Adult_test_csv.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": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "Yn98E5bDvsOC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Machine Learning Project: Income Prediction using Adult Dataset**\n",
        "\n",
        "## **Project Overview**\n",
        "\n",
        "### **Title**: Income Prediction using Adult Dataset\n",
        "- **Objective**: To predict whether an individual's income exceeds $50K/year based on various demographic factors using machine learning techniques.\n",
        "- **Dataset**: The dataset used is the \"Adult Income\" dataset. It contains information such as age, work class, education, marital status, occupation, race, sex, and more.\n",
        "- **Key Steps Involved**:\n",
        "  1. Data Loading\n",
        "  2. Exploratory Data Analysis (EDA)\n",
        "  3. Data Preprocessing\n",
        "  4. Handling Categorical Variables\n",
        "  5. Outlier Detection and Removal\n",
        "  6. Feature Scaling\n",
        "  7. Predictive Modeling\n",
        "  8. Regression Analysis\n",
        "  9. Clustering Analysis\n",
        "  10. Feature Engineering\n",
        "\n",
        "---\n",
        "\n",
        "## **Step-by-Step Explanations**\n",
        "\n",
        "### **1. Data Loading**\n",
        "- **Purpose**: Load the dataset into a DataFrame for analysis.\n",
        "- **Explanation**: The dataset is read using `pd.read_csv()` to check its structure and contents.\n",
        "\n",
        "### **2. Exploratory Data Analysis (EDA)**\n",
        "- **Objective**: Understand the dataset's structure and distribution.\n",
        "- **Actions**:\n",
        "  - **`df.head()`**: Displays the first few rows to get an overview of the dataset.\n",
        "  - **`df.info()`**: Provides details about the data types and missing values.\n",
        "  - **`df.describe()`**: Offers summary statistics of numerical columns.\n",
        "\n",
        "### **3. Data Visualization**\n",
        "- **Objective**: Visualize numerical and categorical variables.\n",
        "- **Explanation**:\n",
        "  - Histograms are used for numerical variables to understand their distribution.\n",
        "  - Count plots are used for categorical variables to show the frequency of each category.\n",
        "\n",
        "### **4. Missing Values Check and Handling**\n",
        "- **Purpose**: Detect and handle missing values to ensure a clean dataset.\n",
        "- **Strategy**:\n",
        "  - **`df.isnull().sum()`**: Identifies the number of missing values per column.\n",
        "  - Missing values are filled with the mean (numerical) or mode (categorical) using `fillna()`.\n",
        "\n",
        "### **5. Duplicate Values Check and Removal**\n",
        "- **Explanation**: Checking for duplicate entries using `df.duplicated().sum()`.\n",
        "- **Action**: Remove duplicate rows using `df.drop_duplicates()` to avoid skewing the analysis.\n",
        "\n",
        "### **6. Data Distribution Check**\n",
        "- **Purpose**: Visualize the distribution of numerical features.\n",
        "- **Explanation**: Histograms help detect skewness or outliers in numerical data.\n",
        "\n",
        "### **7. Correlation Analysis**\n",
        "- **Objective**: Identify relationships between features using a correlation matrix.\n",
        "- **Visualization**: A heatmap shows the correlation coefficients, indicating the strength of relationships between variables.\n",
        "\n",
        "### **8. Handling Categorical Variables**\n",
        "- **Purpose**: Convert categorical variables to numerical using One-Hot Encoding (`pd.get_dummies()`).\n",
        "- **Importance**: Many machine learning algorithms require numerical inputs.\n",
        "\n",
        "### **9. Outlier Detection and Removal**\n",
        "- **Method**: Interquartile Range (IQR) method.\n",
        "  - **Formula**:\n",
        "    - **IQR** = Q3 - Q1\n",
        "    - Lower bound: Q1 - 1.5 * IQR\n",
        "    - Upper bound: Q3 + 1.5 * IQR\n",
        "  - Outliers are identified using these bounds and removed.\n",
        "\n",
        "### **10. Feature Scaling**\n",
        "- **Purpose**: Standardize features using `StandardScaler` to ensure equal weightage across features.\n",
        "- **Importance**: Scaled features help in better model performance, especially for distance-based algorithms.\n",
        "\n",
        "### **11. Predictive Modeling**\n",
        "- **Objective**: Build a Random Forest model for classification.\n",
        "- **Explanation**:\n",
        "  - **Train-Test Split**: Data is split into training (80%) and testing (20%) sets using `train_test_split`.\n",
        "  - **Model Training**: RandomForestClassifier is trained on the data.\n",
        "  - **Prediction**: Model predicts labels on the test set.\n",
        "\n",
        "### **12. Regression Analysis**\n",
        "- **Objective**: Implement Linear Regression to predict income.\n",
        "- **Explanation**:\n",
        "  - **Model Training**: Linear Regression model is fit on training data.\n",
        "  - **Prediction**: Income predictions are made for the test data.\n",
        "\n",
        "### **13. Clustering Analysis**\n",
        "- **Objective**: Use K-Means clustering to segment data into clusters based on features.\n",
        "- **Explanation**:\n",
        "  - **K-Means Algorithm**: Groups data points into k clusters based on feature similarity.\n",
        "  - **Output**: Cluster labels indicating the segment each data point belongs to.\n",
        "\n",
        "### **14. Feature Engineering**\n",
        "- **Objective**: Create new features to enhance model performance.\n",
        "- **Example**: A new feature `Total_Capital` is created by summing `Capital_Gain` and `Capital_Loss`.\n",
        "\n",
        "### **15. Final Dataset Preparation**\n",
        "- **Explanation**: The cleaned and preprocessed dataset is saved as 'preprocessed_data.csv' for future analysis or modeling tasks.\n",
        "\n",
        "---\n",
        "\n",
        "## **Conclusion**\n",
        "This project demonstrates the end-to-end process of data analysis and machine learning, from loading and exploring the dataset to preprocessing, modeling, and evaluation. Various techniques such as handling missing values, scaling, feature engineering, and using predictive models (Random Forest, Linear Regression, K-Means Clustering) have been applied to predict income levels and uncover insights from the dataset. The final dataset is prepared and saved for further analysis."
      ],
      "metadata": {
        "id": "_4-b_WICr869"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "JiZEm550spp3"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CCjPl__YsoHQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-2AFkSe4soOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nc_XtYBmsooV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "EBwUU2Dns2Hw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📚 Import Necessary Libraries\n",
        "\n",
        "Import essential libraries for data manipulation and visualization, such as `pandas`, `numpy`, `matplotlib`, and `seaborn`. These libraries provide powerful tools for data analysis and help in visualizing data trends effectively."
      ],
      "metadata": {
        "id": "t2XIXXXeTBZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import Necessary Libraries\n",
        "# We import essential libraries for data analysis and visualization."
      ],
      "metadata": {
        "id": "VIy8fVKsGqA6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "eFXBad1RGqOH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "O-omK063tVyG"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VoEVsJD8tW1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9_t-xVuXtXKr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I6-JuqhHGqRF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "hoK7ii5YtYDt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📥 Load the Data\n",
        "\n",
        "Load the dataset from the CSV file (`adult_test.csv`) using `pd.read_csv()`. This step initializes the data into a pandas DataFrame, allowing us to manipulate and analyze the dataset easily."
