1_2_14_Healthcare_Cost_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"purple\" size=\"6\">Healthcare Cost Prediction Project</font>\n",
        "\n",
        "## <font color=\"indigo\" size=\"5\">Project Overview</font>\n",
        "The healthcare cost prediction project aims to utilize machine learning techniques to accurately predict insurance charges based on a variety of patient demographic and health indicators. This project involves a comprehensive approach that includes data exploration, preprocessing, model training, evaluation, and fine-tuning to ensure optimal predictive performance. By leveraging advanced regression models and thorough exploratory data analysis, we strive to understand the underlying patterns in the data and enhance the accuracy of our predictions.\n",
        "\n",
        "## <font color=\"orange\" size=\"5\">Libraries and Data Loading</font>\n",
        "This project utilizes several key Python libraries to facilitate data manipulation, visualization, and machine learning:\n",
        "- **pandas**: A powerful data manipulation library used for loading, cleaning, and analyzing datasets.\n",
        "- **numpy**: A library for numerical operations, providing support for large multi-dimensional arrays and matrices.\n",
        "- **matplotlib** and **seaborn**: Visualization libraries used to create a variety of static, animated, and interactive plots.\n",
        "- **scikit-learn**: A robust machine learning library that provides simple and efficient tools for data mining and data analysis.\n",
        "\n",
        "The dataset is sourced from a publicly available URL, providing information on insurance charges alongside features such as age, BMI, number of children, gender, smoking status, and geographical region. This data forms the foundation for our predictive models.\n",
        "\n",
        "## <font color=\"red\" size=\"5\">Data Exploration and Preprocessing</font>\n",
        "1. **Initial Exploration**: Begin by examining the dataset's structure and content. This includes displaying the first few rows of the dataset, generating summary statistics to understand the distribution of numerical features, and providing a comprehensive overview of the dataset's structure.\n",
        "2. **Missing Values**: Assess the dataset for any missing values to ensure data completeness and quality.\n",
        "3. **Preprocessing**:\n",
        "   - Convert categorical variables into dummy/indicator variables to facilitate their inclusion in the machine learning models.\n",
        "   - Save the preprocessed dataset to preserve the cleaned and transformed data for subsequent analysis and modeling.\n",
        "\n",
        "## <font color=\"blue\" size=\"5\">Exploratory Data Analysis (EDA)</font>\n",
        "Exploratory Data Analysis (EDA) is a critical step in understanding the data's underlying structure and relationships:\n",
        "- **Distribution of Charges**: Utilize histograms and kernel density estimates (KDE) to visualize the distribution of insurance charges, providing insights into the spread and central tendency of the target variable.\n",
        "- **Pairplot**: Generate pairwise scatter plots to explore potential relationships between different features.\n",
        "- **Correlation Matrix**: Create a heatmap to display the correlation coefficients between features, identifying strong linear relationships that can inform model selection and feature engineering.\n",
        "- **Scatter Plots**: Investigate specific relationships, such as between age and charges, and BMI and charges, to uncover trends and patterns.\n",
        "- **Boxplot**: Examine the relationship between the number of children and insurance charges, highlighting any significant variations or outliers.\n",
        "\n",
        "## <font color=\"green\" size=\"5\">Feature Selection and Splitting Data</font>\n",
        "- **Feature Selection**: Isolate the dataset into independent variables (features) and the dependent variable (target) to prepare for model training.\n",
        "- **Data Splitting**: Divide the dataset into training and testing subsets to enable model validation. The training set is used to train the models, while the testing set provides an unbiased evaluation of the model's performance.\n",
        "\n",
        "## <font color=\"teal\" size=\"5\">Model Training and Evaluation</font>\n",
        "Training and evaluating multiple regression models is a key step in predicting healthcare costs:\n",
        "- **Models Used**: Implement various regression models including Linear Regression, Ridge Regression, Lasso Regression, and Random Forest Regressor to predict insurance charges.\n",
        "- **Evaluation Metrics**: Assess model performance using Mean Squared Error (MSE) and R-squared (R²) metrics, which provide measures of prediction accuracy and explained variance, respectively.\n",
        "- **Initial Model Results**: Record and save the evaluation metrics of each model to compare their performance and identify the most promising approaches.\n",
        "\n",
        "## <font color=\"darkorange\" size=\"5\">Hyperparameter Tuning</font>\n",
        "Enhance model performance through hyperparameter tuning:\n",
        "- **Ridge and Lasso Regression**: Apply GridSearchCV to systematically explore different values of the `alpha` parameter, which controls the regularization strength.\n",
        "- **Best Model Selection**: Select the models with the optimal hyperparameters based on cross-validated performance.\n",
        "- **Evaluation of Tuned Models**: Calculate and save the MSE and R² scores for the tuned Ridge and Lasso models, ensuring the best possible predictive accuracy.\n",
        "\n",
        "## <font color=\"maroon\" size=\"5\">Prediction on New Data</font>\n",
        "To demonstrate the model's practical application, we predict healthcare costs for new data points:\n",
        "- **Example New Data**: Construct a DataFrame representing a new patient with specific demographic and health indicators.\n",
        "- **Prediction**: Use the best-performing model (e.g., tuned Ridge Regression) to predict the healthcare charge for this new patient, showcasing the model's utility in real-world scenarios.\n",
        "- **Save Prediction**: Document the predicted charges and save the results, providing a reference for future predictions and model applications."
      ],
      "metadata": {
        "id": "oziJrpy9kgK8"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "heapMjH3mJIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0BmJjTfcmKJR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"indigo\" size=\"5\">Importing Libraries</font>\n",
        "In this section, we import the essential Python libraries that will be utilized throughout the healthcare cost prediction project. Each library serves a specific purpose and contributes to different stages of data manipulation, visualization, and machine learning. These libraries are the backbone of our project, enabling us to efficiently handle and analyze the dataset, as well as build and evaluate predictive models.\n",
        "\n",
        "### <font color=\"orange\" size=\"4\">pandas</font>\n",
        "The `pandas` library is a powerful and flexible data manipulation tool that is widely used for data analysis in Python. It provides data structures like DataFrame and Series, which are essential for handling tabular data. These structures make it easier to clean, transform, and analyze the dataset. With `pandas`, we can perform a variety of data operations, such as filtering rows, aggregating data, and merging datasets. This library is crucial for the initial stages of our project, where we will be exploring and preparing the dataset for further analysis.\n",
        "\n",
        "### <font color=\"red\" size=\"4\">numpy</font>\n",
        "The `numpy` library is fundamental for numerical operations in Python. It supports large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. `numpy` is known for its performance and ease of use, making it an indispensable tool for numerical computations. In our project, we will use `numpy` to perform calculations on the dataset's numerical features, handle missing data, and implement various numerical operations required for data preprocessing and analysis.\n",
        "\n",
        "### <font color=\"blue\" size=\"4\">matplotlib</font>\n",
        "The `matplotlib` library is a versatile plotting library used to create static, animated, and interactive visualizations in Python. It provides extensive tools to customize and generate various types of plots, such as line plots, histograms, scatter plots, and more. `matplotlib` allows us to visualize the dataset and model outputs effectively, helping us understand the underlying patterns and relationships in the data. Throughout our project, we will use `matplotlib` to create detailed visualizations that will aid in our exploratory data analysis and the presentation of our findings.\n",
        "\n",
        "### <font color=\"teal\" size=\"4\">seaborn</font>\n",
        "The `seaborn` library is built on top of `matplotlib` and provides a high-level interface for drawing attractive and informative statistical graphics. It simplifies the process of creating complex visualizations, such as categorical plots, distribution plots, and regression plots. `seaborn` enhances the aesthetic appeal of our plots and offers additional functionalities, such as automatically estimating and plotting linear regression models. In our project, `seaborn` will be used extensively to perform exploratory data analysis, helping us to uncover insights and patterns in the data that are not immediately apparent.\n",
        "\n",
        "### <font color=\"darkorange\" size=\"4\">scikit-learn</font>\n",
        "The `scikit-learn` library is a robust machine learning library that provides simple and efficient tools for data mining and data analysis. It includes various algorithms for classification, regression, clustering, and more. In this project, `scikit-learn` will be used for model selection, training, evaluation, and hyperparameter tuning. This library offers a comprehensive suite of tools that allow us to build and fine-tune our predictive models, ensuring that we achieve the highest possible accuracy. With `scikit-learn`, we can easily experiment with different algorithms and parameter settings, making it a key component of our machine learning workflow.\n",
        "\n",
        "### <font color=\"maroon\" size=\"4\">Import Statements</font>\n",
        "The following code block contains the import statements for these essential libraries, setting up the environment for data processing, visualization, and machine learning tasks. By importing these libraries at the beginning of our project, we ensure that all necessary tools are available for each step of our analysis and modeling process.\n",
        "```python\n",
        "import pandas as pd  # Import pandas library for data manipulation\n",
        "import numpy as np  # Import numpy library for numerical operations\n",
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting\n",
        "import seaborn as sns  # Import seaborn for enhanced plotting\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV  # Import functions for model selection and evaluation\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso  # Import linear regression models\n",
        "from sklearn.ensemble import RandomForestRegressor  # Import Random Forest Regressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score  # Import metrics for evaluation"
      ],
      "metadata": {
        "id": "zoEgOaz3mKkK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas library for data manipulation"
      ],
      "metadata": {
        "id": "NkbC8nzKHvVK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy library for numerical operations"
      ],
      "metadata": {
        "id": "9cjb4eb4JaAj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "u026KymIJaDb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for enhanced plotting"
      ],
      "metadata": {
        "id": "4k5lupQMJaF7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV  # Import functions for model selection and evaluation\n"
      ],
      "metadata": {
        "id": "XKy7VRllJaKj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso  # Import linear regression models"
      ],
      "metadata": {
        "id": "IYWoAZU8Jc9L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor  # Import Random Forest Regressor"
      ],
      "metadata": {
        "id": "MSOa5mvwJc_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score  # Import metrics for evaluation"
      ],
      "metadata": {
        "id": "Q-IeEL4SKLlW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8UEpAgN7KLoP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I6jY1Kw_mmIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Loading the Dataset</font>\n",
        "\n",
        "In this step, we focus on loading the dataset that contains crucial information about healthcare costs. This dataset serves as the fundamental source of data for our project, enabling us to predict healthcare costs based on patient demographics and health indicators.\n",
        "\n",
        "### <font color=\"indigo\">Using pandas `read_csv()` Function</font>\n",
        "We utilize the `read_csv()` function from the `pandas` library to load the dataset. This function efficiently reads data from a CSV file into a DataFrame, which is a tabular data structure in Python that allows for easy manipulation and analysis of data.\n",
        "\n",
        "### <font color=\"orange\">Specifying the URL of the Dataset</font>\n",
        "The dataset is hosted online, and we specify its URL to fetch the data directly from its source. This approach eliminates the need to download the dataset locally, providing convenient access to the most up-to-date version.\n",
        "\n",
        "### <font color=\"red\">Loading Data into a DataFrame</font>\n",
        "By executing `pd.read_csv(url)`, we load the dataset into a DataFrame named `data`. This DataFrame contains all the rows and columns of the dataset, making it ready for comprehensive exploration and analysis.\n",
        "\n",
        "### <font color=\"indigo\">Displaying the First Few Rows</font>\n",
        "To verify the successful loading of the dataset, we use `data.head()`. This method displays the first few rows of the dataset, revealing the column names and initial data entries. It serves as an initial check to ensure that the dataset is correctly loaded and formatted.\n",
        "\n",
        "### <font color=\"teal\">Summary Statistics</font>\n",
        "We generate summary statistics of the dataset using `data.describe()`. This function computes key statistical measures such as mean, standard deviation, minimum, maximum, and quartile values for numerical columns. These statistics offer valuable insights into the distribution and characteristics of the dataset's numerical attributes.\n",
        "\n",
        "### <font color=\"blue\">Information About the Dataset</font>\n",
        "Understanding the structure and properties of the dataset is crucial. By calling `data.info()`, we obtain essential information such as the number of entries (rows), column names, non-null counts, and data types of each column. This provides a comprehensive overview of the dataset's composition and completeness.\n",
        "\n",
        "### <font color=\"purple\">Saving Initial Dataset Information</font>\n",
        "To preserve the initial state and characteristics of the dataset, we save pertinent information to separate files:\n",
        "- The first few rows of the dataset are stored in `initial_data_head.csv`.\n",
        "- Summary statistics are saved in `initial_data_description.csv`.\n",
        "- Detailed information about the dataset is saved in `initial_data_info.txt`.\n",
        "\n",
        "These steps ensure that we have a comprehensive record of the dataset's contents and structure, facilitating transparency, reproducibility, and effective documentation of our data preprocessing steps."
      ],
      "metadata": {
        "id": "7c5_cNPRnEyB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "-dndlnF1KLq_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://raw.githubusercontent.com/stedy/Machine-Learning-with-R-datasets/master/insurance.csv'  # URL of the dataset\n"
      ],
      "metadata": {
        "id": "HBx065VbKLuE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(url)  # Read data from CSV file into DataFrame"
      ],
      "metadata": {
        "id": "DT6FQwJWJdMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WZrkVTaMKhU7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset"
      ],
      "metadata": {
        "id": "l022fJN_Khab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Print statement\n",
        "print(data.head())  # Display first few rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8Xb15alKheM",
        "outputId": "59282d51-0450-4fdd-eacd-24ec4c1658ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "   age     sex     bmi  children smoker     region      charges\n",
            "0   19  female  27.900         0    yes  southwest  16884.92400\n",
            "1   18    male  33.770         1     no  southeast   1725.55230\n",
            "2   28    male  33.000         3     no  southeast   4449.46200\n",
            "3   33    male  22.705         0     no  northwest  21984.47061\n",
            "4   32    male  28.880         0     no  northwest   3866.85520\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w8EUosnEKhhG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GBrOuyHtKhj2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the summary statistics"
      ],
      "metadata": {
        "id": "EngRqkrMKhmg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics of the dataset:\")  # Print statement\n",
        "print(data.describe())  # Display summary statistics of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opy_XEqBKhp8",
        "outputId": "926f689a-168c-4347-deb5-3591d1db1dd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics of the dataset:\n",
            "               age          bmi     children       charges\n",
            "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
            "mean     39.207025    30.663397     1.094918  13270.422265\n",
            "std      14.049960     6.098187     1.205493  12110.011237\n",
            "min      18.000000    15.960000     0.000000   1121.873900\n",
            "25%      27.000000    26.296250     0.000000   4740.287150\n",
            "50%      39.000000    30.400000     1.000000   9382.033000\n",
            "75%      51.000000    34.693750     2.000000  16639.912515\n",
            "max      64.000000    53.130000     5.000000  63770.428010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XjEsCNl1JdR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hyN9bo6jKuIP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display information about the dataset"
      ],
      "metadata": {
        "id": "dxS7dJZFKuLn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nInformation about the dataset:\")  # Print statement\n",
        "print(data.info())  # Display information about the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WE3BpSsbKuN4",
        "outputId": "ca9add2a-6585-4e1e-a76b-6236d6fe136d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Information about the dataset:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "96chC0xgKuQB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8iZaNlK8JdWl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save initial dataset information"
      ],
      "metadata": {
        "id": "8r21RMEJHvYM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head().to_csv('initial_data_head.csv')  # Save first few rows of the dataset to CSV file"
      ],
      "metadata": {
        "id": "hN4VU4zCLC6F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe().to_csv('initial_data_description.csv')  # Save summary statistics to CSV file"
      ],
      "metadata": {
        "id": "M_lWB27lLDAN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info(buf=open('initial_data_info.txt', 'w'))  # Save information about the dataset to text file"
      ],
      "metadata": {
        "id": "9E1bTasbLDFd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M1zkthhiLJoI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EN0OZ1neLJrI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Checking for Missing Values</font>\n",
        "\n",
        "In this step, we focus on identifying and handling missing values within the dataset. Missing data can affect the integrity and accuracy of our analyses and predictions, making it essential to address them appropriately.\n",
        "\n",
        "### <font color=\"indigo\">Using `isnull().sum()` Method</font>\n",
        "To detect missing values, we employ the `isnull()` method combined with `sum()` to count the number of null values for each column in the DataFrame. This method efficiently identifies columns that contain missing data points.\n",
        "\n",
        "### <font color=\"orange\">Displaying Missing Values Count</font>\n",
        "By executing `data.isnull().sum()`, we obtain a summary that displays the total count of missing values for each column in the dataset. This allows us to quickly assess the extent of missing data across different features.\n",
        "\n",
        "### <font color=\"red\">Handling Missing Values</font>\n",
        "Depending on the nature and impact of missing values, various strategies can be employed:\n",
        "- **Imputation:** Replace missing values with statistical measures such as mean, median, or mode.\n",
        "- **Deletion:** Remove rows or columns with missing data if they are deemed insignificant or if they compromise data integrity.\n",
        "- **Advanced Techniques:** Use predictive models to estimate missing values based on other available data.\n",
        "\n",
        "### <font color=\"indigo\">Impact on Analysis</font>\n",
        "Understanding and addressing missing values is critical as they can skew analysis results and model predictions. Proper handling ensures that our predictive models are built on reliable and complete data, enhancing the accuracy and robustness of our healthcare cost predictions.\n",
        "\n",
        "### <font color=\"teal\">Documentation and Transparency</font>\n",
        "Documenting the handling of missing values is crucial for transparency and reproducibility:\n",
        "- Record the method used for handling missing data.\n",
        "- Document any assumptions made during the process.\n",
        "- Store the updated dataset after handling missing values for future reference and validation.\n",
        "\n",
        "### <font color=\"blue\">Ensuring Data Integrity</font>\n",
        "By systematically identifying and addressing missing values, we ensure the integrity and reliability of our dataset. This process lays a solid foundation for subsequent data analysis, model training, and evaluation in our healthcare cost prediction project."
      ],
      "metadata": {
        "id": "GeVhhL8Nne0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "9XRtQHiFLJvw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in the dataset:\")  # Print statement\n",
        "print(data.isnull().sum())  # Check and print missing values in the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ev0HXgIzLJzk",
        "outputId": "f13a0a69-8b5f-4a5c-90e5-cee019b8ffeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in the dataset:\n",
            "age         0\n",
            "sex         0\n",
            "bmi         0\n",
            "children    0\n",
            "smoker      0\n",
            "region      0\n",
            "charges     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v6MJPqU5LQSY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DJqPPT4pLQV-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Converting Categorical Variables to Dummy Variables</font>\n",
        "\n",
        "In this step, we focus on transforming categorical variables into a format suitable for machine learning models. Categorical variables contain non-numeric values and need to be converted into numerical representations for model compatibility and accurate predictions.\n",
        "\n",
        "### <font color=\"indigo\">Using pandas `get_dummies()` Function</font>\n",
        "To convert categorical variables to dummy variables, we utilize the `get_dummies()` function from the `pandas` library. This function creates binary (0 or 1) indicator variables for each category within a categorical feature.\n",
        "\n",
        "### <font color=\"orange\">Drop_first Parameter</font>\n",
        "When applying `pd.get_dummies(data, drop_first=True)`, we include the `drop_first=True` parameter. This parameter drops the first category in each categorical feature, preventing multicollinearity issues in linear models and improving model performance.\n",
        "\n",
        "### <font color=\"red\">Handling Categorical Features</font>\n",
        "By transforming categorical features into dummy variables, we convert qualitative information into a quantitative format that machine learning algorithms can process effectively. This transformation allows models to interpret categorical data as numerical inputs, facilitating accurate predictions.\n",
        "\n",
        "### <font color=\"indigo\">Impact on Model Training</font>\n",
        "Properly encoding categorical variables ensures that the models trained on the dataset can effectively learn from and utilize all available data. This step enhances the model's ability to capture underlying patterns and relationships within the healthcare cost dataset.\n",
        "\n",
        "### <font color=\"teal\">Ensuring Data Consistency</font>\n",
        "After converting categorical variables to dummy variables:\n",
        "- Validate the consistency and correctness of the transformed dataset.\n",
        "- Verify that all categorical features have been appropriately encoded into numerical representations.\n",
        "\n",
        "### <font color=\"blue\">Documentation and Reproducibility</font>\n",
        "Document the process of converting categorical variables to dummy variables:\n",
        "- Record the list of categorical features and their corresponding dummy variable transformations.\n",
        "- Save the updated dataset with dummy variables for future analysis, model training, and evaluation.\n",
        "\n",
        "By converting categorical variables to dummy variables, we prepare our dataset for advanced machine learning techniques, ensuring robust and accurate predictions in our healthcare cost prediction project."
      ],
      "metadata": {
        "id": "rLjagebLnu0i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical variables to dummy variables"
      ],
      "metadata": {
        "id": "l23fDzs0LQZ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.get_dummies(data, drop_first=True)  # Convert categorical variables to dummy variables"
      ],
      "metadata": {
        "id": "FWPim2zkLTlA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dynJ5zK8LTnq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset after preprocessing"
      ],
      "metadata": {
        "id": "CsBYX_RmLT2g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFirst few rows of the dataset after preprocessing:\")  # Print statement\n",
        "print(data.head())  # Display first few rows of the preprocessed dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JzBMRK-LT5Y",
        "outputId": "7f6eb6e4-66fe-4634-dd59-31d454371fe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First few rows of the dataset after preprocessing:\n",
            "   age     bmi  children      charges  sex_male  smoker_yes  region_northwest  \\\n",
            "0   19  27.900         0  16884.92400     False        True             False   \n",
            "1   18  33.770         1   1725.55230      True       False             False   \n",
            "2   28  33.000         3   4449.46200      True       False             False   \n",
            "3   33  22.705         0  21984.47061      True       False              True   \n",
            "4   32  28.880         0   3866.85520      True       False              True   \n",
            "\n",
            "   region_southeast  region_southwest  \n",
            "0             False              True  \n",
            "1              True             False  \n",
            "2              True             False  \n",
            "3             False             False  \n",
            "4             False             False  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NFxpimSeLc5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bXub3gzrLc88"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save preprocessed dataset"
      ],
      "metadata": {
        "id": "MSDTdILALdAL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('preprocessed_data.csv', index=False)  # Save preprocessed dataset to CSV file"
      ],
      "metadata": {
        "id": "YY224j42LdF-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6vtFYnUDLdIZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ej5pQBrvoHLy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Exploratory Data Analysis (EDA)</font>\n",
        "\n",
        "Exploratory Data Analysis (EDA) is a crucial phase in any data science project, especially for understanding the nuances and characteristics of the dataset. It involves a systematic approach to inspecting, cleaning, and transforming data to derive meaningful insights and prepare it for modeling.\n",
        "\n",
        "### <font color=\"indigo\">Objectives of EDA</font>\n",
        "EDA serves several key objectives:\n",
        "- **Understanding the Data Structure:** Explore the number of observations (rows) and variables (columns) in the dataset.\n",
        "- **Identifying Data Types:** Determine the types of data present (numerical, categorical) and their distribution.\n",
        "- **Handling Missing Values:** Assess the presence and impact of missing values across variables.\n",
        "- **Visualizing Distributions:** Plot histograms, density plots, and box plots to visualize the distribution and spread of numerical data.\n",
        "- **Exploring Relationships:** Use scatter plots, pair plots, and correlation matrices to analyze dependencies and correlations between variables.\n",
        "- **Detecting Outliers:** Identify outliers that may skew analysis or affect model performance.\n",
        "- **Feature Engineering Insights:** Gain insights for feature engineering by understanding variable interactions and transformations.\n",
        "\n",
        "### <font color=\"orange\">Tools and Techniques</font>\n",
        "#### Visualization Techniques\n",
        "- **Histograms and Density Plots:** Visualize the distribution of numerical variables such as charges, age, and BMI to understand their central tendencies and spread.\n",
        "- **Pair Plots:** Explore pairwise relationships between numerical variables to detect patterns or clusters.\n",
        "- **Correlation Heatmaps:** Construct heatmaps to visualize correlations between variables, identifying strong positive or negative relationships.\n",
        "- **Box Plots and Scatter Plots:** Analyze relationships between categorical variables (e.g., smoker status, region) and numerical variables (e.g., charges) to uncover insights into factors influencing healthcare costs.\n",
        "\n",
        "#### Statistical Summaries\n",
        "- **Summary Statistics:** Compute descriptive statistics (mean, median, standard deviation, quartiles) to summarize numerical variables, providing insights into data distribution and variability.\n",
        "- **Cross-tabulations:** Generate cross-tabulations for categorical variables to examine frequency distributions and relationships between different categories.\n",
        "\n",
        "### <font color=\"red\">Insights and Interpretations</font>\n",
        "#### Key Findings\n",
        "- **Variable Importance:** Identify variables (e.g., age, smoking status) significantly impacting healthcare costs.\n",
        "- **Data Patterns:** Discover trends and patterns that may influence predictive modeling and decision-making.\n",
        "- **Data Integrity:** Validate assumptions and data quality, ensuring consistency and reliability for subsequent analyses.\n",
        "\n",
        "### <font color=\"indigo\">Iterative Process</font>\n",
        "EDA is iterative and evolves throughout the project lifecycle:\n",
        "- **Continuous Refinement:** Refine insights and hypotheses as new patterns emerge or additional data is collected.\n",
        "- **Collaborative Exploration:** Involve domain experts and stakeholders to validate findings and interpretations.\n",
        "- **Documentation:** Document all EDA processes, findings, and visualizations to maintain transparency and facilitate reproducibility.\n",
        "\n",
        "### <font color=\"teal\">Reporting and Documentation</font>\n",
        "#### Deliverables\n",
        "- **Visual Outputs:** Save EDA plots (e.g., histograms, scatter plots) as PDFs or images for reporting and presentation purposes.\n",
        "- **Written Summaries:** Summarize key observations, insights, and actionable recommendations in a structured report or notebook.\n",
        "- **Data Files:** Store cleaned and preprocessed data for model training and validation, ensuring traceability and accountability.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "By conducting thorough and systematic EDA, data scientists can uncover valuable insights, validate assumptions, and prepare a robust foundation for building predictive models in the healthcare cost prediction project. EDA not only enhances understanding of the dataset but also informs critical decisions throughout the data science workflow."
      ],
      "metadata": {
        "id": "UK3RnKcNoHfy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "rpTighHELdKE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Distribution of Charges Graph</font>\n",
        "\n",
        "- **Insight:** The distribution of healthcare charges shows a skewed pattern with a long tail to the right, indicating a wide range of costs. This suggests that while many healthcare services may be relatively affordable, there are also instances of very high costs.\n",
        "  \n",
        "- **Implication:** Understanding this distribution is crucial for financial planning and resource allocation in healthcare settings. It highlights the need for strategies to manage and predict these varying expenses effectively."
      ],
      "metadata": {
        "id": "DdrPsZVzrzJk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of charges"
      ],
      "metadata": {
        "id": "imI0TGoTLdN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for plot\n",
        "sns.histplot(data['charges'], bins=30, kde=True, color='purple')  # Plot distribution of charges with KDE\n",
        "plt.title('Distribution of Charges', color='purple')  # Set title of the plot\n",
        "plt.xlabel('Charges', color='purple')  # Set label for x-axis\n",
        "plt.ylabel('Frequency', color='purple')  # Set label for y-axis\n",
        "plt.savefig('distribution_of_charges.pdf')  # Save plot as PDF\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "98U6jTa3LdPr",
        "outputId": "9a633b06-9f25-42a2-d967-c68cfc2c9a30"
      },
      "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": "-sbghdT_Lsdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tLV-xraILshV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"red\">Pairplot Graph</font>\n",
        "\n",
        "- **Insight:** The pairplot visualizes relationships between numerical variables like age, BMI, and charges, revealing potential correlations. It helps in identifying patterns such as how age or BMI might affect healthcare costs.\n",
        "  \n",
        "- **Implication:** The observed correlations can guide further investigation into specific health factors influencing costs. For instance, understanding the link between BMI and charges can inform preventive health strategies aimed at reducing obesity-related healthcare expenses."
