=\"color:green\">Summary</span>\n",
        "- <span style=\"color:green\">**Insights**</span>: Cross-validation provides a robust method to estimate the model's performance and helps in comparing different regression techniques (Ridge vs. Polynomial) objectively. It ensures that the model's performance metrics are reliable and not overly optimistic due to overfitting to a particular subset of data."
      ],
      "metadata": {
        "id": "-LDqGqZzyuI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score  # Import cross_val_score from sklearn.model_selection"
      ],
      "metadata": {
        "id": "BhD3Xs78I_KX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bWEA52BJI_ML"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform cross-validation with Ridge Regression with description"
      ],
      "metadata": {
        "id": "dbmikns8JQLR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_cv_scores = cross_val_score(ridge_model, X_train_scaled, y_train, cv=5, scoring='r2')  # Perform cross-validation with Ridge Regression\n"
      ],
      "metadata": {
        "id": "UUxGdyoEJQR1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Cross-validated R^2 scores for Ridge Regression: {ridge_cv_scores}\")  # Print cross-validated R^2 scores for Ridge Regression\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbjN_6YfJQVH",
        "outputId": "64fcf3d1-c9b7-418a-b263-83516c773f9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validated R^2 scores for Ridge Regression: [ 0.42430267  0.88972508  0.85400434 -0.51122565  0.34873656]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hq5P3fp7JQYY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform cross-validation with Polynomial Regression with description"
      ],
      "metadata": {
        "id": "wRg2PzgsJ9BW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_cv_scores = cross_val_score(poly_model, X_train_poly, y_train, cv=5, scoring='r2')  # Perform cross-validation with Polynomial Regression\n"
      ],
      "metadata": {
        "id": "Qx1qF-IuJ9E-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Cross-validated R^2 scores for Polynomial Regression: {poly_cv_scores}\")  # Print cross-validated R^2 scores for Polynomial Regression\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92he2IxgJ9Ic",
        "outputId": "7385d53d-8ace-4f0e-f8d8-a4fe08c61387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validated R^2 scores for Polynomial Regression: [1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CLf1Xh3wJ9K2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qXpZXtWAJ9Mu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Step 11: Saving Predictions</span>\n",
        "\n",
        "#### <span style=\"color:green\">Saving Ridge Regression Predictions</span>\n",
        "- <span style=\"color:green\">**Description**</span>: After training and evaluating the Ridge Regression model, it's essential to save the predictions to a CSV file. This step ensures that the predicted values can be easily accessed for further analysis, visualization, and comparison with actual values. Saving predictions is crucial in machine learning projects for reporting model performance and sharing results with stakeholders.\n",
        "\n",
        "- <span style=\"color:green\">**Procedure**</span>:\n",
        "  - Create a DataFrame `predictions_ridge_df` to store the predicted total prices (`y_pred_ridge`). The DataFrame is structured with a column named `'PredictedTotalPrice_Ridge'` to clearly identify the predictions made by the Ridge Regression model.\n",
        "  - Use the `to_csv` method to export `predictions_ridge_df` to a CSV file located at `/content/predicted_customer_spending_ridge.csv`. The parameter `index=False` ensures that the CSV file does not include row indices, maintaining a clean structure suitable for direct analysis or integration with other tools.\n",
        "  - Print a confirmation message to indicate that the Ridge Regression predictions have been successfully saved as CSV, providing feedback to the user about the completion of this task.\n",
        "  - To verify the correctness and structure of the saved predictions, display the first few rows of `predictions_ridge_df` using the `head()` method, which shows a snapshot of the data.\n",
        "\n",
        "#### <span style=\"color:green\">Saving Polynomial Regression Predictions</span>\n",
        "- <span style=\"color:green\">**Description**</span>: Similarly, after training and evaluating the Polynomial Regression model, saving its predictions is critical for comprehensive analysis and reporting. This step ensures that the predicted values from the Polynomial Regression model are stored in a format that facilitates easy access, comparison, and visualization alongside actual data.\n",
        "\n",
        "- <span style=\"color:green\">**Procedure**</span>:\n",
        "  - Construct a DataFrame `predictions_poly_df` to encapsulate the predicted total prices (`y_pred_poly`) generated by the Polynomial Regression model. The DataFrame includes a column named `'PredictedTotalPrice_Polynomial'`, clearly identifying these predictions.\n",
        "  - Utilize the `to_csv` method to export `predictions_poly_df` to a CSV file located at `/content/predicted_customer_spending_poly.csv`. Ensuring `index=False` maintains the CSV file without including row indices, preserving a straightforward format suitable for direct analysis or integration into reporting tools.\n",
        "  - Print a confirmation message indicating the successful completion of saving Polynomial Regression predictions as CSV, providing feedback to the user on the status of this operation.\n",
        "  - To validate the integrity and content of the saved predictions, display the initial rows of `predictions_poly_df` using the `head()` method, offering a quick glimpse into the stored data.\n",
        "\n",
        "#### <span style=\"color:green\">Summary</span>\n",
        "- <span style=\"color:green\">**Purpose**</span>: The primary purpose of saving predictions is to preserve model outputs in a structured format that supports subsequent analysis and presentation. By exporting predictions from both Ridge and Polynomial Regression models to CSV files, this step ensures that stakeholders can easily access and interpret the model's performance metrics. Saved predictions facilitate comparisons with actual data, enable visualization of model accuracy, and support informed decision-making in business or research contexts."
      ],
      "metadata": {
        "id": "duXciQdazDpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save predictions from Ridge Regression as CSV with description"
      ],
      "metadata": {
        "id": "Xlzne7AzKM6P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_ridge_df = pd.DataFrame(y_pred_ridge, columns=['PredictedTotalPrice_Ridge'])  # Create DataFrame with Ridge Regression predictions\n"
      ],
      "metadata": {
        "id": "VdItz_ZgKM8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_ridge_df.to_csv('/content/predicted_customer_spending_ridge.csv', index=False)  # Save predictions to CSV without index\n"
      ],
      "metadata": {
        "id": "5VncEo6PKM_P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nRidge Regression predictions saved as CSV.\")  # Print message indicating Ridge Regression predictions saved\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CreluPdGKNDY",
        "outputId": "c4283260-058c-4bdd-a7db-1d515fcd870d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Ridge Regression predictions saved as CSV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nQks31v9KNGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions_ridge_df.head())  # Display the first few rows of Ridge Regression predictions DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94NCgnbPKNI4",
        "outputId": "a21c463b-c282-4c24-8c95-7f1a1e6019c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   PredictedTotalPrice_Ridge\n",
            "0                   0.341206\n",
            "1                   7.204538\n",
            "2                  -2.504209\n",
            "3                   8.653997\n",
            "4                  41.729637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R_4XBshEKNLZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zcNr-xmMKNNS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save predictions from Polynomial Regression as CSV with description"
      ],
      "metadata": {
        "id": "yilrOkv3J9P9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_poly_df = pd.DataFrame(y_pred_poly, columns=['PredictedTotalPrice_Polynomial'])  # Create DataFrame with Polynomial Regression predictions\n"
      ],
      "metadata": {
        "id": "ZPAlKbd3Kg5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_poly_df.to_csv('/content/predicted_customer_spending_poly.csv', index=False)  # Save predictions to CSV without index\n"
      ],
      "metadata": {
        "id": "aEk9GIdZKg9L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nPolynomial Regression predictions saved as CSV.\")  # Print message indicating Polynomial Regression predictions saved\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqWMgL4wKhAs",