      ],
      "metadata": {
        "id": "IK5cx5hbtZKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Load the Data\n",
        "# We load the dataset from a CSV file named 'adult_test.csv' into a pandas DataFrame."
      ],
      "metadata": {
        "id": "MWfPTzNqGqUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/adult_test.csv')"
      ],
      "metadata": {
        "id": "7DRvG5WhGqWb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "fvK1z9cNtdM2"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M9u_DAZ-teVu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VAsE-FriteZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mOEqUiGuGqYj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "dhOpflDbtfOV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 Inspect the Data\n",
        "\n",
        "**Why?**  \n",
        "Inspecting the dataset helps us understand its structure, identify the data types of each column, and detect potential issues like missing values.\n",
        "\n",
        "- `df.head()`: Displays the first few rows of the dataset.\n",
        "- `df.info()`: Provides information about the data types and non-null counts of each column.\n",
        "- `df.describe()`: Summarizes the statistics of numerical columns, such as mean, median, and standard deviation."
      ],
      "metadata": {
        "id": "Tzvotr8ctgLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Inspect the Data\n",
        "# We inspect the dataset to understand its structure and contents.\n",
        "# This helps us identify potential issues and plan our data preprocessing steps."
      ],
      "metadata": {
        "id": "rpHZxfY9GqbY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's take a quick look at the first few rows of our dataset to understand its structure."
      ],
      "metadata": {
        "id": "QCVhmGZCGqd1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "c9vomuLuGqgT",
        "outputId": "7ad822ef-c839-4307-b5ce-239715472734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Age   Workclass    fnlwgt      Education  Education_Num  \\\n",
              "0  |1x3 Cross validator         NaN       NaN            NaN            NaN   \n",
              "1                    25     Private  226802.0           11th            7.0   \n",
              "2                    38     Private   89814.0        HS-grad            9.0   \n",
              "3                    28   Local-gov  336951.0     Assoc-acdm           12.0   \n",
              "4                    44     Private  160323.0   Some-college           10.0   \n",
              "\n",
              "        Martial_Status          Occupation Relationship    Race    Sex  \\\n",
              "0                  NaN                 NaN          NaN     NaN    NaN   \n",
              "1        Never-married   Machine-op-inspct    Own-child   Black   Male   \n",
              "2   Married-civ-spouse                 NaN      Husband   White   Male   \n",
              "3   Married-civ-spouse     Protective-serv      Husband   White   Male   \n",
              "4   Married-civ-spouse   Machine-op-inspct      Husband   Black   Male   \n",
              "\n",
              "   Capital_Gain  Capital_Loss  Hours_per_week         Country   Target  \n",
              "0           NaN           NaN             NaN             NaN      NaN  \n",
              "1           0.0           0.0            40.0   United-States   <=50K.  \n",
              "2           0.0           0.0            50.0   United-States   <=50K.  \n",
              "3           0.0           0.0            40.0   United-States    >50K.  \n",
              "4        7688.0           0.0            40.0   United-States    >50K.  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2afb235e-e9c3-4cd1-8c0d-f4517c9103d6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>Education</th>\n",
              "      <th>Education_Num</th>\n",
              "      <th>Martial_Status</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Relationship</th>\n",
              "      <th>Race</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Capital_Gain</th>\n",
              "      <th>Capital_Loss</th>\n",
              "      <th>Hours_per_week</th>\n",
              "      <th>Country</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>|1x3 Cross validator</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>Private</td>\n",
              "      <td>226802.0</td>\n",
              "      <td>11th</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>89814.0</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>Local-gov</td>\n",
              "      <td>336951.0</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Protective-serv</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44</td>\n",
              "      <td>Private</td>\n",
              "      <td>160323.0</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>7688.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2afb235e-e9c3-4cd1-8c0d-f4517c9103d6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2afb235e-e9c3-4cd1-8c0d-f4517c9103d6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2afb235e-e9c3-4cd1-8c0d-f4517c9103d6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-424577b1-229c-497d-a034-363d9112a5ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-424577b1-229c-497d-a034-363d9112a5ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-424577b1-229c-497d-a034-363d9112a5ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 16282,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 74,\n        \"samples\": [\n          \"44\",\n          \"81\",\n          \"37\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \" Local-gov\",\n          \" Self-emp-inc\",\n          \" Private\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105714.9076708327,\n        \"min\": 13492.0,\n        \"max\": 1490400.0,\n        \"num_unique_values\": 12787,\n        \"samples\": [\n          98035.0,\n          155632.0,\n          34173.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \" 11th\",\n          \" HS-grad\",\n          \" Prof-school\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5675452586676824,\n        \"min\": 1.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          7.0,\n          9.0,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Martial_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \" Never-married\",\n          \" Married-civ-spouse\",\n          \" Married-spouse-absent\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \" Priv-house-serv\",\n          \" Transport-moving\",\n          \" Machine-op-inspct\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \" Own-child\",\n          \" Husband\",\n          \" Other-relative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Race\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \" White\",\n          \" Amer-Indian-Eskimo\",\n          \" Asian-Pac-Islander\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" Female\",\n          \" Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Capital_Gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7583.935967887084,\n        \"min\": 0.0,\n        \"max\": 99999.0,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          34095.0,\n          7298.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Capital_Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 403.10528562931455,\n        \"min\": 0.0,\n        \"max\": 3770.0,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          1258.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours_per_week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.479332247112273,\n        \"min\": 1.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          17.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \" Laos\",\n          \" India\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \" >50K.\",\n          \" <=50K.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Next, we'll check the information about each column in our dataset, including data types and missing values."
      ],
      "metadata": {
        "id": "G0R6u68TGqit"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QAqIV6TGqlY",
        "outputId": "f775d631-bb2e-4cbb-a53a-5003395e5d9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16282 entries, 0 to 16281\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             16282 non-null  object \n",
            " 1   Workclass       15318 non-null  object \n",
            " 2   fnlwgt          16281 non-null  float64\n",
            " 3   Education       16281 non-null  object \n",
            " 4   Education_Num   16281 non-null  float64\n",
            " 5   Martial_Status  16281 non-null  object \n",
            " 6   Occupation      15314 non-null  object \n",
            " 7   Relationship    16281 non-null  object \n",
            " 8   Race            16281 non-null  object \n",
            " 9   Sex             16281 non-null  object \n",
            " 10  Capital_Gain    16281 non-null  float64\n",
            " 11  Capital_Loss    16281 non-null  float64\n",
            " 12  Hours_per_week  16281 non-null  float64\n",
            " 13  Country         16007 non-null  object \n",
            " 14  Target          16281 non-null  object \n",
            "dtypes: float64(5), object(10)\n",
            "memory usage: 1.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xE5bqb6EGqnp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, we'll get a summary of the numerical columns in our dataset to understand their distribution and statistics."