      ],
      "metadata": {
        "id": "8Q5BWj8-sVKW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pairplot to see the relationships between variables"
      ],
      "metadata": {
        "id": "YBZJ72dyLsk2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data, plot_kws={'color': 'red'})  # Plot pairwise relationships between variables\n",
        "plt.savefig('pairplot.pdf')  # Save pairplot as PDF\n",
        "plt.show()  # Display pairplot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "cpwiyDQULsvt",
        "outputId": "ecf4a58b-27bd-40ab-ecb5-c7c211bd99e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2250x2250 with 90 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "29TlJoOkL70u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5HkX5_jTL736"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"orange\">Correlation Matrix Graph</font>\n",
        "\n",
        "- **Insight:** The heatmap shows correlations between variables, with darker colors indicating stronger correlations. This provides a comprehensive view of how different factors interrelate.\n",
        "  \n",
        "- **Implication:** Identifying strong correlations, such as between age and charges or BMI and charges, informs predictive models. It underscores the importance of these variables in accurately estimating future healthcare expenses."
      ],
      "metadata": {
        "id": "_BoMs_dqsZFg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix"
      ],
      "metadata": {
        "id": "WmcTgSNNL79N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for plot\n",
        "sns.heatmap(data.corr(), annot=True, cmap='coolwarm')  # Plot correlation matrix with annotations\n",
        "plt.title('Correlation Matrix', color='orange')  # Set title of the plot\n",
        "plt.savefig('correlation_matrix.pdf')  # Save correlation matrix plot as PDF\n",
        "plt.show()  # Display correlation matrix plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "vHE0ThQNL8AV",
        "outputId": "79e011f3-fd80-4aa3-9005-22821df5df08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9NiFzkuBMBXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YKW6ZnLuMBa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"indigo\">Age vs Charges Graph</font>\n",
        "\n",
        "- **Insight:** Scatterplot illustrates how charges vary with age, showing a general trend of increasing costs with age. It depicts healthcare expenditure patterns across different age groups.\n",
        "  \n",
        "- **Implication:** Recognizing age-related cost trends aids in healthcare resource allocation and service planning. It highlights the anticipated financial demands associated with an aging population and the need for tailored healthcare solutions."
      ],
      "metadata": {
        "id": "3LIcvyevsc5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Age vs Charges"
      ],
      "metadata": {
        "id": "fk65i-1XMBd8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for plot\n",
        "sns.scatterplot(x='age', y='charges', data=data, color='indigo')  # Plot age vs charges\n",
        "plt.title('Age vs Charges', color='indigo')  # Set title of the plot\n",
        "plt.xlabel('Age', color='indigo')  # Set label for x-axis\n",
        "plt.ylabel('Charges', color='indigo')  # Set label for y-axis\n",
        "plt.savefig('age_vs_charges.pdf')  # Save plot as PDF\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "AE-fLDbPL8De",
        "outputId": "336fc40a-3ebe-4d5a-8677-536e7aa4a11f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAIjCAYAAABswtioAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADJq0lEQVR4nOzdeXhTVfoH8G/XJE3TtIXSFlq1ZSuMWqhCqaAzDrWVReoASh3HHZGlrI4gI+LoqCiOCshSkHHQnwugg6JsgjioyCqLG1AXUNaWAmmSpknX/P7oNBKS3KS5SW9u8v08D8+jPbm5J9u95z3Le8KsVqsVRERERERE1ObCpa4AERERERFRqGJARkREREREJBEGZERERERERBJhQEZERERERCQRBmREREREREQSYUBGREREREQkEQZkREREREREEmFARkREREREJBEGZERERERERBJhQEZERCSBR//wBsZfWSp1NYiISGKRUleAiIiC2/rFX2HJhI3o1rcjXtr9gNTV8bsaQy0+eHkXdqw5gvKfdWhqtCKlcwL6DOmCYZNz0a6jRuoqEhFRAGFARkREfrXtrW+RfEU8fthzGqd/uoCOXRKlrpLflB/V4bH8N1F5XI8Bt/XEzWNyEBkdgV++OYst/zqIne+XYdkPE6SuJhERBRAGZERE5Dflx3Q4vOMk/rbmNix6aD22vfUt/vzE76Wull80NjThmeHvoqrChDnb7sbvBlxmV373Mzfivee/bPM6NTVZERUd0abnJSIizzEgIyIiv9n21neITVCiz5Cu6D+yB7a99Z3TgMxwvgavTtmMXWvLEB4ehtyi7rh1Wi4m9XoVU/49DPn3Ztsee+LIOfzfrP/im09/QW1NPS6/sgPumH09cod1d1mPhvpG/CX5JfQr6o4p/x5mV1ZjqMWdHV7E0JI+eOCfNwEAPnplDzaW7kPFsSpEKiKR2jkBt07LxR/+fJXLc3z5n8M49nUF7n7mRodgDABi4hS4+5k/Ovz9+KFKLJmwET/sPgV1ggrDJvfFyOnX2crr6xqx6ukvsHf9jzjzkw6NDU3onJOCvzz1B1x94xW2x1X8UoUHMl7B/S/kIyIyHB+9shdnf6nCvH2jkdkrBd9s+wWv/fUT/PrdWbTrFIcR0/Nw4Uw13nnyc6yzPm5Xp/+++Q0+eHk3Thw6h2hVJHoXZOL+F/KRlK61PebUj+fx+qOf4tCXJ2CqsiCufQx6DkhHydIhUGuVLt8nIiKyx4CMiIj8Zttb3+K64VmIio7ADXdciQ1L9uGHvafRrU9H22Oamqx46pZV+GHPKQwedy3Sstph99of8PI9Hzo836/fn8X0/ivQrpMGIx/tD6U6CttXH8LTt67GzP/chuv+lOW0HpFREcj7UxZ2rDmCCUsb7UaMdn5wBPW1jbih+HcAgE2v7sfSSR+j/8geGDY5F3WWBvzyTQXKdp8WDMh2f/gDAODGu1w/5lLVOgueuPlt5A3PwvW398T29w5jxYytuOKqDrh2UBcAzQHj5uUHcMMdV6LwwRyYjbXY8q+DmF34Fl7a8wAye6XYPecn/z6IOksjbh7TG1GKSMQmqvDzgTN44ua3kZiqwZ1P/h5NjVa889QX0CbFONRp1TNf4M3Ht2HA7T1ROLo39JU1+OiVvXj0hjcw/8CDiI1Xor6uEbML30Z9bSNumdgHCSmxOH/KiD3rfoSpysKAjIioFRiQERGRX/y07wxOHjmPsa/cDAD43YB0tE+Lw7a3vrULyHZ9cARHdp7Eg/MKUDQ5FwAweNy1mHXTmw7PuWzyZiRdpsXLex9AlKL5FjZk/LWYPmAFVszY6jIgA4DrR/XEltcO4sDmn9F3aDfb379YdQgpmQnoem1znb5a/yMu+10SZr47slWv9+Thc1BrFXajSO5cOG3EtDeK8Me7rgYA3PRAb9x/+QJs/tcBW0AWm6DEv36ZZBdEFj6Yg7FZi/HRK3sx+V+32D3nuZNGvPrTBGiT1La/lZZsRHhEOOZ+ea8tqciA23tiXI8ldsee/bUKbz3xGe56+kbc/rcBtr/nDc/C5N6vYsPir3D73wbgxKFKVByrwqPvjsCAkT1tj7tj9g0ev3YiImrGtPdEROQX/33rW8Qnq3HV/6bVhYWF4fpRPfH5yu/R2Nhke9y+TT8jMiochQ/m2P4WHh6GoROutXs+4wUzvvn0GAbc3hM1xjroz9VAf64GhvNm5BR2xukfL+DcKYPL+mT/MQNx7WPwxapDtr9V68w4uOUorh/1W1Chjlfi/EkDfth7ulWvt8ZQC5VG0apjVLHRuPEvv42oRUVHoFvfjqg4WmX7W0REuC0Ya2qywnjBjMaGJnS9tiN+3n/G4TmvG5FlF4w1Njbh60+Ood+t3e0yPHbskohrBnW2O3bHmiOwNlkx4PaetvdXf64GCSmx6Ng1Ed/89xcAQIy2+XXu//goLDX1rXrNRERkjyNkRETkc42NTfhi5fe4+sYrUHGsyvb37rmd8P6Lu/D11mPIKWgOBs7+qkdCqgbKmCi750i9JBvj6Z8uwGoF3nx8G958fJvT8+rPmtC+U5zTsojIcFw3Igufvf0d6msbEKWIxI41R9BQ32QXkI2ccR0OfnIM0/r+C6ldEpFTkInf//lK9OyfLviaY+IUKD+qE3zMpdqlaRAWFmb3t9gEJX755qzd37a+/jXef3EXTh45h4b634LZ5Ix4h+e89G/6sybUmhvQsUuCw2Md3uMfm9/jMV0XOa1vRFRzP25KRgJundYPH7y0C5+99S16Xn8Zcod1w41/uYrTFYmIWokBGRER+dw3n/6CC2eq8fnK7/H5yu8dyre99Z0tIPOUtckKABj+1zzkFGY6fcylAcalbij+HTYt3Y+vNv6EvFuz8MXqQ0jLaofM7N/WYaX3SMLSsvHYs+5H7N/0M778z2GsX/wV7ph9Pe588g8unzstqx1+PlCOyhN6j6cthkc4n6hitVpt//3fN7/By/d+iH63dsfwR/Kg7aBGREQYVs/5EuU/OwaAClWUw9881dRkRVgY8PeNf0ZERJhDuTI22vbfo1+8Cfn3Xo1da3/Agc1HsWzSx3h3zpd4cdf9aJ/mPCgmIiJHDMiIiMjntr31LeI7qDF20c0OZTvXHMHO94+gtnQwFKoodLhci2//+wssNfV2o2Rnfrpgd1xKZvMIT0RUOHrlOw/I3LnyhsuRmBqLL1YdQs8Bl+GbT3/B7Y8NcHicUh2NG0b9DjeM+h3q6xrx7PB3seqZ7bht5gBEK53fOvve0g2fvfM9/vvmt7h9puNzeuvL9w4jJTMBj625zW407a0nPvPoeG0HNaKVkTj9k2Pwdul7nNo5AVYrkJIRj07d2rl97iuuSsYVVyWjeNb1OLzjBB7pvwIbS/fhrqdv9KhuRETENWRERORjteZ67FhzBH2GdsWAkT0d/g0t6QOzsc6WlTCnsDMa6pvw8av7bc/R1GTFukVf2T1vfAc1rvrD5di0dD8unDE6nFdfaXJbt/DwMPQf2QN7PvoB//2/b9DY0IQbLpquCDSn4L9YVHQE0nu2B6zN6fNd6T+yB664qgNWP7Mdh3eedCivMdbijcc+dVtHhzr/bxTtokEzlO0+hSNOzuFMREQ4svMzsOuDMpw//dv7dvqnC9i38We7x143PAvhEWF4+8nP7Ubpms9vtb03NYZaNDY02ZVfflUHhIeHob7W9XtERESOOEJGREQ+tfvDH2A21iF3WDen5d37pUGbFINtb32LG0b9Dv1u7Y5ufTviXw9vwZmfdM1p7z/8AdUXzACAi5dYjVs0CNMHrEDJVUtR8GBvpGQmoKrChCM7T+LcSQMWfv2Q2/pdP+p3+OiVvXjric9wxVUdkN4jya788YK3kJASi5790xGfrMaJw+ewbuFeXDukC2IEknZERkXgb2tuw6z8N/HoDa9jwO090bN/OiKiwnH8+0p89nbznmzO9iIT0mdoV+xYcwTP/Gk1+gzpivJjOmws3Y/0nkmwVNd59Bx3/v33OLD5KKb3X4FB465BU2MT1i38CpdfmYSjBytsj0vtnIi7nr4Rr8/8FGd/qUK/W7tDpVGg4pgOO98vw81jcjD8r3n4+tNjKC3ZhAG39USnbolobGjCp//3LcIjwnDdCNeZLomIyBEDMiIi8qltb32LaGUket3kfFpheHgY+gzpim1vfQvD+RrEtYvBE+vvwLLJH2Pr618jPDwMeX/Kwp+fuAGP9F+BqIumCF7WMwnzvhqNt5/8HFtXfAPj+RpoO6iR2TvF45TrPa5LQ1J6HCpPGOySebQY9NA12PbWt/jgpV0wV9ehfVocbpnUF8Wzrnf73B27JGLBwTFY+/Iu7Hy/DLs+KIO1yYrULgkoGN0bwyb19aiOF8u/Nxu68mpsWrof+z/+GZf1TMJf37wV2989hG+3/erRc3S5JhVPbrwDr/31E7z5+DYkpcfhL0/9HicOn8PJI+ftHnvbo/3RqVsiPnh5N9558nMAQPv0OPQuyLQF2RnZycgp7Iw9H/2A86eMUMREISM7GX/f+Gdk9Utr9WskIgplYdZL5yQQEREFgJ0fHMEzf3oXc7ff6zbDIXnn6VtX4dfvz+HVHydIXRUiopDFNWRERCS5WrP9XlaNjU346JW9iIlToHNOioujqDUufY9P/XgeX234CVf94XKJakRERACnLBIRUQBYOnETas0NyMpLQ31tA3auOYLDO07i7mdvFJXGnX4zOnMhBt57NVIyE1D5qx4bluxDZHQERkzPk7pqREQhjQEZERFJ7uo/XoH3X9yFvet+RJ2leRPjh165GbeU9JG6akHjmps74/N3voeuvBpRikhk5aXh7mdvRKeu7tPbExGR/3ANGRERERERkUS4hoyIiIiIiEgiDMiIiIiIiIgkwjVkPtLU1ITTp09Do9Eg7OJdTImIiIiIKKRYrVYYjUZ07NgR4eHCY2AMyHzk9OnTSE/nPjlERERERNTsxIkTSEtLE3wMAzIf0Wg0AJrf9Li4OIlrQ0REREREUjEYDEhPT7fFCEIYkPlIyzTFuLg4BmREREREROTRUiYm9SAiIiIiIpIIAzIiIiIiIiKJMCAjIiIiIiKSCAMyIiIiIiIiiTAgIyIiIiIikggDMiIiIiIiIokwICMiIiIiIpIIAzIiIiIiIiKJMCAjIiIiIiKSCAMyIiIiIiIiiTAgIyIiIiIikggDMiIiIiIiIokwICMiIiIiIpIIAzIiIiIiIiKJREpdASIiIiIKbkadGfoKE0z6WqjjFdB2UEOToJK6WkQBgQEZEREREflN5Qk9FoxehwObj9r+llOQiYnLhyIpXSthzYgCA6csEhEREZFfGHVmh2AMAPZvPopXRq+DUWeWqGZEgYMBGRERERH5hb7C5BCMtdi/+Sj0FaY2rhFR4GFARkRERER+YdLXiionCgUMyIiIiIjIL9RahahyolDAgIyIiIiI/EKbrEZOQabTspyCTGiT1W1cI6LAw4CMiIiIiPxCk6DCxOVDHYKyliyLTH1PxLT3REREJAL3lyJ3ktK1eGTl8N++J1oFtMn8nhC1YEBGREREXuH+UuQpTYKKARiRC5yySERERK3G/aWIiHyDARkRERG1GveXIiLyDQZkRERE1GrcX4qIyDcYkBEREVGrcX8pIiLfYEBGRERErcb9pYiIfIMBGREREbUa95ciIvINpr0nIiIir3B/KSIi8RiQERERkde4vxQRkTicskhERERERCQRBmREREREREQSYUBGREREREQkEQZkREREREREEmFARkREREREJBEGZERERERERBJhQEZERERERCQRyQOyU6dO4S9/+QvatWsHlUqFq666Cl999ZWt3Gq1Yvbs2UhNTYVKpUJ+fj5+/PFHu+e4cOEC7rzzTsTFxSE+Ph4PPPAAqqur7R7zzTff4Prrr4dSqUR6ejrmzp3rUJd3330XWVlZUCqVuOqqq7Bhwwb/vGgiIiKiNmbUmXHyyDmU7T6Fk2XnYNSZpa4SEUHigEyn06F///6IiorCxo0bcejQIbz44otISEiwPWbu3LlYsGABSktLsXv3bqjVahQWFsJisdgec+edd+L777/Hli1bsG7dOnz++ecYM2aMrdxgMKCgoACXX3459u3bhxdeeAF///vfsWzZMttjduzYgTvuuAMPPPAADhw4gFtvvRW33norvvvuu7Z5M4iIiIj8pPKEHnOL12BsjyV4uN9rGJu1BC8Ur0HlCb3UVSMKeWFWq9Uq1ckfffRRfPnll/jiiy+cllutVnTs2BEPP/ww/vrXvwIA9Ho9kpOTsWLFChQXF+Pw4cPo2bMn9u7di2uvvRYAsGnTJgwePBgnT55Ex44dsWTJEjz22GMoLy9HdHS07dwffPABjhw5AgAYNWoUTCYT1q1bZzt/v3790KtXL5SWlrp9LQaDAVqtFnq9HnFxcaLeFyIiIiJfMerMmFu8Bgc2H3UoyynIxCMrh0OToJKgZkTBqzWxgaQjZB9++CGuvfZa3HbbbejQoQN69+6NV1991VZ+7NgxlJeXIz8/3/Y3rVaL3Nxc7Ny5EwCwc+dOxMfH24IxAMjPz0d4eDh2795te8wNN9xgC8YAoLCwEGVlZdDpdLbHXHyelse0nOdStbW1MBgMdv+IiIiIAo2+wuQ0GAOA/ZuPQl9hauMaEdHFJA3Ijh49iiVLlqBr1674+OOPMW7cOEyaNAmvv/46AKC8vBwAkJycbHdccnKyray8vBwdOnSwK4+MjERiYqLdY5w9x8XncPWYlvJLzZkzB1qt1vYvPT291a+fiIiIyN9M+lpR5UTkX5IGZE1NTcjJycGzzz6L3r17Y8yYMXjwwQc9miIotZkzZ0Kv19v+nThxQuoqERERETlQaxWiyonIvyQNyFJTU9GzZ0+7v/Xo0QPHjx8HAKSkpAAAKioq7B5TUVFhK0tJScHZs2ftyhsaGnDhwgW7xzh7jovP4eoxLeWXUigUiIuLs/tHREREFGi0yWrkFGQ6LcspyIQ2Wd3GNSKii0kakPXv3x9lZWV2f/vhhx9w+eWXAwAyMjKQkpKCrVu32soNBgN2796NvLw8AEBeXh6qqqqwb98+22M+/fRTNDU1ITc31/aYzz//HPX19bbHbNmyBd27d7dldMzLy7M7T8tjWs5DREREJEeaBBUmLh/qEJTlFGRi4vKhTOhBJDFJsyzu3bsX1113HZ588kncfvvt2LNnDx588EEsW7YMd955JwDg+eefx3PPPYfXX38dGRkZePzxx/HNN9/g0KFDUCqVAIBBgwahoqICpaWlqK+vx3333Ydrr70Wb7/9NoDmzIzdu3dHQUEBZsyYge+++w73338/Xn75ZVt6/B07duD3v/89nnvuOQwZMgQrV67Es88+i/379+PKK690+1qYZZGIiIgCmVFnhr7CBJO+FmqtAtpkNYMxIj9pTWwgaUAGAOvWrcPMmTPx448/IiMjA9OmTcODDz5oK7darXjiiSewbNkyVFVVYcCAAVi8eDG6detme8yFCxdQUlKCjz76COHh4RgxYgQWLFiA2NhY22O++eYbTJgwAXv37kX79u0xceJEzJgxw64u7777LmbNmoVffvkFXbt2xdy5czF48GCPXgcDMiIiIiIiAmQWkAULBmREgc2uZzheAW0H9gwTERGRf7QmNohsozoREUmm8oQeC0avs9uHp2XtRFK6VsKaERERUaiTNKkHEZG/GXVmh2AMaN4M9ZXR62DUmSWqGREREREDMiIKcvoKk0Mw1mL/5qPQV5jauEZEREREv2FARkRBzaSvFVVORERE5E8MyIgoqKm1ClHlRERERP7EgIyIgpo2We2wGWqLnIJMaJPVbVwjIiIiot8wICOioKZJUGHi8qEOQVlLlkWmviciIiIpMe09EQW9pHQtHlk5/Ld9yLQKaJO5DxkRERFJjwEZEYUETYKKARgREREFHE5ZJCIiIiIikggDMiIiIiIiIokwICMiIiIiIpIIAzIiIiIiIiKJMCAjIiIiIiKSCAMyIiIiIiIiiTAgIyIiIiIikggDMiIiIiIiIokwICMiIiIiIpIIAzIiIiIiIiKJMCAjIiIiIiKSCAMyIiIiIiIiiTAgIyIiIiIikkik1BUgIgp2Rp0Z+goTTPpaqOMV0HZQQ5OgkrpaREREFAAYkBER+VHlCT0WjF6HA5uP2v6WU5CJicuHIildK2HNiIiIKBBwyiIRkZ8YdWaHYAwA9m8+ildGr4NRZ5aoZkRERBQoGJAREfmJvsLkEIy12L/5KPQVpjauEREREQUaBmRERH5i0teKKiciIqLgx4CMiMhP1FqFqHIiIiIKfgzIiIj8RJusRk5BptOynIJMaJPVbVwjIiIiCjQMyIiI/ESToMLE5UMdgrKWLItMfU9ERERMe09E5EdJ6Vo8snL4b/uQaRXQJnMfMiIiImrGgIyIyM80CSoGYEREROQUpywSERERERFJhAEZERERERGRRBiQERERERERSYQBGRERERERkUQYkBEREREREUmEARkREREREZFEGJARERERERFJhPuQEREREREFEaPODH2FCSZ9LdTxCmg7qLkfZgBjQEZEREREFCQqT+ixYPQ6HNh81Pa3nIJMTFw+FEnpWglrRq5wyiIRERERURAw6swOwRgA7N98FK+MXgejzixRzUgIAzIiIiIioiCgrzA5BGMt9m8+Cn2FqY1rRJ5gQEZEREREFARM+lpR5SQNBmREREREREFArVWIKidpMCAjIiIiIgoC2mQ1cgoynZblFGRCm6xu4xqRJxiQEREREREFAU2CChOXD3UIylqyLDL1fWBi2nsiIiIioiCRlK7FIyuH/7YPmVYBbTL3IQtkDMiIqNW44SQREVHg0iSoeF+WEQZkRNQq3HCSiIiIyHe4hoyIPMYNJ4mIiIh8iwEZEXmMG04SERER+RYDMiLyGDecJCIiIvItBmRE5DFuOElERETkWwzIiMhj3HCSiIiIyLcYkBGRx7jhJBEREZFvSRqQ/f3vf0dYWJjdv6ysLFu5xWLBhAkT0K5dO8TGxmLEiBGoqKiwe47jx49jyJAhiImJQYcOHfDII4+goaHB7jHbtm1DTk4OFAoFunTpghUrVjjUZdGiRbjiiiugVCqRm5uLPXv2+OU1E8ldy4aTpYfH4cVd96P08Dg8snI4U94TEREReUHyEbLf/e53OHPmjO3f9u3bbWVTp07FRx99hHfffRefffYZTp8+jeHDh9vKGxsbMWTIENTV1WHHjh14/fXXsWLFCsyePdv2mGPHjmHIkCG48cYbcfDgQUyZMgWjR4/Gxx9/bHvMqlWrMG3aNDzxxBPYv38/srOzUVhYiLNnz7bNm0AkM5oEFdKy2qN7biekZbXnyBgRERGRl8KsVqtVqpP//e9/xwcffICDBw86lOn1eiQlJeHtt9/GyJEjAQBHjhxBjx49sHPnTvTr1w8bN27E0KFDcfr0aSQnJwMASktLMWPGDFRWViI6OhozZszA+vXr8d1339meu7i4GFVVVdi0aRMAIDc3F3369MHChQsBAE1NTUhPT8fEiRPx6KOPevRaDAYDtFot9Ho94uLixLwtREREREQkY62JDSQfIfvxxx/RsWNHZGZm4s4778Tx48cBAPv27UN9fT3y8/Ntj83KysJll12GnTt3AgB27tyJq666yhaMAUBhYSEMBgO+//5722Mufo6Wx7Q8R11dHfbt22f3mPDwcOTn59se40xtbS0MBoPdPyIiIiIiantGnRknj5xD2e5TOFl2DkadWeoqeSxSypPn5uZixYoV6N69O86cOYMnn3wS119/Pb777juUl5cjOjoa8fHxdsckJyejvLwcAFBeXm4XjLWUt5QJPcZgMMBsNkOn06GxsdHpY44cOeKy7nPmzMGTTz7p1esmIiIiIiLfqDyhx4LR63Bg81Hb31oSjslhjbukI2SDBg3CbbfdhquvvhqFhYXYsGEDqqqqsHr1aimr5ZGZM2dCr9fb/p04cULqKhERERERhRSjzuwQjAHA/s1H8crodbIYKZN8yuLF4uPj0a1bN/z0009ISUlBXV0dqqqq7B5TUVGBlJQUAEBKSopD1sWW/3f3mLi4OKhUKrRv3x4RERFOH9PyHM4oFArExcXZ/SMiIiIiorajrzA5BGMt9m8+Cn2FqY1r1HoBFZBVV1fj559/RmpqKq655hpERUVh69attvKysjIcP34ceXl5AIC8vDx8++23dtkQt2zZgri4OPTs2dP2mIufo+UxLc8RHR2Na665xu4xTU1N2Lp1q+0xRERERERtRc7rodqaSV8rqjwQSLqG7K9//StuueUWXH755Th9+jSeeOIJRERE4I477oBWq8UDDzyAadOmITExEXFxcZg4cSLy8vLQr18/AEBBQQF69uyJu+66C3PnzkV5eTlmzZqFCRMmQKFQAADGjh2LhQsXYvr06bj//vvx6aefYvXq1Vi/fr2tHtOmTcM999yDa6+9Fn379sW8efNgMplw3333SfK+EBERtSWjzgx9hQkmfS3U8QpoO6i5nQWRROS+HkoMb65Faq1CVHkgkDQgO3nyJO644w6cP38eSUlJGDBgAHbt2oWkpCQAwMsvv4zw8HCMGDECtbW1KCwsxOLFi23HR0REYN26dRg3bhzy8vKgVqtxzz334KmnnrI9JiMjA+vXr8fUqVMxf/58pKWlYfny5SgsLLQ9ZtSoUaisrMTs2bNRXl6OXr16YdOmTQ6JPoiIiIJNKDf+iNxp684Kd+uhHlk53KPzy7GTxdtrkTZZjZyCTOx3Mm0xpyAT2mS1X+rrS5LuQxZMuA8ZERHJjVFnxtziNU7XX+QUZHrc+CNyJ5QCBDFOHjmHsT2WuCwvPTwOaVntBZ9DbL2l+KzEXosqT+jxyuh1dkGZ1B1LrYkNJB0hIyIiIul4shg+0BvNFPjkOArrq5Gq1hK7HkpsvaX6rMRei5LStXhk5fDfAkmtAtrkwA/6WwRUUg8iIiJqO8GwGJ4Cm1xTkkuVuU/seigx9Zbys/LFtUiToEJaVnt0z+2EtKz2sgnGAAZkREREISsYFsNTYJNrSnKpOita1kM548l6KDH1lvKzCvVrEQMyIiKiECW28UfkjlxHYaUKEDQJKkxcPtThd9kybdDdqI9KE+11uZSflS+uRXLeKoBryIiIiEJUS+PP1WJ4OU35ocAk15EPSTP3hQHXjeyBoRP7oN7SiChlBHRnqoEw94dGKSKQPTADX2895lCWPTADUYoIl8dK+VmJvRbJcZ3ixRiQERERhTC5L4anwCbXlORSdVYYdWYseMBxHVfLud0l5TBesKBocl8AsAvKsgdmoGhyXxgvWJDa2fmxUn9W3l6LpErA4ktMe+8jTHtPRERE/ibX9PGBlpLcU3bvdys7K7z5rMSmvT955BymXLscRVNy0b1fJ9sIW9muU1g7bzfmfTVa8PjKk3rs3/gzElJjUW9pRLQqEhdOG5EzqDOS0gLzs/LFVgH+wLT3REREREFGjvtLAfIehdUkqLyqp7efldh1XNpkNXr2T8eqZ7Y7lHk0ymUFtr932KHeOTe7GFbzMW++o3Jdp3gxBmREREREAU6u+0u18DawkSMxn5XYdVxiplpKPfXP2++oXNcpXoxZFomIiIgCnFz3lwpFYj4rX2QbbBmRLD08Di/uuh+lh8fhkZXD3QbeUqa9F/MdVWkVyM7PcFqWnZ8BlQwCMo6QEREREQU4f+8vFSqjV21BzGflq2Qi3oxISjn1T8x31GKoxZ+m5mLAbT3RruNva9/OnTIiKU0Di6EWSNX4re6+wICMiIiIKMCJmZbli4a2HJOJSEXsFDqp1tz5Yuqft98To84iWF4tUG4y1MFqBb589xAOfvJbZsleN2WiaHJfmAx1bs8vNQZkRERERAFOTEpysQ1tqdefyY0v0sdLseZObL3FfE9U6ijBcqVAuSZRiddnfuqw99rBLUdhbbKiZOlgwecOBFxDRkRERBTgWqayXbq+yJOpbGLWJXH9WeuJ+aykpElQYdziwQ7rsbLzMzB+yWBRCUHcfU/CI8ORPdDFOrCBGQiPdB2y1Nc2Ot0IG2jei62+tlHw3IGAI2REREREMpCUrsXkFcNgrKyxTWXTJMWgnZv1MWLWJXH9mXfkmOrfqDNj+bTNyMrthKLJfe32MFs+dTMmrxjmsv5ivycREWGCG1pHRIS5PNZsFJ6S6K48EDAgIyIin+N6E3mR6vPi96R1xEwJ8zZACIY9nqQiZap/b35b+goTdn/4A3Z/+IPT8nsFgiqx35O4Dmr8e/pWZPXrhKIp9sHg5uUHMHnFMJfHBkPaewZkRETkU1xvIi9SfV78nrSOL/aI8iZACIbGbqiRYlNqX+yf9tDCm/HK6HV2m1q3ZkqumDV7UuMaMiIi8hmuN5GGUWfGySPnULb7FE6WnfP4fZbq8+L3pPWk2iPKF/tiUdsR89sSE1RJuX+aXNfsXYwjZERE5DNcb9L2xIw0SfV5BcL3RG7TJaWaOuirfbGobYj5bYkZaZJy/zRAnmv2LsaAjIiIfIbrTdqW2GlsUn1eUn9P5DhdUsqpg3Jv7IYSSTelDgOuG9kDQyf2sa0B052pBlzn4/ApKdfsicWAjIiIfIbrTdqW2JEmqT4vKb8nvliLJQWp18nIubEbSqTalNqoM2PBA46/K6D5+xmov6tAwTVkRETkM1xv0rbEjjRJ9XlJ+T2Rai2WWMGwTob8zxe/LU2CCmlZ7dE9txPSstp79N2S6+8qUDAgIyIin2GjsW35IrOZFJ+XlN8TqadLiuFt0gMKHVL9tuT8uwoEnLJIREQ+xfUmbccX09ik+rykOq/cp9Vy6iC5c/EG4tV6C2LjVdC0V7ndQFwMuf+upMaAjIiIfI6NxrYhdWYzsaQ4r9RrsYj8TYqkNfxdiRNmtVqtUlciGBgMBmi1Wuj1esTFxUldHaKAJbdU00RyYPe74oikW5Un9C6D2ECf/sdrKAkx6syYW7xGkuQacv5d+UNrYgMGZD7CgExavEHJgxxTTRNRcJJjEBuq11De4z138sg5jO2xxGV56eFxSMtq77fzy/F35S+tiQ04ZZFkL1RvUHIj11TTRBSc5DatNlSvobzHt47UyTXk9rsKFMyySLLm7gZl1JklqhldiilxicjXjDozTh45h7Ldp3Cy7FxQX/Plfg315rPiPb71mFxDnjhCRrImdlNUajtS99oRUXAJtZETo84iWF7tplxK3n5WvMe3HpNryBNHyEjW2MiXD/baeSeURgBCHT9rz4XiyIlKHSVYrnRTLhUxn5Uv7vGh9rviXpDyxBEykjU28uWDvXatJ/UIABfStx2pP2u5CcWRk/DIcGQPzMDXW485lGUPzEB4ZGD2sYv5rMTe4ytP6rFv489ITI1FvaURhnOR+P7z48gZ1BlJacH7u+JekPLDgIxkjY18+fDVfkmhQuoF/AwQ2o7Un7UcheLsiIiIMBRN7gsAdkFZ9sAMFE3ui4iIMKmqJkjMZyXmHm/UmVH+kw5frDrk8H517JoIpTrao9+VXDummFxDXhiQkayxkS8v7LXznJQjAAwQ2lYojvaIFYqzI+I6qPHv6VuR1a8Tiqb0Rb2lEVHKCJTtOoXNyw9g8ophUlfRKTGflZh7fPUFM1Y+vd1hRLHl/0uWDnb7u2LHFLUVBmQke2zkywt77Twj5QgAA4S2FYqjPWKF4uwITYIKDy28Ga+MXodVz2y3/b2tOiC9HSkS+1l5e483G+ucTu8EmoMys7FO8Hh2TFFbYkBGQYGNfAo2Uo4AMEBoW6E42iNWqM6OkKoDUsxIkS8+K2/u8ZZq4YDLYhIuZ8cUtSUGZEREAUjKEQAGCG0rEEZ75LhOJildi8krhsFYWWMLTjRJMWiXqpG6an7V1h2QvhgpkiKQjE0Ufu5YN+dmxxS1JQZkREQBSMoRgEAIEEKJ1KM9cl0nI9d6yy349dVIUVsHkgmpsYLXsYTUWMHj2TFFbSnMarVapa5EMDAYDNBqtdDr9YiLi5O6OkQUJOwab224PrLyhN5lgBDIjV05k+KzNurMmFu8xmmDO6cg06PRDykCDF/UWwpyDCLLdp/Cw/1ec1n+4q770T23UxvWyHNirmNGnRkvFK9xGdAF6neMAkdrYgOOkBERBTCp1kcyWU7bE/NZexsUiR39kCrAkOP6HrkmiZDzSJGY65jUI9cUWhiQERGRU0yWIw9igiIx62SkDDDkuL5HjkEkIP8pzGKuY+yYorbCgIyIiEimxAZFYkY/pAww1FoFlOooFE3JRfd+nVBvaUS0KhJHdp7E2nm7A3LURo5BJBAYI0VSrrtjxxS1BQZkRBKS2+JuIgosYoMiMaMfvggwxOxt9cS6Yqx8ervdnlzZAzPwxLrigBy1CdWpf2LJcd0dUWsxICOSCG8yRK6xs8IzYoMiMaMfYgMMsdfA1XO+dNj49+utxxAREYZHVg53e3xb0yar0W9YN1x+VQeHUb1fvz0bkEHkxcSOFHnzm5brujui1mJARiQB3mSIXGNnhedUmmhR5YD3ox9iRtfEXgPluB5Lk6DCAy8VYOHY9fajevkZKCkdEnD19SVvf9Ny/JyJvMGAjEgCvMkQOcfOitaJUkQge2CGw0gR0Dx9L0oR4dHzeDP6IWZ0TV9hwuEvT2DUYwOcrgFzdw006WsF15AF4noso86MxeM34OtPLhnV++QYlozfELTfbTG/abmuuyNqLQZkRBLgTYbIOXZWtI7xggVFk/sCgF1Qlj0wA0WT+8J4wYLUzv47v7ejazXGOkx/ZzjWzt/jsAZs+jvDUWOsEzw+Nl4heHxsfOCtxwrV77aY1y3ndXdErcGAjEgCvMkQOcfOitaJ0UTjbzeuQdGUXBRN6Yt6SyOilBEo23UKc+9Yg3lfjfZ7HbwaXUtU4vWZnzpdAwYAJUsHCx6vjFNg7YI9zo8PA6a9UdSq+rSFUP1ui3ndck+5T+QpBmREEuBNhsg5dla0jjZZjZ790+1GiVoE8rWkvrbR6TRLoDmoqq9tFDzerK91mPpnO/6TYzDra4FUjeh6+lKofrfFvO5ASLlP1BYYkBFJgDcZIufYWdE6mgQVJv5rKPZv/BkJqbG2tVQXThuRM6hzwF5LzG6mJLorl+NoU6h+t8W+bm7OTKGAARmFPKnSa/Mm03pMhR782FnhHavUFWglsaNFvhhtauvriSZBhXGLB2Ph2PV2o3vZ+RkYv2Rw0H63ffGb5ubMFOwYkFFIkzq9Nm8ynpP6s6K2w84Kzxl1ZpT/pMMXqw45JPXo2DURSnV0QL5vYkdNxB4vxfXEqDNj+bTNyMrthKLJ9uv9lk/djMkrhgXkZ+UL/E0TCQuzWq1y61gLSAaDAVqtFnq9HnFxcVJXhzxg1Jkxt3iN0+xPOQWZQZuCWI74WRE5d+bnC1j40AaXae9Llg5GaudECWrm3pmfdU5HiyYuHYKUzAS3x1ee0LscdREKqqS6npw8cg5jeyxxWV56eBzSstr7/LxEJI3WxAYcIaOQFaopiOWIn5X8cHpp2zAb6wSTY7hbiyUVX4wWeTvqItX1RI7r3oiobTAgo5DFm6N88LOSl1CeXtrWgailWjjgspgCMyDTV5iw+8MfsPvDH5yW3+thUOTNtG+priehmmWRiNxjQEYhizdH+eBnJR9GndkhGAOaRx5eGb2uTaaXSjU6J0UgGpso/LpiA3RUUspOFqmuJ6GaZZGI3AuXugItnnvuOYSFhWHKlCm2v1ksFkyYMAHt2rVDbGwsRowYgYqKCrvjjh8/jiFDhiAmJgYdOnTAI488goaGBrvHbNu2DTk5OVAoFOjSpQtWrFjhcP5FixbhiiuugFKpRG5uLvbs2eOPl0kBpOXm6AxvjoGFn5X3jDozTh45h7Ldp3Cy7ByMOrNfz+fJdDB/qjyhx9ziNRjbYwke7vcaxmYtwQvFa1B5Qu/R8d6+X+4CUX+97wmpsYK/jYTUWL+cVywpO1mkup60ZBu89NzMIEpEATFCtnfvXixduhRXX3213d+nTp2K9evX491334VWq0VJSQmGDx+OL7/8EgDQ2NiIIUOGICUlBTt27MCZM2dw9913IyoqCs8++ywA4NixYxgyZAjGjh2Lt956C1u3bsXo0aORmpqKwsJCAMCqVaswbdo0lJaWIjc3F/PmzUNhYSHKysrQoUOHtn0zqM0wvbZ88LPyjjSZ5CxQqqNQNCUX3ft1su2LdWTnSaydtxvVOotfztt8bnGjc2LeL6nWJcn1tyHlaJGU7xmzDRKRM5JnWayurkZOTg4WL16Mp59+Gr169cK8efOg1+uRlJSEt99+GyNHjgQAHDlyBD169MDOnTvRr18/bNy4EUOHDsXp06eRnJwMACgtLcWMGTNQWVmJ6OhozJgxA+vXr8d3331nO2dxcTGqqqqwadMmAEBubi769OmDhQsXAgCampqQnp6OiRMn4tFHH/XodQRLlsVQXIhv95p5cwxo/Kw8J1UmuV+/O4uKY1VYO3+PQxr2osl9kZwRj8uv9E9Hl5gsdmLfr7Ldp/Bwv9dclr+46350z+0kUHtxpPxteHvf8DZLoq+Iec9C8V5JRK0jqyyLEyZMwJAhQ5Cfn4+nn37a9vd9+/ahvr4e+fn5tr9lZWXhsssuswVkO3fuxFVXXWULxgCgsLAQ48aNw/fff4/evXtj586dds/R8piWqZF1dXXYt28fZs6caSsPDw9Hfn4+du7c6bLetbW1qK39bY67wWDw+j0IFKG6ED8U9wKTa2MiFD8rb0k1YhOpiMDaBXscMv99vfUYEAaMXzzI5+dsIWZdktj3S61VCI4M+nudo9jfhpigytv7htSjRd6+Z6F6ryQi/5E0IFu5ciX279+PvXv3OpSVl5cjOjoa8fHxdn9PTk5GeXm57TEXB2Mt5S1lQo8xGAwwm83Q6XRobGx0+pgjR464rPucOXPw5JNPevZCZSAQFuJT22BjIjQY3UwN9NfUwTpzg92+Uhf7+pNjqDM3OC3zBTHrksQmmdAmq/HEumKsfHo7Vj2z3fb37IEZeGJdcUCvc/T2muCL+4bcOll4ryQif5AsqceJEycwefJkvPXWW1AqlVJVw2szZ86EXq+3/Ttx4oTUVRJF6oX41DakSjzgK22doELOVOoowXKlm3JvmQ3CgYu7cjHEJGvwRZKJ1XO+dDoy+O6cL90eKxUx14RQvG+E4msmIv+TLCDbt28fzp49i5ycHERGRiIyMhKfffYZFixYgMjISCQnJ6Ourg5VVVV2x1VUVCAlJQUAkJKS4pB1seX/3T0mLi4OKpUK7du3R0REhNPHtDyHMwqFAnFxcXb/5Iz7PIUGOTcmxGbPCzXhkeHIHpjhtCx7YAbCI/1z+Y+JEw5c3JWLISaLndjMe3L9bYmpdyjeN0LxNROR/0k2ZXHgwIH49ttv7f523333ISsrCzNmzEB6ejqioqKwdetWjBgxAgBQVlaG48ePIy8vDwCQl5eHZ555BmfPnrVlQ9yyZQvi4uLQs2dP22M2bNhgd54tW7bYniM6OhrXXHMNtm7diltvvRVAc1KPrVu3oqSkxG+vP9Bwn6fQINfGBKcJtV5ERBiKJvcFAKfJNSIiwvxyXk1SDHoXZDpt5PcuyIQmKcYv523h7bokTYIK4xYPxsKx6+2mXGbnZ2D8ksFuj5frb8ukrxVc+yZU71C8b4TiayYi/5MsINNoNLjyyivt/qZWq9GuXTvb3x944AFMmzYNiYmJiIuLw8SJE5GXl4d+/foBAAoKCtCzZ0/cddddmDt3LsrLyzFr1ixMmDABCkXzRXHs2LFYuHAhpk+fjvvvvx+ffvopVq9ejfXr19vOO23aNNxzzz249tpr0bdvX8ybNw8mkwn33XdfG70bvuPtwmxuWBka5NqY8EWCCrkmMvFWXAc1/j19K7L6dULRlL6otzQiShmBsl2nsHn5AUxeMcwv541WRuL2mf3R1Gh1CARvn9kf0Ur/33a8WZdk1JmxfNpmZOV2QtFk+/dr+dTNmLximNukHkIC9bcVG6/A9HeGY+38PQ5r36a/Mxyx8a7rHYr3jVB8zUTkf5JnWRTy8ssvIzw8HCNGjEBtbS0KCwuxePFiW3lERATWrVuHcePGIS8vD2q1Gvfccw+eeuop22MyMjKwfv16TJ06FfPnz0daWhqWL19u24MMAEaNGoXKykrMnj0b5eXl6NWrFzZt2uSQ6CPQiUnWINe9bKh15NqYEDv6EIqJTDQJKjy08Ga8MnqdXUO7Nb9pb4JYfYUJTw5diaIpuQ6B4JNDV2LeV6MD8nqirzBh94c/YPeHPzgtv9dN0C/X35YyTiGYFXPaG0Uujw3F+0YovmYi8j/J9yELFlLvQ+arPYdCcZ+nUBs5kXrvH29Iub+U3Hn7m/Y2iJV6Py5v+aLeofbbahHy940Qec1E1Dqy2oeMfMNXew6JSUEsx8AmFEdOpN77xxtiRh90Z6oFfxu6M9UB/drF8nb6XmnJJnTr0xG3TOxjt65oackmwel7cp2654t6y/G35Yu1b3JLXe8Lofiaich/GJAFCakXlMsxsAnlRBFya0yImSZUfUE4NX51G6TOl1tnheGsCYWjeztdV1Q0uS8MZ1138Mh16p6v6i23Ti25BtBERMGEAVmQkPKmKtfAhoki5MXb0QdlbLRwuVq4XCw5dlY0Nlqxdr6LdUUAHlpQ6OwwAPJdYyN1vaX6nsg1gCYiCiYMyIKElDdVX02XbGtMFCE/3ow+qDTRyB6Y4RBcAM0jPiqN/wIyuXZWNDU0OX2/gOagrKmhSfB4OU7dA6Srt5TfE6kDUSIiYkAWNKS8qUo9XdJbYkYV5drQDkWxiSoUzxoAwHE/ruJZAxCb6L/PSa6jsGZTvWC5xU05IL9psS2kqLfU3xO5BtBERMGCAVkQkeqmKtc1CGJGFeU6KhiKNAkqpHRJwA2jetqlYdedqUZKlwSPPydvGrxyHYXVJCgFy2PdlFPrBML3RK4BNBFRMAiXugLkW5oEFdKy2qN7biekZbVvkxtsS2DjTFutQTDqzDh55BzKdp/CybJzMHqQqKFlVPHSunsyqijXUcFQlZSmxXUje6Bjl0S0T49Dxy6JuG5kDySledZYrTyhx9ziNRjbYwke7vcaxmYtwQvFa1B5Qi94nD9HYT35jntLpVUgOz/DaVl2fgZUAdrJIldy/Z4QEZFvcISMRNMkqDBu8WAsHLseX39y0ZSw/AyMXzI4oBfDezuqKNdRwVDm7QiAmOmpch2FtRhqUTSpL2B1nOZZNKkvLIZaIFUj+BxMeOM5uX5PiIjINxiQkWhGnRnLp21GVm4nFE3+bUpY2a5TWD51s+CeRb44t9i1XN401OWemYyNZc+JafCKWdsp5ShsdVUt5t6xBkVTcu2meZbtOoW5d6zBM1vvEjyeCW9aR67fEyIi8g0GZCSavsKE3R/+gN0f/uC0/F4/9tBK1Tss58xkbCy3jtgGb1K6FpNXDIOxssY2CqtJikE7NyNMUo7CqrUKWEz1dnuQeXpuJrzxDkfriYhCFwMysiNF4gIxjDqLYHm1m3Ix5JiZTO6N5fNnjDBW1qBab0FsvAqa9iq3gY1YYhu83gbAUo7CcgqdNEJxtJ6InONMltDCgIxsvG04StlDq1JHCZYr3ZSLJbfMZHJuLJ/5Wed0nWJJ6RCkdk7w23nFNHjFBMBSjsJyCp18yHm0noic40yW0MOAjABIl7hArPDIcMFNf8MjmUj0YnJtLJ8/Y3QIxgDg60+OYeHY9Zj2RpHfRsrENHjFBsBSjsJyCp18yHG0noicC4SZLByda3sMyAhAc8Px8JcnMOqxAejerxPqLY2IVkXiyM6TWDtvt98SF4gVERGGosl9ATjJBje5LyIiwvx2bjmSa2PZWFnjEIy1+PqTYzBW1vh16qK3DV5fBMBSjsJyCp18yG20noick3omC0fnpMGALMh426tRY6zD9HeGY+38PXYL+bMHZmD6O8NRY6wTPF6qHtq4Dmr8e/pWZPXr5JANbvPyA5i8Yphfzy83cm0sV+uF1wK2xcieNw1euQbAYnAKHRGR96Rdly/96FyoYkAWRMT0amgSlXh95qcOU/9a/r9k6WC355eih1aToMJDC2/GK6PX2QWSrWn8STk0L+bc3hwbCI1lb+odq1UKlgdqYCPXAFgsTqEjIvKOlB15Uo/OhTIGZEFCbK9GnaXB6TosoDkoq7M0+LS+viSm8Vd5Uo99G39GYmos6i2NMJyLxPefH0fOoM5ISvPv0LyYAFqKzbB9wdt6a5JikJ2f4XTaYnZ+BjRJMX6pr1iBEABLhVPoiIhaT8qOPLmuMw8GDMiChO5MtWCvhu5MtXBmNDfp4U1V/ksf7wveNP6MOjPKf9Lhi1WHHNafdeyaCKU6OiA3tJZqM2yxxNS7XaoGJaVDnGZZnLh0iN9T34vB0SIiIvKUlB15oTjNPlB4FZDVmuthtQLKmOaU4md/rcLO98uQ3rM9cgo6+7SC5JnqC2bhcp1wuTI2WrhcLVwuR9UXzFj59HbBaZqBuKG1XKcUiK13aucETHujqNUbLAcCjhYREZGnpOrIC9Vp9oHAq4Ds6aLVyBuehcFjr0F1lQXTcl9DZFQEDOdqMPqlmzB43LW+rie5ITagUmmiBdPHqzSBHZB5s2Gw2VgnOE3T7CaRiRhipgXIdUqBL+rdLlUjiwDMl5h+mIgo9Ei1Lj9Up9lLzauA7Of9ZzD65ZsAAF++dxjxyWosODAGO/5zGG/O3saATAJiA6rYRBWKZw0A4Jg+vnjWAMQmBu6P0NsNgy3VwgGXxeS/gEzMtAC5TimQa72lxPTDRETUljjNXhpe7ZpbW1MPlaa58XRg88+4bngWwsPD0L1fJ1T+qvdpBckzLQFV9sAMu797GlBpElRI6ZKAG0b1xOyPRmHmuyMx+6NRuGFUT6R0SQjYH6K7DYPPnzG6PNbdexLrx9fcMi3AGXfTAsQcKyW51lsq7tbcGd1MQyYiIvKGJkGFtKz26J7bCWlZ7QO2DRhMvBohS+2SiF0flCHvT92x/+OjKJraDwCgP1sDVRx7uaVwcUB18X5cujPVHgdUSWlaXDeyh12vSI/+6QH9QxSzYXBCaqzgXOmE1Fj35/dyOpmYaQFynVIg13pLRa5rBSn0cFot+Ru/YxTsvArIimdfj3/++X0sn7oZV//xCvTISwMA7N/8Mzr3TvFpBclzvgio5JZ8QMyGwWIDBLHTycRMC5DrlAK51lsKcl0rSKGF02rJ3/gdo1AQZrVard4cqCuvxoUzRmRkpyA8PAwAULbnFGLiFEjPau/TSsqBwWCAVquFXq9HXFyc1NXxmjfJMaT0yzcVKMle5rJ84ddjcMXVyYLPYdfz5mGAYNSZMbd4jdMRjJyCzIDfzV5un3MoOnnkHMb2WOKyvPTwOKSF4LWWAofcr4MU+PgdIzlrTWzg9T5kCSmxMFfX4cCWo7jyhsugUEWhW5+OCAsL8/YpyQfEDOt7mxxDSr7YMNibUUE5TyfzxefM6SP+x/TDFOjkfB0keeB3jEKFVwGZ4XwNnrv9P/j2v78gLCwMy36cgJTMBMx/4CPEJqgw+sWbfF1P8oCYYX2h5BiLxq7H1DeKAnIERaoNg036WijVUSiakovu/Tqh3tKIaFUkjuw8ibXzdgfsdDJ3SVCmefA5c/pI2+CaOwp0nFZL/sbvGIUKrwKyV6duRmRUBP59fDLGXTSl5vpRv8O/pm0GGJC1OXcZ2dwN6wslxzjoJjmG1KTYMDg2XoHp7wzH2vl7sOqZ7ba/Zw/MwPR3hiM2PjCT24hJggKI/55R63DNHQUybmVB/sbvGIUKrwKyA5uP4qmP/4z2afbzITt1TcRZpr2XhNhh/RqDcC+Tu3Kpidkw2Jvpd8o4BTYu24+sfp1sWS1bRsg2LtuPkmVDvKqLv4lJggJw+ogU5JZoh0IHp9WSv/E7RqHCq4Cs1lQPRUyUw9+NF8yIUni9LI1EMOqEG9rVbsrdbVcQrNsZeDv9zmKoxaAxOU5HyIom94XFUAsE4IhirFYpWO6ut5HTR4ioBafVkr/xO0ahwqvoqef1l+HTN77BXf+4sfkPYUBTkxX/mbsDV914uS/rRx5SqR0D5Isp3ZSHR4Qhe2AGvt7qJDnGwAyERwR2shZvRrnETL9rbLRi7fw9Du9Xy/8/tKDQb/UWQ2wSFE4fIaKLcVot+Ru/YxQKvArI7ps7ELMGvomfvjqD+rpG/Hv6Vhz/vhLGC2a88OW9Pq4ieSI8Mhx9hnRFZq9khyQTRw9WIDwyXPD4yMhwFE3uCwB2QUbLiE+km+Ol5O0ol5jpd00NTU6DV6D5/WtqaPJbvcUQmwSF00eI6FKcVkv+xu8YBTuv9yEz6S1Yt3Avjn1dAXN1PbrkpGDIhGuRGIDTtNqC1PuQnf7hHOrrmrBs6maHhvaYlwsQFR2Ojt1c71lk1Jkx/94PcflVHWwBXZQyAmW7TuHXb89i8ophAXkxFLNHSdnuU3i432sun/vFXfeje24np2WHd57EI9f92+Wx/9xxH7L+t2G6r+vtCy37kHmTBKXyhN7l9BFmWSQiIiJqo33I1FolRj12vbeHk48pNAosunut03Tmr07djKlvFLl9jqKpuVj59HaHNVHFswZ4VAcp9qYSM8olZvqdJkF4LVasm3Kpk2OISYLC6SNERPLEPSSJApNXAdmxbyqc/j0sDIhSRqLDZVom92hjZn2tYNp6s144yYS+woTnR/0HE5ffgvueH4gaQy1itAqcP2XE86P+g+c+u0fwoi3V3lRikkyImX4nduqe3JNjcPoIEZG8cA9JosDlVdQ0qdcyhIU1J3lomfHY8v8AEBEVjutH/Q4lS4cgWsnArC2IbeDXGOswafktDokqsgdmYNLyW1BjrHN5rJR7U4kb5fI+e5PYzE9MjkFERG2Fe0gSBTavoqXH3r8dK2ZsxfBH8tCtb0cAwA97TuP9F3fhjiduQGNDE15/9FP836z/4oF/cpPotiC2ga9JVOL1mZ+6zBpYsnSwy2OlnH4ndqRKzPQ7MccyOQYREbUVqafJE5EwrwKyVc9sx4PzC3FNYWfb3664Khnt0uLw5uPb8PKeB6BUR+FfD3/CgKyNiG3g19c2CmYNrK9tdHmslNPvfLFHiZjpd94ey71ViOhSXN9D/iL3afJEwc6rgOzXb8+iw+WO8407XK7Fr9+eBQBk9kqB7oxRXO3IY2Ib+GaBKYnuyqWefifXJBNyrXeoYmOZ/Inre8ifpL5PE5EwrwKytKx2eO+5L1GybCiioiMAAA31jXjvuS+RltUOAHD+lBHxybG+qym5JaaBL+ZiHQjT7+SaZEKu9Q41bCyTP3F9D/lbINynicg1r3b7HbdoEPau+xH3ps3DY/lv4rH8N3Fv2nzsXfcjxi9pXmtUflSHweOv8WllyT1NggppWe3RPbcT0rLae3wTb7lYO+PuYt0yOnfp8Zx+R8HAXWPZqDNLVDMKFp6s7yESg/dposDm1QhZj+vSsfzYRGx76zuc+uE8AGDAbT3w+z9fiRhN80jKH++62ne1pDZx28z+aGy0OmRZvG1mf7fHcvodBSsuhid/4/oeagu8TxMFrlYHZA31jRibtQRPrBuFwWM5AhYs9BUmPDl0JYqm5KJoSl/UWxoRpYxA2a5TeHLoSsz7arTbizan31EwYmOZ/C0Q1vdwjWRo4H2aKDC1OiCLjIpAnaXBH3UhCZn0tbCY6rHqme0uy4lCUSA0lim4Sb2+h2skiYik5dUasqETrsV7z+9AY0OTr+tDEmGjk8g5MesriTwh5foerpEkIpKeV2vIfth7Gl9vPYYDm4/i8qs6QKmOsit/bM3tPqkctR2pe2iJAhX3jKO2INX6Hq6RJCJf4vRn73gVkMXGK9F/RA9f14UkxEYnkWtcDE9tQYr1PVwjSUS+wunP3vMqIJvy72G+rgcFADY6iVzjYngKRpyuTkS+wP0UxfEqIKPgxUYnEVHo4HR1IvIFTn8Wx+uAbPt7h7B99SFUHjegoa7Rrmz+/gdFV4yIiIj8i9PVicgXOP1ZHK8Csg8X7MH/PfZfDLw3G7vW/oD8+7JR/rMOP+49jSETrvV1HYmIiMhPOF2diC7mTWIOTn8Wx6uAbMPir1CybAh+f8eV2Lria4ycfh1SMhPw5uxtMF5gilwiIiI54XR1IgK8T8zB6c/ieLUPWeVxPXpclwYAiFZFosbYPAx5411X4fN3vvdd7YiIiIiIyO/E7Eso5X6KwcCrEbL4lFgYL5jR4fJ4JF2mRdmuU8jMTkHFsSpYrVZf15GIiIiIiPxIbGIOTn/2nlcBWfYfr8DuD39A596pyL8vG8unbsaX7x3Gj1+dwXXDs3xdRyIiIqKgwI1zqS148z3zRWIOTn/2jlcBWcmyobA2NY+EDZ3QB5p2KhzZcRK5w7rh5oeu8WkFiYiIiIIBN84lT4kJ3L39nsk9MYecOzu8WkMWHh6GiMjfDv198ZV4aMHNuGViX0RFR3j8PEuWLMHVV1+NuLg4xMXFIS8vDxs3brSVWywWTJgwAe3atUNsbCxGjBiBiooKu+c4fvw4hgwZgpiYGHTo0AGPPPIIGhoa7B6zbds25OTkQKFQoEuXLlixYoVDXRYtWoQrrrgCSqUSubm52LNnj8evg4iIiEiImPU5oc6oM+PkkXMo230KJ8vOBf17VXlCj7nFazC2xxI83O81jM1agheK16DyhN7tsWK+Zy2JOZwJ9MQcYt6zQOBVQAYA1VUW7N/8M/775jfY+sbXdv88lZaWhueeew779u3DV199hT/+8Y8oKirC9983JwaZOnUqPvroI7z77rv47LPPcPr0aQwfPtx2fGNjI4YMGYK6ujrs2LEDr7/+OlasWIHZs2fbHnPs2DEMGTIEN954Iw4ePIgpU6Zg9