        "outputId": "c8962b1f-8b90-4c44-d4af-4848dd4ae099"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Polynomial Regression predictions saved as CSV.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h72mX7R2KhEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions_poly_df.head())  # Display the first few rows of Polynomial Regression predictions DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lw_2aEmKhGV",
        "outputId": "369b3692-1bd9-4419-f54d-660b6c2d3061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   PredictedTotalPrice_Polynomial\n",
            "0                            15.9\n",
            "1                            15.8\n",
            "2                            -3.4\n",
            "3                             6.6\n",
            "4                            13.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HrI99HB8KhIM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6hTr6kHLKhKB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Step 12: Visualization of Predictions</span>\n",
        "\n",
        "#### <span style=\"color:green\">Visualizing Ridge Regression Predictions</span>\n",
        "- <span style=\"color:green\">**Description**</span>: Visualizing the predictions made by the Ridge Regression model provides insights into how well the model predicts actual total prices. This scatter plot juxtaposes the actual total prices (`y_test`) against the predicted values (`y_pred_ridge`), using blue dots to represent Ridge Regression predictions. Additionally, a dashed black line illustrates perfect predictions, where actual and predicted values align perfectly.\n",
        "\n",
        "- <span style=\"color:green\">**Procedure**</span>:\n",
        "  - Create a new figure with a size of 10x6 inches (`plt.figure(figsize=(10, 6))`) to accommodate the Ridge Regression visualization.\n",
        "  - Generate a scatter plot (`plt.scatter`) with `y_test` as the x-axis (actual total price) and `y_pred_ridge` as the y-axis (predicted total price). Use the color blue (`color='blue'`) to differentiate Ridge Regression predictions and label it as 'Predicted (Ridge)'.\n",
        "  - Plot a diagonal dashed line (`plt.plot`) across the range of `y_test` values (`[y_test.min(), y_test.max()]`) to visually represent perfect predictions. This line is plotted in black (`'k--'`) with a line width of 3 (`lw=3`) and is labeled as 'Actual'.\n",
        "  - Set the title of the plot (`plt.title`) to 'Ridge Regression: Actual vs Predicted Total Price' to clearly indicate the visualization's focus.\n",
        "  - Label the x-axis (`plt.xlabel`) as 'Actual Total Price' and the y-axis (`plt.ylabel`) as 'Predicted Total Price' for clarity.\n",
        "  - Display a legend (`plt.legend()`) to distinguish between actual and predicted values.\n",
        "  - Save the plot as a PDF file (`plt.savefig('/content/ridge_regression_predictions.pdf')`) for future reference and reporting purposes.\n",
        "  - Finally, display the plot (`plt.show()`) to visualize and interpret the Ridge Regression predictions.\n",
        "\n",
        "#### <span style=\"color:green\">Visualizing Polynomial Regression Predictions</span>\n",
        "- <span style=\"color:green\">**Description**</span>: Similarly, visualizing predictions from the Polynomial Regression model helps assess its accuracy in predicting actual total prices. This scatter plot contrasts actual total prices (`y_test`) with predicted values (`y_pred_poly`), represented by green dots for Polynomial Regression predictions. A dashed black line signifies perfect predictions, providing a benchmark for evaluating model performance.\n",
        "\n",
        "- <span style=\"color:green\">**Procedure**</span>:\n",
        "  - Create another figure with dimensions 10x6 inches (`plt.figure(figsize=(10, 6))`) to accommodate the Polynomial Regression visualization.\n",
        "  - Generate a scatter plot (`plt.scatter`) with `y_test` as the x-axis (actual total price) and `y_pred_poly` as the y-axis (predicted total price). Use the color green (`color='green'`) to distinguish Polynomial Regression predictions and label it as 'Predicted (Polynomial)'.\n",
        "  - Plot a diagonal dashed line (`plt.plot`) spanning the range of `y_test` values (`[y_test.min(), y_test.max()]`) to visually represent perfect predictions. This line is drawn in black (`'k--'`) with a line width of 3 (`lw=3`) and is labeled as 'Actual'.\n",
        "  - Set the plot title (`plt.title`) to 'Polynomial Regression: Actual vs Predicted Total Price' to clearly delineate the focus of the visualization.\n",
        "  - Label the x-axis (`plt.xlabel`) as 'Actual Total Price' and the y-axis (`plt.ylabel`) as 'Predicted Total Price' for clarity and interpretation.\n",
        "  - Display a legend (`plt.legend()`) to differentiate between actual and predicted values in the plot.\n",
        "  - Save the visualization as a PDF file (`plt.savefig('/content/polynomial_regression_predictions.pdf')`) for archival and reporting purposes.\n",
        "  - Finally, display the plot (`plt.show()`) to observe and interpret the Polynomial Regression predictions in relation to actual total prices."
      ],
      "metadata": {
        "id": "yvJFJjD7zZg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize predictions from Ridge Regression with description"
      ],
      "metadata": {
        "id": "LdblHYPgKhMQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for Ridge Regression visualization\n",
        "plt.scatter(y_test, y_pred_ridge, color='blue', label='Predicted (Ridge)')  # Scatter plot of actual vs predicted values for Ridge Regression\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=3, label='Actual')  # Plotting the line of perfect predictions\n",
        "plt.title('Ridge Regression: Actual vs Predicted Total Price')  # Set title for the plot\n",
        "plt.xlabel('Actual Total Price')  # Set label for x-axis\n",
        "plt.ylabel('Predicted Total Price')  # Set label for y-axis\n",
        "plt.legend()  # Display legend\n",
        "plt.savefig('/content/ridge_regression_predictions.pdf')  # Save the plot as PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "3STsawZ0K10a",
        "outputId": "4a4480a4-749f-4f0d-cae3-8c87c621a545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Il6pVkEYK12i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EIsCOT6EK16x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize predictions from Polynomial Regression with description"
      ],
      "metadata": {
        "id": "kfkkJPuqK19-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for Polynomial Regression visualization\n",
        "plt.scatter(y_test, y_pred_poly, color='green', label='Predicted (Polynomial)')  # Scatter plot of actual vs predicted values for Polynomial Regression\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=3, label='Actual')  # Plotting the line of perfect predictions\n",
        "plt.title('Polynomial Regression: Actual vs Predicted Total Price')  # Set title for the plot\n",
        "plt.xlabel('Actual Total Price')  # Set label for x-axis\n",
        "plt.ylabel('Predicted Total Price')  # Set label for y-axis\n",
        "plt.legend()  # Display legend\n",
        "plt.savefig('/content/polynomial_regression_predictions.pdf')  # Save the plot as PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "BTwQsMqnK2Bc",
        "outputId": "8cf12d8f-9fb9-4241-e81f-ca26e8bb34e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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48Dm6d+8eV69eJV++fGTIkMFuGGlsvfbaa1y+fNluyf0VK1ZgsVh47bXXbPfp5ubGpk2buH79+hPFXbBgQbJmzUqePHl4/fXXyZcvH6tXr7br6XN3d6dTp052x3399dcULlyYQoUK2b3mo4fGRb/mo9+f+vTpY3d8bBa9WLduHTdv3mTo0KGkS5fOri42z19ixPg4cX1PdMbV1dX292OxWLh27RpRUVGULl36iV5b0UaPHk3WrFnx9/cnODiY48ePM3HiRJo0aWLXrlu3bo+dXxab5you710iKY2GNYo8Yz788EMKFChAmjRpyJYtGwULFrT9R3/q1ClcXFzIly+f3TH+/v5kyJDBljg54+LiQps2bZg7d65tkv/ixYtJly4dzZs3d2j/8Afc6IQi+oNh9H0VLFjQrp2bmxvPPfecQywBAQEOH7J8fX0JDAx0KHvwfmJy8OBBRo4cyYYNGxyWA39wDlVcBAUF8cknn2CxWDh+/Djjxo3jypUrdh9Ajh49imEY5M+f3+k5oodyRl//w89VmjRpYhyClidPHrvbR48eBaxJW0xCQ0PJmDEjkyZNokOHDgQGBlKqVCnq1q1L+/btee6552znHjBgAFOnTmXx4sVUqVKFBg0a0LZt2xiHNEZfx4PJc7To1elOnTplG3YHj3/dxNXhw4f566+/aN++vd3cteDgYD788EPCwsLw8fGxJX8PxpJQ7ty5w4QJE1iwYAHnzp3DMAxb3ZO89qLn5CxbtowaNWoA1iGNJUqUoECBAoA1aZo4cSIDBw4kW7ZslC9fnvr169O+fXv8/f1jdT8rV67Ex8eHtGnTEhAQQN68eR3a5MyZ0y65Buvr8PDhw2TNmtXpeaPnY0a/Pz183offI5yJHmb3pM9fYsT4OHF9T4zJokWLmDJlCkeOHOHevXu28offH+Kie/fuNG/eHBcXFzJkyGCbE/ew2NxHbJ6ruLx3iaQ0Ss5EnjFly5a1rdYYkyftCWjfvj2TJ0/m22+/pVWrVixZsoT69es7/XAe07enD34QjYuYzvck93Pjxg2qVauGj48P77zzDnnz5iVdunTs2bOHIUOGPPE3sp6entSsWdN2u1KlSpQsWZLhw4czc+ZMwPpttslkYs2aNU5jj5478iQe7JGJvi+AyZMnxzjXJPr+WrRoQZUqVfjmm2/45ZdfmDx5MhMnTmTVqlW88sorAEyZMoWOHTvy3Xff8csvv9CnTx8mTJjA77//7nQO3JOI79fNl19+CUD//v3p37+/Q/3KlSsdenqeREx/U84WmOnduzcLFiygX79+VKhQAV9fX0wmEy1btnyi1567uzuNGjXim2++Yc6cOVy6dIlt27Yxfvx4u3b9+vXj1Vdf5dtvv+Xnn39m1KhRTJgwgQ0bNvDiiy8+9n6qVq1qm2cXk4dfg2B9HRYrVoypU6c6PebhL1iSQkqIMTa+/PJLOnbsSKNGjRg8eDB+fn62BZuik6InkT9/frv3tpg4e/6fRFzeu0RSGiVnImKTO3duLBYLR48etdtX59KlS9y4cYPcuXM/8vjnn3+eF198kcWLFxMQEMDp06eZNWvWE8cC1n2xontnwLra2IkTJ2L1QeBJbdq0iatXr7Jq1SqqVq1qK49eeSy+vPDCC7Rt25aPPvqIQYMGkStXLvLmzYthGOTJk8fWq+FM9ONz7NgxqlevbiuPiori5MmTsdoLKfrbfR8fn1g9ntmzZ6dHjx706NGDy5cvU7JkScaNG2dLzgCKFStGsWLFGDlyJNu3b6dSpUrMmzeP9957L8brCAkJcSiPHjr6uNfc0zAMgyVLllC9enV69OjhUP/uu++yePFiOnXqZHsNRq9sGJOYkrDob/AfXmnUWW/HihUr6NChA1OmTLGV3b1796k2tn7ttddYtGgR69ev5/DhwxiGYRvS+KC8efMycOBABg4cyNGjRylRogRTpkyxJbEJIW/evOzbt48aNWo88ouh6Pen48eP2/UeOXv9OLsPsD5/D/c2Pyim+0+MGB8nLu+JMcW4YsUKnnvuOVatWmXXZvTo0U8dX3yJzXMV1/cukZREc85ExKZu3boATJ8+3a48+tvi2Kw41q5dO3755RemT59O5syZ7T64x0XNmjVxc3Nj5syZdr0i8+fPJzQ0NN5WP3MmunfmwfuNjIxkzpw58X5fb731Fvfu3bM9xk2aNMHV1ZWxY8c69AYZhsHVq1cBKF26NJkzZ+aTTz4hKirK1mbx4sWxHuJXqlQp8ubNywcffMCtW7cc6qNXGDSbzQ7D6fz8/MiRI4dtGfGwsDC7OMCaqLm4uDxyG4a6deuya9cuduzYYSu7ffs2H3/8MUFBQRQpUiRW1/Kg2C6lv23bNk6ePEmnTp1o1qyZw89rr73Gxo0bOX/+PFmzZqVq1ap89tlnnD592u48Dz5Pnp6egGMS5uPjQ5YsWdiyZYtdubPXlKurq8NzP2vWrKfaxqFmzZpkypSJZcuWsWzZMsqWLWs3xCw8PJy7d+/aHZM3b168vb0f+fzFhxYtWnDu3Dk++eQTh7o7d+7YVvWLfi+J7mWO9vD7lTMvv/wy3t7eTJgwweE6H37+nA0dTYwYHycu74kxXYez97adO3fa/f0ltdg8V7F97xJJidRzJiI2xYsXp0OHDnz88ce2oX27du1i0aJFNGrUyK6HJiatW7fmrbfe4ptvvuHNN9+0W+4+LrJmzcqwYcMYO3YsderUoUGDBoSEhDBnzhzKlCnjdJGR+FKxYkUyZsxIhw4d6NOnDyaTiS+++OKJh849SpEiRahbty6ffvopo0aNIm/evLz33nsMGzbMtjS+t7c3J06c4JtvvqF79+4MGjQINzc3xowZQ+/evXnppZdo0aIFJ0+eZOHCheTNmzdWQ1NdXFz49NNPeeWVVyhatCidOnUiZ86cnDt3jo0bN+Lj48MPP/zAzZs3CQgIoFmzZhQvXhwvLy9+/fVX/vjjD1vvzoYNG+jVqxfNmzenQIECREVF8cUXX+Dq6krTpk1jjGHo0KEsXbqUV155hT59+pApUyYWLVrEiRMnWLly5RNtkh3bpfQXL16Mq6trjIl+gwYNGDFiBF999RUDBgxg5syZVK5cmZIlS9K9e3fy5MnDyZMnWb16NXv37gWsHxoBRowYQcuWLUmbNi2vvvoqnp6edO3alffff5+uXbtSunRptmzZwj///ONwv/Xr1+eLL77A19eXIkWKsGPHDn799VeHZcnjIm3atDRp0oSvvvqK27dv88EHH9jV//PPP9SoUYMWLVpQpEgR0qRJwzfffMOlS5fsFtpJCO3atWP58uW88cYbbNy4kUqVKmE2mzly5AjLly/n559/pnTp0pQoUYJWrVoxZ84cQkNDqVixIuvXr3fY584ZHx8fpk2bRteuXSlTpgytW7cmY8aM7Nu3j/DwcNs2CqVKlWLZsmUMGDCAMmXK4OXlxauvvpooMT5OXN4TY7qO+vXrs2rVKho3bky9evU4ceIE8+bNo0iRIk6TnKQQm+cqtu9dIilS4i4OKSJJJXq54j/++OOR7e7du2eMHTvWyJMnj5E2bVojMDDQGDZsmN3y8YbhuJT+g+rWrWsAxvbt22MdR0zLgs+ePdsoVKiQkTZtWiNbtmzGm2++aVy/ft0hlqJFizrcV+7cue2WqY8G2C0L72wp/W3bthnly5c3PDw8jBw5chhvvfWWbRn+B2OMy1L6zmI0DMPYtGmTw7LVK1euNCpXrmx4enoanp6eRqFChYyePXsaISEhdsfOnDnTyJ07t+Hu7m6ULVvW2LZtm1GqVCmjTp06tjbRj21My9z/9ddfRpMmTYzMmTMb7u7uRu7cuY0WLVoY69evNwzDumz/4MGDjeLFixve3t6Gp6enUbx4cWPOnDm2c/z7779G586djbx58xrp0qUzMmXKZFSvXt349ddf7e7r4aX0DcMwjh8/bjRr1szIkCGDkS5dOqNs2bLGjz/+aNcmpmtwtkR9bJbSj4yMNDJnzmxUqVIlxjaGYRh58uQxXnzxRdvtAwcOGI0bN7bFWrBgQWPUqFF2x7z77rtGzpw5DRcXF7vXVXh4uNGlSxfD19fX8Pb2Nlq0aGFcvnzZIdbr168bnTp1MrJkyWJ4eXkZtWvXNo4cOeLw2MV2Kf1o69atMwDDZDIZZ86csav777//jJ49exqFChUyPD09DV9fX6NcuXLG8uXLH3ve6KX0r1y58sh2j/obiIyMNCZOnGgULVrUcHd3NzJmzGiUKlXKGDt2rBEaGmprd+fOHaNPnz5G5syZDU9PT+PVV181zpw589il9KN9//33RsWKFQ0PDw/Dx8fHKFu2rLF06VJb/a1bt4zWrVsbGTJkMAC7v+34jvFxHl5KP1ps3hNjug6LxWKMHz/e9p7x4osvGj/++KPT97HYxBv99/e4LRge9f/Pkz5XhvH49y6RlMhkGAnwVbCIPNMaN27M33//HS/fFkvcWCwWsmbNSpMmTZwOwRIREZHkS3PORCReXbhwgdWrV9OuXbukDiXVu3v3rsNQy88//5xr164RHBycNEGJiIjIE1PPmYjEixMnTrBt2zY+/fRT/vjjD44fPx7r/ZHkyWzatIn+/fvTvHlzMmfOzJ49e5g/fz6FCxdm9+7dDvtJiYiISPKmBUFEJF5s3ryZTp06kStXLhYtWqTELBEEBQURGBjIzJkzuXbtGpkyZaJ9+/a8//77SsxERERSIPWciYiIiIiIJAOacyYiIiIiIpIMKDkTERERERFJBjTnLAFYLBbOnz+Pt7d3rDaCFRERERGR1MkwDG7evEmOHDlwcXl035iSswRw/vx5AgMDkzoMERERERFJJs6cOUNAQMAj2yg5SwDe3t6A9Qnw8fFJ4mhERERERCSphIWFERgYaMsRHkXJWQKIHsro4+Oj5ExERERERGI13UkLgoiIiIiIiCQDSs5ERERERESSASVnIiIiIiIiyYDmnCURwzCIiorCbDYndSgiyZqrqytp0qTRthQiIiKS6ik5SwKRkZFcuHCB8PDwpA5FJEVInz492bNnx83NLalDEREREUkwSs4SmcVi4cSJE7i6upIjRw7c3NzUIyASA8MwiIyM5MqVK5w4cYL8+fM/dvNGERERkZRKyVkii4yMxGKxEBgYSPr06ZM6HJFkz8PDg7Rp03Lq1CkiIyNJly5dUockIiIikiD0FXQS0bf/IrGnvxcRERF5FugTj4iIiIiISDKg5ExERERERCQZUHImyU7Hjh1p1KiR7XZwcDD9+vVL9Dg2bdqEyWTixo0bj2y3fv16ChcuHG/bIpw8eRKTycTevXvj5XwJKbaP0YMefj7Lly/PypUr4z84ERERkRRGyZnESseOHTGZTJhMJtzc3MiXLx/vvPMOUVFRCX7fq1at4t13341V2ydJFp7WW2+9xciRI3F1dQVg4cKFtsfKxcWFgIAAOnXqxOXLlxMtpsRSsWJFLly4gK+v7xOfY+TIkQwdOhSLxRKPkYmIiIikPErOUiizxcymk5tY+vdSNp3chNmS8JtZ16lThwsXLnD06FEGDhzImDFjmDx5stO2kZGR8Xa/mTJlwtvbO97OF59+++03jh8/TtOmTe3KfXx8uHDhAmfPnuWTTz5hzZo1tGvXLomiTDhubm74+/s/1XYQr7zyCjdv3mTNmjXxGJmIiIhIyqPkLAVadXgVQTOCqL6oOq1Xtab6ouoEzQhi1eFVCXq/7u7u+Pv7kzt3bt58801q1qzJ999/D9wfijhu3Dhy5MhBwYIFAThz5gwtWrQgQ4YMZMqUiYYNG3Ly5EnbOc1mMwMGDCBDhgxkzpyZt956C8Mw7O734WFwERERDBkyhMDAQNzd3cmXLx/z58/n5MmTVK9eHYCMGTNiMpno2LEjYN1fbsKECeTJkwcPDw+KFy/OihUr7O7np59+okCBAnh4eFC9enW7OGPy1VdfUatWLYfl3U0mE/7+/uTIkYNXXnmFPn368Ouvv3Lnzh0sFgvvvPMOAQEBuLu7U6JECdauXev0/IZhkC9fPj744AO78r1792IymTh27Jjt/j799FMaN25M+vTpyZ8/v+25ibZ582bKli2Lu7s72bNnZ+jQoXY9n8HBwfTu3Zt+/fqRMWNGsmXLxieffMLt27fp1KkT3t7e5MuXzy6Jerin8urVq7Rq1YqcOXOSPn16ihUrxtKlSx/5GLq6ulK3bl2++uqrRz/YIiIiIrGQFJ0Y8UXJWQqz6vAqmi1vxtmws3bl58LO0Wx5swRP0B7k4eFh10O2fv16QkJCWLduHT/++CP37t2jdu3aeHt7s3XrVrZt24aXlxd16tSxHTdlyhQWLlzIZ599xm+//ca1a9f45ptvHnm/7du3Z+nSpcycOZPDhw/z0Ucf4eXlRWBgoG3uUkhICBcuXGDGjBkATJgwgc8//5x58+Zx8OBB+vfvT9u2bdm8eTNgTSKbNGnCq6++yt69e+natStDhw597GOwdetWSpcuHavHymKxEBUVxYwZM5gyZQoffPAB+/fvp3bt2jRo0ICjR486HGcymejcuTMLFiywK1+wYAFVq1YlX758trKxY8fSokUL9u/fT926dWnTpg3Xrl0D4Ny5c9StW5cyZcqwb98+5s6dy/z583nvvffszrto0SKyZMnCrl276N27N2+++SbNmzenYsWK7Nmzh5dffpl27doRHh7u9Drv3r1LqVKlWL16NQcOHKB79+60a9eOXbt2PfLxKVu2LFu3bn3s4ygiIiLyKEnViRFvDIl3oaGhBmCEhoY61N25c8c4dOiQcefOnTifN8ocZQRMDTAYg9Mf0xiTETg10IgyR8XHZdjp0KGD0bBhQ8MwDMNisRjr1q0z3N3djUGDBtnqs2XLZkRERNiO+eKLL4yCBQsaFovFVhYREWF4eHgYP//8s2EYhpE9e3Zj0qRJtvp79+4ZAQEBtvsyDMOoVq2a0bdvX8MwDCMkJMQAjHXr1jmNc+PGjQZgXL9+3VZ29+5dI3369Mb27dvt2nbp0sVo1aqVYRiGMWzYMKNIkSJ29UOGDHE418N8fX2Nzz//3K5swYIFhq+vr+32P//8YxQoUMAoXbq0YRiGkSNHDmPcuHF2x5QpU8bo0aOHYRiGceLECQMw/vrrL8MwDOPcuXOGq6ursXPnTsMwDCMyMtLIkiWLsXDhQtvxgDFy5Ejb7Vu3bhmAsWbNGsMwDGP48OEOz8WHH35oeHl5GWaz2TAM6+NcuXJlW31UVJTh6elptGvXzlZ24cIFAzB27NhhGIbzx/th9erVMwYOHGi7/eDzGe27774zXFxcbLE87Gn+bkREROTZsPLQSsM0xmTwtuNnZNMYk7Hy0MokietRucHD0iRVUihxt/X0VoceswcZGJwJO8PW01sJDgqO9/v/8ccf8fLy4t69e1gsFlq3bs2YMWNs9cWKFcPNzc12e9++fRw7dsxhvtjdu3c5fvw4oaGhXLhwgXLlytnq0qRJQ+nSpR2GNkbbu3cvrq6uVKtWLdZxHzt2jPDwcGrVqmVXHhkZyYsvvgjA4cOH7eIAqFChwmPPfefOHYchjQChoaF4eXlhsVi4e/culStX5tNPPyUsLIzz589TqVIlu/aVKlVi3759Tu8jR44c1KtXj88++4yyZcvyww8/EBERQfPmze3avfDCC7bfPT098fHxsS1CcvjwYSpUqGA3N6xSpUrcunWLs2fPkitXLodzuLq6kjlzZooVK2Yry5YtG0CMi5uYzWbGjx/P8uXLOXfuHJGRkURERJA+fXqn7aNF9yxGRETg4eHxyLYiIiIiDzNbzPRZ0wfjgAEbgXZABmudgYEJE/3W9qNhwYa4urgmYaSPpuQsBblw80K8tour6tWrM3fuXNzc3MiRIwdp0ti/fDw9Pe1u37p1i1KlSrF48WKHc2XNmvWJYniSD+63bt0CYPXq1eTMmdOuzt3d/YniiJYlSxauX7/uUO7t7c2ePXtwcXEhe/bstrjDwsKe6H66du1Ku3btmDZtGgsWLOC1115zSHjSpk1rd9tkMsV5BURn53iwLDq5i+m8kydPZsaMGUyfPp1ixYrh6elJv379HrtAzLVr1/D09FRiJiIiIk9k6+mtnJt/Dg79v+AXoMX9+oTuxIgvKWbOmdlsZtSoUbYFHfLmzcu7775r18NiGAZvv/227cNwzZo1HebxXLt2jTZt2uDj40OGDBno0qWL7cN7tP3791OlShXSpUtHYGAgkyZNSpRrfJzs3tnjtV1ceXp6ki9fPnLlyuWQmDlTsmRJjh49ip+fH/ny5bP78fX1xdfXl+zZs7Nz507bMVFRUezevTvGcxYrVgyLxWKbK/aw6J67B/ccK1KkCO7u7pw+fdohjsDAQAAKFy7sMC/q999/f+w1vvjiixw6dMih3MXFhXz58vHcc8/ZJRw+Pj7kyJGDbdu22bXftm0bRYoUifF+6tati6enJ3PnzmXt2rV07tz5sbE9qHDhwuzYscPu72Xbtm14e3sTEBAQp3M9yrZt22jYsCFt27alePHiPPfcc/zzzz+PPe7AgQO2XkwRERGRuLpw8wLkeaDgEHA8hnbJWIpJziZOnMjcuXOZPXs2hw8fZuLEiUyaNIlZs2bZ2kyaNImZM2cyb948du7ciaenJ7Vr1+bu3bu2Nm3atOHgwYO2RSu2bNlC9+7dbfVhYWG8/PLL5M6dm927dzN58mTGjBnDxx9/nKjX60yVXFUI8AnAhPNly02YCPQJpEquKokcmXNt2rQhS5YsNGzYkK1bt3LixAk2bdpEnz59OHvWOjyzb9++vP/++3z77bccOXKEHj16PHKPsqCgIDp06EDnzp359ttvbedcvnw5ALlz58ZkMvHjjz9y5coVbt26hbe3N4MGDaJ///4sWrSI48ePs2fPHmbNmsWiRYsAeOONNzh69CiDBw8mJCSEJUuWsHDhwsdeY+3atfntt9/i9LgMHjyYiRMnsmzZMkJCQhg6dCh79+6lb9++MR7j6upKx44dGTZsGPnz54/VkMsH9ejRgzNnztC7d2+OHDnCd999x+jRoxkwYAAuLvH3NpA/f37WrVvH9u3bOXz4MK+//jqXLl167HFbt27l5Zdfjrc4RERE5NmS3Ts7lAIe7KM4HEO7ZCzFJGfbt2+nYcOG1KtXj6CgIJo1a8bLL79s6+0wDIPp06czcuRIGjZsyAsvvMDnn3/O+fPn+fbbbwHrvJu1a9fy6aefUq5cOSpXrsysWbP46quvOH/+PACLFy8mMjKSzz77jKJFi9KyZUv69OnD1KlTk+rSbVxdXJlRx7r64MMJWvTt6XWmJ5txtOnTp2fLli3kypWLJk2aULhwYbp06cLdu3fx8fEBYODAgbRr144OHTpQoUIFvL29ady48SPPO3fuXJo1a0aPHj0oVKgQ3bp14/bt2wDkzJmTsWPHMnToULJly0avXr0AePfddxk1ahQTJkygcOHC1KlTh9WrV5Mnj/Urlly5crFy5Uq+/fZbihcvzrx58xg/fvxjrzE62Q8JCYn149KnTx8GDBjAwIEDKVasGGvXruX7778nf/78jzyuS5cuREZG0qlTp1jfV7ScOXPy008/sWvXLooXL84bb7xBly5dGDlyZJzP9SgjR46kZMmS1K5dm+DgYPz9/WnUqNEjjzl37hzbt29/ousSERGRZ4+ztQmq5KpCQIYAqAv4Yh3SWO9+fXLrxIiJyYhp5YVkZvz48Xz88cf88ssvFChQgH379vHyyy8zdepU2rRpw7///kvevHn566+/KFGihO24atWqUaJECWbMmMFnn33GwIED7eYIRUVFkS5dOr7++msaN25M+/btCQsLsyV0ABs3buSll17i2rVrZMyY0SG2iIgIIiIibLfDwsIIDAwkNDTUloREu3v3LidOnCBPnjxOF5KIjVWHV9F3bV+7xUECfQKZXmc6TQo3eaJzypMbPHgwYWFhfPTRRwl6P1u3bqVGjRqcOXPGtjBHajBkyBCuX7/+yN7p+Pi7ERERkZTtxo0bjB49mvDwcD755BOH+ugtpwyzAQ/0VUR3YqxosSJJPiuHhYXh6+vrNDd4WIpZEGTo0KGEhYVRqFAhXF1dMZvNjBs3jjZt2gBw8eJFAIcPrdmyZbPVXbx4ET8/P7v6NGnSkClTJrs20b0pD54jus5ZcjZhwgTGjh0bD1cZO00KN6FhwYZsPb2VCzcvkN07O1VyVUk2PWbPmhEjRjBnzhwsFku8DhGMFhERwZUrVxgzZgzNmzdPVYkZgJ+fHwMGDEjqMERERCSZslgsfP755wwZMsS2YnTnzp0dpnk0KdyEFS1WOHRiBPgEpJhOjBSTnC1fvpzFixezZMkSihYtyt69e+nXrx85cuSgQ4cOSRrbsGHD7D5cRvecJSRXF9dkvdLMsyRDhgwMHz48wc6/dOlSunTpQokSJfj8888T7H6SysCBA5M6BBEREUmm9uzZQ69evdixY4ddec+ePfnjjz9wdbXvnEjpnRgpJjkbPHgwQ4cOpWXLloB11b5Tp04xYcIEOnTogL+/PwCXLl0ie/b7E/0uXbpkG+bo7+/vsD9TVFQU165dsx3v7+/vsIBB9O3oNg9zd3d/6iXZRWLSsWNHOnbsmNRhiIiIiCSaa9euMXLkSObNm+d0jtmhQ4f466+/KF26tENdSu7ESDELgoSHhzsMGXN1dbXtt5QnTx78/f1Zv369rT4sLIydO3faujwrVKjAjRs37JZq37BhAxaLxbYBcYUKFdiyZQv37t2ztVm3bh0FCxZ0OqRRRERERETih8Vi4ZNPPqFAgQLMnTvXaWLWoEEDDh065DQxS+lSTHL26quvMm7cOFavXs3Jkyf55ptvmDp1qm1lP5PJRL9+/Xjvvff4/vvv+fvvv2nfvj05cuSwrRYXvUpft27d2LVrF9u2baNXr160bNmSHDlyANC6dWvc3Nzo0qULBw8eZNmyZcyYMUNzYkREREREEtAff/xB+fLl6d69O1evXnWoz5cvH6tXr+a7777jueeeS4IIE16KGdY4a9YsRo0aRY8ePbh8+TI5cuTg9ddf5+2337a1eeutt7h9+zbdu3fnxo0bVK5cmbVr19qt7rZ48WJ69epFjRo1cHFxoWnTpsycOdNW7+vryy+//ELPnj0pVaoUWbJk4e2337bbC01EREREROLHf//9x7