      ],
      "metadata": {
        "id": "W6L-DkGaGqqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aKJnIjsFGqth",
        "outputId": "608f06a1-3d36-45cd-a2cd-59993d170a8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             fnlwgt  Education_Num  Capital_Gain  Capital_Loss  Hours_per_week\n",
              "count  1.628100e+04   16281.000000  16281.000000  16281.000000    16281.000000\n",
              "mean   1.894357e+05      10.072907   1081.905104     87.899269       40.392236\n",
              "std    1.057149e+05       2.567545   7583.935968    403.105286       12.479332\n",
              "min    1.349200e+04       1.000000      0.000000      0.000000        1.000000\n",
              "25%    1.167360e+05       9.000000      0.000000      0.000000       40.000000\n",
              "50%    1.778310e+05      10.000000      0.000000      0.000000       40.000000\n",
              "75%    2.383840e+05      12.000000      0.000000      0.000000       45.000000\n",
              "max    1.490400e+06      16.000000  99999.000000   3770.000000       99.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75d5c1a1-c135-4aa6-8b96-c852d1033460\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>Education_Num</th>\n",
              "      <th>Capital_Gain</th>\n",
              "      <th>Capital_Loss</th>\n",
              "      <th>Hours_per_week</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.628100e+04</td>\n",
              "      <td>16281.000000</td>\n",
              "      <td>16281.000000</td>\n",
              "      <td>16281.000000</td>\n",
              "      <td>16281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.894357e+05</td>\n",
              "      <td>10.072907</td>\n",
              "      <td>1081.905104</td>\n",
              "      <td>87.899269</td>\n",
              "      <td>40.392236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.057149e+05</td>\n",
              "      <td>2.567545</td>\n",
              "      <td>7583.935968</td>\n",
              "      <td>403.105286</td>\n",
              "      <td>12.479332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.349200e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.167360e+05</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.778310e+05</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.383840e+05</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.490400e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>3770.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75d5c1a1-c135-4aa6-8b96-c852d1033460')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-75d5c1a1-c135-4aa6-8b96-c852d1033460 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-75d5c1a1-c135-4aa6-8b96-c852d1033460');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e8c3e2e8-2060-4247-a81b-43e4ed45dd3f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8c3e2e8-2060-4247-a81b-43e4ed45dd3f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8c3e2e8-2060-4247-a81b-43e4ed45dd3f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 490128.216661244,\n        \"min\": 13492.0,\n        \"max\": 1490400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          189435.6777839199,\n          177831.0,\n          16281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education_Num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5753.141992134126,\n        \"min\": 1.0,\n        \"max\": 16281.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.07290706959032,\n          10.0,\n          16281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Capital_Gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34583.49041557824,\n        \"min\": 0.0,\n        \"max\": 99999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1081.9051041090843,\n          99999.0,\n          7583.935967887084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Capital_Loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5690.936870675057,\n        \"min\": 0.0,\n        \"max\": 16281.