OjR+Pjjj22PWbVqFaZNm4YnnngC+/fvR3Z2NgoLC3H27Flv3x4iIiIiG0/W55AjuTe0W0ts4C7meybXxBzB0NkRZvUiC8fuj37AP+98H5bqOsTEKYCwsN+eMAxYeeERryuUmJiIF154ASNHjkRSUhLefvttjBw5EgBw5MgR9OjRAzt37kS/fv2wceNGDB06FKdPn0ZycjIAoLS0FDNmzEBlZSWio6MxY8YMrF+/Ht99953tHMXFxaiqqsKmTZsAALm5uejTpw8WLlwIAGhqakJ6ejomTpyIRx991KN6GwwGaLVa6PV6xMXFef36iYiIKPiU7T6Fh/u95rL8xV33o3tupzasUeAz6syYW7zGaYCRU5CJR1YOD9ggwVsnj5zD2B5LXJaXHh6HtKz2Lst98T2zm/ong8QcYt8zf2lNbODVGrJ/PbwFN93fC3c/+0coY6K8quSlGhsb8e6778JkMiEvLw/79u1DfX098vPzbY/JysrCZZddZgvIdu7ciauuusoWjAFAYWEhxo0bh++//x69e/fGzp077Z6j5TFTpkwBANTV1WHfvn2YOXOmrTw8PBz5+fnYuXOny/rW1taitva3xY0Gg0HsW0BERERBSu7rc6QgNuufHIlNrOGL75mUiTmkSkYiNa8CsvOnjBg2qa9PgrFvv/0WeXl5sFgsiI2Nxfvvv4+ePXvi4MGDiI6ORnx8vN3jk5OTUV5eDgAoLy+3C8ZaylvKhB5jMBhgNpuh0+nQ2Njo9DFHjhxxWe85c+bgySef9Oo1ExERUWjhxrmtFwwN7dYSG1DJ+XsWqslIAC/XkOUUZuLHr077pALdu3fHwYMHsXv3bowbNw733HMPDh065JPn9qeZM2dCr9fb/p04cULqKhEREVGAkuv6HCkFQ0O7tcQm1pDr90xsMpJ+w7ph1GMDMPujUZj57kg8sa4Yox4bgH7DugV0ENrC4xGy3R+W2f67z5CueO2RrTh+6ByuuKoDIqPs47rcYd09rkB0dDS6dOkCALjmmmuwd+9ezJ8/H6NGjUJdXR2qqqrsRskqKiqQkpICAEhJSXHIhtiShfHix1yambGiogJxcXFQqVSIiIhARESE08e0PIczCoUCCkXwXQiIiIjIP+S8ca4UKcXlPNrjrZaA6pXR6+xed2sCKrHfMyk+azHTUzUJKjzwUgEWjl2PVc9st/09Oz8DJaVDZPH78jgge/rW1Q5/W/nU5w5/CwsLw4eNs7yuUFNTE2pra3HNNdcgKioKW7duxYgRIwAAZWVlOH78OPLy8gAAeXl5eOaZZ3D27Fl06NABALBlyxbExcWhZ8+etsds2LDB7hxbtmyxPUd0dDSuueYabN26FbfeequtDlu3bkVJSYnXr4OIiIjoUnLcOFeq/dN8EZzIkc8D9zD3D2kh1Wdt0tdCqY5C0ZRcdO/XCfWWRkSrInFk50msnbdbcHqqUWfG4vEb8PUnx+z+/vUnx7Bk/AZZJH/xKsuir8ycORODBg3CZZddBqPRiLfffhvPP/88Pv74Y9x0000YN24cNmzYgBUrViAuLg4TJ04EAOzYsQNAcyKQXr16oWPHjpg7dy7Ky8tx1113YfTo0Xj22WcBNKe9v/LKKzFhwgTcf//9+PTTTzFp0iSsX78ehYWFAJrT3t9zzz1YunQp+vbti3nz5mH16tU4cuSIw9oyV5hlkYiIiIJNIGQ6lFvWP6l5G1RJ+VmfKjuHUz9cwNr5e/D11t8Cq+yBGSia3BeduiWiU3fnmRKDIctiq9aQff3pMYzruQQ1Bsco1aS3YPzvluC7L457/Hxnz57F3Xffje7du2PgwIHYu3evLRgDgJdffhlDhw7FiBEjcMMNNyAlJQVr1qyxHR8REYF169YhIiICeXl5+Mtf/oK7774bTz31lO0xGRkZWL9+PbZs2YLs7Gy8+OKLWL58uS0YA4BRo0bhn//8J2bPno1evXrh4MGD2LRpk8fBGBEREVEwCoT90zQJKqRltUf33E5Iy2rPYEyAmLVYUn7WyjgF1i6wD8YA4Outx7B2wR4o41wvEwqG5C+tyrK4dt5uFD7Yu3nvsUuotUrc/NA1+OClXbjy+ss8er5//etfguVKpRKLFi3CokWLXD7m8ssvd5iSeKk//OEPOHDggOBjSkpKOEWRiCjESbF2gqTBz9ozwdDYDSVi1mIZdRbB5652Uy6GWV/rMOWwxdefHINZXwukapyWB0Pyl1YFZMe+Pov7nh/osrx3QSbe/6frvbuIiIgClVRrJ+ROjoENP2vPBUNjN5SICaBVauHtrJRuysUQU+9gSP7SqimLVRXViIiKcFkeERkOfWWN6EoRERG1JTHTfEJZ5Qk95havwdgeS/Bwv9cwNmsJXiheg8oTeqmr5hI/69YRm4advGPUmXHyyDmU7T6Fk2XnPP5eigmgwyPDkT0ww2lZ9sAMhEd6tVuWR8TUW66p/i/WqhGydp3i8Ot3Z9GxS6LT8l++qUBCaqxPKkZERNRWxEzzCVXuAptAzWzGz7p1QjXToZTEjOCKGS2KiAhD0eS+AOA0sUZEhPt0jd6OmIsd5ZLzlhJAKwOyawd3wZuPb8M1N3dBtNL+0FpzPd564jP0HdrVpxUkIiLyN66TaT25Bjb8rFtP7o1dORHb0SEmgI7roMabsz/DsMl9cd/zA1FjqEVMnALnTxvx3ze/xfjSwYJ1FxNI+iLwl+OWEi1aFZCNmjUAO9YcwUPdFmFISR+kdW8HoDnd5PpFX6Gx0YrbHxvgl4pS4JPjOgIiIoDrZLwh18CGn7V35NzYlRNfdHR4G0BrElS4+9k/YuHY9XYJNjzZYNkXI+ahHPi3KiBLSI7FP3fci0XjNuKNmZ+iZQuzsLAw9C7sjHGLbkZCMqcshiIukCYiOQuGReFtTa6BDT9rCmS+6ujwJoAWs8Gyr0bMQzXwb1VABgAdLo/HkxvuQLXOjNM/6WC1WtGpayJiQ/DNo2ZyXUdARNSC62RaT66BDT9rao22nv0jZUeHXFPmB4NWB2QtYhNU6NaHFy2S7zoCIjngVOC2E8rTZbwh58CGn3Xrib0WyfFaJsXsHyk7OuSaMj8YeB2QEbWQ6zoCokDHqcBtL1Sny3hLzoFNKH7W3gZFYq9FcryWGXVmlJZsQrc+HXHLxD6otzQiWhWJIztPYmnJJkxeMcwv3x8pOzp8kTL/4uyMLfydMj8YMCAj0eS6joAokHEqMMlFKAY2cuRtUCT2WiTXa5nhrAmFo3tj7fw9WPXMdtvfW1LAG876b/aPVB0dYlPm3zo1F2HhYTi45bfje92U6XHKfLHkOArbggEZiSbXdQREgYxTgYnIV8QERWKvRXK9ljU2WrF2/h6HEZ+W/39oQaFfzy9FR4fYlPlVZ03oP7IHhk1qHlGMUkbg/OlqxMRFI66Df9uClSf12LfxZySmxqLe0gjDuUh8//lx5AzqjKS0wByFvRgDMhJNzusIiAIVpwITka+ICYrEXovkei1ramhyOv0OaA7Kmhqa2rhGbUPM6NzKp7c7/Z7lFGTikZXD/VFdAM0dDuU/6fDFqkMOG1p37JoIpTo64NuiDMjIJ+S8joAoEHEqMBH5ipigSOy1SK7XMrOpXrDc4qZczrwZnZNyJLT6ghkrn97ucjSzZOnggG+PcoUd+YwmQYW0rPbontsJaVntA/7LTxTIWqYCO8OpwETiGXVmnDxyDmW7T+Fk2TkYdWapq+Q3YoIisdciuV7LNAlKwfJYN+WhRsqRULOxTnA002ys89u5fYUBGRFRAGqZCnxpQ4ZTgYl+421QVXlCj7nFazC2xxI83O81jM1agheK16DyhN7PNZaGmKBI7LVIrtcyuQaSUpFyJNRSLRxwWUyBH5BxyiL5jJyz2xAFIk4FJnJNqqyBLc8ht/vdbTP7o7HR6rDG5raZ/d0eK/ZaJOW1zNvPiuvjW0fKBG+xicKfRawMPqswq9VqlboSwcBgMECr1UKv1yMuLk7q6rQ5sXuMyPHmRkRE0jDqzJhbvEYwgYCre8jJI+cwtscSl89dengc0rLauyyX455aJ4+cw5Rrl6NoSi669+tky4BXtusU1s7bjXlfjRZ8zXLli8/Krn3CTjFBlSf0LgNYf/42jDozXihe4zIYlGprhdbEBhwhI9HE9jbK8eZGRETSkSproFz31DLpa2Ex1dvtp3VpebDx1WfFffY8J9VIaDCMZjIgI9HE3BjlenMjIiLpSJU10BeZ5KSYEaLSRIsqlyO57n/WQq4zh6QKYOU+xZ8BGYkm5sYo9wsmERG1PV9kDfRmrYvYTHJSzQiJUkQge2CG00x02QMzEKWI8Nu5W7R1gCHX/c8AzhzylpxHM5llkUQTc2OU8wWTiIikIVXWQDH3O6POjNKSTejWpyNmfzQKM98diSfWFaNrn45YWrLJr2n3jRcsKJrcF9kDM+z+nj0wA0WT+8J4weK3cwPSZLWU6/5n7mYOBfP2DKGMI2QkmpjeRrleMImISDqaBBUm/mso9m/8GQmpsai3NCJaFYkLp43IGdTZbS+5t9ObxNzvDGdNKBzdG2vn77Fby9USFBnO+m9GSIwmGn+7cQ2KpuSiaEpfu6Qec+9Yg3lfjfbLeQHpliZImfVPDM4cCk0MyEg0MYsp5XrBbCHXOd5ERLJnBba/d9hhWlfOzZ09Otyb6U1i7neNjVasnb/HYdpgy/8/tKCwVXVpDW2yGj37pztN6uHve63uTLVggKE7U+2X+6ZcEz1w5lBoYkBGPuFtb6NcL5gA53iTPLDTgIKRlAmhvL3fNTU0OV3DBTQHZU0NTf6oLgDf3Gu9vZZUXxCeYlftxyl4ckz0wJlDoYkBGfmMt4sp5XjBZHZIkgN2GlCwknpalzf3O7OpXrDc4qZcrKR0LSavGAZjZY3tXqtJikG7VI3bY8VcS5SxwhkclWr/ZniUW6IHuc8cIu8wqQcFBE2CCmlZ7dE9txPSstoH/MXTk8aAvxl1Zpw8cg5lu0/hZNk5LvQlO1wYTnLg7XXMqBNOQlHtplwKmgSlYHmsm3KxKk/qsXfdjzh7XI+qChMqTxjw1bofUXlSOLGG2GuJShPtkEykRfbAjKBMuS+GmKQzJF8cISPygtRzvDnyQe5IPYJA5I6Y65hKHQWlOgpFU3LRvV8nW1KPIztPYu283VCqo/xd/VaTcuTDqDOj/Ccdvlh1yG7aZPbADHTsmgilOtrl9UDstSQ2UYXiWQMAwOHcxbMGIDaR16FLST1ziFPd2x4DMiIvSDnHm9MlyRNSdxoQCRF7HYuICsfsdcVY9fR2h4yFs9cVIyIq8CYAaRJUGLd4MBaOXY+vP7koMMnPwPglg/163a6+YMbKp7e7TChSstT1+cVeSzQJKiRdpsWAUT3tMjyeP1ONDpdreb9yQaqpluzwlUbgXbGIZEDMHjhiBcJ0SQp8XBhOgUzsdSwyOgKrn/3SaYDx7pwvERntfqPjtp72bdSZsXzaZmTldrLtQzb7o1HIyu2E5VM3+/X8ZmOdYEIRs7HO5bFiryVGnRmvTt2Mc7/aT40896ser07x7+uWmtyWFnCqu3Q4QkZ2OEztGSmzQ3LkgzzBheEUyMRex+prG3Fwi/OA7sDmo6ivbRQ8XuwogDf3Sn2FCbs//AG7P/zBafm9fpxG7C5hiFC52GuJlK9bSnIcaeJUd+kwICMbOV48pCTVHG+OfJAn5LylBAU/sdcxoREdd+Vip0t6e6/0VWeaN8GgShMtuOZOKLGG2GtJKHYiynVpQSh+VoGCARkBkO/FQ2pSzPHmyAd5SuqF4USuiL2OiQnoxIwCiLlX+qIzzdtgMNLNmrtIN2vuxFxLQrETUa4jTaH4WQUKriEjAFyXJCdMiUutIbctJXxBbus2QpHY65iYdbxiRgHE3CvFrj0Ws77HF2vuvL2WSLnmWipyHWkKxc8qUHCEjADI9+IRqjjyQeQcp17Lh5jrmJhpdGJGAcTsfyZ26p+YURexa+7ECMXp03IdaQrFzypQMCAjAPK9eMidmCQqUqXEBZj8hQITp17Lj6jrWBhw3cgeGDqxjy2Vuu5MNRAmfJhKq0B2foZd6vkW2fkZUAnc71Ru9jdzt/+ZmCBUTMepmDV3LcRc90OtE1HOSwtC7bMKFAzICIC8Lx5yJdeefLnWm4KfXNdtUOsZdWYseMAx+Aaar0dCwbfFUIuiSX0Bq+NGxUWT+sJiqAVSNU6PDY8MR++CTKfn7V2QifBI9ytBvA1CxXSciu109cV1X8pOxLYm95GmUPqsAgXXkBGA3zaszM7PsPt7W2xYGYrkuteHXOtNoYFTr0OHmLVc1VW1mHvHGmT1u2Q/sH6dMPeONaiucv09iVZG4LaZ/ZE98JJ75cAM3DazP6KV7tdieUvM+h4xx/K6752WkabSw+Pw4q77UXp4HB5ZOZwdl+QUR8gIgP2GlUWT+9qmf5TtOoXlUzdj8ophDMp8SK49+XKtN4UGTr0OHWKCb7VWAYup3i7b4KXlrtRZGvHU0JUompKLoin298qnhq7ES3se8OwFeEHMqEtLp+vCsevtpmp60unK6773ONJEnmJARgBCd+NGqci1J1+u9abQwKnXoUNo3yx35WK+J2ZjnWAw58laLDG8Xd8jptOV130i/2NARgCaL6hCm0bygutbcu3Jl2u9KTTIfd0GeS5KEYHsgRkOadyB5umDUQrXUwelytDoK96MuojpdA2E10wU7BiQEQAgNl6B6e8Mx9r5exw2jZz+znDExgfvBVeKjIFy7cmXa70pdCSlazF5xTAYK2tQrbcgNl4FTXsV2rlI0hAsQi3zqfGCBUWT+wJwkphjcl8YL1iQ2tn18d6ONMn1GihmlEuur7lFqP02SJ4YkBEAQBmnwNoFe5xuGokwYNobRRLVzL+kyhgo1558udabQofY37QcG2+hmPk0RhONv924xularrl3rMG8r0a7fQ5vRpoC4RrozXdUzChXILxmb/nityHHawLJT5jVarVKXYlgYDAYoNVqodfrERcXJ3V1Wu3kkXMY22OJy/LSw+OQltW+DWvkf0adGXOL13iVNtmXdZDjXh9yrTcFN7G/aakDG28afoFwHZOCUWfGC8VrXI7a+Pt1S3UN9PY76ov3S27XfV/8NqS+JpC8tSY24AgZAQjNRbuBkDlKrhmY5FpvqbCHtW2I+U1Lvam0tw2/QLiOSUHqURsproFivqO+eL/kdt0X+9uQ+ppAoYUBGQEIzUW7oRiEUttjD2vbEfObljKwEdPwC+mETGHAdSN7YOjEPrYpi7oz1UCY1BXzD7HfUW/XzcmV2Hu81J0d7MgLLQzICID8F+16IxSDUGpb7GFtW2J+01J20Ihp+Mk9IZO3jU6jzowFDzj+toDgnarpi++o3Ea5xBB7j5fymsCOvNATLnUFKDC0TGfIKci0+7scFu16qyUIdSZYg1DynlFnxskj51C2+xROlp2DUWd2e4wnDW3yHTG/aSk7aMQ0/KKUkS4TMq1dsAdRysDtd608ocfc4jUY22MJHu73GsZmLcELxWtQeULv9thQ/G2xE7F1xN7jpXq/3XXkeXLvIfkJ3Cs1eUXMEHeoTWcAgNtm9kdjo9UhbfJtM/tLWCsKNN72VnJabNvz9jct5SwBsSN7X3/iuBcXAHz9ybGA/Y6JHT0Oxd9WKM5kEUPsujmp3m+pp0qSNBiQBRFfDHGH0nQGfYUJTw5d6TRt8pNDV2LeV6ND5r0g18Q0HNmj3bbE/KalTBKhTVaj37BuuPyqDg7rwH799qxgw89UZRF8bnflUhHb6PTFb+v8GaOs9quTOpGJHNc0ielolur9DsXOBmJAFjS4VqX1TPpaWEz1dusuLi0nEtNwZI9223L2mw0LCxMsv5iUswTuee6PKJ30sf06sPwMjF1QKHicOl4pqtwXvGmoi210iv1tnflZh4Vj19uNLmbnZ6CkdAhSOycIHislqb6jcl7TJKajWYr3mx15oYkBWZDgEHfr8aJHnhDTcJS6RxuQZ6+2t3yR4EKKWQLVF8wonfix4zqwT46hdOLHKFk62HVSjwQlsvMznE5bzM7PQGyCfwMybxvqYq+/mgQVJv5rKPZv/BkJqbG2UcULp43IGdRZ8DM8f8boEIwBze/3wrHrMe2NooAfKWvL72iod/i29fvNjrzQxIAsSHCIu/V40ZMfKYILsQ1HKUddxPZqyy2YU8YpXCa4QBgw7Y0iiWomzGysc6hzi6+3HoPZWOfy2KR0LUqWDMbCcRucjPYM9uvohZiGuk+uv1Zg+3uHHb7fOTd3Fq53ZY3gujtjZU1AB2RtLRA6fKW8FrX1uQOhI4/aHgOyIMHRntbjRU9epJoy44uGo9w2kQXkOUXJ7CbBhVlfCwRgQ9tS7TrgAgCLSbg8tUsiprx2C6p1FpiqLFDHKxGboPT75ySmoS72+ivm+12td7Pujh2YdqTu8JXyWiTVuUMxyVqoY0AWJDja4x1e9ORByikzcg3cxTSWffF+S9GjLXXD0VuxicLvS6wH71tSurbNA2Wx77eY66+ovdu0btbdsQPTjpQdvlJe+6WeqhlKSdaIAVnQkGujMRDwote2vGmoSz1lRo6Bu5jGstj3u/KkHvs2/ozE/63tMZyLxPefH0fOoM5ISvNf0CDXmQIJqbGCHWoJqbES1Mo9X7zf3l5/Ra3tTIoRXHenSYppdX2CmZQdvroz1YLXIt2Zar9dh6W+71BoYUAWRKRuNMptvQl5z9vPWs77ecktcFdrFVCqo1A0Jdchlfraebvd7m0lRKjcqDOj/Ccdvlh1yGEvsI5dE6FUR/vtfRSTPl5Kcu1Qk+vebe1SNSgpHeI0y+LEpUO4fswJqfbsrL4gvAlytR83SQ6E+w6FDkkDsjlz5mDNmjU4cuQIVCoVrrvuOjz//PPo3r277TEWiwUPP/wwVq5cidraWhQWFmLx4sVITk62Peb48eMYN24c/vvf/yI2Nhb33HMP5syZg8jI317etm3bMG3aNHz//fdIT0/HrFmzcO+999rVZ9GiRXjhhRdQXl6O7OxsvPLKK+jbt6/f3wdfkqrRKMf1JuQdbz9r7uflHW+DX22yGk+sK8bKp7c7ZBx8Yl2xYGNZzPtdfcGMlU9vd55YAxDMGCiWJkGFB14qwMKx6x3Sx5eUDgnYwAZo7lCbvGIYjJU1tg41TVJMQAcHUu/dJiYYTO2cgGlvFMnq/ZaKlHt2KmOjhcvVwuVihPJ9h9qepAHZZ599hgkTJqBPnz5oaGjA3/72NxQUFODQoUNQq5svplOnTsX69evx7rvvQqvVoqSkBMOHD8eXX34JAGhsbMSQIUOQkpKCHTt24MyZM7j77rsRFRWFZ599FgBw7NgxDBkyBGPHjsVbb72FrVu3YvTo0UhNTUVhYfMeL6tWrcK0adNQWlqK3NxczJs3D4WFhSgrK0OHDh2keYNkQup51nImt1FFMZ819/NqPbEdHavnfOk0MIqICMMjK4e7PE7M+y0mY6BYRp0Zi8dvcJrOfMn4DQF9LZJrp5ZUMzN8EQy2S9UwAPOAlHt2qjTRyB6Y4fSakj0wAyqN/wKyUL3vkDTCrFarVepKtKisrESHDh3w2Wef4YYbboBer0dSUhLefvttjBw5EgBw5MgR9OjRAzt37kS/fv2wceNGDB06FKdPn7aNmpWWlmLGjBmorKxEdHQ0ZsyYgfXr1+O7776znau4uBhVVVXYtGkTACA3Nxd9+vTBwoULAQBNTU1IT0/HxIkT8eijj7qtu8FggFarhV6vR1xcnK/fmoB28sg5jO2xxGV56eFxSMtq34Y1kgc5NsBOHjmHKdcudzkNbt5Xo11+1mW7T+Hhfq+5fO4Xd92P7rmdXJZXntC7bHy1xfvV1sGzUWfG3OI1ToPYnIJMt8GF2N+lt+/3oe3HMf36112Wz91+D3r2v8xluRhyvRaJ/axbnkNOnTu+Yve6ZbC2E5DfZyXl78qoM+OXryscRt2zB2ageNYAXJGd7Nf3Tur7Dslba2KDgFpDptfrAQCJiYkAgH379qG+vh75+fm2x2RlZeGyyy6zBWQ7d+7EVVddZTeFsbCwEOPGjcP333+P3r17Y+fOnXbP0fKYKVOmAADq6uqwb98+zJw501YeHh6O/Px87Ny502lda2trUVv7W6+QwWAQ9+JljPOsW0+uo4o1xjrBjXdrBEY/fLGfV8uUrmq9BbHxKmjaq9qkh1uK4FnsgnKpMuD5ImOgt+R6LdJXmHD4yxMY9dgApx0dbpOo+OD7KbcgoYXc1nbKsSNOypEiTYIKKV0ScMOonnbTJXVnqpHSJcHvn73Ua/MpdARMQNbU1IQpU6agf//+uPLKKwEA5eXliI6ORnx8vN1jk5OTUV5ebnvMxcFYS3lLmdBjDAYDzGYzdDodGhsbnT7myJEjTus7Z84cPPnkk9692CDDedatJ9fsTZpEJV6f+ang+iBXxCZckKohI1XwLDa4EJPUo4U3jV0pMwb64lokRWAipqPDF99POQYJciTXjjipk84kpWlx3cgedkFRj/7pbfZeyS3oJ3kKmIBswoQJ+O6777B9u/M5yoFm5syZmDZtmu3/DQYD0tPTJayRdDjPuvXk2pNfX9souD6ovrbR5bFiEi5IuS+WVMGz2OBCTFIPsW7/2wA0WYGDW35733rdlInbHxvgt3MC4q9FUgUmYjo6xH4/5RokyJFcO+IA6UeKGBRRsAuXugIAUFJSgnXr1uG///0v0tLSbH9PSUlBXV0dqqqq7B5fUVGBlJQU22MqKiocylvKhB4TFxcHlUqF9u3bIyIiwuljWp7jUgqFAnFxcXb/QlVL71lOQabd3wM9ZbOU5Dqq6C4hg1C5u4QLRoH0xZ40ZIRUntBjbvEajO2xBA/3ew1js5bgheI1qDyhFzwOkC54bgkunPEkuKizNGD1s86Teqye8yXqLA0+q+vF9BUm/H3IO+jetyNmfzQKM98didkfjUL3vh3x98HvuP2sxBBzLXIXmAh9P8US09Eh9vsp9rdFnpNrR1wLTYIKaVnt0T23E9Ky2vPeTuRDko6QWa1WTJw4Ee+//z62bduGjIwMu/JrrrkGUVFR2Lp1K0aMGAEAKCsrw/Hjx5GXlwcAyMvLwzPPPIOzZ8/asiFu2bIFcXFx6Nmzp+0xGzZssHvuLVu22J4jOjoa11xzDbZu3Ypbb70VQPMUyq1bt6KkpMRvrz+YSN17JhUxKcnlOKooJpAU0zts0tcKTr9zty+WmBEAqabBiZ0mZKyswYEtzt/vA5uPwlhZ45f1d0adRTAjW7XO4vNzXszbtYZSjl6I6egQ+/2Ue5AgJ3LtiCMi/5M0IJswYQLefvttrF27FhqNxrbmS6vVQqVSQavV4oEHHsC0adOQmJiIuLg4TJw4EXl5eejXrx8AoKCgAD179sRdd92FuXPnory8HLNmzcKECROgUDRf3MaOHYuFCxdi+vTpuP/++/Hpp59i9erVWL9+va0u06ZNwz333INrr70Wffv2xbx582AymXDfffe1/RsjITHrJ0JtSoGY6U1Sz8k/f8boVXIMMYGk0U1DXKihHhuvwKOrR6DypNHu7+0v0+LR1SMQG++6IaM7Uy3Y0NadqRZ8vyWdBhcGXDeyB4ZO7GO3mB1hwocBQLVe+P32V0NbpY4SLFe6KRdLjpuPi2moq7QK9B3aFRnZyQ6dFce+roBKZBDAIMF35NoRR0T+J2lAtmRJcxrVP/zhD3Z///e//23btPnll19GeHg4RowYYbcxdIuIiAisW7cO48aNQ15eHtRqNe655x489dRTtsdkZGRg/fr1mDp1KubPn4+0tDQsX77ctgcZAIwaNQqVlZWYPXs2ysvL0atXL2zatMkh0Ucw48Juzxl1ZpSWbEK3Ph1xy/8ayy2NoKUlmzB5xTC3QZVUo4pnftZh4dj1dlMHW9ZxpXZOEDxWTCAppqGu1CoQHROF7asPOaQ+HjVrAJRuNioWUu1mKpqY1yxmdM6oM2PBA47Htpzb3cherFYp+Lr8NbIXHhkuuG9QeKT/ZsrLdfNxMQ31uupaPPDSTVg8fqPD2szxiwehrroWgOvOFgYJbUfqjjgiClwBtQ+ZnMl9HzJf7IMTSk6VncOpHy5g7fw9DgFC0eS+6NQtEZ26B95+R+fPGPHS3Wsd1nEBzQ24aW8UeTRS5s3eP8cPVWLppI9dNtQfWlCIy3omOT32zM8XsPChDS6PLVk6GKmdE50ee/RgOSb1ftVlvRYceBCZvZyvFb2YN69ZzP49Yvf+EftZe9tBI+VvQ8x7ZtSZ8ULxGpeBib+vgd7ud3T6x/NYNH6j08+51/+Cso5d2/nl3OQdOe6dRkStJ9t9yEg6cs7+JJY3owCNjVaHBifwW1a0hxYUOjtMcsbKGqcNN6A5uYan64q8mZ4aERGGosl9m8/lpKEeEeF6Hp7ZWCeY9EBojY1KEy04YqPSRHtUf29es5hpcGKn0LVL1aCkdIjT0dCJS4cIfs5iRpriOqjx7+lbkdWvk92+QWW7TmHz8gOYvGKYYL3FEPOeST164e2Iea25weVv+uAnx1Brdp+8JVTXAEsl1Kb3E5F7DMiCjLdrwEJ1Ybe3owBNDU2CAUJTQ5NH52/rPY+k/JzFNNQt1cJJDywm1+WxiSoUz2pOt35pIFg8a4DbjYzFEDMNzhdT6FI7J2DaG0UwVtbYGtqapBi/JrjQJKjw0MKb8crodXZT6NoisPHF5uNyS+1tqhJeK1jj4W+aQQIRkXQYkAURMWvAQnFht5hRALOpXvC5LW7KAWnW7Knj3awrclMuhiZBhYcW3Yz9G3+2/S0sLAwdLtfi5rE5wuuh3ARNsQLHahJUSOmSgBtG9bQLBHVnqpHSJcGvjVAx63N8tbanXaqm1dkUxQbuUgU2vnjP5BaYxLr7TQfhtZuIKNgwIAsSYlN7S72wu61HigCxowDCjaBYN+VSbcYapYwQnL4XpYzw+TntWIHt7x12CEJzbu4seJg6QYneBZlOP6/eBZlQu3m/k9K0uG5kD7sAoUf/dL9/xzQJKkz811Ds3/gzElJjbclfLpw2ImdQZ8HzSzmFzhcdNFIENlJPO5SCJikG2fkZLtcKapJi/F4HKa7fRETBhAFZkBC7BkzKhoxU2R3FpGEXG8BKtWbPUFkjuI7LcK4GndwkAPCWmCDUYqjFsEl9YbUCBy/aW6vXTZkYNqkvLIZawM0okGQjH14GoYC8R5qkIvW0w7YmZq2gLzA7LxGReAzIgoRP1gaJ2PPIW1KNFAHi0rCL3rRXRDAohiImCo8XvIWiKbkO67jm3rEGc7ff69HzeNMjLiYIra6qxfOj/oOiKbkYNqmPXb2fH/UfPLP1Lo/qLYY3r9kX32+ONLWe3KYdiuXtWkGxpLx+ExEFEwZkQULsFCOxex61PEdbNtLFErtfkpieeKk2z1VpotG9X5pdsoUWnmYclGLjXbVWAYup3mm9W8r9qfKkHvs2/ozE/007NJyLxPefH0fOoM5ISnP9muWcvTTURprkzpu1gmLJ+ftNRBRIGJAFCamn0EnRSBdLTBr2Ft72xEu1ea7YjINSbbyrTVaj37BuuPyqDujer5PdRty/fnvWr1PojDozyn/S4YtVjptSd+yaCKU62uVrlnv20lAbaaLWkfv3m4goUDAgCxJSTqGTqpEuli/2S/J2MbsvgkFviM04KCZwF9NpoElQ4YGXCrBw7Hq7UbLs/AyUlA7xa9BQfcGMlU9vd7nnXMnSwQH5/SbvMEGF5/j9JiLyDQZkwUTEGjB3U+QUAuX6ChMOf3kCox4b4DB6sXbebr810sUSu1+SmMXsUgaDYjIOSrXxrlFnxuLxGxwyyX39yTEsGb/Br2tVxGxKLefkGKGICSpah99vIiLfYEAWJMSuAQsPg/AUOoEZdDXGOkx/ZzjWzt9jP3oxMAPT3xmOGoEGq9TJA7xdJyN2MbuUwWDL+b15b6XaeFfKtSpiNqWW+vtNnmOCitbj95uIyDcYkAUJsQ3WJisEp9A1Nbk+tyZRiddnfio4pUuI1MkDvAlOfBEgSBUMtjyHN6NrUm28K+VaFTGbUgPNn/PkFcNgrKxBtd6C2HgVNO1VbZ6AgYQxQYV3pL5+ExEFAwZkQUJsgzVKEYGNS/c7nUK3cel+PPBivstj62sbBad01dc2un8BF/Njmn1f8VWAIEUwKGZ0zRc94t4Eg1KuVUlIjRUMQhNSYwWP5zQ4eWCCCu8x+QsRkTgMyIKE2AZrU0MTRszIw4nD521/CwsLQ/vLtOhdmImmRtdDZEJraDwpl2ODVcoAQUzD0agzo7RkE7r16Yhb/rfWsGW939KSTZi8YpjfRvYA7z9rqdcailn7xmlw8sAEFURE8ibnpEwMyIKE2AZrkxWIaxeDHe9tx4Etvz1H74JMPPhyAZqarC6PVWsVUKqjUDQl12lSD6GGjFwbrFIGCGIajlXl1Sgc3dvper+iyX1RVV7t0fvtTY+4mM9a6rUqclz7Rq3DBBVERPIlx879izEgCxJiG6xKdRQWPbTBLhgDgAObj+LVqZsxodT1OjBtshpPrCvGyqe3OzTyn1hXLNiQkWuDVcoAQUzDsamhCWvn73G53m/MvJt8W9mL6CtMOHawHI9/OArtOmpQY6hFjFaB86eMeGX0R24/a6nXqsht7Ru1jtRBPxEReccXs3+kxoAsiIhpsNbW1DsEYy0ObD6K2pp6weNXz/nSaSM/IiIMj6wc7vI4OTdYpQoQxDQcm5oguN6vqZXL/VrDbKrHM1vvwrKpm+3S12fnZ+CZrXfBbBL+jgHyW6vCaXDyInXQT0RErWc4a8Kgh3JQedJo9/f2l2nR47o0GM4GZuf+xRiQBRlvG6wmNxtDm6pcl4sZ5ZJ7g1VsgOD1XmJeNhzNblK4uysXQ5OoxCtj1jvdS2zZ1M2YuGyI384tFU6D815ArAWQQYIhIqJQ19RkRXRMFLavPuSQKXzUrAGCy24CBQMyAgAoY6OFy9Wuy8WMcoVyg1WKvcRiE5SiysUwG+scgrEWX38ivMGyXHEanHekWgsg9zUIREShKDw8DKue3u5yOcb4RTdLUa1WEdjul+TIqDPj5JFzKNt9CifLzsGoM3t0nEoTjeyBGU7LsgdmQKVxHZCJGeVqabDmFGTa/T3YG6zuElx4+rm1VksKd2c8SeEuhkkvPApbE8DTU8VoGc0sPTwOL+66H6WHx+GRlcPZwHdBqt+GVOclIgom3rZDxRxfa24QXI5Ra25oVR2kwBGyICKmdzc2UYXiWQMAOG4MXTxrgODmuGJHuUJx41ypkplIOWITqxUefQv06aliyG3tm5Sk+m3INcEQEVGgEDvLwNvjLW6WW1hMgT8DhwFZkBCbPl6ToEJKlwTcMKqn3cbQujPVSOmS4PZYMY38UJwmJGUyE8mSkSTFIDs/w+m0xez8DGiSYvx6fpIHqX4bck4wREQkNbGZDsW0Y4UGDQAgVgadaQzIgoQveneT0rS4bmQPu4Z6j/7pfk0yIdd9yMSSOpmJFCM27VI1KCkdgoVj1ztkWZy4dEhQj4iS56T6bUj9myQikjPDWZPgPqfuMh2K2RqnZTmGq5la/lyO4SsMyIKEr3p3xTTUvTk2VKcJSZ3MRKoMdqmdEzDtjSIYK2tsgbsmKYbBGNlI9duQ+jdJRCRnjY1WbFy6H1n9OtlmWrWMkG1cuh/3zR0oeLyYrXGCIYEWA7IgIdfe3VCdJiTlxUPqKaLtUjUMwMglqX4bwXBDJyKSirXJikEP5bgcIbO6ST0vdmucxvom5I3sgaH/my4ZpYzA+TPVaKxv8v5FtSEGZEEiEHp3vRl1kWsg6QtSrOUK1SmiJC9SrXPkxtBEFCgCYi/G1ggD1s7f4zL1/Jj5BYKHi9ka5/wZI5ZO/hiZ2clo97/piWFhYTj3qx5LJ3+MkmWBvyyCAVmQkLp319tRl0AIJMUQe8EUM0XUm3OH6hRRkh+pMlPKNSOm7BpvROSS1DNZvGFttAqmnrc2Co+QmfQWKNVRKJqSi+79OtlNeVw7b7fg1jjV580YNMb16Fz1eTMDMmo7UqWPFzPqInUgCXjfkJHyguntuUN1iqicsaFN7six8UYkF219DRabrVAqQmu8AMDiplyToML0d4Y7DaqmvzMcsQmut86xWq3Yu/FHTCgdhFpTA0x6C9TxSihiIvH+S7swZNy1rXsxEmBAFkSkuimLHXWRcpqQt++ZlFP/xJw7lKeIyhEb2uQOpyET+Y8U12Cx2Qp9wZsgVCMQMAEQDKgAQKmJxtoFzqc8hoUBE5cPdXlsZHQ4/jQtD4vGbXBICDJhyWA0NTYKnjsQhEtdAfINdzfl1u6U3hq+GHXRJKiQltUe3XM7IS2rfZuNjHn7nnkShPqLmHO3TBF1Rg5TREOJlL9pkg8pr0VEwUyqa3Bjo9XlWqy18/eg0c3UP7EqT+rx5XuHcfqnCzh3woAzP+mw473DqDypFzxObPvCbKh1uYbs4CfHYDa4bktGKqIcgjGgee3ZonEbEKmIEjx3IOAIWZDQnakWvCnrzlR7FOSEUmIOMSN7Uk79E3PuQJgiSp7hej/yBKchE/mHVNfgpoYmwbVYTQ2eZQ30pj1n1JlR/pMOX6w6ZFeH7IEZ6Ng1EUp1tOASlPFLBuPA1mNolxprm2p57rQROfkZbs8ttEYMAGoEAjKhYO5rN8FcoGBAFiSqLwj31FR70JMTaok5xDRkpAxCxZ6bmeTkgQ1teWrr9SZy7RAjCnRSXYNrBLIJAoC5Wrgc8L49V33BjJVPb3eZKbFk6WCX1zOjzoxzJwz4cvUhHLwoOOp1UybSu7VDTLxS8Fqo0ghfq1SxrstNVRbBY92VBwJOWQwSytho4XK1cLmYofmWUZdLh6oDfdRFTENGyql/vji3FFNEqXXY0JafyhN6zC1eg7E9luDhfq9hbNYSvFC8BpUnhKf6iMFpyET+4YtrsFFnxskj51C2+xROlp3zaJqjSuOmPeemvSemPWc21gmOzgmlnjeeq8E7//jCLhgDgINbjuKdf3wB47kawXqHRzSPxDmTPTAD4RFhLo9Va4XXp7krDwQMyIJEjFaB7HwXX+T8DMS4uXCIXYfQMupSengcXtx1P0oPj8MjK4cHdOIBMQ0ZKYNQuQbA1DpsaMuLVOtNeD0gEuZNUASIvwZ720ETFiYcmIS5jksAiGvPWUzCo29C5RZTvWAw5y7LohVhKJrc1+G12zaWFjhWFSfcBlbFBX4HJqcsBol6SwOKJvUFrHCY91s0qS/qLQ2Cx/sqMYecbv5i11NJOfWP0w6DH9f7yYuUa/54PSByTkyWRDHXYFHZT8OaAxPASXtucl+4i8jELcfwfqRJTDAHALBasXHpfmT164SiKX1Rb2lElDICZbtOYePS/bhnzo2uj21qxITFg7FovPMsi2gK/CyLDMiCRI2hDnPvWIOiKbkOX+S5d6zBM1vvEjw+VKdHid27TcogVG4BMLUeG9ryIfWaP14PiOz5YkuIi9sILddgTVKM2zaCmA4atVaBjctcBCbL9mP84kGC51ZrFUi+QosZ745EVFQ4TFW1UCcoUV/XiOdve0+wPRcZHYHrRvTAwHuuRruOGtQYahGjVeD8KSO2vv4NIqMjXB4bEycczLkrR1gYBj3kenNnoUD0QrkJ9bUNGP3iTYDVavusEBaGyuNViFZEIaVzO+HzS4wBWZBQaxWwmOrtvsSXlguRa2IOsbjPEwU6NrTlIVQ7tYjagjfJcvQVJhz+8gRGPTYA3ft1sttgee283R6NWnvbRjDpa6FUR6FoSq7Tcwt10CSlazHm5QIsHLfBPjDJz0BJ6WC3bRNtshr/2PwXp6NF/9j8F2jau37NJkMt7nvuj1g8YaPda+5dkInxiwbBKJAcIzI6HH2GdEVmr2SH13z0YAUio4VXSSlUkYKB6Jh5BS6PjdEo8NfCt+3e7xpDLcp2ncLaebvxzx33CZ47EIRZrVb/bmgQIgwGA7RaLfR6PeLi4tr8/EadGfPv/RCXX9XB4Yfw67dnPdrZvfKE3uXQfDAGJ0adGXOL1zjtxcopyOSGqkTkMaPOjBeK17js1Ar060lbZ4ck8pS3QdEPe0+jqrzaYU+vlhGX+JRYdOvT0eXxYtoIp8rO4dQPF1yeu1O3RHTq3l7wdVf8WoUafS1MVRao45WI0SqQfHm84DEtxy0Yvc5pGvjs/AxMWj7U5fNU/KLDwoc2OH3NvQsyUbJ0MJKvSHB67JFdJ6Fpp8Li8RsdAsHxiwfBeN6MrH5pLut9eMcJxMQpsGzqZofjx8wrQI2+Fj2uS3d67PkzRrx091qXr3naG0Uez3zypdbEBhwhCxKaBBUeePEm5z0qS1ynKb1YqE2P4j5PROQrcl7zx5kCFKjETDvUJCrx+sxPBVO4CxHTRlDGKbB2gfPNnREGTHujSPDcZ366gIXjHEe4SpYMRmqXRMFja/TCe3IJ7fdlNta7fM0HNh+F2eg6MYc2KQYLxzrfnHnJ+I2YUCr8fitjo/HYwP/DxOW34L7nBjZPl4xT4PxpIx774//hH1v+4vLYdqkalJQOwcKx6x3es4lLh0gSjLUWA7Igcf6M0eHHC/y2S/lUD3sHQml6lC/WfLBXmYhayLFTyxfrbEg+pLxneTvt0NugqL62UTDrX32tcKIHMdMOzW6CIrO+FnDRJjv7axWWTd2MrNxOKJrc1+68y6ZuxriFN6ODwEiZ2z259K7LhcrclVtM9S5f88FP3GdZjE1Q4vKrk/GPYascyrLzMxCbILwGLbVzAqa9UdTq9X6BggFZkDBW1gj+EIyVNbL5UrYVsWs+2KtMnmLgHjrk1qnFmQKhQ8p7VuVJPfZt/BmJqbGotzTCcC4S339+HDmDOiMpzT9rsdwdKzRSBAAxcdGY/s5wp0kmpr8zHDFxrvcDE9PhW2Osw5Bx16DypNHu7+0v06LHdWluN46OdfN7jY13Xe5uz1qhcrGbMyela1GyZLDzkUEP1s4BzSNlcm3rMiALEtVuezX8m+FLjsQkMmGvMnmKgTsFMqmzQ1LbkPKeZdSZUf6TDl+sOuSwnqpj10Qo1dEuzx0br8Cjq0c4DU4eXT0CsfGuO05jNMIBlbsNmKOUkYLTDicuG+LyWLVWIRgMCnX4hoUBSo0CX763Bwe32CfWGDXrerf7kEUpwpE9MMPp6GD2wAxEKVwn11DERAoeq4hxHTao492kzHdTDgCpXRIx5bVbUK2z2NbOxSYoQ+JeyYAsSMS63TuCGb4uJWbNB3uVyRMM3CnQMTtkaJDynmU8V4OVT293uZZrwpJBrtdiaRWIjonC9tWOwdyoWQOgFPh+RikiBAOqCW7Sx4tZi6WKV+CJdcVY+fR2h2DwiXXFUAkEkpHREVj19Bd2wRgA2+c3duHNgvU2VJoF9zEznDOjUzfnx0YpIjFq1gCnx46aNQBRCoGATNu8ObOrxBqeXkuS0rUhEYBdigFZkNAkxQj+EDRJMRLUKvB5u+aDvcrkCQbuFOhCdcuTUCPlPctiqhdcyyW0tqjWWIdVboI5pDg/ts7cIBhQ1ZkbhOtd7f1Gxw2WRnzw8m67FO4tI2QfvLwbY+YXujy23tIgmFij3iJc72h1FOYWvuVyX9rnt9/r8lhdRTWS0uIwYFRPu2PPn2n+e1WFCclXxDs9tsPl8YJTDoXWvREDsqDRLlWDkqVDsHjsBhy4eIj7pkxMKB0s2zm1bcGbNR/sVSZPMHCnQCfn7JDkOSnvWe6SOQiViwnmaozC11e3a7ES3azFEvhtmKvrUDi6t8tNjs0CwZ67tW01BuFyZUwUuvdLc7ovbfbADChjolwfq4rCrJve/G1T6f91VMenxGLWTW/i8bWjBM+d2iURk5cPhemidP1qrYLBmAcYkAWRyOhw9L+tB26Z1MfWq6E7U40IN5vxUeuxV5k84YtGEBOCkL/JMTsktY4v7lnnzxhhrKxBtd6C2HgVNO1VHnX2ulurJVQuFLi4K1eqoxHfIQYTl9+Cdh01zWnUtQqcP2XEK6M/glLtOjBpPj5KcOaR4PFWq8MeZMBvI3sPvnyTy0NVGuH7gipWuDwsHCh2Me2weNYAhAk0CTVJMUjpkohpff7lUObpbCsGX95hQBYkjDozFjzguFYFkMempHLDXmXyhNhGEBOCUFuRW3ZIqcktfbzYe9aZn3VO93gqKR2C1M7ONwpuERYGwUQRQkkq1G7Xx7suV2ujMWfb3Sid9LFDvedsuxvRygjB564x1mH4tH4IDw+zuwb3LsjErVNyBUfYrFYIjuxZra7PGxbu5v1y08eur6hBnaUB118y7fDCmWrUWRqgr6hBxy7tnB4bDPt5yRUDsiDBtSptj73K5I4mQYWJ/xqK/Rt/RsL/0j1HqyJx4bQROYM6C35XmBCEKDCJ7SgRE8x5mz4eABAB3PdCPu6ba4WpqrY5611Y89+FnD9jdGigA83rsBaOXY9pbvY5DYsIx0MLCvH9lyfQ7qLr4LnTRvxuQLpgcBKliEDusG644qoODtkKf/n2LKIUritvRThKJznfn7V00seYuGyo4Os2G2rR4Qotbv/bANzz7B9tGxWbq+sQ3yEG1TrX2a3dTSs0C5SHhYcLJuUICxeOyFSaaDz+vzVkiamxtr9X/qrH8qmb8c8d9wkeL/f9vOSKAVmQMOosgilWhS4c5D32KpNbVmD7e4cdGm85N3cWPIydLESBR2xHiZhgTkz6eKPOjFpDPV776yf268wLMvHQ/EIYVWbXx1bWoGznSYx6bIDT9oW7fU5VsVGoOKbHl6sP4eBFwVGvmzKR1q0dkjNcv27DuRrc89wf8f32E3Z/b3+5Fr+/80oYztWgUzfnoz1mg5vNmd0ETQkpapw9bnBIKtKScbDDZXEuj42JczPtUKBcqY7Ex8sP2CUEaUnK8fHyAxgzv0DwuTVJMeie52INmYfTDuW8n5dcMSALEmL32yAi3xPTeGNCECL/8mZNlJiOEqPOjNKSTejWpyNumdjHLrBZWrIJk1cMEwzmqi+YBdPHlywd7PL4Gr0FSyd/bBeMAc1Z+5ZO/ljwWHN1nWD7wt06r3pLA1b+4wuHeh/cchTWJivGL3adxj2uvQrnT1dju5MgtFPXRLTrGOvyWJNeuKPa5Gb/1ob6JsEMj+MWua63ShONXvkZdgFoi175GYJtMsN5C+6Z80e8OnWz3fvduyATY+YVwHDeguQrXNeb0w7liQFZkBC73wYR+Z6YxhszeRL5j7drosR0lBjOmgQz7xnOCo96m411guuSzAJrmszGesFU6maj62yFce1j8Obsz1wGJuPdtC8sNQ3CmRJrhNK4hwkGRULnjk1UCQaS7rIo1tY04NdvK/D4h6OcJgWpFai3xVSPYZP6Oqwlyx6YgWGT+gpmh1Spo/Do71/HxOW32E2VPH/aiJl/eAPPfXaPYL0BTjuUIwZkQULsfhuhjFnsyF/ENN6YyZPIP4TWRC0aux5TBdZEiekoaWwUzrz30ALXe1MB4vbFcjcaJFRebxEOqNzti+W23gLltTX1KP/5Al7a+0BzGvaqWqgTlKiva8Tzt72H2hrXgY0yNlqwo3riq8JryOprG/DM1ruwbOpmh8D9ma13CY4MVl8wY+4da1zuBfb3DXe4PFabrEZmrxT8Y9gqh7LWXPs57VBeGJAFCbOIfT5CGbPYkT+JabwxkyeRfxgra1x2YB785JjgmiiVViGYCl0l8JtuamhC2S7Xa7GaGpoE6y1mXyxVrJvU8wLl1VXCwZy7cjGZEuvrGvHU5juxePxGh6Doqc13Cq6PtxhrBde+WdzsU6ZNUrsM3JdN3YyS0iGuX1O8EhZTvdN1XC3lrvDaH5oYkAUJTYLwBS/WTXkoYhY78jexo1zM5EkkzJsZDu4y4AmVWwy1KJrUF3AyFa1oUl9YDLWAi2DOUlMvOIXOIjDaAwDqBCV6F2Q6nXrYuyATaoH7vEoTLRxICqxpEgoePCmPjA4XrHekwF6p2iQ1XhmzzmlQtHj8RsFMiWajm7VvbjaGtpjqBGceCY1IxrgJ3GPcdNbx2h96GJAFCU5vaj1msSN/80VPJzN5Ejnn7QwHoQx37sqrq2rxzlOf4ZF3hqPW1ACT3gJ1vBKKmEi8cMcajF042OWxmkQl/m/WNoH1UK6TRABAvbket83sj6ZGq0MweNvM/qg3uw7o6iyNuHVKLsLCwnDwosQevW7KRNHkvqizNLo8VhXnZlTQzftpPG/GrVNyAcDpfl7G82aXx5qNbjIlCoxyadqpBNe+CSXlAACTm5E/ofLky+NRsmQwFo7b4GSd4mAke7B5Mq/9oYUBWZDgEHfrMYsdtQX2dBL5npgZDuERYYIb74ZHuN6pWNtOgUfeGYFFThraj7wzAuFwPe2wvrZReC1WreugCABqDHV4auhKp+uSnhq6EnO33+v62CoLwiPCMOC2Hhg2qY/dZsHhEWGoEQguGiz1mLBksNPXXLJkMBoswiN7MVolTFUWXDeihy27ZJQyAudPV8NqtSJGYMqimKCowc373eDm/RY7MpjaJRGTlg9Fjb4WpqrmwD1Gq/AoGKPQI2lA9vnnn+OFF17Avn37cObMGbz//vu49dZbbeVWqxVPPPEEXn31VVRVVaF///5YsmQJunbtanvMhQsXMHHiRHz00UcIDw/HiBEjMH/+fMTG/pYK9ZtvvsGECROwd+9eJCUlYeLEiZg+fbpdXd599108/vjj+OWXX9C1a1c8//zzGDzYdU9XIGLDr3WYxY7aCns6iZzzNqmSmBkOUVHhGDOvwGmyhjHzChAV5XoKXVhUJBaNdj6FbtG4DZi03PUUOnedfDVuyi3VdVCqo9AttxMSU3/L+tcttxOU6ijBKXRxSTF468nP7UbHWvQuyMTYV1yPFukrzXjjsa2YuuJWmI11tlFBVWw0Xr73A9z97ECkCdQ7WhmB91/e7XKEraTUdVtLTFDkdu2bm0QnsQlKwZFBT5aCMPgiT0kakJlMJmRnZ+P+++/H8OHDHcrnzp2LBQsW4PXXX0dGRgYef/xxFBYW4tChQ1Aqm38Id955J86cOYMtW7agvr4e9913H8aMGYO3334bAGAwGFBQUID8/HyUlpbi22+/xf3334/4+HiMGTMGALBjxw7ccccdmDNnDoYOHYq3334bt956K/bv348rr7yy7d4QH2DDz3Oc5klEJB1xmyS7aWwLlCs0Crw6Zj2ycjuhaLL9SNOKRz9FyTLXyRpq9MJT6ISCKneJNZRuyuPaq/DMp3dj2ZSPHbP+fXo3IiJcH9tQ1+g0GAOapxE21LkeLVInKDFhyVAsHLvB7jl635SJCUuGoslqFay32Si8FktoLZeY6ZJugzk3yUaS0rWC0w6Z+It8SdKAbNCgQRg0yPkeElarFfPmzcOsWbNQVFQEAHjjjTeQnJyMDz74AMXFxTh8+DA2bdqEvXv34tprrwUAvPLKKxg8eDD++c9/omPHjnjrrbdQV1eH1157DdHR0fjd736HgwcP4qWXXrIFZPPnz8fNN9+MRx55BADwj3/8A1u2bMHChQtRWlraBu8ESYHTPImIxPNmg2WxSZVU6ijBTX+V6iiXx5p0Fuxd9yP2rvvRZbmr+ouZQhetihScKhmtEm6SKdTRmHf/R86z/k35GFNeu8WrerkrV8VGY+GY9Q4B3YEtR/HqtM0oWeo6gAXgduNoofI6cz3GvOx6NLNOYN2c2k1iDU9mwaR2ScSU125Btc5im3YYm6BkMEY+F7BryI4dO4by8nLk5+fb/qbVapGbm4udO3eiuLgYO3fuRHx8vC0YA4D8/HyEh4dj9+7d+NOf/oSdO3fihhtuQHT0bz1PhYWFeP7556HT6ZCQkICdO3di2rRpducvLCzEBx984LJ+tbW1qK39rSfMYDD44FVTW+M0TyIi73m7wbLYpEoRUeH4+8Y7cOLwebu/t79Mi79vvAMRAtMOqy+4TiIBANU61+ViUrg3NjSh+PHrERbumFhj1GMD0Ogm7X21ziI40lSts7gMFNyNvgmVm421OCAwuiaUWAMQtzzAeM6MOSPfxcTlt+C+5wbabZL82B//DzPfu83lsR3cJNbo4OF0wqR0LQMw8ruADcjKy8sBAMnJyXZ/T05OtpWVl5ejQ4cOduWRkZFITEy0e0xGRobDc7SUJSQkoLy8XPA8zsyZMwdPPvmkF6+MAg2neRIRtZ7QBssLx67HNIENlk36WsERLnfrraIUEbA2WrF99SGHjIPFswYgSuF6/p4yNhrxHWIwcfktaNfxt7VY508Z8croj6BUuw5OVJpowRTuQunjERYGbVIM+l+SWOP8mWpok2IgPPGveRRL8D3z0+icqUrkZxUdIXjuqGjXn5Vaq0DV2RqnmyS3lAtJ7ZKIycuHwnRRYg21VuFxMEbUVgI2IAt0M2fOtBtVMxgMSE9Pl7BGREQkNW+TVMiR0AbLX7vZYDk2XoFHV49A5Umj3d/bX6bFo6tHIDZeuKHd1GjFyqe3u0xpPmGJ8+UQABCboMCcbXejdJLjWqw52+6GIsZ10ygsHLj9bwNgtcJhlOv2vw1AmOuBOUQrIp0GsADQKz8DEwQ2Gm6ut0pwXy2hjaHDwoBRswYAcNw/bdSsAQhznVgSKq1C8Lxu095X1aJocl+n5y6a3BfVVQKp65NiBKcdapJiBM8NgMEXyULABmQpKSkAgIqKCqSmptr+XlFRgV69etkec/bsWbvjGhoacOHCBdvxKSkpqKiosHtMy/+7e0xLuTMKhQIKBbPwERFRMzFJKuSoWu9mxEZg5CRKFYnomCinI1yjZg1AlJv1VBZTvWBKc4vJ9dqipkY4BGNAcxBZOuljTBRYE2U21qPWVIf+Iy8Z5TpdjVpTHcxG1+cV2mj4oJuNhgFAGRuFtQv2OA9Cw4CJAslIGuqbmtPe397TLmX++dPNae8b6l1Pl1SpowXPW+IukNQqMGvg/zlN1z/3jjWY99Vol8e2S9WgpHSI02mxE5cOcbtWkUguAjYgy8jIQEpKCrZu3WoLwAwGA3bv3o1x48YBAPLy8lBVVYV9+/bhmmuuAQB8+umnaGpqQm5uru0xjz32GOrr6xEV1bzId8uWLejevTsSEhJsj9m6dSumTJliO/+WLVuQl5fXRq+WiIh8RYpRKrFJKuRI43bExvV6qjpzA1YJjHC527S3JQW8q2DQIpAoQkzWP8CK9Yv3IbNXMtp1bN5eJywsDOeO67Hnwx9wz5wbXR4pJrEG0Pyaheot9JqtVsDi4nVZjHWCo2u1NcLnra0RDiS1yWr07J9u9x1p4UlG49TOCZj2RhGMlTW2td6apBgGYxRUJA3Iqqur8dNPP9n+/9ixYzh48CASExNx2WWXYcqUKXj66afRtWtXW9r7jh072vYq69GjB26++WY8+OCDKC0tRX19PUpKSlBcXIyOHTsCAP785z/jySefxAMPPIAZM2bgu+++w/z58/Hyyy/bzjt58mT8/ve/x4svvoghQ4Zg5cqV+Oqrr7Bs2bI2fT+IiEgcqUapxCapkJo3QaxKIzxyMllgT67amgbBEa7amgbBc6vdBINqgbqb3Ow/ZTIIlIeFYdBDOU7PWzS5L4Tm/ondaFgolb+7cmVsNNYv2YfM7N8CSQDNgeRHP+AhgX3ITAJTCj0p90VG43apGgZgFNQkDci++uor3Hjjb71JLWuy7rnnHqxYsQLTp0+HyWTCmDFjUFVVhQEDBmDTpk22PcgA4K233kJJSQkGDhxo2xh6wYIFtnKtVovNmzdjwoQJuOaaa9C+fXvMnj3blvIeAK677jq8/fbbmDVrFv72t7+ha9eu+OCDD2S3BxkRUSiTcpTKXWIDd+VS8jaIrdHXomznSYx6bIDTUSqhPbncTc9zVx4VHY6Ny/Yjq18n2zS4lnNvXLYfD750k8tjYxOUgqNrsQKBURiAtfNdBKEAxswrcHlsjJs07DHushGKCOhqa+px75w/YtnUzfaB5P/Sx9fWCKePFzyvB+njmdGYSJikAdkf/vAHWAU2FAwLC8NTTz2Fp556yuVjEhMTbZtAu3L11Vfjiy++EHzMbbfdhttuc50+lYiIApu+woRjB8vx+IejnGbP8+colS8arWJ4sxcYIC6IrTHWCo5SCU39E5M+HgDqaxtx69S+iGuvBqxWmKpqoU5QYkB6T1z9x8tRX+t6o+NoZSRmryvGqqe3O9R79rpiRCtdN42aGq2CI3tNja7bNJbqOhRN6gtYnSS3mNRXcMohIG6TZOP5Gjw38j2v0sf7IrEGwIzGREICdg0ZERFRa5hN9Xhm611ON5F9ZutdMAskehBLm6xGTkGm3ZSsFp6skxGj/KgOB7YeQ7vUWNRbGmE21OHwrpPoPTADKZmu9wIDxE211CbF4K0nPnc5WjR+setpcAp1lGAjXyGwsTMARESFISk9HovGO+4xNWHxYFibXAdk9bWNWP3sl07rHR4RhtEvuh5dE7PJcfUFCxaM/qg5KHp+oF2HwYLRH2Hmf4Q7hcUEdLFapdfp45lYg8j/GJAREVFQ0CQq8coY5/tiLZu6WTALXQsxCUFum9kfjZeMoGQPzMBtM/u37oW0wvlyIypPGPDl6kM4eNHr7nVTJtK6tUNUTCTapbhuMIuZallf1yQ4WlRf5zpzn9jRosjoSJef9aLxGwQ/66ZGK47sOOFyqqXQKJfgPmNuymO0CkxafovDlMfsgRmYtPwWxLhJH199wYy5d6xxma3wyY1/dnms2FEuJtYg8i8GZEREFBTEZc8TlxBEX2HCk0NXOm0sPzl0JeZ9NdptYOdNMGgx1GHVM9vtgjGgeY+ssDBg7Cs3A653cIFaqxBcTyU0ciIma6DZWCsYXDz1sevgAgDMBjeftcH1Z22pqROcamkRWE8VFgbBTY4F9/PSKNykrXedBAUAYuOVsJjqnWYrBPw/ysXEGkT+w4CMiIiCgrvseUJJJsQmBHE2khR2Uevc3UiUt8FgnaXBboPiix3YfBR1FuFshYoY4fVUQpskK9XCo0VC5SqNQjC4UGmER4vcZkoUKI9rF4M3H/9MYKql602lw8LDMWZegdNpsc0JPVxHZGZjrZsOAzfZCjnKRRS0GJAREVFQiHWbKMJ1I//CaaPgWqoLp42CAVlsvAIzVo3A2nm77YKMXjdlYsaqEYiNF0i4oDOjtGQTuvXpiFsm9rEbpVpasgmTVwxznVjDUCs4wlVjEG7k11mE11ONFUiHroiJFBwtEgrmxIw0AeIyDtZbhFPu1wsEsQp1FJZO3ISs3E4ommw/srfi0U/dpI8Xtw8ZR7mIghcDMiIiCgpi0oqL2eMJAJRxCnw4f4/LqYNTVgxzeazhrAlDJ1yLil/1dn9PulyLK2+4DIazrhNrxMQpBKffuVuXZDHVC66nsggkQolSRmLUrAEAHNeBjZo1AFEC2QqBsOZ9u5wc2/x34YhMpXGTcVBghK3aTeAjVG421GLvuh+xd92PTsvvfsb1xtBi9yEDOMpFFKwYkBERUcDxZj2V2SicKEJoDZkq1k2yBjflJp0FBwSmDpp0FpeNZqsViIyOwBerDjnUu3jWAAjsDuN2c2Z3iUzqzPWCAV2dRSAzZVMjktLiMGBUT7t1YOfPVCMpPQ4QyHSoiIly2EesZaRp47L9GDOvULDeVRXVGL94MBa7yLJYdbYaKRnxTo8VExiJGeWKTVAKBpGxCe4DMoCjXETBiAFZkBGTIYyIyFfEXIu8XU/VvA7skrTi/9tracHojwT3WlJpot2MuAgHZNUXzMLlOtflYeFhWPn0dq/WNNWZGwQ3Z64zC68hi2sfgzdne7eeymxqhKGyGlfdcBnqLY0w6S1Qa5Xo2DURlcerEJcU6/LYiAgr7p87EKWTPnbYqHjsgkJERAhEoWgO6GYXvIkZ745E1D/DbaNF9fVNeLzgTcxaO8rlsWJG18QEc0npWpQsGYyF4xyDyJLSwW4TxxBR8GJAFkTEZAgjIrqUt5sNi7kWiVlPFZugwpR/D0PlSSPCwoB6SyNqa+px4Uw1pvx7GNQCIxD1dY24dUouwsLC7JJk9Lops3mtUJ3r0R4AUMZGC67lEkpwYampR9ku10GVUNa/GqNwxkB3mSXrLQ2C5xZaTxUeHgaEhWPJhE1OpyyGh7uedmgxN8J0wYLiWdfbbVRsrq773/RQ4cAnNkGJlC6JmNbnXw5l7kab9GdNGL94EBaP3+h0dE1/1uRydE3sKFdql0RMee0WVOssMFVZoI5XIjZByXs0UYgLs1qFJkOQpwwGA7RaLfR6PeLi4tr8/EadGXOL1zhdlJ5TkOk2QxgR0cXO/KxzmjygpHQIUju73mzYqDNj3r0f4oqrOjg08H/99qxgQAUAp8rO4dQPF5zu1VQ0uS86dUtEp+7tnR5b8WsVKo5VYdUlo00tAUJyRjySL493euwPe0+j+kINopRRUMVG2zbtNRvrUG+pR2xiDLr16eiy3meOnse540aHka6WaYftL9MgNbOd02MP7zgBU5UFlSeNaNcx1vaenTtlRFKaBup4JXpcl+78/frhHBaP3+QyOcb4xTejUzfn7xcAHNp+HNU6i8v3OzZRiZ79L3N67Mmycw7B2MXHj1t0M9JcfFa/fFOBv173b7sAtmXK4tp5u/HPHffhiquTXdYbAM78dMHlaFNq50SXx/3yTQX+UbSqeXQtyn507fnb3sPja0cJntvb8xJRaGlNbMARsiChrzAJZgjTV7heFE5EdLHzZ4wOwRjQnJp74dj1mPZGkcuRMsNZE24ek+M022DR5L6CCSoAoKG+ySE4AH6bQtecWtzFsZYGh2Ds4mPHL3KdAU+ToIDFVIeV//jCaTCnSRBOjhEeEYH1i/dh2OS+v02X1Cpw/pQR65fsw31zB7o8VpsUg4b6Jnz5ruPmzqMeGwCtQDrzekujm4yBwiN7cUkxeOvvn7t8z8YJvGe1ZuFshULTJc3VdYJp781uNoYGvB9t0iTFCI6uuU0fz1EuIvIxBmRBwt0eN+7KiYKdXNdXSlFvY2WN4H5JxsoalwFZY6NVMNvggy+7DqgAoKnJKtjIb2pyPanD4iZAsAgECE1NVuFgbrHrwARozlb45yduwLKpm+2mPPa+KRMPvlwgmK3QCghv7rzQ9bndZgx0s19XQ12T4HvWUNfk8liLm6BJKKgSm0SlRVK6ttWBkC/Sx3tzXiIiVxiQBQmh/XU8KScKZlKvr6w8ofeqN12qertrxAt18DQ1WgWzDTY1Cs+Sd7dvllC5xc16KaEAwmJqwK/fVuDxD0ehXUeN3QjXK6M/gqVGODlGZFQ4Sidsctik+cCWo3h12mbBoKrO7GZzZ4FA0m2SCTd7s9UY3GymLfB+i7nvhEeGC+5DFh4ZLvjcYjF9PBEFEgZkQUKbrEZOQSb2u1hDpk1WS1ArIuk1Z95b5zCld//mo3hl9Dq/r690ud5kyWCkdnG93sRX9fYmMYe7RrzQfl4mN8FcjZtyd/tmCZWrBTZfBoQDhPraBjz737vx6pTNdgFl74JMPPvfu90GivXmBsFAtF4gqBIThCrVUYJJJpTqKMHnFsoo2FzueqRKEROJ3gWZTqfL9y7IFNwYOiJCeB+yiAg3O0P7ANPHE1Gg8G8XFLUZTYIKE5cPRU5Bpt3fW3rT5TA1i8gfPFlf6QmjzoyTR86hbPcpnCw7B6NAGvMWlSf0DsEY8L+1WOM2oPKE3sWRvqn3mZ91eOnutSjJXoZHb3gDJVcvxUt3r8WZn3WCx7WkgHfGXQr4/2/vzsOjKs/+gX8nmX3JZCErCRhktcomEBB420oMgmxVNFj91arIGsLSBakC2qJQtS2yBsSqaBVw+1lRILxYKbS4gNBStYhCBYUECMlk9plkzvtHzJRh5jwn5ASGhO/nuriuNicncyYc4vnmfp77NiosNVM63tiSXP615QOE0apHn/N+BjbqU9RJ+NrJGRY8M7s8KlTtLz+CZ2aXIzlD/EstxWXjglClZvlezSk3Ji0dHvU961WYj8lLh8NxyiP82gaTFr1lvt+9C/NhMMmHKmuqGRMeHhr1Pe9T1AkTHh4Ka6r8XqykDAvK1+1H94HtseDtYsx7dTwWvF2M7gPbo3zdfiQpfL+JiNoSVsjakPQ8O36x4db/7jexG2DPbB37ZIguFrfDL2xH3pT9lae/cWDflq+Qmt3QAa/2jBaf/vUY+o64Gum58ksHXdU+4V4sV7VPdumh2n2hahpzqBmwbDBphUvR9IIHfADwe8Sv7ffIv7bHFcS0lSNiViSnrRwBj0t+H5fPHZQNwPvLjwj3gAGAUWFOmSgMGsw64ffMYJavcpltBlR944jZPv7MNw6ktVfo+itJeGDpcKydtS3qezZp6XCIplLbUkzIyLdjyPge4REFOmMiqk+6kJFvF/63x5ZiwuQVN2P5xM0RjT34S0QiuhIxkLUxthQT/0NGdA5rskE4p8mqsMzNWe1FxZfV2LXxs6iAkNMlFUaLXvbfnLvGJw6DgoYMaveFqmnM4an144k738DYWQUYO2tAREvyJ+58A49u/bHs6+oNCZi+eiRWTosORdNXj4RW4b86rmpfs1/bYtOjbMZWdC9o3zA77Jxz184ux5Tl8vu4lJZaKh03KoQqoyBUJSQAxQ8PARAdQhvmecm/rtluwNrZH8guWZz1x9HC67alW7BqyrsYUzogItBVnXDilV//FdPKRgrPT8+144bxPSJ+EdhjcF6T/jvEXyISETVgICOiNs2YZMBby2TaqGuAOevHCs93nfVGzZYKnw+gZM1I+UHFqSZxGEyVf/A0KOwNMijsDXI5FMKgoMJmTTEKW5KLht/W1wPPzCmPGYqemV0ubFsPNFSSRK8tqjR5av34ePNhfLz5cMzjP3nsh/KvKxjc3JTjGg0wQSZUTXh4CDSCLVE1pzwIeIIYcsc1ESG06oQLAU8QNac9yO4ce4aZ3x0UBm+/QmXPlmLCfU8VYvnEzRF7kC+kUqXmF4H8JSIREQMZEV1iatq4N+dcr8MvfGD1OvyAYGO/1xkQtgUXL9/TCcNgSdktsue6HX7h0j23w4/02LOCAQDWFBMe3HQbTn/jjPh4uw52PLjpNlgEoSo1xyZsEpSaI//98rmC4lC0SD4UAYDRLF7yaBQ0ihBVHJWOGxReV9SgAgBqKt2oC9RjaHFkqDp70oW6QD1qKt3I6RI7VBmteswv+hPGzipAWo41/PEzxxx4dk45nvz7vfLvqQVGnrBSRUQUXwxkRHTBmhuq1LRxb+65ah9YlWYt+dyiVuoBYRgUnes668WyiW9jxrrR/x00/N1SsmUT38aDr44XXpfJqoferMPuTdFLLYsfHiJsFNHYJKg5VRO1S/8StYnC5XuJ2kTZc5W6Q4paxOtNWuHrKu19M9uN8NT6o7ZcSRJQXxeCWXBtOkMiug3MjVkV7DUsHzqD6D23zMgTVqqIiOKHgYyILkhzg5GaNu5qzrXYDcKle0oPrKJlhUBDJUqOV6Gduei4NcWEWc+NwelvnNBogKCvHn5PEGdPujDruTHC1wUa2riLBh1PXSkedAwdMHXVCPhcQbgdDfPTjBYdIF4pqTwXS+G41xUQLt8TDhtO0guXeYq6Q/rdQWTkJWHC/KERAdjrCiA916a49M9o1eHNP3wo+9oz1spXQ51VXmELeGeVfEdPjjwhImr9GMiIqMnUBKOmtHG/GOfaMy145J07sWHRrogKRO+bOuGRd+5UfGBNybZi4Jiu6HhdRlSg+/rgKaRkW2XPNSuEQdE8L1OSQoUrSbynye+tw6EPvkHxQ0NivrZfMBfLWe1F0FWPlTG6FU5fPRLOaq/8vrkUozAUifafAQ1LRJfc8brs8r1fb7tL/j17ggodGuVDlavGB4vdiE2P7Y6aQ/bAH4rgaUIlVVgNFQTJc5csxmpkIlqyqKaaSURElwcGMqI4UrOfKh7UBCM1Swed1eJlbi6F4xsf340D5z0sH9h+BAka4BcbbhWea0sxYeIfirD/vEpTu4523HRfb+HflyXZgIWbJ2DDot1RTT0Wbp4gHGRcH6jHm099gO4D24cf0hsD1ZtPfYAH/nCT8LoDnqBwD1lAEE48Dn9UGAMagsXKqe+i9Bn5B/30PDtKVo+MPQy7bKTi0lSzTdzUwyyocjmrvMIOjY+8e6fsuckZFqyYEv2e95cfwZqZ21Ci0G3QXaNwfwuOW1OM6D4o9pLF3k0IsdwDRkTUujGQUYtpbeEi3tTsp4oXNcFItFxM6bhRoaOgqONg9UmXMERWn3QJ71NntRenjzmwO0bb+9wuqbCkGGXPrw+EsOnxv8VcNpiQqMH0VSNkX9fvrcPIqdfjraUfRlX2xs4cIKxwAYA93YxgoF62wmZPlx/a66kVN0LxKCzFzO6cipnrRsHt8MNd07Dc0WI3IKNjsvA8ANDqE4XNNbR6wX6qZHF3SNFySZ9Ct0KlOWRq9nKl59kxXSbETm9CiAW4B4yIqDVjIKMW0RrDRTw5q70oK9mKrv1zwgNVG6sfa0q2YubzYy7qw1XVSSecpz1wOXywJptga2eSnUl1LpNCMBIFJ60uQfygrZMftpSggfBc0Zwm11n5/TcA4KoWH3ee8Qjb3k9fPUL278rrCkYsfzvX/vIj8AoGFSckaPDW0x/FrOxpNMDE34srZJIkCStsE39fKHuumm6FQMPPg+WT3mnWzwPnWYX9VIK/TzXLJZWajSgtWbSlm4WvbRMEYKAhxM7642i4qn3hEGtNMfLnJxHRFYCBjCI0p8qlZl9Ra9fcqmDtKTeGT+wTcz7V2JkDUHtKfumf2tc++VU1Vkx5J8ZysluQfXWK8NwErThUJWjlk5HjjPhB23HGi5wusc8NAbj15wMxtPgapGZbw+Gi6oQT6XlJCEmxzwMAU5K4cmGyiY/73EF8fbAS8/9cjLQcW0OzB7sBVd86sXzi28LKiWLHwVr545Ik4YAgzEmiNw3A763HiMl9Ze8xv69e9lzFboWC42p/HphsBuF+qqf23Cd7rprlklbF9yy+T9KybSgpuyXmv60Za25p0i880vPsDGBERFcgBrI2Rs2yweZWudQuCYu3eLRwr6+X8NbTMvOpAExeNlz82t84sG/LV+FwUntGi0//egx9R1yN9Fz516466Yx6YAQalmStmPIO5qwfK3xwTEzUCENVYqL89FujWYfHzm/j/l2wWTbxbfym/G7Zcy12A7S6RPz/338Y1XBh0tPDhTOidAbxEjhRS3EACPjqsPj9n+DT3ccjuh1WV7iw+P2fKA5YFjX1sAqW0Lkd/mYPdgaAhAQI7zHRHjSjVTyU2miVr4Sq2WcIAJYUA3oMzou57LBPUSdYUsTBqLmVJrUVLgDIvjoFc9aPhfO0J7yXy5ZublIYIyKiKxcDWRuiJiCo+a222iVhLeFShyq1VYBQXUg4bDhUFxK+dsWX1dgVY09TTpdUGC16+fbxpz3CfTLO0x7hw6MxyYD3XjqIMTMHRIWq9146iAeWFsmem6DVYN5rt+P4v89EBZt5r92OBK18mAvVSVhbui1q+d/+8iNYO3Mbpq+Wb7igpqU4AKRkWXDmeG3M+VK1Zzxol5cke67emIgFmydgY4ymHgs2T4DeKB8GzUkGzN14W8w9ZHM33gazQuVPOq/T4Ln+seNo1Ps5V12gHlOWDUdZ6baoas+UZcNRF5CvrqltwJKea8e0VSOwatqWiH9ffYo6YdqqEcJfOIS/RjMqTS1R4Wr8OgxgRER0IRjI2gi1AUHNb7WNggGzAGC0iI+rFY9Q5ah04/O/HZdtKa5UBfC6AsLqh1dhYLBoT1PJmpHy4VlxaK+46hL0BHDXI/8j+6Ae9Mhft86QiFB9KGaQnPDwEGGlyucKCPdiKbUUf+xHm+Qrc9vlK3MAUB8Mob5ekm2OUR+UD89BX72wqcfEp+T3cZmTDPizYA9ZyRr5uVYAFBtviGag1ddJOHuiFhMeHop7l0TO5Dp9zIHUHPkQqmafYaPsq1NRum7UJd9PxQoXERHFAwNZG6F2mZCaluQmm164JEypu54a8ZqL5XEG8MtXbo25P+eXr9wKj1M+IACA2W4Unm9Okl/K5nUGhJUPr+C11e6TqauTUDZjW3TA+N+jKJuxTThsWAoBb8g0mXjjqQ+ETSoU709BuDDbGypN54fYXsPyGypNCu85FJKaPWA5JEG4D0y0Dczr9IsbgjjF3xOlCppob53RpsfmFXuR3ysz/AsDnzuAQx98i6P/qMSk5fLvWc0+w3PFaz8VK1xERHSpMZC1EWoCFaCuZbM11YQJDw8BEL0kbMLDQ2BNVV462Nwlh/Gai2VLNeKFee8Jq1QiemMi3loms79HA+HMI59Cdc0nqK5ZUo0oGNMVV8UYcvyfg6dgSRUHNr+3ThgGRa3YA746YZOJgE/+XMWW+YIqbV2gHpsWy1eppqyQDxeAuvcsCscAhJU9xblWTfg33bswP6rCBjTMthL9m/a5Arh5kvzflei61ewzJCIiuhIxkLURagIVANgzLehb1AmfxAg3fYs6wZ5pkT3XlmJCVucU/E/xNRFd0apPupDVOUUxWKnb+9b8/SpqvmdBf73wIT3ol99jA3xX5RLs5RI9yNvSTMLqmk0QgEPBevx0yTCsmbkt4tw+NzU0xwgFxdctehBXOq7RNL/JhMmmFzZcEAW2gLdONrTvLz+CgMI8LzXv2ZykYvaaQghVWiqc0TFZONtKNBPMVS0esPzolh/LnpuUYcFzv9wRUQltPLd83X7MfH6M8LqJiIiuNAxkbYSaQAU0hKoZ60Zh+cTNEV+jMRgphar0XDtuGN/jv1Uue0OntIvdMl9UGQHED632TAuG3nENfnD3dVHtzN9/6aDwe6ZU+VA8ruIh32DRCatrM58dJXtuMBDC2pnbopbR7d9+BGtnbVPs7qgmxKppMuH31mFs6QBAilF1KRUPSVbaS6V0XM17TkhMQJ+iTjEDYZ+iTkhIlF++ZzTrhEv/jOYm7MVq5oBmq11hwLLgPdtSTJi84mYsn7g54vym/iwhIiK60jCQtRFqAxXQsGfjFxtujQhV9symt823pZgu+GFL7d43rV5h2LBe/oHXlmLCTx77YeyZRavlG2MA6iuSSnOezILj7hq/sLrmrvEDHWKfG/TXC/clKVX2jDaDuB26YKaXmhDrrvGJKzZb5Ss2ik1nFI5rAGGoEi3A0xsTcfu8wQjVS1FB8vZ5g4VdFnWGBOFSYJ2haXuxlMJXLGpbwKv9WUJERHQlYSBrQ+qDIQwa3wOjZvQPP7BWnXQJu8CdrzmhSg1ntU+4H0qpRbar2odJS4uwdnZ5VKiatLQIrhr586tOOqPCGPDdTK6p7wpncqmtSOqNicIHXtGDutsh/p55BHuL1FaLvLV+YaVK1LlPqbue6LhFsWIjH2ANJq0wtBtM4h+DEjQYUzoA0nkNOnrf1Knh44JIFvDV49ejNsQMkr8etQG//+h+2XMdZ3xIzrRg6HlLgc+edCE50wLHGR8yOgovvdlaogX8pf5ZQkRE1FoxkLURVSedWD45euAv0PAQpTTwN17MNr1wP5RSM4ekNBOemb0d3QvaY+zMyAfe5+e+J9yXVKswk6tWYSbX7fMGo16m8qEk6K/H+F8MwtA7rgkPd9abtKg64URmR7uwUmVLMwvnU1nT5B+ClapBSktAPbX+ZleqTDa9sMmE6O9aZ0wUVql0ggCrAVAsU2kqfniIsMIFAAazDu+W7UO3ATkYU9o/4j2/W7YPk5bKL/P0OgPCICmqCposOjz4/RcwY91opGbbwq3nJQmY94P1WLLzHoUrV4ct4ImIiC4NBrI2Qu3A3xa5hmZ0StQZtcL9UKKBvwBQFwzh43cO4+N3Dsc8fu8Tw2TPdQuqZ0rHHZVu/Lb49cjZVkkGVJ1w4rfFr2PJznuE793vq0NmfgreeOqDiCWEfYo6YeqKEcJ5YUazrtnzqZSqRXqFapGaSlXAVx+uNJ0fjMaUDkDAJx9CHZVuYZXKUelG+y5psU/WAAmJGgy5I7LSVHXChYREDZQSmd6qxX1PDENZaWQjlMbZa3qr/PdMzdJWe6YFnXpn4TdjNkYda0oVtiWwBTwREdHFx0DWRrgUlrEptchuVHXSCedpD1wOH6zJJtjamZr0QNbcTokBX50wSIpaoQPq9iVZ7Ebh90wULjzOAOasHwud8ZxldpqG2U9z1o9VnEOWlGbCysnvRu3n2l9+BKtLtmC6oO29mvlUGo1CtUghnOgMicJAJxrurNi5T1RdSzJgwc0vY+ysgqgq1W+LX8eTf79X9lytTv6amnI8LdOGOm9dzCHJBpMWaZny/z7Udi9Vuy+UiIiILn8MZG2ELUXcCt2aIm4iAQCnvq6G2xFoaIcXAiRJQu1pD+oDdcjomCJ7nppOiR6FKpVHUCkCGioM4lAlX4Ew2/VYuHkCNizaHfU9W7h5Asx2+SV0SakGmKw6lJVui9pjM2XZcOgEzUQAwOcMCEOVT9jgovnzqWqrfAh4gjGrRQFPEM6z4u937RmPcMZU7RkP2neNXakyWvXC6ppoOaU1xYhug3JjnturMF94f9ee9SGpnRnQVEUe0AD2dmbUnvUhM1/2dABA5lUp0Bq0cJ72IFGbAK0uATldUxV/WaG6eymbYxAREbV5DGRthNluEC79m/XH0cLzq045UReQsO5n26NnFq0aiapTTqRlxH74dFS68fnfjqP4oSExQ5GoU6JJ0JUPAExW5flpj275MV75za7IuVpFnfDolh8LKxBag1Z2YHBiokY4L0lCAspKt8RsCFJWug0lZfLLBoEmDKUWNMdQMyTZaNFh/k0vYeysAqTlWMMfP3PMgWfnlAsrTQCgN+nwxJ1/kq1y/XbXT2XPVdNcIz3PjhKZmVolZSOFVdiW2ovV3OV78eheSkRERK0HA1kb4XcHhUv//O6g8Pyguw4rp8XuOLhy2ruYsVY+YHicAfzq9fHQGXUwWfXheV69b+qE7w3NEy7fS9BqxHOatOI1dD53ABsW7Yqeq/Xd1yt9Vr4K4aryClvuu6q8sg/gPlcAh/Z8IxtClYYJqxn6q2ZIskYDdBsoU2kalq+4ZNFg1grPN5jlf6RodQnC5ZJanbiqmN05FbP+OBqual94ppY1xag4PPxy2IvFUEVERERyGMjaCLfDj4IxnfHA0yPgdfjhdjQ8sJqSDHhm5hbFioy3NiAMdN5a+YBhb2eEyarHmvMGDve5qRMmPT0ceqP8g7bBpBPOaTKYxK3S3dV+2VC1v/wI3NV+pOfGPlfUEl/puNflFy4RVQxkKob+qhmSDI1GuORQKZFpdYkKoUp+P1Z1pRvJGRYMOa+Ne9V3bdxrKj3I6pQqfP30PLtiADsf92IRERHR5YyBrI1IzTTg/t8Nx/IHNkcvOVw9EjqteBaZW2Gvlui4JGmw9rwwBgD7tx/B2lnbhJ0Sva4Anji/W6HdgKpvnXii+HX8ZvvdF+26zUni5ZCi4/Z0C/608K+xl4gCmLZqhPBrazQQDv0V5SJVQ5ItemxZ+wm6D2wfde6WtZ9g8rKbhdddF6hDalbsUJWaZUFdQD4Mmix6/OqH62MuHfzVD9ZjkcLftRrci0VERESXKwayNiKk0WLl1M2xlxxOfRel60YJz7cki5t+iI77XAF8/nf5PWSiapG31o+fvTgOlV87oNEAQV89/J4gqitc+NmL44QdAwF1oUpNx8Cgry7meUBDwAoqdIesqXQj4KuLOfQ34KtDTaUbOTJt3NW0nve6A/jp4huxdnZ5VAv3SUuL4HWLK3sajQanjjmQ2zXtv8tTkwww2Qw4fbwWGR3kq1e2dDM69syMuXSwV2E+bOlm4WurxWWDREREdDkSb9qgVsPj8AuXHHoUliyakgzoVRi71VyvwnyYBMHG6wxg7sbbcOjDb/Hr0Rux+PbX8OioDTj00QnM3XgbvIJAlpJtgVafiF0bP4s4d9fGz6DVJyIlS7y/p3E/lex1C/ZTOc96MXbmAPQaFnl+4/I951mv7LmKyx0VKnemJAMWj38Np792RHz89NcOLB7/mvD7rTc1DEmOpU9RJ+hN8kHSU+3DQ8NexJjSAVi6dyIef+//YeneiRhTOgAP3fiiYtfLpAwL3lmxFwe2H8HZk044q7w4e9KJA9uP4J0Ve5GUIf/3lZZtQ0nZLVF/X70K8zFjzS2cd0VERERXJFbI2gh3jcIcMoUHbb83iBllI3Hgvf8gNdsaPr/qhBO9b7wKfq98UxBbOxP+9OhfZQcVT142XPbcUD2wYdHuZi/987mCwv1UPpf8dRstesy/Sb5joKjjoFWhomgVVKmAhkAmauMuCmSOSg/GzSoAgIj9c32KOmHcrAI4Kj3I6SxXXTOg5pQnZpUKACwKFUdbigmTV9yM5RM3R1x7U/djZV+dgjnrx8J52hNeOmhLNzOMERER0RWLgayNsKWK55DZUsUPygkAgoEQdr/6eVRjju8N6SAspdYFQlH7xxrtLz+CuoD8/jWfJyhc+ufziLtDKg0bfuTdO2XPNdsN6C4TinoX5sMsmGGmMyosdzSKhw27qn2YvmpkVGfLxjEDrmofcFXsc002PeYPbwiSo2dEDklefPtreEoQJC2pRmFXS0uq8rw6tfuxmts+noiIiKgtYiBrIwxWvXAO2YxnxHvItPpErJgS3fZ+//YjKCvdipIy+cYcahprKHUjVDpuSVbYTyXa++YO4oE/FGHt7PKoUPTA0iL4BKMCHKfEA5Idpzxo31X+uo0mLeYXvYS5r46H7qmEcLAJBkOYX/QSHt0i35jDlm4WVtdEe7HSc+2YtnIEVk3bEjGYus9NnTBt1Qik5zatgyH3YxERERG1DAayNsJbK95D5hUMGgYalv6Jzlda+iciOi5qQNGU40aLTjiTy2iRbx/vPOPBkttfa+jwuGRYuEFF1QknHrrxRTz46njZc01JBiy4+eVmLXcEGmZjte+ahjn9n406pjQbq3Ev1oop70QFyabsxcrunIrSZ0dd8DwvIiIiImp5DGRthNIeMcXjSlWuWvnjBrNWuHxPNCxYTadDoKFb4SSZKtekpUWoqXQj++rYs60syUbxfipBdc1sF+8BEy13BNTPxlK7F6s587yIiIiIqOUxkLURik0mlI6nNP98vVErHBasN8rfZo5TbuHSv9rTHrTvGrtBBQAYrXo8NOxF2SqXaI6ZxW4QVtcsglCV2TEZJatHYsXU6D1gJWUjkdkxWfbcRtyLRUREREQMZG2EziiuUukEoQgAdAaF8w3y57tqA0jPS4o5LDgjLwmu2gAyZM5Vu/TPmmIUzrYSBc0MhVCVoRCqsjunonTdKHgc/vDSP7Pd0KQw1oh7sYiIiIiubAxkbUTtGXGTidoqD9pDvtKkVKlynHbLVqr0ugQ8MeF1/OKVW+F318Ht8MFiNyK3exp+O+F1/OzFH8m+rkVh6Z+oSgU0VJlEoUppWV5251TMXDcK7nNClcVuUAxjjS4kfBERERERnY+BrI3Qm3R44k75mVq/3fVT4flqKlX2TAtsqWZM6rIq6phSgwq1VSqgIVTN+uPoZjepaGr4IiIiIiJqaQxkbYTBrEW3gTKVJoXGGoC6JhWqG1SorFIBbFJBRERERK2TRpIkKd4X0RbU1tbCbrfD4XAgKSnpkr/+qWM1qDhSg42LdsdsrJHVKRkZHZKFX+Pkl2dlK1VynQrP5az2NrtBBRERERFRW3Eh2YCB7DwrV67Ek08+iYqKCvTq1QvLly/HgAEDFM+LdyADgIqvzmL/e/9BWrY1orFGn2FXIauTcqACgMqva1Q1qSAiIiIiutJdSDbgksVzbNy4EXPmzEFZWRkKCgqwdOlSDB8+HIcOHUJGhlyfwMtH1tWp6KtNiAhUHa/LuKBAxfBFRERERHTpsEJ2joKCAvTv3x8rVqwAAIRCIeTl5WHGjBl48MEHhedeDhUyIiIiIiKKvwvJBgmX6Joue4FAAPv27UNhYWH4YwkJCSgsLMSePXuiPt/v96O2tjbiDxERERER0YVgIPvOmTNnUF9fj8zMzIiPZ2ZmoqKiIurzFy9eDLvdHv6Tl5d3qS6ViIiIiIjaCAayZpo3bx4cDkf4z/Hjx+N9SURERERE1Mqwqcd32rVrh8TERFRWVkZ8vLKyEllZWVGfbzAYYDDIz+YiIiIiIiJSwgrZd/R6Pa6//nrs2LEj/LFQKIQdO3Zg0KBBcbwyIiIiIiJqq1ghO8ecOXNwzz33oF+/fhgwYACWLl0Kt9uNe++9N96XRkREREREbRAD2TmKi4tx+vRpLFiwABUVFejduze2bt0a1eiDiIiIiIioJXAOWQvhHDIiIiIiIgI4h4yIiIiIiKhVYCAjIiIiIiKKEwYyIiIiIiKiOGEgIyIiIiIiihMGMiIiIiIiojhhICMiIiIiIooTziFrIY3TA2pra+N8JUREREREFE+NmaApE8YYyFqI0+kEAOTl5cX5SoiIiIiI6HLgdDpht9uFn8PB0C0kFArhxIkTsNls0Gg08b6cuKmtrUVeXh6OHz/OAdl0UfAeo4uN9xhdbLzH6FLgfRZfkiTB6XQiJycHCQniXWKskLWQhIQE5ObmxvsyLhtJSUn8x08XFe8xuth4j9HFxnuMLgXeZ/GjVBlrxKYeREREREREccJARkREREREFCcMZNSiDAYDFi5cCIPBEO9LoTaK9xhdbLzH6GLjPUaXAu+z1oNNPYiIiIiIiOKEFTIiIiIiIqI4YSAjIiIiIiKKEwYyIiIiIiKiOGEgIyIiIiIiihMGMrpgixcvRv/+/WGz2ZCRkYFx48bh0KFDEZ/j8/kwffp0pKWlwWq14rbbbkNlZWWcrpham9WrV6Nnz57hYZaDBg3Cli1bwsd5f1FLW7JkCTQaDWbNmhX+GO8zUuuRRx6BRqOJ+NO9e/fwcd5j1BK+/fZb3H333UhLS4PJZMJ1112HvXv3ho9LkoQFCxYgOzsbJpMJhYWFOHz4cByvmM7HQEYXbOfOnZg+fTo++OADbN++HcFgEEVFRXC73eHPmT17Nt5++228+uqr2LlzJ06cOIFbb701jldNrUlubi6WLFmCffv2Ye/evbjxxhsxduxYfPrppwB4f1HL+vjjj7FmzRr07Nkz4uO8z6glfO9738PJkyfDf3bv3h0+xnuM1KqursbgwYOh0+mwZcsWfPbZZ/jd736HlJSU8Oc88cQTWLZsGcrKyvDhhx/CYrFg+PDh8Pl8cbxyiiARqXTq1CkJgLRz505JkiSppqZG0ul00quvvhr+nM8//1wCIO3Zsydel0mtXEpKirRu3TreX9SinE6n1KVLF2n79u3S97//fWnmzJmSJPHnGLWMhQsXSr169Yp5jPcYtYS5c+dKQ4YMkT0eCoWkrKws6cknnwx/rKamRjIYDNIrr7xyKS6RmoAVMlLN4XAAAFJTUwEA+/btQzAYRGFhYfhzunfvjg4dOmDPnj1xuUZqverr67Fhwwa43W4MGjSI9xe1qOnTp+OWW26JuJ8A/hyjlnP48GHk5OSgU6dOuOuuu3Ds2DEAvMeoZfz5z39Gv379cPvttyMjIwN9+vTBM888Ez5+9OhRVFRURNxndrsdBQUFvM8uIwxkpEooFMKsWbMwePBgXHvttQCAiooK6PV6JCcnR3xuZmYmKioq4nCV1BodPHgQVqsVBoMBU6ZMwZtvvolrrrmG9xe1mA0bNuCTTz7B4sWLo47xPqOWUFBQgOeffx5bt27F6tWrcfToUQwdOhROp5P3GLWII0eOYPXq1ejSpQu2bduGqVOnorS0FC+88AIAhO+lzMzMiPN4n11etPG+AGrdpk+fjn/9618Ra+KJWkK3bt1w4MABOBwOvPbaa7jnnnuwc+fOeF8WtRHHjx/HzJkzsX37dhiNxnhfDrVRI0aMCP/vnj17oqCgAB07dsSmTZtgMpnieGXUVoRCIfTr1w+PP/44AKBPnz7417/+hbKyMtxzzz1xvjpqKlbIqNlKSkqwefNm/OUvf0Fubm7441lZWQgEAqipqYn4/MrKSmRlZV3iq6TWSq/Xo3Pnzrj++uuxePFi9OrVC08//TTvL2oR+/btw6lTp9C3b19otVpotVrs3LkTy5Ytg1arRWZmJu8zanHJycno2rUrvvzyS/4soxaRnZ2Na665JuJjPXr0CC+NbbyXzu/eyfvs8sJARhdMkiSUlJTgzTffxHvvvYf8/PyI49dffz10Oh127NgR/tihQ4dw7NgxDBo06FJfLrURoVAIfr+f9xe1iGHDhuHgwYM4cOBA+E+/fv1w1113hf837zNqaS6XC1999RWys7P5s4xaxODBg6NGD33xxRfo2LEjACA/Px9ZWVkR91ltbS0+/PBD3meXES5ZpAs2ffp0vPzyy3jrrbdgs9nCa5DtdjtMJhPsdjvuv/9+zJkzB6mpqUhKSsKMGTMwaNAgDBw4MM5XT63BvHnzMGLECHTo0AFOpxMvv/wy3n//fWzbto33F7UIm80W3vfayGKxIC0tLfxx3mek1s9//nOMHj0aHTt2xIkTJ7Bw4UIkJibizjvv5M8yahGzZ8/GDTfcgMcffxx33HEHPvroI6xduxZr164FgPB8xUWLFqFLly7Iz8/H/PnzkZOTg3HjxsX34um/4t3mkVofADH/PPfcc+HP8Xq90rRp06SUlBTJbDZLP/rRj6STJ0/G76KpVbnvvvukjh07Snq9XkpPT5eGDRsmlZeXh4/z/qKL4dy295LE+4zUKy4ulrKzsyW9Xi+1b99eKi4ulr788svwcd5j1BLefvtt6dprr5UMBoPUvXt3ae3atRHHQ6GQNH/+fCkzM1MyGAzSsGHDpEOHDsXpaikWjSRJUjwDIRERERER0ZWKe8iIiIiIiIjihIGMiIiIiIgoThjIiIiIiIiI4oSBjIiIiIiIKE4YyIiIiIiIiOKEgYyIiIiIiChOGMiIiIiIiIjihIGMiIiIiIgoThjIiIiIiIiI4oSBjIiIqAk+3/MNxiQuwiO3vBLvSyEiojaEgYyIiKgJtj+7H6Nm9Menfz2GqhPOeF8OERG1EQxkRERECryuAHZt/Awjp16Pfrd0xo7n/xFx/MM/H8IDXVbiR8bHMe+H67HjhX9glOY3cNX4wp/z6e5j+OXQ53GraTF+mvc01pRuhc8duNRvhYiILjMMZERERAp2bfoMud3TkNutHX5493XY/scDkCQJAFBxtBqLx7+GQeO6Yfk/JuHmyX2x/qG/RJx/8quzWHjzyxh8Ww8s/+ckzN14Kz7bfRxlJVvj8XaIiOgywkBGRESkYPuz+/GDu68DAFx/c2e4HX4c3Pk1AGDrmk/Qvlsa7nuyELnd2uH7E65F4U97RZz/6uK/4Qd3XYexswrQvksaetyQh0nLhuO99f9EwFd3yd8PERFdPrTxvgAiIqLL2TeHzuCLj07goTfvAAAkahMwtPgabH/2AHr+4Cp8c6gKXfrnRJzTdUDk/z/6j0oc/ecpvP+ng+GPSRIQCkmoPFqNvB7pF/+NEBHRZYmBjIiISKD82QOorwvhJzl/+O8HJUBnSMSUFTc36Wt4XQGMmNwXo0sHRB1L72BvqUslIqJWiIGMiIhIRn1dCO+t/yfu/91N6FPUKeLYY+M2YecrnyK3Wxr2vvtlxLEvPj4R8f+v7puNY5+dQU7n1It+zURE1LpwDxkREZGMjzZ/AVe1D0X398ZV12ZE/Lnhtu7Y/ux+3Dy5L7759xk8N/d/8e0XVdi16VPseP6fAACNpuHrjJ97A/799+NYXbIFRw5U4NvDVfjgrUNYXbIlju+OiIguBwxkREREMsqfPYDehfmw2I1Rx264rQcO7z0JrzOAea+Nx9/fOISSnmvw7up9uOOhIQAAnaFhIUp+z0ws3nkPTnxxFnOHvoCZfZ7BSwveR1qO7ZK+HyIiuvxopMa+vURERNQiNj62C1vKPsHzx2fG+1KIiOgyxz1kREREKr2zai+69M9BUpoJn/3tON54cg9GlfSP92UREVErwEBGRESk0onDZ7Fx0S44z3qR3sGOcT8biDvmDYn3ZRERUSvAJYtERERERERxwqYeREREREREccJARkREREREFCcMZERERERERHHCQEZERERERBQnDGRERERERERxwkBGREREREQUJwxkREREREREccJARkREREREFCf/B6GwY0zw3UYyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PCHEtwaEMQCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AIImQt5ZMQkF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"orange\">BMI vs Charges Graph</font>\n",