Bhw5g/f77TnjIPDw9GjhzJgAEDUv2qzSlmKf2U5FHLZWpJcJG409+NiIhI6vTxxx8zdOhQu62uHtS0aVOmTp1Krly5Ejmy+JMql9IXEREREZHU5fDhw04Ts4IFCzJr1ixq1aqVBFElnRQz50xERERERFKXMWPG2O3h6unpycSJE9m/f/8zl5iBkjNJJUwmE99++21ShyEiIiIiceDr68ukSZMAaNmyJSEhIbz11lu4ubklcWRJQ8mZxNmOHTtwdXWlXr16cTouKCiI6dOnJ0xQIiIiIpIsbdmyhUWLFsVY37ZtW3bu3MnSpUvJmTNnIkaW/GjOWTJy5cqVJz7Wy8sLDw8Pp3X//fefw8o3WbNmfeL7mj9/Pr1792b+/PmcP3/etg2BiIiIiEi08+fP89Zbb7F48WLSp0/PSy+9RGBgoEM7FxcXypYtmwQRJj/qOUtG/Pz8nvjns88+i/G8hQsXdmj/pG7dusWyZct48803qVevHgsXLrSr/+GHHyhTpgzp0qUjS5Ystn3ogoODOXXqFP3798dkMmEymQDrOOMSJUrYnWP69OkEBQXZbv/xxx/UqlWLLFmy4OvrS7Vq1dizZ88TX4OIiIiIJJx79+7xwQcfULBgQRYvXgxAeHg4gwYNSuLIkj8lZxIny5cvp1ChQhQsWJC2bdvy2Wef2XrlVq9eTePGjalbty5//fUX69evt30LsmrVKgICAnjnnXe4cOECFy5ciPV93rx5kw4dOvDbb7/x+++/kz9/furWrcvNmzcT5BpFRERE5MmsX7+e4sWLM3jwYG7dumVXt3z5cn777bckiixl0LBGiZP58+fTtm1bAOrUqUNoaCibN28mODiYcePG0bJlS8aOHWtrX7x4cQAyZcqEq6sr3t7e+Pv7x+k+X3rpJbvbH3/8MRkyZGDz5s3Ur1//Ka9IRERERJ7WmTNnGDhwIF9//bXTel9fX9577z3Kly+fyJGlLOo5k1gLCQlh165dtGrVCoA0adLw2muvMX/+fAD27t1LjRo14v1+L126RLdu3cifPz++vr74+Phw69YtTp8+He/3JSIiIiKxFxERwYQJEyhUqFCMiVnnzp35559/6NWrF2nSqG/oUfToJCOXL19+4mO9vLxirDt8+LDDgiBPYv78+URFRdktAGIYBu7u7syePTvGBUkexcXFxSG2e/fu2d3u0KEDV69eZcaMGeTOnRt3d3cqVKhAZGTkk12IiIiIiDy1tWvX0qdPH44ePeq0vmTJknz44YfqLYsDJWfJyNOsoPgoWbJkeepzREVF8fnnnzNlyhRefvllu7pGjRqxdOlSXnjhBdavX0+nTp2cnsPNzQ2z2WxXljVrVi5evIhhGLZFQvbu3WvXZtu2bcyZM4e6desC1m7z//7776mvSURERETi7uTJk/Tv3z/GPWYzZcrE+PHj6dq1K66urokbXAqn5Exi5ccff+T69et06dIFX19fu7qmTZsyf/58Jk+eTI0aNcibNy8tW7YkKiqKn376iSFDhgDWfc62bNlCy5YtcXd3J0uWLAQHB3PlyhUmTZpEs2bNWLt2LWvWrMHHx8d2/vz58/PFF19QunRpwsLCGDx48BP10omIiIjI0xswYIDTxMxkMtG9e3fGjRtH5syZEz+wVEBzziRW5s+fT82aNR0SM7AmZ3/++SeZMmXi66+/5vvvv6dEiRK89NJL7Nq1y9bunXfe4eTJk+TNm9fWS1i4cGHmzJnDhx9+SPHixdm1a5fDMqvz58/n+vXrlCxZknbt2tGnT5+n2g5ARERERJ7cxIkTcXNzsysrV64cu3btYt68eUrMnoLJiI/JSGInLCwMX19fQkND7XqAAO7evcuJEyfIkycP6dKlS6IIRVIW/d2IiIgkLyNGjGD8+PFkyZKFiRMn0rFjR1xc1O/jzKNyg4dpWKOIiIiIiNgJDw/n33//5fnnn3daP3z4cAzDYPDgwWTMmDGRo0u9lN6KiIiIiAhgXYl71apVFC5cmHr16hEeHu60naenJ+PHj1diFs+UnImIiIiICCEhIdSpU4emTZty+vRpTp8+zfvvv5/UYT1TlJyJiIiIiDzDbt26xdChQylWrBi//PKLXd2kSZM4fvx4EkX27NGcsySidVhEYk9/LyIiIvHPMAyWL1/OwIEDOXfunNM25cuXx2KxJHJkzy71nCWytGnTAsQ4fldEHEX/vUT//YiIiMjTOXToEDVr1qRly5ZOE7McOXKwdOlSNm7cSP78+ZMgwmeTes4SmaurKxkyZODy5csApE+fHpPJlMRRiSRPhmEQHh7O5cuXyZAhA66urkkdkoiISIoWFhbG2LFjmTlzJlFRUQ71adKkYcCAAYwaNQovL68kiPDZpuQsCfj7+wPYEjQRebQMGTLY/m5EREQk7gzDYPHixQwePJiLFy86bVOzZk1mzZpFoUKFEjk6iabkLAmYTCayZ8+On58f9+7dS+pwRJK1tGnTqsdMRETkKXXt2pXPPvvMaV1gYCDTpk2jSZMmGtGVxJScJSFXV1d96BQRERGRBNeyZUuH5MzNzY3BgwczbNgwPD09kygyeZAWBBERERERSeVq1apF06ZNbbfr1q3LwYMHee+995SYJSNKzkREREREUglni3xEmzp1Ks8//zzff/89P/74I/ny5UvEyCQ2NKxRRERERCSFu3btGiNGjODw4cNs3LjR6dyxXLlysX//fs0rS8bUcyYiIiIikkKZzWY++eQTChQowLx589i8eTOLFy+Osb0Ss+RNyZmIiIiISAq0a9cuypcvT/fu3bl69aqtfPDgwYSFhSVhZPKklJyJiIiIiKQgV65coVu3bpQvX54///zToT40NJQ//vgjCSKTp6XkTEREREQkBTCbzcyZM4eCBQvy6aefYhiGQ5tmzZpx5MgRatSokQQRytPSgiAiIiIiIsnc9u3b6dmzJ3v37nVaX6hQIWbOnEmtWrUSNzCJV+o5ExERERFJpi5dukTHjh2pVKmS08TM09OTSZMmsW/fPiVmqYB6zkREREREkqFPP/2UgQMHxri4R6tWrZg8eTI5c+ZM5MgkoSg5ExERERFJhu7eves0MStatCizZ88mODg48YOSBKVhjSIiIiIiydAbb7xB8eLFbbd9fHyYNm0af/31lxKzVErJmYiIiIhIMpQmTRo+/PBDTCYT7du3JyQkhH79+pE2bdqkDk0SiIY1ioiIiIgkkfXr17N9+3ZGjRrltL5SpUqEhISQP3/+RI5MkoKSMxERERGRRHbmzBkGDhzI119/DUDt2rUpW7as07ZKzJ4dGtYoIiIiIpJIIiIimDBhAoUKFbIlZgA9e/bEbDYnYWSSHCg5ExERERFJBGvXrqVYsWIMHz6c8PBwu7o///yTFStWJFFkklwoORMRERERSUAnT56kcePGvPLKKxw9etShPlOmTHz00Uc0a9YsCaKT5ERzzkREREREEsDdu3eZNGkSEyZM4O7duw71JpOJ119/nffee4/MmTMnQYSS3Cg5ExERERGJZz/88AP9+vXj33//dVpfrlw5PvzwQ0qVKpXIkUlypmGNIiIiIiLx5Pjx49SvX58GDRo4TcyyZs3KZ599xvbt25WYiQMlZyIiIiIi8WTWrFmsXr3aodzFxYXevXsTEhJCp06dcHHRx3BxlKJeFefOnaNt27ZkzpwZDw8PihUrxp9//mmrNwyDt99+m+zZs+Ph4UHNmjUdJl1eu3aNNm3a4OPjQ4YMGejSpQu3bt2ya7N//36qVKlCunTpCAwMZNKkSYlyfSIiIiKSso0ZM4asWbPalVWuXJk9e/Ywc+ZMMmbMmESRSUqQYpKz69evU6lSJdKmTcuaNWs4dOgQU6ZMsXuBT5o0iZkzZzJv3jx27tyJp6cntWvXtpuA2aZNGw4ePMi6dev48ccf2bJlC927d7fVh4WF8fLLL5M7d252797N5MmTGTNmDB9//HGiXq+IiIiIpDwZMmRg4sSJAPj7+/PFF1+wZcsWihcvnsSRSUpgMgzDSOogYmPo0KFs27aNrVu3Oq03DIMcOXIwcOBABg0aBEBoaCjZsmVj4cKFtGzZksOHD1OkSBH++OMPSpcuDVj3m6hbty5nz54lR44czJ07lxEjRnDx4kXc3Nxs9/3tt99y5MiRWMUaFhaGr68voaGh+Pj4xMPVi4iIiEhycevWLfbs2UPVqlWd1lssFmbMmEGXLl30WVDilBukmJ6z77//ntKlS9O8eXP8/Px48cUX+eSTT2z1J06c4OLFi9SsWdNW5uvrS7ly5dixYwcAO3bsIEOGDLbEDKBmzZq4uLiwc+dOW5uqVavaEjOA2rVrExISwvXr153GFhERQVhYmN2PiIiIiKQuhmGwfPlyChcuTL169Th37pzTdi4uLvTv31+JmcRZiknO/v33X+bOnUv+/Pn5+eefefPNN+nTpw+LFi0C4OLFiwBky5bN7rhs2bLZ6i5evIifn59dfZo0aciUKZNdG2fnePA+HjZhwgR8fX1tP4GBgU95tSIiIiKSnBw6dIiaNWvy2muvcfbsWW7dusXgwYOTOixJZVJMcmaxWChZsiTjx4/nxRdfpHv37nTr1o158+YldWgMGzaM0NBQ28+ZM2eSOiQRERERiQdhYWEMHDiQ4sWLs2HDBru6pUuXsmnTpqQJTFKlFJOcZc+enSJFitiVFS5cmNOnTwPWCZcAly5dsmtz6dIlW52/vz+XL1+2q4+KiuLatWt2bZyd48H7eJi7uzs+Pj52PyIiIiKSchmGweLFiylUqBBTp04lKirKoU2tWrXIkSNHEkQnqVWKSc4qVapESEiIXdk///xD7ty5AciTJw/+/v6sX7/eVh8WFsbOnTupUKECABUqVODGjRvs3r3b1mbDhg1YLBbKlStna7Nlyxbu3btna7Nu3ToKFiyopU9FREREngH79++nWrVqtG3blgsXLjjU58qVi5UrV/Lzzz9ToECBJIhQUqsUk5z179+f33//nfHjx3Ps2DGWLFnCxx9/TM+ePQEwmUz069eP9957j++//56///6b9u3bkyNHDho1agRYe9rq1KlDt27d2LVrF9u2baNXr160bNnS9q1H69atcXNzo0uXLhw8eJBly5YxY8YMBgwYkFSXLiIiIiKJ4MaNG/Tt25eSJUs6XSHczc2NESNGcPjwYZo0aYLJZEqCKCU1SzFL6QP8+OOPDBs2jKNHj5InTx4GDBhAt27dbPWGYTB69Gg+/vhjbty4QeXKlZkzZ47dNxrXrl2jV69e/PDDD7i4uNC0aVNmzpyJl5eXrc3+/fvp2bMnf/zxB1myZKF3794MGTIk1nFqKX0RERGRlMNisfD5558zZMgQhykw0erWrcuMGTPIly9fIkcnKV1ccoMUlZylFErORERERFKOQYMGMWXKFKd1efLkYcaMGdSvX189ZfJEUuU+ZyIiIiIiCaFbt26kTZvWrixdunSMGTOGgwcP8uqrryoxk0Sh5ExEREREnmkFCxakf//+ttsNGzbk0KFDjB49Gg8PjySMTJ41Ss5ERERE5Jlw69atGOtGjRpFcHAwP/30E99++y158uRJxMhErJSciYiIiEiqduXKFbp27Urx4sW5c+eO0zZeXl5s3LiRV155JZGjE7lPyZmIiIiIpEpms5kPP/yQAgUKMH/+fP79918mTZqU1GGJxEjJmYiIiIikOtu2baN06dL06tWLGzdu2Mrff/99Tpw4kXSBiTyCkjMRERERSTUuXrxIhw4dqFy5Mnv37nWod3V1Zd++fYkfmEgsKDkTERERkRQvKiqK6dOnU7BgQT7//HOnbVq1akVISAiNGjVK3OBEYilNUgcgIiIiIvI0Nm/eTK9evThw4IDT+qJFizJ79myCg4MTNzCROFLPmYiIiIikSOfPn6d169YEBwc7Tcx8fHyYNm0af/31lxIzSRHUcyYiIiIiKc7ChQvp3bt3jHuXtWvXjkmTJuHv75/IkYk8OSVnIiIiIpLi+Pv7O03MihcvzuzZs6lcuXISRCXydDSsUURERERSnDp16tgt7JEhQwZmz57Nn3/+qcRMUiwlZyIiIiKSIk2bNo306dPTpUsXQkJC6NmzJ2nSaGCYpFx69YqIiIhIsrR27Vq+/vprPv30U0wmk0N9UFAQ//77L9myZUuC6ETin3rORERERCRZOXHiBI0aNeKVV17hs88+Y9myZTG2VWImqYmSMxERERFJFu7cucPYsWMpUqQI3333na184MCB3Lx5MwkjE0kcSs5EREREJEkZhsH3339P0aJFGTNmDHfv3rWrP3/+PAsWLEii6EQSj5IzEREREUkyx44do379+jRs2JATJ0441GfNmpXPPvuMXr16JUF0IolLyZmIiIiIJLrw8HBGjhxJ0aJF+emnnxzqXVxc6N27N//88w+dOnXCxUUfWyX102qNIiIiIpJoDMPgm2++oX///pw+fdppm8qVK/Phhx/ywgsvJHJ0IklLX0GIiIiISKIICQmhdu3aNG3a1Gli5u/vz5dffsmWLVuUmMkzScmZiIiIiCSKtWvXsm7dOodyV1dXBgwYQEhICG3atHG6p5nIs0DJmYiIiIgkip49e/L888/blVWvXp19+/YxZcoUfHx8kigykeRByZmIiIiIJIo0adLw4YcfApAzZ06++uor1q9fT9GiRZM4MpHkQcmZiIiIiMSbsLAwvv766xjrq1atypIlSzhy5AivvfaahjCKPEDJmYiIiIg8NcMw+PLLLylYsCCvvfYaf/75Z4xtW7VqhZeXVyJGJ5IyKDkTERERkaeyb98+qlatSrt27bh48SKGYdCrVy8sFktShyaSoig5ExEREZEncuPGDfr06UPJkiX57bff7Op27tzJggULkigykZRJm1CLiIiISJxYLBYWLVrEkCFDuHLlitM2devWpVq1aokcmUjKpuRMRERERGJt9+7d9OrVi99//91pfZ48eZgxYwavvvpqIkcmkvJpWKOIiIiIPNa1a9d48803KVOmjNPELF26dIwdO5aDBw8qMRN5Quo5ExEREZEYmc1m5s+fz/Dhw7l69arTNo0aNWLq1KnkyZMnkaMTSV2UnImIiIhIjCZNmsTw4cOd1uXPn5+ZM2dSp06dRI5KJHXSsEYRERERidHrr79O5syZ7crSp0/P+PHj+fvvv5WYicQjJWciIiIiEqNMmTIxYcIE2+1mzZpx+PBhhg0bhru7exJGJpL6aFijiIiIiHD58mX8/Pyc1nXp0oXNmzfTsWNHatasmciRiTw71HMmIiIi8gy7ePEiHTp0IH/+/Fy4cMFpGxcXF7788kslZiIJTMmZiIiIyDMoKiqK6dOnU7BgQT7//HPCwsJ46623kjoskWeakjMRERGRZ8zmzZt58cUX6d+/P2FhYbbyL7/8kq1btyZhZCLPNiVnIiIiIs+I8+fP07p1a4KDgzlw4IBDvY+PD2fPnk2CyEQEtCCIiIiISKpktpjZenorF25eIIt7FvZ8u4f33n2PW7duOW3foUMH3n//ffz9/RM5UhGJpuRMREREJJVZdXgVfdf25WzYWTgOrAH+c962RIkSzJ49m0qVKiVmiCLihJIzERERkVRk1eFVNFveDCPUgJ+BQ87bZciQgXHjxvH666/j6uqaqDGKiHNKzkRERERSCbPFTN+1fTEOGPAdcM95u86dO/P++++TNWvWRI1PRB5NyZmIiIhIKrH19FbrUEY/wOykQQ6gLrQb0U6JmUgylGJXa3z//fcxmUz069fPVnb37l169uxJ5syZ8fLyomnTply6dMnuuNOnT1OvXj3Sp0+Pn58fgwcPJioqyq7Npk2bKFmyJO7u7uTLl4+FCxcmwhWJiIiIPJ0LN/+/ibQfUO6BCg+gPtAVCHignYgkKykyOfvjjz/46KOPeOGFF+zK+/fvzw8//MDXX3/N5s2bOX/+PE2aNLHVm81m6tWrR2RkJNu3b2fRokUsXLiQt99+29bmxIkT1KtXj+rVq7N371769etH165d+fnnnxPt+kRERESeRHbv7PdvBAM+QGmg9///dXHSTkSSDZNhGEZSBxEXt27domTJksyZM4f33nuPEiVKMH36dEJDQ8maNStLliyhWbNmABw5coTChQuzY8cOypcvz5o1a6hfvz7nz58nW7ZsAMybN48hQ4Zw5coV3NzcGDJkCKtXr7bb+6Nly5bcuHGDtWvXxirGsLAwfH19CQ0NxcfHJ/4fBBEREXnm/fDDD8yaNYvvv/+edOnSAdY5Z0EzgjgXdg4DAyIA9/vHmDAR4BPAib4ncHXRIiAiiSEuuUGK6znr2bMn9erVo2bNmnblu3fv5t69e3blhQoVIleuXOzYsQOAHTt2UKxYMVtiBlC7dm3CwsI4ePCgrc3D565du7btHM5EREQQFhZm9yMiIiKSEI4dO0b9+vVp0KAB69at44MPPrDVubq4MqPODMCaiD2cmAFMrzNdiZlIMpWikrOvvvqKPXv2MGHCBIe6ixcv4ubmRoYMGezKs2XLxsWLF21tHkzMouuj6x7VJiwsjDt37jiNa8KECfj6+tp+AgMDn+j6RERERGISHh7OyJEjKVq0KKtXr7aVjx8/nlOnTtluNynchBUtVpDTJ6fd8QE+AaxosYImhZsgIslTilmt8cyZM/Tt25d169bZuu6Ti2HDhjFgwADb7bCwMCVoIiIiEi8Mw2DVqlUMGDCA06dPO9TfuXOHTz75hPfee89W1qRwExoWbMjW01u5cPMC2b2zUyVXFfWYiSRzKSY52717N5cvX6ZkyZK2MrPZzJYtW5g9ezY///wzkZGR3Lhxw6737NKlS/j7+wPg7+/Prl277M4bvZrjg20eXuHx0qVL+Pj44OHh4TQ2d3d33N3dndaJiIiIPKmQkBB69+7NunXrnNb7+/szefJk2rRp41Dn6uJKcFBwAkcoIvEpxQxrrFGjBn///Td79+61/ZQuXZo2bdrYfk+bNi3r16+3HRMSEsLp06epUKECABUqVODvv//m8uXLtjbr1q3Dx8eHIkWK2No8eI7oNtHnEBEREUlot27dYsiQIRQrVsxpYubq6sqAAQMICQmhbdu2mEymJIhSROJbiuk58/b25vnnn7cr8/T0JHPmzLbyLl26MGDAADJlyoSPjw+9e/emQoUKlC9fHoCXX36ZIkWK0K5dOyZNmsTFixcZOXIkPXv2tPV8vfHGG8yePZu33nqLzp07s2HDBpYvX243tltEREQkIRiGwbJlyxg0aBDnzp1z2iY4OJjZs2dTtGjRRI5ORBJaiknOYmPatGm4uLjQtGlTIiIiqF27NnPmzLHVu7q68uOPP/Lmm29SoUIFPD096dChA++8846tTZ48eVi9ejX9+/dnxowZBAQE8Omnn1K7du2kuCQRERF5Rhw6dIiePXuyadMmp/U5c+ZkypQptGjRQj1lIqlUitvnLCXQPmciIiISV6tXr6Z+/foO5WnTpqV///6MGjUKLy+vJIhMRJ5Gqt7nTERERCQ1qlevHq+++qpdWa1atdi/fz8TJ05UYibyDFByJiIiIpJMTJ8+HXd3d3LlysXKlSv5+eefKVSoUFKHJSKJRMmZiIiISCK5ceMGM2fOJKZZJc899xw//fQThw8fpkmTJppbJvKMUXImIiIiksAsFgufffYZBQoUoG/fvqxYsSLGti+99BLp06dPxOhEJLlQciYiIiKSgHbv3k3FihXp0qULV65cAWDAgAHcunUriSMTkeRGyZmIiIhIArh69SpvvPEGZcqUYefOnXZ1Z8+eZfz48UkUmYgkV6lqnzMRERGRpGY2m/n0008ZPnw4165dc9qmYcOGdOvWLZEjE5HkTsmZiIiISDzZuXMnPXv2ZPfu3U7r8+XLx8yZM3nllVcSOTIRSQk0rFFERETkKV25coUuXbpQvnx5p4mZh4cH48aN48CBA0rMRCRG6jkTERERiQOzxczW01u5cPMCfh5+HFxzkNFvj+bGjRtO2zdr1owpU6aQK1euxA1URFIcJWciIiIisbTq8Cr6ru3L2bCz1oK/gO+cty1UqBCzZs2iZs2aiRafiKRsSs5EREREHiG6p+y7I98xfed0+8oXgO3AlftFXl5ejB49mj59+uDm5paIkYpISqfkTERERMSJyKhIXv/xdb4+9DW379123sgVqAssst5s1aoVkydPJmfOnIkVpoikIkrORERERB7y1rq3mLJ9ChYs1oKrQOYYGucBqgLPQffR3ZWYicgTU3ImIiIi8oC31r3F5O2TrTfCgF+Ag0A3IEcMB71k/efCzQsJHp+IpF5aSl9ERETk/yKjIpm6YypEAduA2cABwAB+guiOtJhk986e0CGKSCqmnjMRERGR/5vz5xzMx8ywBvjvocqzwD7gRcfjTJgI8AmgSq4qCR+kiKRaT9Rz9sUXX1CpUiVy5MjBqVOnAJg+fTrffRfDWrIiIiIiydzp06eZPWg2fIFjYgaQDjA5Fpv+Xzi9znRcXVwTMkQRSeXinJzNnTuXAQMGULduXW7cuIHZbAYgQ4YMTJ8+Pb7jExEREYlXZouZTSc3sfTvpWw6uYnwO+GMHz+ewoULc3zbcecHvQj0Bko4VgX4BLCixQqaFG6SgFGLyLMgzsMaZ82axSeffEKjRo14//33beWlS5dm0KBB8RqciIiISHxy2ET6KKT5OQ1R/0U5PyAH1qXyAxyr+pXvR8OCDamSq4p6zEQkXsQ5OTtx4gQvvug42Nrd3Z3bt2PYA0REREQkia06vIpmy5thYMB1YC0QAlE4Scw8gJpYe8weGmfk5ebFokaL1FMmIvEuzslZnjx52Lt3L7lz57YrX7t2LYULF463wERERETiQ/Qwxm4/dLMmZv8Ay8FZTmYymXjjjTdwreHK3INzMRvm+3WYaFG0BYubLFZPmYgkiDgnZwMGDKBnz57cvXsXwzDYtWsXS5cuZcKECXz66acJEaOIiIjIE3EYxgjWIYppcUzOAmDe3Hl0r98dgCkNpzDnzzkcv3acvJny0qN0D9zSuCVW6CLyDIpzcta1a1c8PDwYOXIk4eHhtG7dmhw5cjBjxgxatmyZEDGKiIiIxJrZYmbr6a18d+Q7pu+c7tggPVAD+PGB27WA4uCd29vWzC2NG/3K90vocEVEbJ5on7M2bdrQpk0bwsPDuXXrFn5+fvEdl4iIiEic2fWUGThd+h6AksBfQE6gOtY5ZmgTaRFJWk+0IEhUVBT58+cnffr0pE+fHoCjR4+SNm1agoKC4jtGERERkceyLfhhGHAY2Ay0A7ycNHYBOgP/nzqmTaRFJDmI8z5nHTt2ZPv27Q7lO3fupGPHjvERk4iIiEicmC1m+q7ti3HFsG4ivRy4BKx7xEEPJGagTaRFJOnFOTn766+/qFSpkkN5+fLl2bt3b3zEJCIiIhInPx/6mbMrz8Jc4N8HKvYBpx99rDaRFpHkIs7DGk0mEzdv3nQoDw0NxWw2OzlCREREJH5EL/Zx4eYFsntnp3JgZVZ8vYIefXvA5RgO+hvI5Vic2SMzy5otIzgoWD1mIpIsxDk5q1q1KhMmTGDp0qW4ulrfyMxmMxMmTKBy5crxHqCIiIgIOFkW/zK4/+JOxLEI5wd4A7WBovbF0cMYP371Y2o8VyPB4hURias4J2cTJ06katWqFCxYkCpVrJNmt27dSlhYGBs2bIj3AEVEROTZ5XRZ/LvAJmAnRBhOEjMXoAJQFXB3rA7wCWB6nekaxigiyU6ck7MiRYqwf/9+Zs+ezb59+/Dw8KB9+/b06tWLTJkyJUSMIiIi8gxy6CkzgP3AL8DtGA7KC7wCZHGs6le+Hw0LNqRKrioaxigiyZLJMAwjqYNIbcLCwvD19SU0NBQfH5+kDkdERCTFsS2Lz/8/plwBfiDmxT18Yez7YylatSj9fu53P6EDAn0C1VMmIkkmLrlBrHrO9u/fz/PPP4+Liwv79+9/ZNsXXngh9pGKiIiIPMS2LD4PfH9sAc44aewKVAIqQ/5K+WlapCmNCjWyWzREPWUiklLEKjkrUaIEFy9exM/PjxIlSmAymXDW4WYymbRio4iIiMTZg6swXrp9ya7nC4BsQDng9wfK8gN1gMzWm9m9swPg6uJKcFBwgscsIhLfYpWcnThxgqxZs9p+FxEREYkvDnPLYhKMdVn8tFjnlRW0FpswEeATQJVcVRI0ThGRhBar5Cx37twA3Lt3j7FjxzJq1Cjy5MmToIGJiIhI6mc3tywca89YVZx/QkkHtMPaU5bWWhS9LP70OtM1dFFEUjyXuDROmzYtK1euTKhYRERE5Blim1tmMeBPYBawBdjxiIP8sSVmYF0Wf0WLFVrsQ0RShTgvpd+oUSO+/fZb+vfvnxDxiIiISCr14Lyy7N7ZMVvMnD10FlYDFx5ouAUoBmSI+VxaFl9EUqM4J2f58+fnnXfeYdu2bZQqVQpPT0+7+j59+sRbcCIiIpI6OMwruw1uG92sPWYPuwesB5o6VmlZfBFJzeK8z9mj5pqZTCb+/fffpw4qpdM+ZyIiIvfZzSszA7uBDcDdGA4oAryMredsWu1pZPPMpmXxRSRFivd9zh6k1RpFREQktuz2LDuNdQjjpRgaZ8G6CmNe683oVRh7l+2thExEnglxSs5+//13fvjhByIjI6lRowZ16tRJqLhEREQkhXLYs+zcWfgV2BfDAW5ANaz7mP3/k4lWYRSRZ1Gsk7MVK1bw2muv4eHhQdq0aZk6dSoTJ05k0KBBCRmfiIiIpCB2c8vMwC5gExARwwHFgFqQKVsmrt25ZisO8AnQ3DIReebEes5ZqVKlKFOmDB9++CGurq5MmDCByZMnc+3atccf/IzRnDMREXmWRPeUfXfkO6bvnH6/4hjwZQwH+QF1gSDrzV/b/Yqri6ttJUfNLROR1CIuuUGskzMvLy/27t1Lvnz5AIiMjMTT05Nz587h5+f39FGnIkrORETkWeGwCuODDGAx1iQtmjtQHSgDuN6fV3ai7wklYyKSKsUlN4j1JtTh4eF2J3NzcyNdunTcunXrySMVERGRFMlsMfPO5ndouryp88QMwIR1gY/onKs40Asojy0xA80rExGJFuvkDODTTz9l5syZtp+oqCgWLlxoV5ZQJkyYQJkyZfD29sbPz49GjRoREhJi1+bu3bv07NmTzJkz4+XlRdOmTbl0yX5JqNOnT1OvXj3Sp0+Pn58fgwcPJioqyq7Npk2bKFmyJO7u7uTLl4+FCxcm2HWJiIikNKsOryJoehCjN422FpzD2kvmTGasCVpnoDHgfb8qwCeAFS1WaF6ZiMj/xXpYY1BQECaT6dEnS8B9zurUqUPLli0pU6YMUVFRDB8+nAMHDnDo0CHbRthvvvkmq1evZuHChfj6+tKrVy9cXFzYtm0bAGazmRIlSuDv78/kyZO5cOEC7du3p1u3bowfPx6wbhXw/PPP88Ybb9C1a1fWr19Pv379WL16NbVr145VrBrWKCIiqZXdnmU3gJ+Bw0ALrPuTPYb2LBORZ02CzDlLbq5cuYKfnx+bN2+matWqhIaGkjVrVpYsWUKzZs0AOHLkCIULF2bHjh2UL1+eNWvWUL9+fc6fP0+2bNkAmDdvHkOGDOHKlSu4ubkxZMgQVq9ezYEDB2z31bJlS27cuMHatWudxhIREUFExP1lqMLCwggMDFRyJiIiqYrZYiZoRhBnr52F7cAWIHrwiQ/WIYtuzo/V3DIReVYlyJyz5CY0NBSATJkyAbB7927u3btHzZo1bW0KFSpErly52LFjBwA7duygWLFitsQMoHbt2oSFhXHw4EFbmwfPEd0m+hzOTJgwAV9fX9tPYGBg/FykiIhIMrL19FbO7j4Lc4AN3E/MAMKArc6P09wyEZHYidMm1MmFxWKhX79+VKpUieeffx6Aixcv4ubmRoYMGezaZsuWjYsXL9raPJiYRddH1z2qTVhYGHfu3MHDw8MhnmHDhjFgwADb7eieMxERkZTIbDGz6eQmNp3cBEBwUDC5LLkY1G2Qdc8yZzyATM6rtGeZiEjspMjkrGfPnhw4cIDffvstqUMBwN3dHXd396QOQ0RE5KmtOryK7j905+qdq9aCe/Deb+/BNux7yh5UGngJSG9f3K98PxoWbKi5ZSIisZTikrNevXrx448/smXLFgICAmzl/v7+REZGcuPGDbves0uXLuHv729rs2vXLrvzRa/m+GCbh1d4vHTpEj4+Pk57zURERFKLVYdX0XR5U+sNAwgB1mJd+MOZAKwbSed4qNg7gBmvzFBPmYhIHKWYOWeGYdCrVy+++eYbNmzYQJ48eezqS5UqRdq0aVm/fr2tLCQkhNOnT1OhQgUAKlSowN9//83ly5dtbdatW4ePjw9FihSxtXnwHNFtos8hIiKSmkRGRTL99+n0XN2TTt92shZexbp59Fc4T8zSAw2xLo//UGI2NngsJ/udVGImIvIEYrVaY1hYWKxPmFCrE/bo0YMlS5bw3XffUbBgQVu5r6+vrUfrzTff5KeffmLhwoX4+PjQu3dvALZv3w7cX0o/R44cTJo0iYsXL9KuXTu6du3qsJR+z5496dy5Mxs2bKBPnz5aSl9ERFKdQb8MYtrv07AYlvuFp4DPAbOTA0xAGRg6aihfHv3SbvPpQJ9AzSsTEXEi3pfSd3FxeeweZ4ZhYDKZMJudvZs/vZjuf8GCBXTs2BGwbkI9cOBAli5dSkREBLVr12bOnDm2IYsAp06d4s0332TTpk14enrSoUMH3n//fdKkuT/Cc9OmTfTv359Dhw4REBDAqFGjbPcRG0rOREQkObsTeYcic4pwMvSkY2UUMA/476HyXFiHMPrDkiZLaFG0BVtPb+XCzQvas0xE5BHiPTnbvHlzrO+8WrVqsW6bWik5ExGR5KrRV434LuS7Rzc6Dnzx/9+9gFrAC/D/FfHZ2GEjwUHBCRWiiEiqEpfcIFYLgijhEhERSfnsEjMzEFNHV17gecAbqAaku18V4BNAlVxVEjJMEZFn1hOv1hgeHs7p06eJjIy0K3/hhReeOigRERGJH2aLma2nt3LqxilrYmYAB4BfgZZA9hgObIqtp+xBM+rM0PBFEZEEEufk7MqVK3Tq1Ik1a9Y4rU+oOWciIiISe2aLmXFbxzFj5wyu3blmLbwErAFO/r/RT1hXXHQ2rfuhsswemfn41Y+14IeISAKKc3LWr18/bty4wc6dOwkODuabb77h0qVLvPfee0yZMiUhYhQREZE4cNhI+i6wCdiJtecs2hlgP1Dc8RwmTAypNIQ0LmkIDgomOChYPWYiIgkszsnZhg0b+O677yhdujQuLi7kzp2bWrVq4ePjw4QJE6hXr15CxCkiIiKxsOLgCpqvaG69YWBNvn4BbsdwwHGcJmcDKgxgQs0JCRKjiIg4F+fk7Pbt2/j5+QGQMWNGrly5QoECBShWrBh79uyJ9wBFRETk0a6EXaHs/LKcuXkGs/H/6QUXsQ5bPB3DQb5AHaCQY1XDgg354OUPEiRWERGJWZyTs4IFCxISEkJQUBDFixfno48+IigoiHnz5pE9e0yzikVERCS+mS1mfCb4EB4Vfr/wDrAR+AP7IYzRXIFKQGXAzb4qjUsavmz8Ja89/1oCRSwiIo8S5+Ssb9++XLhwAYDRo0dTp04dFi9ejJubGwsXLozv+ERERMSJxfsX0/abtvcLLMBerKswhjs/hvzAK0Amx6pKgZXY3HGz5pWJiCShWG1C/Sjh4eEcOXKEXLlykSVLlviKK0XTJtQiIpJQIqMiyTE1x/3FPgCuAquAczEclBHrEMaC94s803pSMaAiBbIUYHLNyXi4eSRYzCIiz7K45AYucT35O++8Q3j4/a/k0qdPT8mSJfH09OSdd96Je7QiIiISK4N+GYT7OHf7xAysm0RfdXJAGqA60AO7xAzg88af80v7X5hdd7YSMxGRZCLOPWeurq5cuHDBtihItKtXr+Ln56d9zlDPmYiIxC+zxUzVBVXZfnZ7zI12YV0AJFohoDbWXrMHaL8yEZHEFZfcIM5zzgzDwGRy3K1y3759ZMrkZBC7iIiIPJHojaTHbxlPhCXi0Y1LA3uASKzzyvLbV7vgwqhqoxhVdZTmlYmIJFOxTs4yZsyIyWTCZDJRoEABuwTNbDZz69Yt3njjjQQJUkRE5Flitph5d8u7TNo2iTtRd6yFt7FuJF0N8HJykAvQ8v91Tv53/6rZVzQv2jxhAhYRkXgR6+Rs+vTpGIZB586dGTt2LL6+vrY6Nzc3goKCqFChQoIEKSIi8iwwW8yM3TyWcVvGYcHy/0LgT6zL498FooCGMZwgg2ORZ1pPPm/8uYYxioikALFOzjp06ABAnjx5qFixImnTpk2woERERJ41yw4so+2qtkQZUfcLTwOrgUsPNPwLKAkEPv6cWh5fRCRlifOcs2rVqmE2m1m5ciWHDx8GoGjRojRo0ABXV735i4iIxIXZYqbS/ErsPL/zfuFNYB2wP4aDfgU6Pfq8y5ouo8XzLeInSBERSRRxTs6OHTtG3bp1OXfuHAULWtflnTBhAoGBgaxevZq8efPGe5AiIiKpkcNG0masqy5uAmJa/6MYUCvmc46sMpIxwWPUWyYikgLFeSn9unXrYhgGixcvtq3OePXqVdq2bYuLiwurV69OkEBTEi2lLyIij3In8g5+k/24FXXrfuEJYA1wOYaD/IC6QJDz6uGVh/NO9XeUlImIJDMJupT+5s2b+f333+2Wzc+cOTPvv/8+lSpVinu0IiIiz4hrt66Re2Zubt17ICkLA34BDsRwkDvWjaTLAE7yLq+0XixqvEgLfoiIpAJxTs7c3d25efOmQ/mtW7dwc3OLl6BERERSm2yTsnH5zgPdYlHATmAz1r3JnCmOdQijk6XzPdJ48Falt7RvmYhIKuIS24Zbtmzh3r171K9fn+7du7Nz504Mw8AwDH7//XfeeOMNGjRokJCxioiIpDhmi5m076S1T8wArmFd2MNZYuYPdAYa45CYlc1Zll/b/crNYTc1t0xEJJWJ9ZwzV1dXLly4gJubGx06dOCHH36wLacfFRVFgwYNWLhwod3+Z88qzTkTEZE7kXdo+FVD1p1YF3OjNVh7z6KlA14CSuP069MGBRrwXavv4jVOERFJWAky5yw6h8uQIQPfffcdR48e5ciRIwAULlyYfPnyPUXIIiIiqYPZYqbKZ1XYcW7H4xsHY51rdhvr3mU1AE/nTQdWGMgHL38QX2GKiEgyFKc5ZyaTyfZ7/vz5yZ8/f7wHJCIiklIt2b+ENt+0sS/8F8iF8/9xPYCGQHogwLE6jSkN42uMp2+5vril0bxuEZHULk7JWceOHXF3d39km1WrVj1VQCIiIinNlbAr+E/zx4LlfuF1YC0QAtQEKsdwcAHnxaX8S/Hn63/Ga5wiIpK8xSk58/b2xsPDI6FiERERSVHMFjM+430IN4ffL7wHbAN+w7oiI1hXZCwGxGJatgsuhA4JxSudkyUaRUQkVYtTcjZz5kz8/PwSKhYREZEUY9Ffi+j4fcf7BQbWXrK1wI2HGt/DupdZ80efs6R/SXa/vjv+ghQRkRQl1snZg/PNREREnlWh4aFkmpzJfgjjVaxJ2dEYDkoPPGLdrLwZ8rL39b3qLRMRecbFebVGERGRZ9Vz05/jROiJ+wWRwFZgO2B2coAJKIt1VcYYZgUMrjiYSbUmxW+gIiKSIsU6Odu4cSOZMmVKyFhERESSpWMXj5H/owdWKDaAw8DPQGgMB+UC6mLdUNqJQpkLse+NfVqFUUREbGKdnFWrVi0h4xAREUl2roRdwW/aQ3Otr2DdPPrfGA7yAl7GugCIkxkBQb5BHOpxCA83LbAlIiL24rQgiIiIyLPg1t1b+E70tZ9XBnAZmAcPFwPgApQDqgHpnFW7sKTpEl57/rX4DldERFIJJWciIiIPKPZhMQ78d8B5ZVYgD3D8ofIgrEMYY1jQeFSVUYwOHo2ri2t8hSkiIqmQkjMREREg5HwIhT4p9OhGJuAVYA7W3jNvoDZQFKdDGP3T+3N24FklZSIiEiuxSs7CwsJifUIfH58nDkZERCSxrfhrBc2/f2gDskggLU4TLrIAVbCuzlgFcHdskoY0/Df4P3zTx2LXaRERkf+LVXKWIUOGWO9zZjY7W0tYREQk+TGNfej/NgPYj3XD6FeBmDrSqsd8zlfzv8r3rb+Pl/hEROTZEqvkbOPGjbbfT548ydChQ+nYsSMVKlQAYMeOHSxatIgJEyYkTJQiIiLxaO62ufT4tYd94UXgJ+D0/2+vBfJi7UGLpfBh4VqFUUREnpjJiOPu0jVq1KBr1660atXKrnzJkiV8/PHHbNq0KT7jS5HCwsLw9fUlNDRUwzxFRJKRXcd2UW5xOfvCO8BG4A+sPWcPqsYje8mivZjtRfa8sSdeYhQRkdQlLrlBnBcE2bFjB/PmzXMoL126NF27do3r6URERBKc0/3KLMBe4FcgPKYDsSZsMYzsz+qRlX/7/ItXOq94ilRERJ5lcU7OAgMD+eSTT5g0aZJd+aeffkpgYGC8BSYiIhIfHOaVAZzDOoTxXAwHZQTqAAVjPq+GMIqISHyLc3I2bdo0mjZtypo1ayhXzjo0ZNeuXRw9epSVK1fGe4AiIiJPosL7Ffg94nf7wnBgPbA7hoPSYF2BsSIxzjULDgxmY+eNzitFRESeQpznnAGcOXOGuXPncuTIEQAKFy7MG2+8oZ6z/9OcMxGRpLP35F5eXPSifaEF2IM1MbsTw4GFsO5ZljHmc6u3TERE4iouucETJWfyaErORESShtMhjKHAV8CFGA7KhHVj6fwxn/dUz1PkypLrqeMTEZFnT1xyA5cnuYOtW7fStm1bKlasyLlz1gH7X3zxBb/99tuTnE5EROSpmMaanCdmAJ5YN5V+WFqgBtCDGBOzstnKYow2lJiJiEiiiHNytnLlSmrXro2Hhwd79uwhIiICgNDQUMaPHx/vAYqIiMTkkUlZtDRYe8YeVATohXV+WQyzr8OHhbPzjZ1PHaOIiEhsxTk5e++995g3bx6ffPIJadPeny1dqVIl9uzRHi8iIpI4HpuUPSgf1jllWYD2QAvA13nTJfWXYIw2NLdMREQSXZyTs5CQEKpWrepQ7uvry40bN+IjpmThww8/JCgoiHTp0lGuXDl27dqV1CGJiAjWjaQdErObwDfAxUcc2AB4A3jOeXU60mGMNmhVqlW8xCkiIhJXcV5K39/fn2PHjhEUFGRX/ttvv/HcczH8j5fCLFu2jAEDBjBv3jzKlSvH9OnTqV27NiEhIfj5+T3+BCIiEu/MFjNp3n3ovy0zsAvYiHVe2Q2gI843jU4f87lvDL6Bb/oYutJEREQSSZx7zrp160bfvn3ZuXMnJpOJ8+fPs3jxYgYNGsSbb76ZEDEmuqlTp9KtWzc6depEkSJFmDdvHunTp+ezzz5L6tBERJ5JC3YvcEzMTgDzgJ+5v+DHKeDv2J93aJmhGKMNJWYiIpIsxLnnbOjQoVgsFmrUqEF4eDhVq1bF3d2dQYMG0bt374SIMVFFRkaye/duhg0bZitzcXGhZs2a7Nixw+kxERERtoVRwLpcpoiIPD2zxUz6cemJtDyw3GIosA44EMNBW4DneezXj8Zo7SQjIiLJS5x7zkwmEyNGjODatWscOHCA33//nStXrvDuu+8mRHyJ7r///sNsNpMtWza78mzZsnHxovPJDBMmTMDX19f2o824RUSejtlips9PfUjzbpr7iVkU8Bswm5gTs+JYhzUqMRMRkRQozslZ586duXnzJm5ubhQpUoSyZcvi5eXF7du36dy5c0LEmOwNGzaM0NBQ28+ZM2eSOiQRkRRr6d9LSfNuGmb9Met+4XFgLvArcM/JQf5AZ6Ax4BXzuY3RhhIzERFJtuKcnC1atIg7d+44lN+5c4fPP/88XoJKSlmyZMHV1ZVLly7ZlV+6dAl/f3+nx7i7u+Pj42P3IyIicRMZFUng1EBar2p9v/AGsAz4Arjq5KB0QF2gO/CIfaKVlImISEoQ6+QsLCyM0NBQDMPg5s2bhIWF2X6uX7/OTz/9lCpWMnRzc6NUqVKsX7/eVmaxWFi/fj0VKlRIwshERFKnyKhIXlr0Eu7j3Dl786y1MArr3LHZwOEYDiwJ9AbKEuP/Zi64KCkTEZEUI9YLgmTIkAGTyYTJZKJAgQIO9SaTibFjx8ZrcEllwIABdOjQgdKlS1O2bFmmT5/O7du36dSpU1KHJiKSqgz6ZRBTdkxxrLgLbMOapD0sB9besoBHn1tJmYiIpDSxTs42btyIYRi89NJLrFy5kkyZMtnq3NzcyJ07Nzly5EiQIBPba6+9xpUrV3j77be5ePEiJUqUYO3atQ6LhIiIyJN7dfGr/HjsR+eVXsBLwJoHyjyAmsCLPHLch5IyERFJqUyGYcTpf7FTp06RK1cuTCZnO3wKWIeA+vr6EhoaqvlnIiIPMVvMFPmwCP9c++cxDYGPgctAaaA6j9xI+uOXP6ZbhW7xFqeIiEh8iEtuEOd9zjZs2ICXlxfNmze3K//6668JDw+nQ4cOcT2liIg8I5YdWEbbVW2JMqLAAEKAQMDTSWNXoCFgArLHfM79nfZTLFexBIhWREQkccV5tcYJEyaQJUsWh3I/Pz/Gjx8fL0GJiEjqYraYqfxZZVqubGlNzK4Ci4GvgPWPODAHMSZmM2vPxBhtKDETEZFUI849Z6dPnyZPnjwO5blz5+b06dPxEpSIiKQeKw6uoP237bkTdQciga3AdqzDFgH2AKWAnLE7X5nsZdjRdQeuLq4JEa6IiEiSiXNy5ufnx/79+wkKCrIr37dvH5kzZ46vuEREJIWLjIqkzuI6bDy50TqE8RDwMxDmpPFqoCuPHM/h4eLB1SFX8XDzSIhwRUREklyck7NWrVrRp08fvL29qVq1KgCbN2+mb9++tGzZMt4DFBGRlMVsMdNmVRuWH1yOgQFXsK66+G8MB3gB5bHOLXPCBRc+b/w5bV5okyDxioiIJBdxTs7effddTp48SY0aNUiTxnq4xWKhffv2mnMmIvIMM1vMjNs6jgm/TeBu1F2IADYDvwMWJwe4AOWAakA6x+rKgZUZVXUUNZ6roSGMIiLyTIjzUvrR/vnnH/bt24eHhwfFihUjd+7c8R1biqWl9EXkWbPq8Cq6/9Cdq3euWocwHgB+AW7GcEAe4BXAz7EqjUsaljVbRpPCTRIqXBERkUSToEvpRytQoAAFChR40sNFRCSFM1vMbD29le+OfMf0ndOthZewDmE8GcNBPkBtoAhOhzFWDKjIlk5b1FMmIiLPpFglZwMGDODdd9/F09OTAQMGPLLt1KlT4yUwERFJvlYdXkXftX05G3b2fuFNrJtGm50c4AJUBKoA7o7VQb5BHOpxSIt9iIjIMy1Wydlff/3FvXv3bL/HxGSKYTa3iIikGisOrqD5iuaOFd5ASeCPh8rzYh3C6LhFJiZMDKgwgA9e/iDe4xQREUlpnnjOmcRMc85EJLX6+uDXtFrZCrPhrHsMuAPMAsIBX6AOUAinQxirB1VnbZu1uKVxS6hwRUREklyizDkTEZFny6rDq2ixooU1AUuL8/9BPICXgWtAZcBJ3uWRxoNFjRbRvKiT3jcREZFnWKySsyZNYr9i1qpVq544GBERST6iF/y4cPMCfp5+9F7dG/YAv2JNvCrGcGAJ58UmTLQo2oLFTRZrwQ8REREnYpWc+fr62n43DINvvvkGX19fSpcuDcDu3bu5ceNGnJI4ERFJvhwW/DgH/PT/fwE2Ac9jXX3xMdxd3Wn1fCs+qv+RhjCKiIg8QqySswULFth+HzJkCC1atGDevHm4ulq/+TSbzfTo0UPzq0REUoFVh1fRbHkzDAzr3LH1wO6HGkUC64CmMZ8ns0dm+pTrw4gqI9RTJiIiEgtxXhAka9as/PbbbxQsWNCuPCQkhIoVK3L16tV4DTAl0oIgIpJSmS1mgmYEcfbGWesQxvVY55g5UwhoDjyUd/Ur34+GBRtSJVcVJWUiIvLMS9AFQaKiojhy5IhDcnbkyBEsFktcTyciIkksMiqSOX/O4fi14xgYnD141jqE8UIMB2QC6gL57ItdTa581fQrmhVtlrABi4iIpFJxTs46depEly5dOH78OGXLlgVg586dvP/++3Tq1CneAxQRkfgXvdjHlO1T+OnYT1gMC9zC2lMW03aWaYGqQAWc/u+xtOlSJWYiIiJPIc7J2QcffIC/vz9TpkzhwgXr16rZs2dn8ODBDBw4MN4DFBGR+OWw2IcZ+BPYCNyN4aCiWJfI93WsCvQJZHqd6TQprEWhREREnsZTbUIdFhYGoHlVD9GcMxFJjswWM+O2jmP0ptH3C8OBRcClGA7KgnUI43PWmyZM5PTJycKGC7l8+zLZvbNrbpmIiMgjJPgm1FFRUWzatInjx4/TunVrAM6fP4+Pjw9eXl5PckoREUlAXx/8mh6re/Dfnf/sKzwATycHuAHBQDlsC36YMAEwo84MajxXI8FiFREReVbFOTk7deoUderU4fTp00RERFCrVi28vb2ZOHEiERERzJs3LyHiFBGRJ/TWureYvH2y80oT8AowF4he06kYUAuHPcwCfAI0fFFERCQBxTk569u3L6VLl2bfvn1kzpzZVt64cWO6desWr8GJiMjTWXFwRcyJWbSsQHngGNYhjEH3q3qW6UmlwEoavigiIpII4pycbd26le3bt+Pm5mZXHhQUxLlz5+ItMBEReTLRKzGeCztHz596QhjwC9YesYIxHFQdqIHdnmWuJlemvjwVtzRuMRwkIiIi8SnOyZnFYsFsNjuUnz17Fm9v73gJSkREnozdSoxRwO/AZuAecA7rwh5pnRzopGxAhQFKzERERBKRS1wPePnll5k+fbrttslk4tatW4wePZq6devGZ2wiIhJLZouZdza/Q9PlTa2J2XGs88h+xZqYAVwHtj/+XK4mVwZXHMykWpMSLF4RERFxFOel9M+cOUOdOnUwDIOjR49SunRpjh49SpYsWdiyZQt+fn4JFWuKoaX0RSSxRC+PP+P3GVy7ew1uAD8Dh2M4wBvog0NPmY+7DxUDK1I7b216lO6hHjMREZF4Epfc4In2OYuKimLZsmXs27ePW7duUbJkSdq0aYOHh8cTB52aKDkTkcSw6vAquv/Qnat3rlqHMG4HtmD93ZmSWOeVPbR0/uhqoxlVdZQW+xAREUkACZac3bt3j0KFCvHjjz9SuHDhpw40tVJyJiIJbdXhVTRb3gwDA/4B1gLXYmicA+sqjAGOVRq+KCIikrASbBPqtGnTcvfu3acKTkREno7ZYqbv2r4Y1wzrEMaQGBp6ADWBF3GYYZw1fVbm1J1Ds6LNEjRWERERib04r9bYs2dPJk6cyKeffkqaNHE+XEREntL6o+s5+91Z+A1wXDzXurF0aazL46e/X5w1fVam1Z5GTp+c2rNMREQkGYpzdvXHH3+wfv16fvnlF4oVK4anp/3khVWrVsVbcCIi4ujirYvwN84TswCgHpDdvtiEiXn159GkcJOED1BERESeSJyTswwZMtC0adOEiEVERGIhV+ZcUAdY8kChJ1ALeAGHIYwB3gHMeGWGEjMREZFkLs7J2YIFCxIiDhERiaUquaoQUDqAs7vPWhcDKQsEY51j9pCxwWMZUWWEhjCKiIikALHehNpisTBx4kQqVapEmTJlGDp0KHfu3EnI2EREnlmGYbBy5UoOHTrkUOfq4sqMOjPgFeB1rP8+lJhl9sjMyhYrebva20rMREREUohYJ2fjxo1j+PDheHl5kTNnTmbMmEHPnj0TMjYRkWfSkSNHePnll2nWrBm9evXC2Y4nTQo3YWX3lQQUsF8fP7NHZsYGj+XSoEsaxigiIpLCxHqfs/z58zNo0CBef/11AH799Vfq1avHnTt3cHGJdY73TNA+ZyISG2aLma2nt3Lh5gWye2enRMYSjB83nmnTphEVdX8n6WXLltGiRYtYnUOrMIqIiCQvCbIJtbu7O8eOHSMwMNBWli5dOo4dO0ZAgJOdTZ9hSs5E5HFWHV5F37V9ORt2FgzgALisc8ESZnFomzNnTv755x/Sp0/veCIRERFJ1hJkE+qoqCjSpUtnV5Y2bVru3bv3ZFGKiDyjVh1eRbPlzTAw4BLwE3AKLDgmZgEBAUydOhUPDyerfYiIiEiqEuvkzDAMOnbsiLu7u63s7t27vPHGG3Z7nWmfMxGRmJktZvqu7Ytx14BNwE6sPWcPSZs2LQMHDmTEiBF4eXklcpQiIiKSFGKdnHXo0MGhrG3btvEajIhIarf55GbObjkL64DbMTTKC/M/nk+7l9olZmgiIiKSxGKdnGl/MxGRp7N3717e7PQm7I2hgS/WzaULQZqscd6GUkRERFI4/e8vIpIIbt++zUsvvcT169cdK12BSkBlwM1alN07eyJGJyIiIsmB1sAXEUkEnp6evP32244VBYCewEuAG5gwEegTSJVcVRI5QhEREUlqSs5ERBJJz549KVq0qPVGRqAV0BrIZC0yYQJgep3p2qtMRETkGaTkTEQkHl29etX50EWsKzB++OGHvPPOOyzdsJSAMvZ7RAb4BLCixQqaFG6SGKGKiIhIMpMikrOTJ0/SpUsX8uTJg4eHB3nz5mX06NFERkbatdu/fz9VqlQhXbp0BAYGMmnSJIdzff311xQqVIh06dJRrFgxfvrpJ7t6wzB4++23yZ49Ox4eHtSsWZOjR48m6PWJSMpnNpuZN28eBQoUYPjw4TG2q1atGqNGjaJliZac7HuSjR02sqTJEjZ22MiJvieUmImIiDzDUkRyduTIESwWCx999BEHDx5k2rRpzJs3z+4DUFhYGC+//DK5c+dm9+7dTJ48mTFjxvDxxx/b2mzfvp1WrVrRpUsX/vrrLxo1akSjRo04cOCArc2kSZOYOXMm8+bNY+fOnXh6elK7dm3u3r2bqNcsIinHjh07KFu2LG+++SbXrl3jo48+Ys+ePY89ztXFleCgYFoVa0VwULCGMoqIiDzjTIZhONn+NPmbPHkyc+fO5d9//wVg7ty5jBgxgosXL+LmZl3ubOjQoXz77bccOXIEgNdee43bt2/z448/2s5Tvnx5SpQowbx58zAMgxw5cjBw4EAGDRoEQGhoKNmyZWPhwoW0bNkyVrGFhYXh6+tLaGgoPj4+8XnZIpJEzBYzW09v5cLNC2T3zk6VXFW4+t9Vhg4d6nSrkfLly7Nt2zZcXFLEd2AiIiKSQOKSG6TYpfRDQ0PJlCmT7faOHTuoWrWqLTEDqF27NhMnTuT69etkzJiRHTt2MGDAALvz1K5dm2+//RaAEydOcPHiRWrWrGmr9/X1pVy5cuzYsSPG5CwiIoKIiAjb7bCwsPi4RBFJJlYdXkXftX05G3bWWmCGDAcyEPlrJOE3w50ekytXLm7fvo23t3ciRioiIiIpWYr8SvfYsWPMmjWL119/3VZ28eJFsmXLZtcu+vbFixcf2ebB+gePc9bGmQkTJuDr62v7CQwMfMIrE5HkZtXhVTRb3ux+YnYK+BhufHPDaWJWuHBhfv31V5YtW6bETEREROIkSZOzoUOHYjKZHvkTPSQx2rlz56hTpw7NmzenW7duSRS5vWHDhhEaGmr7OXPmTFKHJCLxwGwx03dtXwwMuAmsAhYAlxzbenl58cEHH7Bv3z5q1KiRyJGKiIhIapCkwxoHDhxIx44dH9nmueees/1+/vx5qlevTsWKFe0W+gDw9/fn0iX7T0zRt/39/R/Z5sH66LLs2bPbtSlRokSMMbq7u+Pu7v7I6xCRlGfr6a2cvX4WdgKbgEjn7Wo2rMnncz+3e98QERERiaskTc6yZs1K1qxZY9X23LlzVK9enVKlSrFgwQKHSfYVKlRgxIgR3Lt3j7Rp0wKwbt06ChYsSMaMGW1t1q9fT79+/WzHrVu3jgoVKgCQJ08e/P39Wb9+vS0ZCwsLY+fOnbz55ptPebUiktKc+u8UfARcjqGBH1AXOg/orMRMREREnlqKmHN27tw5goODyZUrFx988AFXrlzh4sWLdvPAWrdujZubG126dOHgwYMsW7aMGTNm2C0A0rdvX9auXcuUKVM4cuQIY8aM4c8//6RXr14AmEwm+vXrx3vvvcf333/P33//Tfv27cmRIweNGjVK7MsWkSSWO0tuyOWkwh14BXgdCILs3krMRERE5OmliNUa161bx7Fjxzh27BgBAQF2ddE7Afj6+vLLL7/Qs2dPSpUqRZYsWXj77bfp3r27rW3FihVZsmQJI0eOZPjw4eTPn59vv/2W559/3tbmrbfe4vbt23Tv3p0bN25QuXJl1q5dS7p06RLnYkUk2aiSqwrZG2bnwsELcOf/hSWAmoAXmDAR4BNAlVxVki5IERERSTVS7D5nyZn2ORNJeQzDwGQyOZSvOryKpm81hd1AXeD/i7GasLZd0WIFTQo3SbxARUREJEWJS26QIoY1iogklFOnTtG0aVNmzJjhtL5J4SZ8/f7X5ByY05aYAQT4BCgxExERkXilnrMEoJ4zkeTv7t27fPDBB4wfP547d+7g4+NDSEiIbdXWh5ktZrae3sqFmxfI7p2dKrmq4OrimshRi4iISEoTl9wgRcw5ExGJT6tXr6Zv374cP37cVhYWFsaQIUNYtGiR02NcXVwJDgpOpAhFRETkWaRhjSLyzPj3339p0KAB9evXt0vMoq1Zs4arV68mQWQiIiIiSs5E5Blw584dRo8eTZEiRfjhhx8c6l1cXOjRowdHjhwhc+bMSRChiIiIiIY1ikgqZhgG33//Pf369ePkyZNO21SsWJHZs2fz4osvJm5wIiIiIg9Rz5mIpEpHjx6lbt26NGrUyGlili1bNhYtWsTWrVuVmImIiEiyoORMRFKVe/fuMWLECJ5//nnWrl3rUO/q6krfvn0JCQmhffv2uLjobVBERESSBw1rFJFUJU2aNOzYsYPIyEiHuqpVqzJ79myKFSuWBJGJiIiIPJq+MhaRVMVkMjFr1izSpLn/3VP27NlZvHgxmzZtUmImIiIiyZaSMxFJdYoWLUqfPn1IkyYNgwYNIiQkhNatW2MymZI6NBEREZEYmQzDMJI6iNQmLruAi0jcGYbBV199hZ+fHzVq1HDaJiwsjLNnz1KkSJFEjk5ERETkvrjkBppzJiIpyoEDB+jVqxebN28mX758HDhwAHd3d4d2Pj4+SsxEREQkRdGwRhFJEUJDQ+nXrx8lSpRg8+bNABw7dowpU6YkcWQiIiIi8UPJmYgka4Zh8Pnnn1OwYEFmzJiB2Wy2q3/vvfe4dOlSEkUnIiIiEn+UnIlIsrV3716qVKlChw4dnCZguXPnZsmSJfj5+SVBdCIiIiLxS8mZiCQ7169fp1evXpQqVYpt27Y51Lu7u/P2229z6NAhGjVqpFUYRUREJFXQgiAikmxYLBYWLFjA0KFD+e+//5y2efXVV5k2bRp58+ZN5OhEREREEpaSMxFJFv7880969uzJrl27nNbnzZuXGTNmUK9evUSOTERERCRxKDkTkSRnNptp1aoVx44dc6jz8PBg+PDhDBo0iHTp0iVBdCIiIiKJQ3PORCTJubq6MnXqVIfyxo0bc/jwYUaOHKnETERERFI9JWcikuDMFjObTm5i6d9L2XRyE2aL2aHNq6++ahuyWKBAAdauXcuqVavInTt3YocrIiIikiQ0rFFEEtSqw6vou7YvZ8POwi0gCgJyBTCjzgyaFG5i13b69OlUrlyZ/v374+7unjQBi4iIiCQRk2EYRlIHkdqEhYXh6+tLaGgoPj4+SR2OSJJZdXgVzZY3wzAb8CewAcgJtAOTycSKFiscEjQRERGR1CQuuYGGNYpIgjBbzPRd2xfjlAEfA2uACOBf4LC1Tb+1/ZwOcRQRERF5Fik5E5EE8e2f33J24VlYAFx6qHItGJEGZ8LOsPX01qQIT0RERCTZUXImIvHq3r17TJ06lXYvtYP9MTQKAqKsv164eSGRIhMRERFJ3rQgiIjEm40bN9K7d28OHjzovIEfUA94YAHG7N7ZEyM0ERERkWRPyZmIPLWzZ88yaNAgli1b5ryBO/ASUBpwtRaZMBHgE0CVXFUSKUoRERGR5E3JmYg8scjISKZNm8a7777L7du3nTcqAdQEvO4XmTABML3