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          87.89926908666544,\n          3770.0,\n          403.10528562931455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hours_per_week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5742.24006580474,\n        \"min\": 1.0,\n        \"max\": 16281.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          16281.0,\n          40.392236349118605,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "QnQK2Y7Htlh2"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FVUI52_9tmif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0cIb50NbGqvz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "loXOF0deGqyC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "x_VCbH4gtnHB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 Exploratory Data Analysis (EDA)\n",
        "\n",
        "**Why?**  \n",
        "EDA is crucial for understanding the distribution of variables, detecting outliers, and gaining insights from data before modeling.\n",
        "\n",
        "- For **categorical variables**: We use bar charts to visualize the frequency of each category using `sns.countplot()`.\n",
        "- For **numerical variables**: We create histograms with KDE (Kernel Density Estimation) curves to understand their distribution using `sns.histplot()`.\n",
        "\n",
        "**Significance**:  \n",
        "These visualizations help in identifying imbalances, skewness, and potential issues in the data."
      ],
      "metadata": {
        "id": "y9377knGtoTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Visualize the Dataset\n",
        "# Visualizing the dataset helps us understand the distribution of data.\n",
        "# For numerical variables, we create histograms with kernel density estimation (KDE) curves.\n",
        "# For categorical variables, we create bar charts to show the distribution of different categories.\n",
        "# These visualizations aid in explaining the dataset to others and support the presentation of algorithms."
      ],
      "metadata": {
        "id": "N6GRF7YhGq0c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    if df[column].dtype == 'object':\n",
        "        # For categorical variables\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        sns.countplot(y=column, data=df, order=df[column].value_counts().index)\n",
        "        plt.title(f'Distribution of {column}')\n",
        "        plt.show()\n",
        "    else:\n",
        "        # For numerical variables\n",
        "        plt.figure(figsize=(8, 6))\n",
        "        sns.histplot(df[column], kde=True)\n",
        "        plt.title(f'Distribution of {column}')\n",
        "        plt.xlabel(column)\n",
        "        plt.ylabel('Frequency')\n",
        "        plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5nZiPjM7Gq2_",
        "outputId": "b6410299-40ed-49d9-dbbf-c6df083b8e37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAIjCAYAAADx6oYJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABduUlEQVR4nO3deXxU1f3/8ffMJDPZSEKAJEQiIPtORcUILkgkLC5UakWpAkX5qmAFFBFrEW37pdWCIqK0Xyug1apYxf5QkR1cogKCCAoCIhEh7CEQQrY5vz/CXJiEhGQy2WZez8djHsnce+beMycDvHP43HNtxhgjAAAAABZ7bXcAAAAAqGsIyQAAAEAJhGQAAACgBEIyAAAAUAIhGQAAACiBkAwAAACUQEgGAAAASiAkAwAAACUQkgEAAIASCMkA6pypU6fKZrPVyLmuueYaXXPNNdbzVatWyWaz6e23366R848YMUItWrSokXP56sSJE7rrrruUmJgom82mcePG1XaXylTy5+kPNfl5BFB3EJIBVKt58+bJZrNZj7CwMCUlJSktLU3PPfecjh8/7pfz7N27V1OnTtXGjRv9cjx/qst9q4j//d//1bx583Tvvffq1Vdf1R133FFu+6KiIs2dO1fXXHON4uLi5HK51KJFC40cOVLr1q2roV4Xq62x9wTrQ4cO1eh5AfhPSG13AEBwePLJJ9WyZUsVFBQoMzNTq1at0rhx4zRjxgz997//VdeuXa22jz32mB555JFKHX/v3r164okn1KJFC3Xv3r3Cr1uyZEmlzuOL8vr2f//3f3K73dXeh6pYsWKFLr/8cj3++OPnbZubm6ubb75Zixcv1lVXXaVHH31UcXFx+vHHH/XWW29p/vz5ysjIULNmzaqlryV/nr5+LgCAkAygRgwYMECXXHKJ9Xzy5MlasWKFrr/+et1444367rvvFB4eLkkKCQlRSEj1/vV08uRJRUREyOl0Vut5zic0NLRWz18RBw4cUMeOHSvUduLEiVq8eLGeeeaZUmUZjz/+uJ555plq6OEZtf3zBBA4KLcAUGuuvfZa/eEPf9Du3bv1r3/9y9p+rhrQpUuXqnfv3oqNjVVUVJTatWunRx99VFJxHfGll14qSRo5cqRV2jFv3jxJxXWqnTt31vr163XVVVcpIiLCem1ZNaxFRUV69NFHlZiYqMjISN1444366aefvNq0aNFCI0aMKPXas495vr6dqyY5JydHDz74oJKTk+VyudSuXTv97W9/kzHGq53NZtPYsWO1cOFCde7cWS6XS506ddLixYvPPeAlHDhwQKNGjVJCQoLCwsLUrVs3zZ8/39rvqc/etWuX3n//favvP/744zmPt2fPHv3973/Xddddd866ZYfDoYceesiaRd69e7fuu+8+tWvXTuHh4WrUqJFuueWWUsf3lOysWbNG//M//6NGjRopOjpad955p44ePerVtjJj//HHH+uWW27RhRdeKJfLpeTkZI0fP165ubkVGj9/WLFiha688kpFRkYqNjZWN910k7777juvNsePH9e4cePUokULuVwuxcfH67rrrtNXX31ltdm+fbuGDBmixMREhYWFqVmzZho6dKiOHTtWY+8FCDTMJAOoVXfccYceffRRLVmyRHffffc522zZskXXX3+9unbtqieffFIul0s7duzQp59+Kknq0KGDnnzySU2ZMkWjR4/WlVdeKUm64oorrGMcPnxYAwYM0NChQ/Wb3/xGCQkJ5fbrz3/+s2w2myZNmqQDBw7o2WefVWpqqjZu3GjNeFdERfp2NmOMbrzxRq1cuVKjRo1S9+7d9dFHH2nixIn6+eefS83EfvLJJ3rnnXd03333qUGDBnruuec0ZMgQZWRkqFGjRmX2Kzc3V9dcc4127NihsWPHqmXLllqwYIFGjBihrKwsPfDAA+rQoYNeffVVjR8/Xs2aNdODDz4oSWrSpMk5j/nhhx+qsLDwvDXLHmvXrtVnn32moUOHqlmzZvrxxx/14osv6pprrtG3336riIgIr/Zjx45VbGyspk6dqm3btunFF1/U7t27rTBf0vnGfsGCBTp58qTuvfdeNWrUSF9++aVmzZqlPXv2aMGCBRV6D1WxbNkyDRgwQBdddJGmTp2q3NxczZo1S7169dJXX31l/fJ0zz336O2339bYsWPVsWNHHT58WJ988om+++47XXzxxcrPz1daWpry8vJ0//33KzExUT///LMWLVqkrKwsxcTEVPt7AQKSAYBqNHfuXCPJrF27tsw2MTEx5he/+IX1/PHHHzdn//X0zDPPGEnm4MGDZR5j7dq1RpKZO3duqX1XX321kWTmzJlzzn1XX3219XzlypVGkrngggtMdna2tf2tt94ykszMmTOtbc2bNzfDhw8/7zHL69vw4cNN8+bNrecLFy40ksyf/vQnr3a/+tWvjM1mMzt27LC2STJOp9Nr29dff20kmVmzZpU619meffZZI8n861//srbl5+eblJQUExUV5fXemzdvbgYNGlTu8YwxZvz48UaS2bBhw3nbGmPMyZMnS21LT083kswrr7xibfN8hnr06GHy8/Ot7U899ZSRZN577z1rW2XG/lznnzZtmrHZbGb37t3WtpKfx4rwvKa8z2z37t1NfHy8OXz4sLXt66+/Nna73dx5553WtpiYGDNmzJgyj7NhwwYjySxYsKBSfQRQPsotANS6qKiocle5iI2NlSS99957Pl/k5nK5NHLkyAq3v/POO9WgQQPr+a9+9Ss1bdpUH3zwgU/nr6gPPvhADodDv/vd77y2P/jggzLG6MMPP/TanpqaqlatWlnPu3btqujoaP3www/nPU9iYqJuu+02a1toaKh+97vf6cSJE1q9enWl+56dnS1JXuNWnrNn5AsKCnT48GG1bt1asbGxXqUEHqNHj/aq4b733nsVEhLi88/k7PPn5OTo0KFDuuKKK2SM0YYNG3w6ZkXt27dPGzdu1IgRIxQXF2dt79q1q6677jqv9xQbG6svvvhCe/fuPeexPDPFH330kU6ePFmt/QaCCSEZQK07ceJEucHq1ltvVa9evXTXXXcpISFBQ4cO1VtvvVWpwHzBBRdU6qKuNm3aeD232Wxq3bp1mfW4/rJ7924lJSWVGo8OHTpY+8924YUXljpGw4YNS9Xqnus8bdq0kd3u/c9AWeepiOjoaEmq8LJ+ubm5mjJlilV73bhxYzVp0kRZWVnnrKUt+TOJiopS06ZNff6ZZGRkWCE1KipKTZo00dVXXy1J1V7L6xnfdu3aldrXoUMHHTp0SDk5OZKkp556Sps3b1ZycrIuu+wyTZ061euXoJYtW2rChAl66aWX1LhxY6WlpWn27NnUIwNVREgGUKv27NmjY8eOqXXr1mW2CQ8P15o1a7Rs2TLdcccd2rRpk2699VZdd911KioqqtB5KlNHXFFl3WCion3yB4fDcc7tpsRFfjWhffv2kqRvvvmmQu3vv/9+/fnPf9avf/1rvfXWW1qyZImWLl2qRo0aVfuyeEVFRbruuuv0/vvva9KkSVq4cKGWLl1qXdRXl5bl+/Wvf60ffvhBs2bNUlJSkp5++ml16tTJ638Vpk+frk2bNunRRx9Vbm6ufve736lTp07as2dPLfYcqN8IyQBq1auvvipJSktLK7ed3W5X3759NWPGDH377bf685//rBUrVmjlypWSyg6svtq+fbvXc2OMduzY4bUSRcOGDZWVlVXqtSVnYSvTt+bNm2vv3r2lZmO3bt1q7feH5s2ba/v27aXCYFXOM2DAADkcDq+VSsrz9ttva/jw4Zo+fbp+9atf6brrrlPv3r3POaZS6Z/JiRMntG/fvnLvWFjW2H/zzTf6/vvvNX36dE2aNEk33XSTUlNTlZSUVKG+V5VnfLdt21Zq39atW9W4cWNFRkZa25o2bar77rtPCxcu1K5du9SoUSP9+c9/9npdly5d9Nhjj2nNmjX6+OOP9fPPP2vOnDnV+0aAAEZIBlBrVqxYoT/+8Y9q2bKlhg0bVma7I0eOlNrmuTFEXl6eJFmBoqyAVVmvvPKKV1B9++23tW/fPg0YMMDa1qpVK33++efKz8+3ti1atKjUUnGV6dvAgQNVVFSk559/3mv7M888I5vN5nX+qhg4cKAyMzP15ptvWtsKCws1a9YsRUVFWWUHlZGcnKy7775bS5Ys0axZs0rtd7vdmj59ujW76XA4Ss14z5o1q8yZ+H/84x8qKCiwnr/44osqLCwsd0zKGnvPDPzZ5zfGaObMmeW8Q/9p2rSpunfvrvnz53v1bfPmzVqyZIkGDhwoqXjGu2TZRHx8vJKSkqzPfnZ2tgoLC73adOnSRXa73WoDoPJYAg5Ajfjwww+1detWFRYWav/+/VqxYoWWLl2q5s2b67///a/CwsLKfO2TTz6pNWvWaNCgQWrevLkOHDigF154Qc2aNVPv3r0lFQfW2NhYzZkzRw0aNFBkZKR69uypli1b+tTfuLg49e7dWyNHjtT+/fv17LPPqnXr1l7L1N111116++231b9/f/3617/Wzp079a9//cvrQrrK9u2GG25Qnz599Pvf/14//vijunXrpiVLlui9997TuHHjSh3bV6NHj9bf//53jRgxQuvXr1eLFi309ttv69NPP9Wzzz5b4YvvSpo+fbp27typ3/3ud3rnnXd0/fXXq2HDhsrIyNCCBQu0detWDR06VJJ0/fXX69VXX1VMTIw6duyo9PR0LVu2rMyl6/Lz89W3b1/9+te/1rZt2/TCCy+od+/euvHGG8vsT1lj3759e7Vq1UoPPfSQfv75Z0VHR+s///nPeWu5K2vGjBmllrKz2+169NFH9fTTT2vAgAFKSUnRqFGjrCXgYmJiNHXqVEnF9d3NmjXTr371K3Xr1k1RUVFatmyZ1q5dq+nTp0sq/mVz7NixuuWWW9S2bVsVFhbq1VdflcPh0JAhQ/z6foCgUosrawAIAp7luzwPp9NpEhMTzXXXXWdmzpzptdSYR8klt5YvX25uuukmk5SUZJxOp0lKSjK33Xab+f77771e995775mOHTuakJAQr2W/rr76atOpU6dz9q+sJeD+/e9/m8mTJ5v4+HgTHh5uBg0a5LUsmMf06dPNBRdcYFwul+nVq5dZt25dqWOW17eSS8AZY8zx48fN+PHjTVJSkgkNDTVt2rQxTz/9tHG73V7tJJ1zabCylqYraf/+/WbkyJGmcePGxul0mi5dupxzqbSKLgHnUVhYaF566SVz5ZVXmpiYGBMaGmqaN29uRo4c6bU83NGjR63zR0VFmbS0NLN169ZS/fd8hlavXm1Gjx5tGjZsaKKiosywYcO8lk8zpvTP05iyx/7bb781qampJioqyjRu3Njcfffd1hJ6Z49DVZaAO9fD4XBY7ZYtW2Z69eplwsPDTXR0tLnhhhvMt99+a+3Py8szEydONN26dTMNGjQwkZGRplu3buaFF16w2vzwww/mt7/9rWnVqpUJCwszcXFxpk+fPmbZsmWV6jMAbzZjauHqDgAAKmjevHkaOXKk1q5d63VrcwCoTtQkAwAAACVQkwwAQAUdO3ZMubm55bZJTEysod4AqE6EZAAAKuiBBx7Q/Pnzy21DFSMQGKhJBgCggr799tsybw/tkZqaWkO9AVCdCMkAAABACVy4BwAAAJRATbKfuN1u7d27Vw0aNPD77XEBAABQdcYYHT9+XElJSbLby58rJiT7yd69e5WcnFzb3QAAAMB5/PTTT2rWrFm5bQjJfuK5hetPP/2k6OjoWu4NAAAASsrOzlZycrKV28pDSPYTT4lFdHQ0IRkAAKAOq0hpLBfuAQAAACUQkgEAAIASCMkAAABACYRkAAAAoARCMgAAAFACIRkAAAAogZAMAAAAlEBIBgAAAEogJAMAAAAlEJIBAACAEgjJAAAAQAmEZAAAAKAEQjIAAABQAiEZAAAAKIGQDAAAAJRASAYAAABKICQDAAAAJRCS66klWzI1c9l27ThwvLa7AgAAEHBCarsD8M0r6bv1yY5DSooNU+v4BrXdHQAAgIDCTHI91axhuCRpz9HcWu4JAABA4CEk11OekPxzFiEZAADA3wjJ9VSzhhGSpD1HT9ZyTwAAAAIPIbmeuoByCwAAgGpDSK6nPOUWmcdOqbDIXcu9AQAACCyE5HoqvkGYQh02FbqN9h/Pq+3uAAAABBRCcj3lsNvUNOZ0ycUR6pIBAAD8iZBcj7HCBQAAQPUgJNdjrJUMAABQPQjJ9dgFsSwDBwAAUB0IyfUY5RYAAADVg5Bcj1FuAQAAUD0IyfWY54Yie7Ny5XabWu4NAABA4CAk12OJ0WFy2G0qKDI6wFrJAAAAfkNIrsdCHHY1jQmTxMV7AAAA/kRIrueoSwYAAPA/QnI951kGjhUuAAAA/IeQXM+dmUmm3AIAAMBfCMn1HOUWAAAA/kdIruc8y8D9TEgGAADwG0JyPZfc8PStqVkrGQAAwG8IyfVcYkyY7DYpv9CtQydYKxkAAMAfCMn1XKjDroTo4rWS9x07Vcu9AQAACAyE5AAQ5QqRJOXkFdZyTwAAAAIDITkARJwOySfzi2q5JwAAAIGBkBwAIp0OSVJOPjPJAAAA/lCrIXnatGm69NJL1aBBA8XHx2vw4MHatm2bV5trrrlGNpvN63HPPfd4tcnIyNCgQYMUERGh+Ph4TZw4UYWF3oFx1apVuvjii+VyudS6dWvNmzevVH9mz56tFi1aKCwsTD179tSXX37p9/dcHSKczCQDAAD4U62G5NWrV2vMmDH6/PPPtXTpUhUUFKhfv37Kycnxanf33Xdr37591uOpp56y9hUVFWnQoEHKz8/XZ599pvnz52vevHmaMmWK1WbXrl0aNGiQ+vTpo40bN2rcuHG666679NFHH1lt3nzzTU2YMEGPP/64vvrqK3Xr1k1paWk6cOBA9Q9EFUV4ZpKpSQYAAPALmzGmziyue/DgQcXHx2v16tW66qqrJBXPJHfv3l3PPvvsOV/z4Ycf6vrrr9fevXuVkJAgSZozZ44mTZqkgwcPyul0atKkSXr//fe1efNm63VDhw5VVlaWFi9eLEnq2bOnLr30Uj3//POSJLfbreTkZN1///165JFHztv37OxsxcTE6NixY4qOjq7KMFTa5Hc26d9f/qQHr2ur+/u2qdFzAwAA1BeVyWt1qib52LFjkqS4uDiv7a+99poaN26szp07a/LkyTp58qS1Lz09XV26dLECsiSlpaUpOztbW7ZssdqkpqZ6HTMtLU3p6emSpPz8fK1fv96rjd1uV2pqqtWmpLy8PGVnZ3s9aoun3CKHcgsAAAC/CKntDni43W6NGzdOvXr1UufOna3tt99+u5o3b66kpCRt2rRJkyZN0rZt2/TOO+9IkjIzM70CsiTreWZmZrltsrOzlZubq6NHj6qoqOicbbZu3XrO/k6bNk1PPPFE1d60n3jKLU5y4R4AAIBf1JmQPGbMGG3evFmffPKJ1/bRo0db33fp0kVNmzZV3759tXPnTrVq1aqmu2mZPHmyJkyYYD3Pzs5WcnJyrfTFmknOYyYZAADAH+pESB47dqwWLVqkNWvWqFmzZuW27dmzpyRpx44datWqlRITE0utQrF//35JUmJiovXVs+3sNtHR0QoPD5fD4ZDD4ThnG88xSnK5XHK5XBV/k9Uo0lU8k5xbwEwyAACAP9RqTbIxRmPHjtW7776rFStWqGXLlud9zcaNGyVJTZs2lSSlpKTom2++8VqFYunSpYqOjlbHjh2tNsuXL/c6ztKlS5WSkiJJcjqd6tGjh1cbt9ut5cuXW23qMmaSAQAA/KtWZ5LHjBmj119/Xe+9954aNGhg1RDHxMQoPDxcO3fu1Ouvv66BAweqUaNG2rRpk8aPH6+rrrpKXbt2lST169dPHTt21B133KGnnnpKmZmZeuyxxzRmzBhrpveee+7R888/r4cffli//e1vtWLFCr311lt6//33rb5MmDBBw4cP1yWXXKLLLrtMzz77rHJycjRy5MiaH5hKoiYZAADAv2o1JL/44ouSipd5O9vcuXM1YsQIOZ1OLVu2zAqsycnJGjJkiB577DGrrcPh0KJFi3TvvfcqJSVFkZGRGj58uJ588kmrTcuWLfX+++9r/Pjxmjlzppo1a6aXXnpJaWlpVptbb71VBw8e1JQpU5SZmanu3btr8eLFpS7mq4vOhGRmkgEAAPyhTq2TXJ/V5jrJa388olvmpKtl40itfOiaGj03AABAfVFv10mGb7jjHgAAgH8RkgOA58I9yi0AAAD8g5AcACLPunCP6hkAAICqIyQHgAhX8Uyy20h5he5a7g0AAED9R0gOAOGhDut76pIBAACqjpAcABx2m8JCi3+U1CUDAABUHSE5QERy8R4AAIDfEJIDRITr9DJw3HUPAACgygjJASIi9PRMch4zyQAAAFVFSA4Qnpnkk8wkAwAAVBkhOUBQkwwAAOA/hOQAYd2amplkAACAKiMkBwhPSKYmGQAAoOoIyQHCc9c9yi0AAACqjpAcICKdXLgHAADgL4TkABFx+sI9apIBAACqjpAcIKhJBgAA8B9CcoCgJhkAAMB/CMkBIpIl4AAAAPyGkBwgrHILZpIBAACqjJAcICK44x4AAIDfEJIDRKSLJeAAAAD8hZAcIKwl4FjdAgAAoMoIyQEigpuJAAAA+A0hOUB4ZpJzC4rkdpta7g0AAED9RkgOEJ6aZGOkU4WUXAAAAFQFITlAhIU4ZLMVf09dMgAAQNUQkgOE3W5TeCh1yQAAAP5ASA4grJUMAADgH4TkAMJayQAAAP5BSA4gnnILapIBAACqhpAcQCJdlFsAAAD4AyE5gHBDEQAAAP8gJAeQSM+tqZlJBgAAqBJCcgCxZpLzmEkGAACoCkJyAImwVrdgJhkAAKAqCMkBJNJaJ5mZZAAAgKogJAeQCGqSAQAA/IKQHECoSQYAAPAPQnIAoSYZAADAPwjJAeRMTTIhGQAAoCoIyQEk/HS5RQ4X7gEAAFQJITmAeGaSc5lJBgAAqBJCcgDx1CQzkwwAAFA1hOQAYtUk5zGTDAAAUBWE5AASQU0yAACAXxCSA4gnJJ8qcKvIbWq5NwAAAPUXITmAeO64J0m5BZRcAAAA+IqQHEBcIWd+nHmEZAAAAJ8RkgOI3W6T01H8Iz1V6K7l3gAAANRfhOQA4wot/pEykwwAAOA7QnKAcYWcuXgPAAAAviEkB5gwz0xyITPJAAAAviIkB5iwUGaSAQAAqoqQHGA8K1ycYiYZAADAZ4TkAOOZSc5jJhkAAMBnhOQAQ00yAABA1RGSA8yZ1S0IyQAAAL4iJAeYMzPJlFsAAAD4ipAcYJhJBgAAqDpCcoCxZpK5cA8AAMBnhOQAY80kc+EeAACAzwjJAcZ1eiaZm4kAAAD4jpAcYMJOzySzBBwAAIDvCMkBhttSAwAAVB0hOcBYt6VmdQsAAACfEZIDjHVbatZJBgAA8BkhOcAwkwwAAFB1hOQAw0wyAABA1RGSA8yZm4kwkwwAAOArQnKAOXNbamaSAQAAfFWrIXnatGm69NJL1aBBA8XHx2vw4MHatm2bV5tTp05pzJgxatSokaKiojRkyBDt37/fq01GRoYGDRqkiIgIxcfHa+LEiSosLPRqs2rVKl188cVyuVxq3bq15s2bV6o/s2fPVosWLRQWFqaePXvqyy+/9Pt7rm7WTDLrJAMAAPisVkPy6tWrNWbMGH3++edaunSpCgoK1K9fP+Xk5Fhtxo8fr//3//6fFixYoNWrV2vv3r26+eabrf1FRUUaNGiQ8vPz9dlnn2n+/PmaN2+epkyZYrXZtWuXBg0apD59+mjjxo0aN26c7rrrLn300UdWmzfffFMTJkzQ448/rq+++krdunVTWlqaDhw4UDOD4SeskwwAAFB1NmOMqe1OeBw8eFDx8fFavXq1rrrqKh07dkxNmjTR66+/rl/96leSpK1bt6pDhw5KT0/X5Zdfrg8//FDXX3+99u7dq4SEBEnSnDlzNGnSJB08eFBOp1OTJk3S+++/r82bN1vnGjp0qLKysrR48WJJUs+ePXXppZfq+eeflyS53W4lJyfr/vvv1yOPPHLevmdnZysmJkbHjh1TdHS0v4emwrbvP67rnlmjhhGh2jClX631AwAAoK6pTF6rUzXJx44dkyTFxcVJktavX6+CggKlpqZabdq3b68LL7xQ6enpkqT09HR16dLFCsiSlJaWpuzsbG3ZssVqc/YxPG08x8jPz9f69eu92tjtdqWmplptSsrLy1N2drbXoy5gJhkAAKDq6kxIdrvdGjdunHr16qXOnTtLkjIzM+V0OhUbG+vVNiEhQZmZmVabswOyZ79nX3ltsrOzlZubq0OHDqmoqOicbTzHKGnatGmKiYmxHsnJyb69cT+z1kkuLFId+k8CAACAeqXOhOQxY8Zo8+bNeuONN2q7KxUyefJkHTt2zHr89NNPtd0lSZLr9EyyMVJBESEZAADAFyG13QFJGjt2rBYtWqQ1a9aoWbNm1vbExETl5+crKyvLazZ5//79SkxMtNqUXIXCs/rF2W1Kroixf/9+RUdHKzw8XA6HQw6H45xtPMcoyeVyyeVy+faGq5FndQupeDbZGVJnfg8CAACoN2o1QRljNHbsWL377rtasWKFWrZs6bW/R48eCg0N1fLly61t27ZtU0ZGhlJSUiRJKSkp+uabb7xWoVi6dKmio6PVsWNHq83Zx/C08RzD6XSqR48eXm3cbreWL19utakvnA67bLbi77k1NQAAgG9qdSZ5zJgxev311/Xee++pQYMGVv1vTEyMwsPDFRMTo1GjRmnChAmKi4tTdHS07r//fqWkpOjyyy+XJPXr108dO3bUHXfcoaeeekqZmZl67LHHNGbMGGum95577tHzzz+vhx9+WL/97W+1YsUKvfXWW3r//fetvkyYMEHDhw/XJZdcossuu0zPPvuscnJyNHLkyJofmCqw2Wxyhdh1qsCtPC7eAwAA8EmthuQXX3xRknTNNdd4bZ87d65GjBghSXrmmWdkt9s1ZMgQ5eXlKS0tTS+88ILV1uFwaNGiRbr33nuVkpKiyMhIDR8+XE8++aTVpmXLlnr//fc1fvx4zZw5U82aNdNLL72ktLQ0q82tt96qgwcPasqUKcrMzFT37t21ePHiUhfz1QdhoY7ikMwNRQAAAHxSp9ZJrs/qyjrJktTzf5dpf3aeFt3fW50viKnVvgAAANQV9XadZPiHZ61kZpIBAAB8Q0gOQNZaydQkAwAA+ISQHICYSQYAAKgaQnIACgvh1tQAAABVQUgOQK5QT7kFM8kAAAC+ICQHIFeIp9yCmWQAAABfEJIDEDPJAAAAVUNIDkBhzCQDAABUCSE5AIUxkwwAAFAlhOQA5GJ1CwAAgCohJAcgz0wy6yQDAAD4hpAcgDw3E2EmGQAAwDeE5ADkuS11HjXJAAAAPiEkB6Azt6VmJhkAAMAXhOQA5JlJZnULAAAA3xCSAxAzyQAAAFVDSA5ArJMMAABQNYTkAGStk8wScAAAAD4hJAcgl2edZJaAAwAA8AkhOQBZ6yQzkwwAAOATQnIAOrO6BTPJAAAAviAkByBrdQsu3AMAAPAJITkAWTPJLAEHAADgE0JyAPLMJOcXumWMqeXeAAAA1D+E5ADkCckSNxQBAADwBSE5AHnKLSRuKAIAAOALQnIACnXY5bDbJDGTDAAA4AtCcoAKC+HW1AAAAL4iJAcol2cZOGaSAQAAKo2QHKCYSQYAAPAdITlAeWaSueseAABA5RGSA5RnhYu8QmaSAQAAKouQHKDCmEkGAADwGSE5QLmoSQYAAPAZITlAhbG6BQAAgM8IyQEqLJSZZAAAAF8RkgOUK4SZZAAAAF8RkgMUM8kAAAC+IyQHKGsmmZAMAABQaYTkAOWZSabcAgAAoPIIyQHqzDrJzCQDAABUFiE5QJ1ZJ5mZZAAAgMoiJAeoM+skM5MMAABQWYTkAOXittQAAAA+IyQHKE+5BTPJAAAAlUdIDlBhzCQDAAD4jJAcoKwL95hJBgAAqDRCcoCyLtxjJhkAAKDSCMkBKoyZZAAAAJ8RkgOUi5lkAAAAnxGSA9SZ21IzkwwAAFBZhOQA5QphdQsAAABfEZIDFDPJAAAAviMkB6iw0zPJBUVGRW5Ty70BAACoXwjJAcoVeuZHe6qA2WQAAIDKICQHKM9MsiTlFVKXDAAAUBmE5ABlt9vkdJxeK5mZZAAAgErxKST/8MMP/u4HqoHn1tTMJAMAAFSOTyG5devW6tOnj/71r3/p1KlT/u4T/MRzQxFmkgEAACrHp5D81VdfqWvXrpowYYISExP1P//zP/ryyy/93TdUkWcmmZAMAABQOT6F5O7du2vmzJnau3evXn75Ze3bt0+9e/dW586dNWPGDB08eNDf/YQPzqyVTLkFAABAZVTpwr2QkBDdfPPNWrBggf76179qx44deuihh5ScnKw777xT+/bt81c/4YMwyi0AAAB8UqWQvG7dOt13331q2rSpZsyYoYceekg7d+7U0qVLtXfvXt10003+6id8cKbcgplkAACAygjx5UUzZszQ3LlztW3bNg0cOFCvvPKKBg4cKLu9OJS1bNlS8+bNU4sWLfzZV1SSZyaZW1MDAABUjk8h+cUXX9Rvf/tbjRgxQk2bNj1nm/j4eP3zn/+sUudQNVZIZiYZAACgUnwKydu3bz9vG6fTqeHDh/tyeFRQRkaGDh06VOb+kyeyJUnbd/2orxw1dzFl48aNdeGFF9bY+QAAAPzNp5A8d+5cRUVF6ZZbbvHavmDBAp08eZJwXAMyMjLUvkMH5Z48WWabRoMmKKrztfrr357RY2vfrbG+hUdEaOt33xGUAQBAveVTSJ42bZr+/ve/l9oeHx+v0aNHE5JrwKFDh5R78qSGTXpaCRe2Omeb9Ycd+jFH6v3L4eow4o