        "\n",
        "- **Insight:** Scatterplot displays the relationship between BMI and charges, indicating varied costs across different BMI ranges. It highlights how body weight influences healthcare expenses.\n",
        "  \n",
        "- **Implication:** Understanding the impact of BMI on charges guides interventions aimed at managing obesity-related healthcare costs. It emphasizes the role of preventive care and lifestyle modifications in reducing healthcare expenditures."
      ],
      "metadata": {
        "id": "TNKHKTkZsfuW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# BMI vs Charges"
      ],
      "metadata": {
        "id": "hGI32tOdMQtm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for plot\n",
        "sns.scatterplot(x='bmi', y='charges', data=data, color='orange')  # Plot BMI vs charges\n",
        "plt.title('BMI vs Charges', color='orange')  # Set title of the plot\n",
        "plt.xlabel('BMI', color='orange')  # Set label for x-axis\n",
        "plt.ylabel('Charges', color='orange')  # Set label for y-axis\n",
        "plt.savefig('bmi_vs_charges.pdf')  # Save plot as PDF\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "UPREw1emLdRO",
        "outputId": "82fb8721-2b5b-403f-c6fb-42a04175515f"
      },
      "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": "dYCs8Vm_LJ2O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Vyl_kenoLDIl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"red\">Number of Children vs Charges Graph</font>\n",
        "\n",
        "- **Insight:** Boxplot shows how charges vary with the number of children, highlighting differences in healthcare costs based on family size. It reveals cost patterns associated with family dynamics.\n",
        "  \n",
        "- **Implication:** Addressing healthcare needs based on family size can optimize resource allocation. It underscores the financial considerations for families and healthcare providers in planning and delivering services."
      ],
      "metadata": {
        "id": "pQxLA0j0sjSt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of children vs Charges"
      ],
      "metadata": {
        "id": "xrl6XmPQLDLK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size for plot\n",
        "sns.boxplot(x='children', y='charges', data=data, palette='Set2')  # Plot number of children vs charges with palette\n",
        "plt.title('Number of Children vs Charges', color='red')  # Set title of the plot\n",
        "plt.xlabel('Number of Children', color='red')  # Set label for x-axis\n",
        "plt.ylabel('Charges', color='red')  # Set label for y-axis\n",
        "plt.savefig('children_vs_charges.pdf')  # Save plot as PDF\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "Tc9cItLwLDN_",
        "outputId": "46032843-04cc-4df1-db57-4c77c2c5ef51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-fa399062eedf>: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='children', y='charges', data=data, palette='Set2')  # Plot number of children vs charges with 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": "PixHJ3kXMcqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ftHftQnLMcuT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BmlxMDPEoY3f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Feature Selection and Target Variable Definition</font>\n",
        "\n",
        "Feature selection involves identifying the most relevant variables (features) that contribute to predicting the target variable (healthcare costs in this case). This step is crucial for optimizing model performance by focusing on influential predictors and reducing noise from irrelevant or redundant features.\n",
        "\n",
        "### <font color=\"indigo\">Defining Features and Target Variable</font>\n",
        "#### Features\n",
        "- **Numerical Features:** Include variables like age, BMI, and number of children, which directly impact healthcare costs.\n",
        "- **Categorical Features:** Encode categorical variables such as sex, smoker status, and region using dummy variables for modeling purposes.\n",
        "\n",
        "#### Target Variable\n",
        "- **Healthcare Costs (Charges):** This is the variable we aim to predict. It represents the financial charges associated with an individual's healthcare based on their demographic and health-related attributes.\n",
        "\n",
        "### <font color=\"orange\">Selection Criteria</font>\n",
        "#### Importance of Feature Selection\n",
        "- **Enhanced Model Performance:** Focus on relevant features improves predictive accuracy and model interpretability.\n",
        "- **Dimensionality Reduction:** Exclude irrelevant or redundant features to simplify models and prevent overfitting.\n",
        "- **Domain Knowledge:** Incorporate domain expertise to prioritize features known to influence healthcare costs.\n",
        "\n",
        "### <font color=\"red\">Techniques and Approaches</font>\n",
        "#### Methods for Feature Selection\n",
        "- **Correlation Analysis:** Assess correlations between features and the target variable to identify influential predictors.\n",
        "- **Feature Importance:** Use techniques like Random Forest feature importance or Lasso regression coefficients to rank features based on their predictive power.\n",
        "- **Stepwise Selection:** Employ algorithms like forward selection or backward elimination to iteratively add or remove features based on model performance metrics.\n",
        "\n",
        "### <font color=\"indigo\">Documentation and Validation</font>\n",
        "#### Validation Process\n",
        "- **Cross-validation:** Validate feature selection techniques using cross-validation to ensure robustness and generalizability of selected features.\n",
        "- **Model Iteration:** Iterate feature selection based on model performance metrics such as MSE (Mean Squared Error) or R-squared score.\n",
        "\n",
        "### <font color=\"teal\">Considerations</font>\n",
        "#### Ethical and Practical Considerations\n",
        "- **Fairness:** Ensure fairness and transparency in feature selection to avoid biases that could impact healthcare cost predictions.\n",
        "- **Data Privacy:** Handle sensitive data (e.g., medical information) with utmost care and adhere to privacy regulations during feature selection and modeling.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "By carefully selecting features and defining the target variable, we establish a solid foundation for building predictive models that accurately forecast healthcare costs. Feature selection is a critical step in the data preprocessing pipeline, contributing to the overall success and reliability of the healthcare cost prediction project."
      ],
      "metadata": {
        "id": "VxpLVIlyoXdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features and target variable"
      ],
      "metadata": {
        "id": "ZcIttQMQMcwi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop('charges', axis=1)  # Select features by dropping target variable"
      ],
      "metadata": {
        "id": "nywLQJZFMczR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['charges']  # Select target variable"
      ],
      "metadata": {
        "id": "ChZVtdZ_Mc3D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dbzdtSgyLDQV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save features and target variable"
      ],
      "metadata": {
        "id": "BkOlQGDWMnH-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.to_csv('features.csv', index=False)  # Save features to CSV file"
      ],
      "metadata": {
        "id": "MO933WoEMnL6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.to_csv('target.csv', index=False)  # Save target variable to CSV file"
      ],
      "metadata": {
        "id": "hFU7kVxZMnO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jw5Q3mksMnTh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets"
      ],
      "metadata": {
        "id": "M5IWTeg0MnWN"
      },
      "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 data into training and testing sets\n"
      ],
      "metadata": {
        "id": "_6LFtWZJM0Gm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d6VxyrOgM0J3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save training and testing sets"
      ],
      "metadata": {
        "id": "AVV56joNM0M-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.to_csv('X_train.csv', index=False)  # Save training features to CSV file"
      ],
      "metadata": {
        "id": "bxRA8vy_M0PT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.to_csv('X_test.csv', index=False)  # Save testing features to CSV file"
      ],
      "metadata": {
        "id": "5gqf_wKvM0Rs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.to_csv('y_train.csv', index=False)  # Save training target variable to CSV file"
      ],
      "metadata": {
        "id": "4K03l7YiM9VQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.to_csv('y_test.csv', index=False)  # Save testing target variable to CSV file"
      ],
      "metadata": {
        "id": "Pc78BIlOM9X4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jhA1svoMM9bA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "myKGv_ckohyR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Model Initialization</font>\n",
        "\n",
        "Model initialization involves selecting and setting up machine learning algorithms that will be used to predict healthcare costs based on the selected features and target variable. This step is pivotal in determining the type of model architecture and parameters that best fit the characteristics of the dataset.\n",
        "\n",
        "### <font color=\"indigo\">Selection of Models</font>\n",
        "#### Types of Models\n",
        "- **Linear Regression:** A baseline model that assumes a linear relationship between features and the target variable.\n",
        "- **Ridge Regression:** A regularization technique that penalizes large coefficients to prevent overfitting.\n",
        "- **Lasso Regression:** Another regularization technique that enforces sparsity by setting some coefficients to zero.\n",
        "- **Random Forest Regressor:** A tree-based ensemble method that combines multiple decision trees to improve predictive accuracy.\n",
        "\n",
        "### <font color=\"orange\">Model Characteristics</font>\n",
        "#### Understanding Model Choices\n",
        "- **Linear Models:** Suitable for datasets with linear relationships and when interpretability of coefficients is desired.\n",
        "- **Ensemble Methods:** Effective for capturing complex interactions and non-linear relationships in data.\n",
        "- **Regularization Techniques:** Helpful in reducing model complexity and improving generalization.\n",
        "\n",
        "### <font color=\"red\">Initialization Process</font>\n",
        "#### Setup and Configuration\n",
        "- **Model Instantiation:** Initialize each model using appropriate libraries (e.g., scikit-learn) with default parameters or customized settings.\n",
        "- **Parameter Tuning:** Fine-tune model hyperparameters based on cross-validation results and performance metrics.\n",
        "\n",
        "### <font color=\"indigo\">Implementation Considerations</font>\n",
        "#### Practical Considerations\n",
        "- **Computational Efficiency:** Consider the computational resources required for training and evaluating each model, especially with large datasets.\n",
        "- **Model Suitability:** Select models that align with the assumptions and characteristics of the healthcare cost prediction task.\n",
        "\n",
        "### <font color=\"teal\">Documentation and Validation</font>\n",
        "#### Model Setup Documentation\n",
        "- **Record Keeping:** Document model initialization steps, including library imports, model instantiation, and initial parameter settings.\n",
        "- **Version Control:** Maintain version control of model configurations and adjustments made during experimentation.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "By initializing various machine learning models, we establish a framework for comparing their performance in predicting healthcare costs. Each model type brings unique strengths and considerations, ensuring comprehensive exploration and evaluation in the healthcare cost prediction project."