OdFxdXBM6TBEREZEUQXPOROSJWCwWKlWqxNChQ50mZi+++CLbt29n5ZKVBOQIsKsL8AnQMvoiIiIiD1HPmYg8ERcXF1577TX+/PNPu/KMGTMyfvx4unXrhqurtVesYcGGbD29lQs3L5DdOztVclVRj5mIiIjIQ7QJdQLQJtTyrLh37x7Fixfn8OHDmEwmunbtyvjx48mSJUtShyYiIiKSLGgTahGJV1FR/2vv3uOiKvM/gH+GAQa8zEgpNwEBRRRFwRvhJS1ZUdlKpVIzf9K6qCwoJF4gC+/rJW0TNFvT1NxSvP20RYUlrFAjyQsqguAm5g1QV7l4SWDm+f3hz7NOjIoJnIH5vF+vecWc5zsz3/OEZ/x4zjxTZXC7hYUFEhIS0KtXLxw+fBhr1qxhMCMiIiL6nRjOiOiRzp07h1deeQVRUVGPrBk4cCB+/PFH9OzZs/4aIyIiImqE+JkzIqrmzp07WLJkCZYsWYJ79+7BzMwMf/7zn+Hj42OwXqFQ1G+DRERERI0Qz5wRkUQIgV27dsHLywvz5s3DvXv3ANxfmTEiIgL8iCoRERFR3WE4IyIAQH5+PoYOHYrhw4fjl19+qTZ+9uxZFBQUyNAZERERkWlgOCMycbdv38Z7770Hb29vJCcnVxtXKpWIjIxEfn4+3N3dZeiQiIiIyDTwM2dEJkoIge3bt2Pq1Km4dOmSwZoXX3wRK1euhLe3dz13R0RERGR6GM6ITFBubi4mT56MtLQ0g+MODg5YtmwZRo8ezcU+iIiIiOoJL2skMiFCCMycORNdunQxGMzMzc0xbdo05OXl4a233mIwIyIiIqpHPHNGZEIUCgVu3Lhh8EulX375ZSQkJMDLy0uGzoiIiIiIZ86ITMyiRYtgY2Mj3XdycsLWrVvxzTffMJgRERERyYjhjMjEtGzZEgsWLICFhQViY2Nx5swZvPHGG7yEkYiIiEhmCsFvla11ZWVl0Gg0KC0thVqtlrsdMjE6nQ6bNm2CEAIhISEGa7RaLc6fP4+2bdvWb3NEREREJuZpskGDO3N27949+Pj4QKFQICsrS2/s5MmT6NevH6ysrODs7IylS5dWe/y2bdvQoUMHWFlZwdvbG3v37tUbF0IgLi4ODg4OsLa2RkBAAM6ePVuXu0RUa44fP45+/fohJCQEUVFRuHr1qsE6pVLJYEZERERkZBpcOJsxYwYcHR2rbS8rK8OgQYPQpk0bHD16FB9++CHmzJmDNWvWSDU//PADRo8ejfHjx+P48eMYNmwYhg0bhuzsbKlm6dKliI+Px6efforDhw+jadOmCAwMxK+//lov+0f0e9y4cQPh4eHo0aMHfvjhBwBAaWkpYmJiZO6MiIiIiGqqQV3WuG/fPkydOhU7duxAp06dcPz4cfj4+AAAVq9ejVmzZqGoqAiWlpYAgJiYGOzatQtnzpwBAIwcORK3b99GUlKS9JwvvPACfHx88Omnn0IIAUdHR0RHR2PatGkA7v8F187ODhs2bMCoUaNq1Ccva6T6otPp8PnnnyM2NhbXr183WJOTk4OOHTvWc2dEREREBDTSyxqLi4sRGhqKTZs2oUmTJtXGMzIy8OKLL0rBDAACAwORl5eHmzdvSjUBAQF6jwsMDERGRgYAoKCgAEVFRXo1Go0Gfn5+Uo0h9+7dQ1lZmd6NqK4dOXIE/v7+CA0NNRjM2rZti6SkJAYzIiIiogaiQYSzBwsbTJo0CT169DBYU1RUBDs7O71tD+4XFRU9tubh8YcfZ6jGkEWLFkGj0Ug3Z2fnp9g7oqdz/fp1TJgwAb169UJmZma1cWtrayxYsADZ2dkICgqSoUMiIiIi+j1kDWcxMTFQKBSPvZ05cwYJCQkoLy9HbGysnO0+UmxsLEpLS6XbxYsX5W6JGiGtVovVq1ejffv2+Oyzz2DoiuTg4GDk5uZi1qxZsLKykqFLIiIiIvq9zOV88ejo6Ecu9f2Au7s79u/fj4yMDKhUKr2xHj16YMyYMdi4cSPs7e1RXFysN/7gvr29vfRfQzUPjz/Y5uDgoFfz4LNthqhUqmq9EdWmjIwMRERE4NixYwbHPT09ER8fj0GDBtVzZ0RERERUW2QNZ61atUKrVq2eWBcfH48FCxZI969cuYLAwEAkJibCz88PAODv749Zs2ahsrISFhYWAIDU1FR4enrCxsZGqklLS0NUVJT0XKmpqfD39wcAuLm5wd7eHmlpaVIYKysrw+HDhxEWFlYbu0z0u7z33nsGg1nTpk0RFxeHqKgovc9bEhEREVHDI2s4qykXFxe9+82aNQNwf8EDJycnAMBbb72FuXPnYvz48Zg5cyays7OxYsUK/O1vf5MeFxkZif79+2P58uUICgrCli1bcOTIEWm5fYVCgaioKCxYsAAeHh5wc3PDBx98AEdHRwwbNqx+dpbIgPj4ePj6+kKr1UrbRo4ciWXLlkl/BoiIiIioYWsQC4LUhEajwb/+9S8UFBSge/fuiI6ORlxcHCZMmCDV9O7dG1999RXWrFmDrl27Yvv27di1axc6d+4s1cyYMQOTJ0/GhAkT0LNnT9y6dQvJycn8/A7JytvbGxEREQAALy8v7N+/H1u2bGEwIyIiImpEGtT3nDUU/J4z+j0KCwtRVFQEX19fg+MlJSXYtGkTJk2aJF26S0RERETGrVF+zxlRY1VZWYmPPvoInp6eGDVqFCoqKgzWtWjRApMnT2YwIyIiImqkGM6IZPTtt9/Cx8cH0dHRKC8vR35+vt7nJImIiIjIdDCcEcng0qVLGDlyJF5++WXk5OTojc2fPx+XLl2SqTMiIiIikkuDWK2RqKHS6rQ4cOEACssL4dDcAX72fohfEY/58+fj9u3bBh/z5ptv8nvziIiIiEwQwxlRHdmZuxORyZG4VPb/Z8H+DZinmKPqWpXB+m7dumHVqlV44YUX6rFLIiIiIjIWDGdEdWBn7k68vvV1CAigBEAKgFygCtWDmY2NDf76178iNDQUSqWyvlslIiIiIiPBcEZUy7Q6LSKTIyEqBfADgAOAgUwGhUKB0NBQLFy4EC1btqzvNomIiIjIyDCcEdWyAxcO3L+UcTOAc48oag2s/mQ1Jr46sT5bIyIiIiIjxnBGVMsKywvv/9AT1cNZEwABAHwAtRu/oJyIiIiI/ovhjKiWOTR3uP9DBwBtAfwMQAGgB4CXcD+gPVxHRERERAR+zxnRMxFCVFsSv59LPzipnaBQKIAhAFwBTAAQBKAJoIACzmpn9HPpV/8NExEREZHRYjgj+p3y8/MxdOhQBAcHQwghbVeaKbFi8AoAgKKlAggB8P8nyRRQAAA+HvwxlGZcmZGIiIiI/ovhjOgp3b59G++99x68vb2RnJyMlJQU7N69W69mRMcR2P7mdrRWt9bb7qR2wvY3t2NExxH12TIRERERNQAK8fA/+VOtKCsrg0ajQWlpKdRqLvrQWAghsH37dkydOhWXLl3SG2vTpg1yc3NhbW2tt12r0+LAhQMoLC+EQ3MH9HPpxzNmRERERCbkabIBFwQhqoHc3FxMnjwZaWlpBscrKyuRn5+Prl276m1XmikxwHVAPXRIRERERA0dL2skeozy8nJMnz4dXbp0MRjMzM3NMX36dJw5c6ZaMCMiIiIieho8c0ZkgBACmzdvxrRp01BYWGiwZuDAgUhISEDHjh3ruTsiIiIiaowYzoh+49SpU4iIiEB6errBcWdnZ3z00UcIDg6+v1w+EREREVEt4GWNRA+ZNWsWfH19DQYzCwsLxMbGIjc3F6+//jqDGRERERHVKp45I3qIWq2GVquttn3w4MFYsWIF2rdvL0NXRERERGQKeOaM6CHvvvuuXgBzdXXFrl27sHfvXgYzIiIiIqpTDGdED7G0tERCQgJUKhVmz56NnJwcvPbaa7yEkYiIiIjqHC9rJJOi0+mwfv16XLp0CbNnzzZYM2jQIPzyyy+ws7Or5+6IiIiIyJQxnJHJOHLkCMLDw5GZmQmlUokRI0bA29vbYC2DGRERERHVN17WSI3e9evXMWHCBPTq1QuZmZkAAK1Wi4iICAghZO6OiIiIiOg+hjNqtLRaLVavXo327dvjs88+qxbE0tPTH/ldZkRERERE9Y3hjBqljIwM9OzZE3/5y19w8+bNauOenp5ISUlB//79ZeiOiIiIiKg6hjNqVIqLi/HOO++gd+/eOH78eLXxpk2bYvHixTh58iQGDRokQ4dERERERIZxQRBqFKqqqvDJJ58gLi4OpaWlBmtGjhyJZcuWwcnJqZ67IyIiIiJ6MoYzavDS09MRERGBU6dOGRz38vLCypUr8dJLL9VzZ0RERERENcfLGqnB27Bhg8Fg1rx5cyxfvhxZWVkMZkRERERk9BjOqMFbvHgxNBqN3ra3334beXl5mDp1KiwsLGTqjIiIiIio5hjOqMGztbXF/PnzAQBdunRBeno6Nm3aBAcHB5k7IyIiIiKqOX7mjBqEixcvIjc395ErLIaFhUGj0eCtt96CuTl/rYmIiIio4eGZMzJq9+7dw+LFi9GhQweMHDkS165dM1hnbm6O//mf/2EwIyIiIqIGi+GMjFZKSgq6dOmC2NhY3LlzByUlJZg1a5bcbRERERER1QmGMzI658+fx/DhwzF48GDk5+frja1duxY//fSTTJ0REREREdUdhjMyGr/++ivmz5+Pjh07YteuXdXGFQoFQkND4ebmVv/NERERERHVMX5Ah4xCUlISIiMjce7cOYPjvXr1wsqVK9GzZ8967oyIiIiIqH7wzBnJ6ueff8Yrr7yCV155xWAwa9myJdauXYuMjAwGMyIiIiJq1BjOSBZ37txBXFwcOnXqhKSkpGrjZmZmCA8PR35+PsaPHw8zM/6qEhEREVHjxssaSRZ/+tOfkJiYaHCsT58+WLlyJXx8fOq3KSIiIiIiGfF0BMkiJiam2tkwOzs7fPHFFzhw4ACDGRERERGZHIYzkoWPjw/CwsIAAEqlElFRUcjLy8PYsWOhUChk7o6IiIiIqP7xskaqM0IIXLt2Dba2tgbH58+fj8LCQsydOxedO3eu5+6IiIiIiIwLz5xRncjJycEf/vAH9O/fHxUVFQZrbGxssGPHDgYzIiIiIiI0sHC2Z88e+Pn5wdraGjY2Nhg2bJje+IULFxAUFIQmTZrA1tYW06dPR1VVlV7Nd999h27dukGlUqFdu3bYsGFDtddZtWoVXF1dYWVlBT8/P2RmZtbhXjUu5eXlmDZtGrp27Yq0tDScOXMG8fHxcrdFRERERGT0Gkw427FjB8aOHYt33nkHJ06cwKFDh/DWW29J41qtFkFBQaioqMAPP/yAjRs3YsOGDYiLi5NqCgoKEBQUhJdeeglZWVmIiorCn//8Z6SkpEg1iYmJmDp1KmbPno1jx46ha9euCAwMxNWrV+t1fxsaIQS++uoreHp6Yvny5XqheO7cubhy5YqM3RERERERGT+FEELI3cSTVFVVwdXVFXPnzsX48eMN1uzbtw9//OMfceXKFdjZ2QEAPv30U8ycORPXrl2DpaUlZs6ciT179iA7O1t63KhRo1BSUoLk5GQAgJ+fH3r27ImVK1cCAHQ6HZydnTF58mTExMTUqN+ysjJoNBqUlpZCrVY/y643CKdOnUJERATS09MNjjs7OyMxMRH+/v713BkRERERkbyeJhs0iDNnx44dw+XLl2FmZgZfX184ODhgyJAheiErIyMD3t7eUjADgMDAQJSVleH06dNSTUBAgN5zBwYGIiMjAwBQUVGBo0eP6tWYmZkhICBAqjHk3r17KCsr07uZgpKSEkRGRsLX19dgMLO0tMSsWbOQm5vLYEZERERE9AQNIpydO3cOADBnzhy8//77SEpKgo2NDQYMGIAbN24AAIqKivSCGQDpflFR0WNrysrKcPfuXVy/fh1ardZgzYPnMGTRokXQaDTSzdnZ+dl22MjpdDps3LgRnp6eiI+Ph1arrVbzIDwvWLAATZs2laFLIiIiIqKGRdZwFhMTA4VC8djbmTNnoNPpAACzZs1CcHAwunfvjvXr10OhUGDbtm1y7gIAIDY2FqWlpdLt4sWLcrdUZ44fP46+ffsiJCTE4OfwXF1dsXv3buzZswceHh4ydEhERERE1DDJ+j1n0dHRCAkJeWyNu7s7CgsLAQBeXl7SdpVKBXd3d1y4cAEAYG9vX21VxeLiYmnswX8fbHu4Rq1Ww9raGkqlEkql0mDNg+cwRKVSQaVSPXY/GoP58+djzpw5Ulh+mEqlQkxMDGbOnAlra2sZuiMiIiIiathkDWetWrVCq1atnljXvXt3qFQq5OXloW/fvgCAyspKnD9/Hm3atAEA+Pv7Y+HChbh69ar0pcepqalQq9VSqPP398fevXv1njs1NVX6PJSlpSW6d++OtLQ0aZl+nU6HtLQ0RERE1Mo+N2ReXl4Gg9mrr76Kv/3tb3B3d5ehKyIiIiKixqFBfOZMrVZj0qRJmD17Nv71r38hLy8PYWFhAIA33ngDADBo0CB4eXlh7NixOHHiBFJSUvD+++8jPDxcOqs1adIknDt3DjNmzMCZM2fwySefYOvWrXj33Xel15o6dSo+++wzbNy4Ebm5uQgLC8Pt27fxzjvv1P+OG5kRI0boLZbStm1b7NmzB7t372YwIyIiIiJ6RrKeOXsaH374IczNzTF27FjcvXsXfn5+2L9/P2xsbAAASqUSSUlJCAsLg7+/P5o2bYpx48Zh3rx50nO4ublhz549ePfdd7FixQo4OTlh7dq1CAwMlGpGjhyJa9euIS4uDkVFRfDx8UFycnK1RUJMkUKhQEJCAl544QVMnz4d0dHRsLKykrstIiIiIqJGoUF8z1lD01C/50yr1WLNmjXIyMjAF1988ci6W7duoVmzZvXYGRERERFRw/Q02aDBnDmjupWRkYHw8HAcP34cwP3LRV955RWDtQxmRERERES1r0F85ozqTnFxMUJCQtC7d28pmAFAZGQkfv31Vxk7IyIiIiIyLQxnJqqqqgrx8fHw9PTExo0bq40XFBQgKSlJhs6IiIiIiEwTw5kJSk9PR7du3RAZGYnS0tJq415eXti/fz9ef/11GbojIiIiIjJN/MxZI6bVaXHgwgEUlhfCobkD2pq3RWxMLL788kuD9c2bN8fcuXMREREBCwuLeu6WiIiIiMi0MZw1UjtzdyIyORKXyi4BWgCHAcX3Coh7hhfnHDt2LJYuXQp7e/v6bZSIiIiIiAAwnDVKO3N34vWtr0NAAAUA9gC4jvv3f6NLly5YtWoV+vbtW+99EhERERHRf/EzZ42MVqdFZHLkf4PYRQDXq9dpNBokJCTg6NGjDGZEREREREaAZ84amQMXDty/lPGB3gCyANx4qMgXWL9mPYb3GF6/zRERERER0SPxzFkjU1heqL/BHMCQ///ZAcB4AK8Bv6r4HWZERERERMaEZ84aGYfmDtU3egB4C0A7SHHcYB0REREREcmGZ84amX4u/eCkdoICCv2B9gDMAAUUcFY7o59LP1n6IyIiIiIiwxjOGhmlmRIrBq8AgGoB7cH9jwd/DKWZst57IyIiIiKiR2M4a4RGdByB7W9uR2t1a73tTmonbH9zO0Z0HCFTZ0RERERE9CgKIYThbyWm362srAwajQalpaVQq9Wy9aHVaXHgwgEUlhfCobkD+rn04xkzIiIiIqJ69DTZgAuCNGJKMyUGuA6Quw0iIiIiIqoBXtZIRERERERkBBjOiIiIiIiIjADDGRERERERkRFgOCMiIiIiIjICDGdERERERERGgOGMiIiIiIjICDCcERERERERGQGGMyIiIiIiIiPAcEZERERERGQEGM6IiIiIiIiMAMMZERERERGREWA4IyIiIiIiMgIMZ0REREREREbAXO4GGiMhBACgrKxM5k6IiIiIiEhODzLBg4zwOAxndaC8vBwA4OzsLHMnRERERERkDMrLy6HRaB5boxA1iXD0VHQ6Ha5cuYLmzZtDoVDI3U69KCsrg7OzMy5evAi1Wi13OyaH8y8vzr+8OP/y4vzLi/MvL86/vBrK/AshUF5eDkdHR5iZPf5TZTxzVgfMzMzg5OQkdxuyUKvVRv2Ho7Hj/MuL8y8vzr+8OP/y4vzLi/Mvr4Yw/086Y/YAFwQhIiIiIiIyAgxnRERERERERoDhjGqFSqXC7NmzoVKp5G7FJHH+5cX5lxfnX16cf3lx/uXF+ZdXY5x/LghCRERERERkBHjmjIiIiIiIyAgwnBERERERERkBhjMiIiIiIiIjwHBGRERERERkBBjO6In27NkDPz8/WFtbw8bGBsOGDdMbv3DhAoKCgtCkSRPY2tpi+vTpqKqq0qv57rvv0K1bN6hUKrRr1w4bNmyo9jqrVq2Cq6srrKys4Ofnh8zMzDrcq4bl3r178PHxgUKhQFZWlt7YyZMn0a9fP1hZWcHZ2RlLly6t9vht27ahQ4cOsLKygre3N/bu3as3LoRAXFwcHBwcYG1tjYCAAJw9e7Yud8nonT9/HuPHj4ebmxusra3Rtm1bzJ49GxUVFXp1nH/58djx7BYtWoSePXuiefPmsLW1xbBhw5CXl6dX8+uvvyI8PBzPP/88mjVrhuDgYBQXF+vV1Nb7gSlbvHgxFAoFoqKipG2c+7p1+fJlvP3223j++edhbW0Nb29vHDlyRBqvyTH6xo0bGDNmDNRqNVq0aIHx48fj1q1bejU1eb8wNVqtFh988IHee+38+fPx8HqFJjf/gugxtm/fLmxsbMTq1atFXl6eOH36tEhMTJTGq6qqROfOnUVAQIA4fvy42Lt3r2jZsqWIjY2Vas6dOyeaNGkipk6dKnJyckRCQoJQKpUiOTlZqtmyZYuwtLQUn3/+uTh9+rQIDQ0VLVq0EMXFxfW6v8ZqypQpYsiQIQKAOH78uLS9tLRU2NnZiTFjxojs7GyxefNmYW1tLf7+979LNYcOHRJKpVIsXbpU5OTkiPfff19YWFiIU6dOSTWLFy8WGo1G7Nq1S5w4cUK8+uqrws3NTdy9e7c+d9Oo7Nu3T4SEhIiUlBTx888/i927dwtbW1sRHR0t1XD+5cdjR+0IDAwU69evF9nZ2SIrK0sMHTpUuLi4iFu3bkk1kyZNEs7OziItLU0cOXJEvPDCC6J3797SeG29H5iyzMxM4erqKrp06SIiIyOl7Zz7unPjxg3Rpk0bERISIg4fPizOnTsnUlJSxL///W+ppibH6MGDB4uuXbuKH3/8URw4cEC0a9dOjB49WhqvyfuFKVq4cKF4/vnnRVJSkigoKBDbtm0TzZo1EytWrJBqTG3+Gc7okSorK0Xr1q3F2rVrH1mzd+9eYWZmJoqKiqRtq1evFmq1Wty7d08IIcSMGTNEp06d9B43cuRIERgYKN3v1auXCA8Pl+5rtVrh6OgoFi1aVFu702Dt3btXdOjQQZw+fbpaOPvkk0+EjY2NNNdCCDFz5kzh6ekp3X/zzTdFUFCQ3nP6+fmJiRMnCiGE0Ol0wt7eXnz44YfSeElJiVCpVGLz5s11tFcN09KlS4Wbm5t0n/MvPx476sbVq1cFAPH9998LIe7/TlpYWIht27ZJNbm5uQKAyMjIEELU3vuBqSovLxceHh4iNTVV9O/fXwpnnPu6NXPmTNG3b99HjtfkGJ2TkyMAiJ9++kmq2bdvn1AoFOLy5ctCiJq9X5iioKAg8ac//Ulv24gRI8SYMWOEEKY5/7yskR7p2LFjuHz5MszMzODr6wsHBwcMGTIE2dnZUk1GRga8vb1hZ2cnbQsMDERZWRlOnz4t1QQEBOg9d2BgIDIyMgAAFRUVOHr0qF6NmZkZAgICpBpTVVxcjNDQUGzatAlNmjSpNp6RkYEXX3wRlpaW0rbAwEDk5eXh5s2bUs3j5r+goABFRUV6NRqNBn5+fiY//79VWlqK5557TrrP+ZcXjx11p7S0FACk3/ejR4+isrJSb647dOgAFxcXaa5r4/3AlIWHhyMoKKja/HDu69bXX3+NHj164I033oCtrS18fX3x2WefSeM1OUZnZGSgRYsW6NGjh1QTEBAAMzMzHD58WKp50vuFKerduzfS0tKQn58PADhx4gQOHjyIIUOGADDN+Wc4o0c6d+4cAGDOnDl4//33kZSUBBsbGwwYMAA3btwAABQVFem9GQCQ7hcVFT22pqysDHfv3sX169eh1WoN1jx4DlMkhEBISAgmTZqkd8B52LPM/8PjDz/OUA0B//73v5GQkICJEydK2zj/8uKxo27odDpERUWhT58+6Ny5M4D7v6eWlpZo0aKFXu1vf5ef9f3AVG3ZsgXHjh3DokWLqo1x7uvWuXPnsHr1anh4eCAlJQVhYWGYMmUKNm7cCKBmx+iioiLY2trqjZubm+O55557qv9HpigmJgajRo1Chw4dYGFhAV9fX0RFRWHMmDEATHP+Gc5MUExMDBQKxWNvZ86cgU6nAwDMmjULwcHB6N69O9avXw+FQoFt27bJvBcNV03nPyEhAeXl5YiNjZW75UalpvP/sMuXL2Pw4MF44403EBoaKlPnRPUjPDwc2dnZ2LJli9ytmISLFy8iMjISX375JaysrORux+TodDp069YNf/3rX+Hr64sJEyYgNDQUn376qdytmYStW7fiyy+/xFdffYVjx45h48aNWLZsmRSOTZG53A1Q/YuOjkZISMhja9zd3VFYWAgA8PLykrarVCq4u7vjwoULAAB7e/tqK6M9WEHK3t5e+u9vV5UqLi6GWq2GtbU1lEollEqlwZoHz9GY1HT+9+/fj4yMDKhUKr2xHj16YMyYMdi4ceMj5xZ48vw/PP5gm4ODg16Nj4/PU++fsavp/D9w5coVvPTSS+jduzfWrFmjV8f5l1fLli1N6thRHyIiIpCUlIT09HQ4OTlJ2+3t7VFRUYGSkhK9Mzi//V1+1vcDU3T06FFcvXoV3bp1k7ZptVqkp6dj5cqVSElJ4dzXIQcHB72/5wBAx44dsWPHDgA1O0bb29vj6tWres9RVVWFGzduPHH+H34NUzR9+nTp7BkAeHt745dffsGiRYswbtw4k5x/njkzQa1atUKHDh0ee7O0tET37t2hUqn0llOurKzE+fPn0aZNGwCAv78/Tp06pfeHIjU1FWq1WjrY+fv7Iy0tTa+H1NRU+Pv7A4D0Wg/X6HQ6pKWlSTWNSU3nPz4+HidOnEBWVhaysrKk5dcTExOxcOFCAPfnNj09HZWVldLzp6amwtPTEzY2NlLN4+bfzc0N9vb2ejVlZWU4fPiwSc8/cP+M2YABA6SzxmZm+odMzr+8TO3YUZeEEIiIiMD//u//Yv/+/XBzc9Mb7969OywsLPTmOi8vDxcuXJDmujbeD0zRwIEDcerUKelYn5WVJf0j3IOfOfd1p0+fPtW+NiI/P1/6e05NjtH+/v4oKSnB0aNHpZr9+/dDp9PBz89PqnnS+4UpunPnTrX3VqVSKV29ZZLzL/eKJGTcIiMjRevWrUVKSoo4c+aMGD9+vLC1tRU3btwQQvx3+d5BgwaJrKwskZycLFq1amVw+d7p06eL3NxcsWrVKoNL6atUKrFhwwaRk5MjJkyYIFq0aKG38pSpKygoqLZaY0lJibCzsxNjx44V2dnZYsuWLaJJkybVlnI3NzcXy5YtE7m5uWL27NkGl3Jv0aKF2L17tzh58qR47bXXTH4p90uXLol27dqJgQMHikuXLonCwkLp9gDnX348dtSOsLAwodFoxHfffaf3u37nzh2pZtKkScLFxUXs379fHDlyRPj7+wt/f39pvLbeD0jordYoBOe+LmVmZgpzc3OxcOFCcfbsWfHll1+KJk2aiH/84x9STU2O0YMHDxa+vr7i8OHD4uDBg8LDw0NvKfeavF+YonHjxonWrVtLS+nv3LlTtGzZUsyYMUOqMbX5Zzijx6qoqBDR0dHC1tZWNG/eXAQEBIjs7Gy9mvPnz4shQ4YIa2tr0bJlSxEdHS0qKyv1ar799lvh4+MjLC0thbu7u1i/fn2110pISBAuLi7C0tJS9OrVS/z44491uWsNjqFwJoQQJ06cEH379hUqlUq0bt1aLF68uNpjt27dKtq3by8sLS1Fp06dxJ49e/TGdTqd+OCDD4SdnZ1QqVRi4MCBIi8vry53x+itX79eADB4exjnX348djy7R/2uP3ysvnv3rvjLX/4ibGxsRJMmTcTw4cP1/rFCiNp7PzB1vw1nnPu69c9//lN07txZqFQq0aFDB7FmzRq98Zoco//zn/+I0aNHi2bNmgm1Wi3eeecdUV5erldTk/cLU1NWViYiIyOFi4uLsLKyEu7u7mLWrFl6S96b2vwrhHjoK7iJiIiIiIhIFvzMGRERERERkRFgOCMiIiIiIjICDGdERERERERGgOGMiIiIiIjICDCcERERERERGQGGMyIiIiIiIiPAcEZERERERGQEGM6IiIiIiIiMAMMZERHRbygUCuzatUvuNvTUVU+urq74+OOPa/15iYjo6TGcERGRbDIyMqBUKhEUFPTUj5UrVCgUisfe5syZ88jHnj9/HgqFAllZWbXeV0hIiNSDpaUl2rVrh3nz5qGqquqxj/vpp58wYcKEWu+HiIienrncDRARkelat24dJk+ejHXr1uHKlStwdHSUu6UnKiwslH5OTExEXFwc8vLypG3NmjWToy0AwODBg7F+/Xrcu3cPe/fuRXh4OCwsLBAbG1uttqKiApaWlmjVqpUMnRIRkSE8c0ZERLK4desWEhMTERYWhqCgIGzYsKFazT//+U/07NkTVlZWaNmyJYYPHw4AGDBgAH755Re8++670tkiAJgzZw58fHz0nuPjjz+Gq6urdP+nn37CH/7wB7Rs2RIajQb9+/fHsWPHaty3vb29dNNoNFAoFNJ9W1tbfPTRR3BycoJKpYKPjw+Sk5Olx7q5uQEAfH19oVAoMGDAgFrp6QGVSgV7e3u0adMGYWFhCAgIwNdffw3g/pm1YcOGYeHChXB0dISnpyeA6mcgS0pKMHHiRNjZ2cHKygqdO3dGUlKSNH7w4EH069cP1tbWcHZ2xpQpU3D79u2n7pWIiKpjOCMiIlls3boVHTp0gKenJ95++218/vnnEEJI43v27MHw4cMxdOhQHD9+HGlpaejVqxcAYOfOnXBycsK8efNQWFiodzbrScrLyzFu3DgcPHgQP/74Izw8PDB06FCUl5c/8z6tWLECy5cvx7Jly3Dy5EkEBgbi1VdfxdmzZwEAmZmZAIBvvvkGhYWF2LlzZ532ZG1tjYqKCul+Wloa8vLykJqaqhe4HtDpdBgyZAgOHTqEf/zjH8jJycHixYuhVCoBAD///DMGDx6M4OBgnDx5EomJiTh48CAiIiKeqU8iIrqPlzUSEZEs1q1bh7fffhvA/cvxSktL8f3330tnkxYuXIhRo0Zh7ty50mO6du0KAHjuueegVCrRvHlz2NvbP9Xrvvzyy3r316xZgxYtWuD777/HH//4x2fYI2DZsmWYOXMmRo0aBQBYsmQJvv32W3z88cdYtWqVdAnh888/r9d3bfckhEBaWhpSUlIwefJkaXvTpk2xdu1aWFpaGnzcN998g8zMTOTm5qJ9+/YAAHd3d2l80aJFGDNmDKKiogAAHh4eiI+PR//+/bF69WpYWVk9da9ERPRfPHNGRET1Li8vD5mZmRg9ejQAwNzcHCNHjsS6deukmqysLAwcOLDWX7u4uBihoaHw8PCARqOBWq3GrVu3cOHChWd63rKyMly5cgV9+vTR296nTx/k5ubWS09JSUlo1qwZrKysMGTIEIwcOVJvgRJvb+9HBjPg/pw7OTlJwey3Tpw4gQ0bNqBZs2bSLTAwEDqdDgUFBU/VKxERVcczZ0REVO/WrVuHqqoqvQVAhBBQqVRYuXIlNBoNrK2tn/p5zczM9C6NBIDKykq9++PGjcN//vMfrFixAm3atIFKpYK/v7/e5X/1rbZ6eumll7B69WpYWlrC0dER5ub6b/NNmzZ97OOfNOe3bt3CxIkTMWXKlGpjLi4uT9UrERFVxzNnRERUr6qqqvDFF19g+fLlyMrKkm4nTpyAo6MjNm/eDADo0qUL0tLSHvk8lpaW0Gq1ettatWqFoqIivYD222XrDx06hClTpmDo0KHo1KkTVCoVrl+//sz7pVar4ejoiEOHDlV7PS8vL6lnANX6rq2emjZtinbt2sHFxaVaMKuJLl264NKlS8jPzzc43q1bN+Tk5KBdu3bVbo87I0dERDXDcEZERPUqKSkJN2/exPjx49G5c2e9W3BwsHRp4+zZs7F582bMnj0bubm5OHXqFJYsWSI9j6urK9LT03H58mUpyAwYMADXrl3D0qVL8fPPP2PVqlXYt2+f3ut7eHhg06ZNyM3NxeHDhzFmzJjfdZbOkOnTp2PJkiVITExEXl4eYmJikJWVhcjISACAra0trK2tkZycjOLiYpSWltZ5T0+jf//+ePHFFxEcHIzU1FQUFBRg37590oqTM2fOxA8//ICIiAhkZWXh7Nmz2L17NxcEISKqJQxnRERUr9atW4eAgABoNJpqY8HBwThy5AhOnjyJAQMGYNu2bfj666/h4+ODl19+WVrtEADmzZuH8+fPo23bttJCGx07dsQnn3yCVatWoWvXrsjMzMS0adOqvf7NmzfRrVs3jB07FlOmTIGtrW2t7NuUKVMwdepUREdHw9vbG8nJyfj666/h4eEB4P5n6+Lj4/H3v/8djo6OeO211+q8p6e1Y8cO9OzZE6NHj4aXlxdmzJghnenr0qULvv/+e+Tn56Nfv37w9fVFXFxcg/h+OiKihkAhfntxPhEREREREdU7njkjIiIiIiIyAgxnRERERERERoDhjIiIiIiIyAgwnBERERERERkBhjMiIiIiIiIjwHBGRERERERkBBjOiIiIiIiIjADDGRERERERkRFgOCMiIiIiIjICDGdERERERERGgOGMiIiIiIjICPwfuE3bNWdSfxAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S4Tffhj8K2Dg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2bwbOtKMzuxK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <span style=\"color:blue\">Conclusion and Final Notes</span>\n",
        "\n",
        "Congratulations on completing the project on predicting customer spending using Linear Regression, Ridge Regression, and Polynomial Regression models! Here's a summary and some final insights to wrap up your journey:\n",
        "\n",
        "#### <span style=\"color:green\">Summary of the Project</span>\n",
        "- This project aimed to predict customer spending based on various features using machine learning techniques.\n",
        "- The dataset provided valuable insights into customer behavior, allowing us to build models that predict total spending accurately.\n",
        "\n",
        "#### <span style=\"color:green\">Key Steps and Learnings</span>\n",
        "- **Data Preparation and Exploration**:\n",
        "  - We started by loading and cleaning the dataset, handling missing values, and ensuring data quality.\n",
        "  - Exploratory data analysis (EDA) provided insights into the distribution and relationships within the dataset.\n",
        "\n",
        "- **Feature Engineering**:\n",
        "  - Engineered features such as polynomial features helped capture non-linear relationships between variables, enhancing model performance.\n",
        "\n",
        "- **Model Building and Evaluation**:\n",
        "  - Utilized Linear Regression, Ridge Regression (with regularization), and Polynomial Regression models to predict total spending.\n",
        "  - Evaluated model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R^2) score.\n",
        "  - Cross-validation ensured robustness of model performance across different subsets of data.\n",
        "\n",
        "- **Visualization and Interpretation**:\n",
        "  - Visualized model predictions using scatter plots to compare actual vs. predicted values, providing intuitive insights into model accuracy and performance.\n",
        "\n",
        "#### <span style=\"color:green\">Key Takeaways</span>\n",
        "- **Model Selection**: Choose the appropriate regression model based on your data characteristics and performance metrics.\n",
        "- **Feature Engineering**: Explore different techniques like polynomial features to capture complex relationships in your data.\n",
        "- **Evaluation**: Use multiple metrics and cross-validation to comprehensively evaluate model performance.\n",
        "- **Visualization**: Visualize predictions to understand how well your models predict actual outcomes.\n",
        "\n",
        "#### <span style=\"color:green\">Next Steps</span>\n",
        "- Further refine models by tuning hyperparameters or exploring different algorithms.\n",
        "- Explore advanced techniques like ensemble methods or deep learning for potentially improved accuracy.\n",
        "- Consider deploying the model in a production environment for real-time predictions and monitoring.\n",
        "\n",
        "### <span style=\"color:blue\">Final Thoughts</span>\n",
        "This project not only provided hands-on experience with machine learning techniques but also equipped you with valuable skills in data preprocessing, model building, and evaluation. Keep exploring and applying these skills in various domains to continue your journey in data science and machine learning.\n",
        "\n",
        "Enjoy your data science and machine learning adventure, and best of luck with your future projects!"
      ],
      "metadata": {
        "id": "Toy3JFSZzzNT"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LJukct83zwAC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}