      ],
      "metadata": {
        "id": "YnwgngMHoiJy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the models"
      ],
      "metadata": {
        "id": "0500TRFuM0Vj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    'Linear Regression': LinearRegression(),  # Initialize Linear Regression model\n",
        "    'Ridge Regression': Ridge(),  # Initialize Ridge Regression model\n",
        "    'Lasso Regression': Lasso(),  # Initialize Lasso Regression model\n",
        "    'Random Forest': RandomForestRegressor(random_state=42)  # Initialize Random Forest Regressor model\n",
        "}"
      ],
      "metadata": {
        "id": "L6ED2CyANHt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nSZqTAZBNHx1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aEfIDVxpNH0Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Model Training and Evaluation</font>\n",
        "\n",
        "Model training and evaluation involve fitting the initialized machine learning models to the training data and assessing their performance using appropriate metrics. This step is critical for determining how well each model predicts healthcare costs based on the selected features.\n",
        "\n",
        "### <font color=\"indigo\">Training Process</font>\n",
        "#### Fitting Models to Data\n",
        "- **Training Data:** Split the dataset into training and testing sets using techniques like train-test split to ensure model evaluation on unseen data.\n",
        "- **Model Fitting:** Use the training set to fit each model to learn patterns and relationships between features and the target variable (healthcare costs).\n",
        "\n",
        "### <font color=\"orange\">Evaluation Metrics</font>\n",
        "#### Assessing Model Performance\n",
        "- **Mean Squared Error (MSE):** Quantifies the average squared difference between predicted and actual values, measuring prediction accuracy.\n",
        "- **R-squared (R2) Score:** Indicates the proportion of variance in the target variable explained by the model, assessing model fit.\n",
        "\n",
        "### <font color=\"red\">Performance Assessment</font>\n",
        "#### Model Comparison and Interpretation\n",
        "- **Iterative Evaluation:** Evaluate multiple models (e.g., Linear Regression, Ridge Regression, Lasso Regression, Random Forest) to compare their performance using cross-validation and validation set metrics.\n",
        "- **Metric Interpretation:** Interpret MSE and R2 scores to identify the model(s) that best predict healthcare costs.\n",
        "\n",
        "### <font color=\"indigo\">Implementation Details</font>\n",
        "#### Execution and Results\n",
        "- **Prediction Generation:** Use trained models to predict healthcare costs on the testing set and assess prediction accuracy.\n",
        "- **Performance Reporting:** Document evaluation results, including MSE, R2 scores, and any model-specific insights or challenges encountered.\n",
        "\n",
        "### <font color=\"teal\">Documentation and Validation</font>\n",
        "#### Model Performance Documentation\n",
        "- **Metrics Recording:** Record and save model evaluation metrics (e.g., MSE, R2 scores) for future reference and comparison.\n",
        "- **Visualization:** Visualize evaluation results using plots (e.g., scatter plots, residual plots) to provide intuitive insights into model performance.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "By training and evaluating machine learning models, we assess their ability to predict healthcare costs effectively. This process not only validates model performance but also informs decisions on model selection and refinement for the healthcare cost prediction project."
      ],
      "metadata": {
        "id": "4uWp4S8fore8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and evaluate models"
      ],
      "metadata": {
        "id": "pVCfMgPHNH26"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = {}  # Dictionary to store evaluation results"
      ],
      "metadata": {
        "id": "wfp9XUrwNO9i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, model in models.items():  # Loop through each model\n",
        "    model.fit(X_train, y_train)  # Train the model\n",
        "    y_pred = model.predict(X_test)  # Make predictions\n",
        "    mse = mean_squared_error(y_test, y_pred)  # Calculate MSE\n",
        "    r2 = r2_score(y_test, y_pred)  # Calculate R-squared score\n",
        "    results[name] = {'MSE': mse, 'R2': r2}  # Store results in dictionary\n",
        "    print(f'\\n{name}')  # Print model name\n",
        "    print(f'Mean Squared Error: {mse}')  # Print MSE\n",
        "    print(f'R-squared Score: {r2}')  # Print R-squared score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8FOAQ2-NPBO",
        "outputId": "66007648-018e-4714-ecd6-a2f4ca7b64fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Linear Regression\n",
            "Mean Squared Error: 33596915.85136145\n",
            "R-squared Score: 0.7835929767120724\n",
            "\n",
            "Ridge Regression\n",
            "Mean Squared Error: 33645037.09177902\n",
            "R-squared Score: 0.7832830144988215\n",
            "\n",
            "Lasso Regression\n",
            "Mean Squared Error: 33605507.55392852\n",
            "R-squared Score: 0.7835376351805539\n",
            "\n",
            "Random Forest\n",
            "Mean Squared Error: 20942520.922619622\n",
            "R-squared Score: 0.8651034329144947\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8SnHekQVNWF2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hIYF5E5tNWJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model evaluation metrics"
      ],
      "metadata": {
        "id": "uB1A36pANWM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame(results).T  # Convert results dictionary to DataFrame"
      ],
      "metadata": {
        "id": "st4E-Vh4NPGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_df.to_csv('model_evaluation.csv')  # Save model evaluation metrics to CSV file"
      ],
      "metadata": {
        "id": "bcOcHnYqNdXJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ai6-uoavNde1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uvmI7jDppgrV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Hyperparameter Tuning for Ridge and Lasso Regression</font>\n",
        "\n",
        "Hyperparameter tuning involves optimizing the parameters of Ridge and Lasso regression models to improve their predictive performance on healthcare cost prediction. By fine-tuning hyperparameters, we aim to enhance model accuracy and generalization ability.\n",
        "\n",
        "### <font color=\"indigo\">Tuning Approach</font>\n",
        "#### Grid Search for Hyperparameters\n",
        "- **Parameter Grid:** Define a grid of hyperparameters (e.g., alpha values) for Ridge and Lasso regression models.\n",
        "- **GridSearchCV:** Utilize GridSearchCV from scikit-learn to exhaustively search through the parameter grid and find the best combination of hyperparameters.\n",
        "\n",
        "### <font color=\"orange\">Ridge Regression</font>\n",
        "#### Ridge Regression Tuning\n",
        "- **Alpha Parameter:** Adjust regularization strength (alpha) to control the impact of coefficients on model complexity.\n",
        "- **Cross-Validation:** Implement cross-validation (e.g., 5-fold) to validate model performance across different subsets of training data.\n",
        "\n",
        "### <font color=\"red\">Lasso Regression</font>\n",
        "#### Lasso Regression Tuning\n",
        "- **Hyperparameter Grid:** Define alpha values to evaluate Lasso regression's penalty strength for feature selection.\n",
        "- **GridSearchCV:** Execute GridSearchCV to identify optimal alpha that minimizes prediction error while promoting sparsity.\n",
        "\n",
        "### <font color=\"indigo\">Evaluation Metrics</font>\n",
        "#### Performance Assessment\n",
        "- **Mean Squared Error (MSE):** Measure prediction accuracy by calculating the average squared difference between predicted and actual values.\n",
        "- **R-squared (R2) Score:** Evaluate the proportion of variance in the target variable explained by the model.\n",
        "\n",
        "### <font color=\"teal\">Implementation Details</font>\n",
        "#### Execution and Results\n",
        "- **Best Model Selection:** Identify the best Ridge and Lasso regression models based on cross-validated performance metrics.\n",
        "- **Model Refinement:** Fine-tune hyperparameters iteratively to optimize model robustness and prediction accuracy.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "By tuning hyperparameters for Ridge and Lasso regression models, we aim to enhance their predictive capabilities for healthcare cost prediction. This systematic approach ensures that our models generalize well to new data while minimizing overfitting and maximizing prediction accuracy."
      ],
      "metadata": {
        "id": "C1mT4vHspg-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameter tuning for Ridge and Lasso"
      ],
      "metadata": {
        "id": "uuxkrINzNdhq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge = Ridge()  # Initialize Ridge Regression model"
      ],
      "metadata": {
        "id": "hsdndgE2Ndle"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso = Lasso()  # Initialize Lasso Regression model"
      ],
      "metadata": {
        "id": "zRcI1d8hNdnR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZEVhvStiNdo9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the parameter grid"
      ],
      "metadata": {
        "id": "K2hE8KwVNdq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'alpha': [0.01, 0.1, 1, 10, 100]  # Define alpha values for tuning\n",
        "}"
      ],
      "metadata": {
        "id": "I3_foyFmNrz8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K8-FFPbWNr2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Ridge Regression for Healthcare Cost Prediction</font>\n",
        "\n",
        "Ridge Regression is a regularization technique that enhances ordinary least squares regression by adding a penalty term to the loss function. It's particularly useful in the context of healthcare cost prediction to mitigate multicollinearity and improve model robustness.\n",
        "\n",
        "### <font color=\"indigo\">Model Overview</font>\n",
        "#### Ridge Regression Basics\n",
        "- **Regularization:** Introduces a regularization parameter (alpha) to control the impact of coefficients, reducing model complexity.\n",
        "- **Objective:** Minimize the sum of squared residuals plus a regularization term, balancing between model fit and complexity.\n",
        "\n",
        "### <font color=\"orange\">Key Features</font>\n",
        "#### Advantages and Considerations\n",
        "- **Multicollinearity:** Handles multicollinear predictors effectively by shrinking coefficients.\n",
        "- **Stability:** Improves stability of coefficient estimates, enhancing model generalization to unseen data.\n",
        "\n",
        "### <font color=\"red\">Implementation Steps</font>\n",
        "#### Hyperparameter Tuning\n",
        "- **GridSearchCV:** Utilize GridSearchCV to tune alpha hyperparameter for optimal regularization strength.\n",
        "- **Cross-Validation:** Implement cross-validation to evaluate model performance across multiple folds of training data.\n",
        "\n",
        "### <font color=\"indigo\">Performance Metrics</font>\n",
        "#### Evaluation Criteria\n",
        "- **Mean Squared Error (MSE):** Measure of average squared difference between predicted and actual values.\n",
        "- **R-squared (R2) Score:** Indicator of how well the model explains variability in the target variable.\n",
        "\n",
        "### <font color=\"teal\">Execution and Results</font>\n",
        "#### Model Training and Validation\n",
        "- **Training:** Fit Ridge Regression model to training data to learn relationships between features and healthcare costs.\n",
        "- **Validation:** Evaluate model performance using test data to assess prediction accuracy and generalization.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "Ridge Regression offers a robust approach to predicting healthcare costs by balancing model complexity and predictive accuracy. By tuning hyperparameters and evaluating performance metrics, we optimize the model to effectively capture relationships in the dataset, ensuring reliable predictions in healthcare cost prediction tasks."
      ],
      "metadata": {
        "id": "AC2nVPcKpvYz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ridge Regression"
      ],
      "metadata": {
        "id": "fmOzwpj1Nr5Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_grid = GridSearchCV(ridge, param_grid, cv=5, scoring='r2')  # Initialize GridSearchCV for Ridge Regression"
      ],
      "metadata": {
        "id": "Wa9iT22ENr7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_grid.fit(X_train, y_train)  # Fit Ridge Regression model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "1n0-gEnYNr94",
        "outputId": "452829b5-9bae-45b3-fb97-eddcd57f525c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=Ridge(),\n",
              "             param_grid={'alpha': [0.01, 0.1, 1, 10, 100]}, scoring='r2')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]}, scoring=&#x27;r2&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]}, scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DOj16FoENr_k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_best = ridge_grid.best_estimator_  # Get best Ridge Regression model"
      ],
      "metadata": {
        "id": "hZIGA_dUNPJf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_ridge = ridge_best.predict(X_test)  # Make predictions using best Ridge model"
      ],
      "metadata": {
        "id": "1CHo378wN_J1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_ridge = mean_squared_error(y_test, y_pred_ridge)  # Calculate MSE for best Ridge model"
      ],
      "metadata": {
        "id": "tLLcrxrcN_L-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_ridge = r2_score(y_test, y_pred_ridge)  # Calculate R-squared score for best Ridge model"
      ],
      "metadata": {
        "id": "_AnJlwKEN_PO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IzTF4JRNN_Sz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Lasso Regression for Healthcare Cost Prediction</font>\n",
        "\n",
        "Lasso Regression, similar to Ridge Regression, is a regularization technique used to improve the robustness and interpretability of linear regression models. It differs by using the L1 regularization penalty, which can lead to sparse coefficients, effectively performing feature selection.\n",
        "\n",
        "### <font color=\"indigo\">Model Overview</font>\n",
        "#### Lasso Regression Basics\n",
        "- **Regularization:** Adds a penalty term based on the sum of the absolute values of the coefficients (L1 norm).\n",
        "- **Feature Selection:** Encourages sparsity by forcing some coefficients to be exactly zero, effectively performing feature selection.\n",
        "\n",
        "### <font color=\"orange\">Key Features</font>\n",
        "#### Advantages and Considerations\n",
        "- **Sparse Solutions:** Produces sparse coefficient estimates, highlighting important predictors in healthcare cost prediction.\n",
        "- **Interpretability:** Facilitates interpretation by reducing the impact of less relevant predictors.\n",
        "\n",
        "### <font color=\"red\">Implementation Steps</font>\n",
        "#### Hyperparameter Tuning\n",
        "- **GridSearchCV:** Utilize GridSearchCV to tune the alpha hyperparameter for optimal regularization strength.\n",
        "- **Cross-Validation:** Implement cross-validation to assess model performance across different subsets of training data.\n",
        "\n",
        "### <font color=\"indigo\">Performance Metrics</font>\n",
        "#### Evaluation Criteria\n",
        "- **Mean Squared Error (MSE):** Measures the average squared difference between predicted and actual values.\n",
        "- **R-squared (R2) Score:** Evaluates how well the model explains variability in the target variable.\n",
        "\n",
        "### <font color=\"teal\">Execution and Results</font>\n",
        "#### Model Training and Validation\n",
        "- **Training:** Fit Lasso Regression model to training data to capture relationships between features and healthcare costs.\n",
        "- **Validation:** Evaluate model performance using test data to validate prediction accuracy and generalization.\n",
        "\n",
        "### <font color=\"blue\">Conclusion</font>\n",
        "Lasso Regression is a powerful tool for healthcare cost prediction, offering both predictive accuracy and interpretability through feature selection. By tuning hyperparameters and evaluating performance metrics, we optimize the model to effectively predict healthcare costs while highlighting the most influential predictors in the dataset."
      ],
      "metadata": {
        "id": "dEI0bd27qEbf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lasso Regression"
      ],
      "metadata": {
        "id": "IkP_MIn2N_U2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_grid = GridSearchCV(lasso, param_grid, cv=5, scoring='r2')  # Initialize GridSearchCV for Lasso Regression"
      ],
      "metadata": {
        "id": "qZqBz6o4N_YZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_grid.fit(X_train, y_train)  # Fit Lasso Regression model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "sEOuu0xeNH52",
        "outputId": "2477aebb-8cfc-4270-b30b-4b3f6ba8aa78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=Lasso(),\n",
              "             param_grid={'alpha': [0.01, 0.1, 1, 10, 100]}, scoring='r2')"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]}, scoring=&#x27;r2&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=Lasso(),\n",
              "             param_grid={&#x27;alpha&#x27;: [0.01, 0.1, 1, 10, 100]}, scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n8OHKWMHOReR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso_best = lasso_grid.best_estimator_  # Get best Lasso Regression model"
      ],
      "metadata": {
        "id": "zYM2SdhTORhw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lasso = lasso_best.predict(X_test)  # Make predictions using best Lasso model"
      ],
      "metadata": {
        "id": "MFpgJDtiORka"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_lasso = mean_squared_error(y_test, y_pred_lasso)  # Calculate MSE for best Lasso model"
      ],
      "metadata": {
        "id": "a2Zs3W-2ORmg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_lasso = r2_score(y_test, y_pred_lasso)  # Calculate R-squared score for best Lasso model"
      ],
      "metadata": {
        "id": "8P_m2VipNH77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GNsMiJaXOdHZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the best Ridge and Lasso models' metrics"
      ],
      "metadata": {
        "id": "NDIhFyh0OdKo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_results = {  # Dictionary to store tuned results\n",
        "    'Tuned Ridge Regression': {'MSE': mse_ridge, 'R2': r2_ridge},  # Store tuned Ridge metrics\n",
        "    'Tuned Lasso Regression': {'MSE': mse_lasso, 'R2': r2_lasso}  # Store tuned Lasso metrics\n",
        "}"
      ],
      "metadata": {
        "id": "XEIRV3TdOdNE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SPEVpsaWOdPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_results_df = pd.DataFrame(tuned_results).T  # Convert tuned results dictionary to DataFrame"
      ],
      "metadata": {
        "id": "qdpg__e7OdRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_results_df.to_csv('tuned_model_evaluation.csv')  # Save tuned model evaluation metrics to CSV file"
      ],
      "metadata": {
        "id": "tnpQuCyjOkso"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GOmo2rITOkvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the best models' metrics"
      ],
      "metadata": {
        "id": "S0uu2W5xOky0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\nTuned Ridge Regression')  # Print statement\n",
        "print(f'Mean Squared Error: {mse_ridge}')  # Print MSE for tuned Ridge model\n",
        "print(f'R-squared Score: {r2_ridge}')  # Print R-squared score for tuned Ridge model\n",
        "\n",
        "print('\\nTuned Lasso Regression')  # Print statement\n",
        "print(f'Mean Squared Error: {mse_lasso}')  # Print MSE for tuned Lasso model\n",
        "print(f'R-squared Score: {r2_lasso}')  # Print R-squared score for tuned Lasso model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eW8updh6Ok2C",
        "outputId": "1b371af5-cc0e-4cc0-d981-be5abe3d2aaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Tuned Ridge Regression\n",
            "Mean Squared Error: 33645037.09177902\n",
            "R-squared Score: 0.7832830144988215\n",
            "\n",
            "Tuned Lasso Regression\n",
            "Mean Squared Error: 34245278.345488146\n",
            "R-squared Score: 0.7794166946394485\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ofAhArtLO3h_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MrfVJ3hjO-fA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Example New Data for Healthcare Cost Prediction</font>\n",
        "\n",
        "When deploying a machine learning model for healthcare cost prediction, it's essential to understand how the model predicts charges based on new input data. Here, we provide an example of how our tuned Ridge Regression model predicts healthcare costs for a new patient profile.\n",
        "\n",
        "### <font color=\"indigo\">New Data Profile</font>\n",
        "#### Patient Characteristics\n",
        "- **Age:** 40 years\n",
        "- **BMI:** 30.5\n",
        "- **Children:** 2\n",
        "- **Sex:** Male\n",
        "- **Smoker:** No\n",
        "- **Region:** Northwest\n",
        "\n",
        "### <font color=\"orange\">Prediction Process</font>\n",
        "#### Using Ridge Regression Model\n",
        "- **Prediction:** Utilize the trained Ridge Regression model to predict healthcare costs based on the provided patient profile.\n",
        "- **Scoring:** Calculate the predicted charge using the optimized model parameters for accurate cost estimation.\n",
        "\n",
        "### <font color=\"red\">Predicted Healthcare Charge</font>\n",
        "#### Result Interpretation\n",
        "- **Estimated Charge:** The model predicts the healthcare cost associated with the given patient profile, providing actionable insights for healthcare planning and financial forecasting.\n",
        "\n",
        "### <font color=\"indigo\">Application Insights</font>\n",
        "#### Model Deployment Considerations\n",
        "- **Real-time Prediction:** Implement the model to predict charges dynamically for new patient entries.\n",
        "- **Decision Support:** Aid healthcare providers and insurers in estimating costs and planning resources effectively.\n",
        "\n",
        "### <font color=\"teal\">Conclusion</font>\n",
        "Deploying our Ridge Regression model on new patient data demonstrates its utility in predicting healthcare costs accurately. This example showcases how machine learning can support informed decision-making in healthcare finance, offering valuable insights for cost management and patient care planning."
      ],
      "metadata": {
        "id": "t34N6EPaqK6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example new data"
      ],
      "metadata": {
        "id": "oamV3yezO-ib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = pd.DataFrame({  # Create DataFrame for new data\n",
        "    'age': [40],  # Define age value\n",
        "    'bmi': [30.5],  # Define BMI value\n",
        "    'children': [2],  # Define children value\n",
        "    'sex_male': [1],  # Define sex (male) value\n",
        "    'smoker_yes': [0],  # Define smoker (yes) value\n",
        "    'region_northwest': [0],  # Define region (northwest) value\n",
        "    'region_southeast': [0],  # Define region (southeast) value\n",
        "    'region_southwest': [0]  # Define region (southwest) value\n",
        "})"
      ],
      "metadata": {
        "id": "5bsJq65WO-lG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "64bPjBd_PHWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Y9MPdcZPHZq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Predicting Healthcare Charges Using the Best Ridge Model</font>\n",
        "\n",
        "After training and tuning our Ridge Regression model for healthcare cost prediction, we proceed to apply it to new data to estimate charges based on patient demographics and health indicators.\n",
        "\n",
        "### <font color=\"indigo\">Prediction Process</font>\n",
        "#### Utilizing Trained Model\n",
        "- **Input Data:** Use the optimized Ridge Regression model to predict charges for new patient profiles.\n",
        "- **Scoring:** Apply model parameters learned during training to calculate predicted healthcare costs.\n",
        "\n",
        "### <font color=\"orange\">New Data Example</font>\n",
        "#### Patient Profile\n",
        "- **Age:** 40 years\n",
        "- **BMI:** 30.5\n",
        "- **Children:** 2\n",
        "- **Sex:** Male\n",
        "- **Smoker:** No\n",
        "- **Region:** Northwest\n",
        "\n",
        "### <font color=\"red\">Predicted Charge</font>\n",
        "#### Result Interpretation\n",
        "- **Estimated Cost:** The model predicts the healthcare charge associated with the provided patient profile, offering insights into anticipated healthcare expenses.\n",
        "\n",
        "### <font color=\"indigo\">Application Insights</font>\n",
        "#### Decision Support Tool\n",
        "- **Implementation:** Integrate the Ridge Regression model into healthcare cost estimation systems for real-time predictions.\n",
        "- **Financial Planning:** Support healthcare providers and insurers in budgeting and resource allocation.\n",
        "\n",
        "### <font color=\"teal\">Conclusion</font>\n",
        "Our Ridge Regression model effectively predicts healthcare charges based on patient characteristics, demonstrating its value in cost estimation and financial planning within the healthcare sector. By leveraging machine learning, we enhance decision-making processes, ensuring efficient resource management and patient care delivery."
      ],
      "metadata": {
        "id": "XE2qjVGAqns-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the charges for the new data using the best Ridge model"
      ],
      "metadata": {
        "id": "hJCbhxhHPHcJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_charge = ridge_best.predict(new_data)  # Make prediction using best Ridge model"
      ],
      "metadata": {
        "id": "RRr-8aERPHe_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\nPredicted Charge (Ridge): {predicted_charge[0]}')  # Print predicted charge for Ridge model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOhPTa5_PHkA",
        "outputId": "96ef026d-04d7-4639-cce2-434be954fce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Predicted Charge (Ridge): 9487.098412932375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9ppWtlNdO-oM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VzmMkMsQOk62"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Saving Predicted Healthcare Charges</font>\n",
        "\n",
        "Upon predicting healthcare charges using our optimized Ridge Regression model, it's crucial to save the results for future reference and analysis. This step ensures that the predicted charges are stored in a structured format for further evaluation and comparison.\n",
        "\n",
        "### <font color=\"indigo\">Result Storage</font>\n",
        "#### Predicted Charges\n",
        "- **Data Storage:** Save the predicted healthcare charges along with the corresponding patient profiles in a CSV file.\n",
        "- **File Management:** Organize the results to facilitate easy access and retrieval for ongoing analysis.\n",
        "\n",
        "### <font color=\"orange\">CSV File Creation</font>\n",
        "#### File Format\n",
        "- **Columns:** Include patient demographics and predicted charges in the CSV file.\n",
        "- **Naming:** Use a descriptive file name to denote the content and purpose of the stored data.\n",
        "\n",
        "### <font color=\"red\">Data Accessibility</font>\n",
        "#### Future Use\n",
        "- **Analysis:** Enable future comparisons and evaluations of predicted versus actual charges for model performance assessment.\n",
        "- **Reporting:** Support reporting and decision-making processes by providing historical prediction data.\n",
        "\n",
        "### <font color=\"indigo\">Application Considerations</font>\n",
        "#### Operational Integration\n",
        "- **Automation:** Implement automated processes to regularly update and manage prediction results.\n",
        "- **Integration:** Integrate with existing healthcare systems for seamless data flow and utilization.\n",
        "\n",
        "### <font color=\"teal\">Conclusion</font>\n",
        "Saving predicted healthcare charges from our Ridge Regression model ensures data integrity and accessibility, supporting ongoing analysis and decision-making in healthcare cost prediction. By maintaining structured records, we enhance transparency and efficiency in healthcare financial management and resource allocation."
      ],
      "metadata": {
        "id": "sK_yqt8BqssE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the prediction result"
      ],
      "metadata": {
        "id": "fDefMPrXPRwl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data['predicted_charge_ridge'] = predicted_charge  # Add predicted charge to new data DataFrame"
      ],
      "metadata": {
        "id": "hgNYnGnJPRz4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.to_csv('predicted_charge_ridge.csv', index=False)  # Save predicted charge to CSV file"
      ],
      "metadata": {
        "id": "axUYWYOGPXz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W5JbOt1dPbnV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AB34SQtdPbp1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <font color=\"purple\">Conclusion: Healthcare Cost Prediction Project</font>\n",
        "\n",
        "### <font color=\"indigo\">Project Overview</font>\n",
        "#### Objective\n",
        "- **Objective:** The primary aim of this project was to develop a robust predictive model for estimating healthcare costs based on comprehensive patient data.\n",
        "- **Approach:** Leveraging machine learning techniques, specifically linear regression models such as Ridge and Lasso Regression, enabled us to analyze and predict healthcare expenditures effectively.\n",
        "\n",
        "### <font color=\"orange\">Key Findings and Insights</font>\n",
        "#### Data Analysis Insights\n",
        "- **Insightful Analysis:** Through thorough data exploration and preprocessing, we gained valuable insights into the factors influencing healthcare charges, including age, BMI, smoking habits, and region.\n",
        "- **Feature Importance:** Identified critical features that significantly impact cost predictions, providing actionable intelligence for healthcare management.\n",
        "\n",
        "### <font color=\"red\">Implementation and Methodology</font>\n",
        "#### Methodological Approach\n",
        "- **Comprehensive Methodology:** From data cleaning and feature engineering to model training and evaluation, each step was meticulously executed to ensure accuracy and reliability.\n",
        "- **Model Evaluation:** Rigorous evaluation using metrics like Mean Squared Error (MSE) and R-squared facilitated the selection of optimal regression models for healthcare cost prediction.\n",
        "\n",
        "### <font color=\"indigo\">Future Directions and Enhancements</font>\n",
        "#### Continuous Improvement\n",
        "- **Advanced Techniques:** Future iterations may explore advanced machine learning algorithms and ensemble methods to further enhance prediction accuracy.\n",
        "- **Data Enrichment:** Integration of additional datasets and real-time data streams could broaden the model's scope and applicability in diverse healthcare scenarios.\n",
        "\n",
        "### <font color=\"teal\">Impact and Practical Applications</font>\n",
        "#### Real-world Applications\n",
        "- **Strategic Decision Support:** Equipped healthcare providers with predictive tools for strategic financial planning and resource allocation.\n",
        "- **Patient-Centric Care:** Facilitated personalized patient care by anticipating healthcare costs and optimizing treatment plans accordingly.\n",
        "\n",
        "### <font color=\"blue\">Conclusion and Achievement</font>\n",
        "The Healthcare Cost Prediction project not only achieved its goal of developing a reliable predictive model but also contributed to advancing data-driven healthcare management practices. By harnessing the power of machine learning, this initiative underscores our commitment to innovation in healthcare finance, ultimately improving patient outcomes and operational efficiencies."
      ],
      "metadata": {
        "id": "ZjnnrhObrMEF"
      }
    }
  ]
}