4a6df+jJ167a8TdejQIUIyAACot3wKyRkZGWrZsmWp7c2bN1dGRkaVO4WKS7iwlZq16XTOfTvcB6ScY4ps2ETNWjWu4Z4BAADUXz4tARcfH69NmzaV2v7111+rUaNGVe4U/CPEUfzjLXSbWu4JAABA/eJTSL7tttv0u9/9TitXrlRRUZGKioq0YsUKPfDAAxo6dKi/+wgfOew2SVJhESEZAACgMnwKyX/84x/Vs2dP9e3bV+Hh4QoPD1e/fv107bXX6n//938rfJw1a9bohhtuUFJSkmw2mxYuXOi1f8SIEbLZbF6P/v37e7U5cuSIhg0bpujoaMXGxmrUqFE6ceKEV5tNmzbpyiuvVFhYmJKTk/XUU0+V6suCBQvUvn17hYWFqUuXLvrggw8qPiB1VMjpkFzETDIAAECl+BSSnU6n3nzzTW3dulWvvfaa3nnnHe3cuVMvv/yynE5nhY+Tk5Ojbt26afbs2WW26d+/v/bt22c9/v3vf3vtHzZsmLZs2aKlS5dq0aJFWrNmjUaPHm3tz87OVr9+/dS8eXOtX79eTz/9tKZOnap//OMfVpvPPvtMt912m0aNGqUNGzZo8ODBGjx4sDZv3lyJUal7PCG50M06yQAAAJXh04V7Hm3btlXbtm19fv2AAQM0YMCActu4XC4lJiaec993332nxYsXa+3atbrkkkskSbNmzdLAgQP1t7/9TUlJSXrttdeUn59vBfhOnTpp48aNmjFjhhWmZ86cqf79+2vixImSimfKly5dqueff15z5szx+f3VtpDTd0BkJhkAAKByfJpJLioq0j//+U/dfvvtSk1N1bXXXuv18KdVq1YpPj5e7dq107333qvDhw9b+9LT0xUbG2sFZElKTU2V3W7XF198YbW56qqrvGa409LStG3bNh09etRqk5qa6nXetLQ0paenl9mvvLw8ZWdnez3qmhAHNckAAAC+8Gkm+YEHHtC8efM0aNAgde7cWTabzd/9klRcanHzzTerZcuW2rlzpx599FENGDBA6enpcjgcyszMVHx8vNdrQkJCFBcXp8zMTElSZmZmqeXqEhISrH0NGzZUZmamte3sNp5jnMu0adP0xBNP+ONtVhvrwj1mkgEAACrFp5D8xhtv6K233tLAgQP93R8vZ6+U0aVLF3Xt2lWtWrXSqlWr1Ldv32o99/lMnjxZEyZMsJ5nZ2crOTm5FntUGjXJAAAAvvH5wr3WrVv7uy/nddFFF6lx48basWOHJCkxMVEHDhzwalNYWKgjR45YdcyJiYnav3+/VxvP8/O1KasWWiqulY6OjvZ61DWskwwAAOAbn0Lygw8+qJkzZ8qYmg1fe/bs0eHDh9W0aVNJUkpKirKysrR+/XqrzYoVK+R2u9WzZ0+rzZo1a1RQUGC1Wbp0qdq1a6eGDRtabZYvX+51rqVLlyolJaW631K1Yp1kAAAA3/hUbvHJJ59o5cqV+vDDD9WpUyeFhoZ67X/nnXcqdJwTJ05Ys8KStGvXLm3cuFFxcXGKi4vTE088oSFDhigxMVE7d+7Uww8/rNatWystLU2S1KFDB/Xv319333235syZo4KCAo0dO1ZDhw5VUlKSJOn222/XE088oVGjRmnSpEnavHmzZs6cqWeeecY67wMPPKCrr75a06dP16BBg/TGG29o3bp1XsvE1UeskwwAAOAbn0JybGysfvnLX1b55OvWrVOfPn2s554a3+HDh+vFF1/Upk2bNH/+fGVlZSkpKUn9+vXTH//4R7lcLus1r732msaOHau+ffvKbrdryJAheu6556z9MTExWrJkicaMGaMePXqocePGmjJlitdayldccYVef/11PfbYY3r00UfVpk0bLVy4UJ07d67ye6xN1CQDAAD4xqeQPHfuXL+c/Jprrim3ZOOjjz467zHi4uL0+uuvl9uma9eu+vjjj8ttc8stt+iWW2457/nqE09NMjPJAAAAleNTTbJUfIHcsmXL9Pe//13Hjx+XJO3du7fULaFRe0LOqkmu6fpxAACA+synmeTdu3erf//+ysjIUF5enq677jo1aNBAf/3rX5WXl1ev71IXSDwX7hlJbiM5qmc5awAAgIDj00zyAw88oEsuuURHjx5VeHi4tf2Xv/xlqVUiUHs8M8kSdckAAACV4dNM8scff6zPPvvM61bPktSiRQv9/PPPfukYqs5xdkguMnL59NMGAAAIPj7NJLvdbhUVFZXavmfPHjVo0KDKnYJ/2Gw2Kyhz8R4AAEDF+RSS+/Xrp2effdZ6brPZdOLECT3++OPVfqtqVM6ZZeAIyQAAABXl03/AT58+XWlpaerYsaNOnTql22+/Xdu3b1fjxo3173//2999RBWE2G3KEzXJAAAAleFTSG7WrJm+/vprvfHGG9q0aZNOnDihUaNGadiwYV4X8qH2Fa+VXES5BQAAQCX4fClXSEiIfvOb3/izL6gGZ6+VDAAAgIrxKSS/8sor5e6/8847feoM/M9BTTIAAECl+RSSH3jgAa/nBQUFOnnypJxOpyIiIgjJdciZC/eoSQYAAKgon1a3OHr0qNfjxIkT2rZtm3r37s2Fe3VMcU2yVES5BQAAQIX5FJLPpU2bNvrLX/5SapYZtYtyCwAAgMrzW0iWii/m27t3rz8PiSpinWQAAIDK86km+b///a/Xc2OM9u3bp+eff169evXyS8fgH9QkAwAAVJ5PIXnw4MFez202m5o0aaJrr71W06dP90e/4CcOx+nbUlOTDAAAUGE+hWQ3s5L1Roi9uKKGcgsAAICK82tNMuoeapIBAAAqz6eZ5AkTJlS47YwZM3w5BfyEmmQAAIDK8ykkb9iwQRs2bFBBQYHatWsnSfr+++/lcDh08cUXW+1sNpt/egmfsU4yAABA5fkUkm+44QY1aNBA8+fPV8OGDSUV32Bk5MiRuvLKK/Xggw/6tZPwHeskAwAAVJ5PNcnTp0/XtGnTrIAsSQ0bNtSf/vQnVreoY6hJBgAAqDyfQnJ2drYOHjxYavvBgwd1/PjxKncK/kNNMgAAQOX5FJJ/+ctfauTIkXrnnXe0Z88e7dmzR//5z380atQo3Xzzzf7uI6qAdZIBAAAqz6ea5Dlz5uihhx7S7bffroKCguIDhYRo1KhRevrpp/3aQVQN6yQDAABUnk8hOSIiQi+88IKefvpp7dy5U5LUqlUrRUZG+rVzqDpqkgEAACqvSjcT2bdvn/bt26c2bdooMjJSxhDE6poQT7kFIRkAAKDCfArJhw8fVt++fdW2bVsNHDhQ+/btkySNGjWK5d/qGKvcoogL9wAAACrKp5A8fvx4hYaGKiMjQxEREdb2W2+9VYsXL/Zb51B1rJMMAABQeT7VJC9ZskQfffSRmjVr5rW9TZs22r17t186Bv+gJhkAAKDyfJpJzsnJ8ZpB9jhy5IhcLleVOwX/8cwkF7kNNeMAAAAV5FNIvvLKK/XKK69Yz202m9xut5566in16dPHb51D1Xku3JO4eA8AAKCifCq3eOqpp9S3b1+tW7dO+fn5evjhh7VlyxYdOXJEn376qb/7iCrwXLgnFZdchDhqsTMAAAD1hE8zyZ07d9b333+v3r1766abblJOTo5uvvlmbdiwQa1atfJ3H1EFdpvkmUumLhkAAKBiKj2TXFBQoP79+2vOnDn6/e9/Xx19gh/ZbDaFOGwqKDKUWwAAAFRQpWeSQ0NDtWnTpuroC6oJayUDAABUjk/lFr/5zW/0z3/+0999QTVhrWQAAIDK8enCvcLCQr388statmyZevToocjISK/9M2bM8Evn4B+slQwAAFA5lQrJP/zwg1q0aKHNmzfr4osvliR9//33Xm1sNtu5Xopa5Di9DBzlFgAAABVTqZDcpk0b7du3TytXrpRUfBvq5557TgkJCdXSOfhHyFk3FAEAAMD5VaomueQd2z788EPl5OT4tUPwP+vCPUIyAABAhfh04Z4HtzmuH6hJBgAAqJxKhWSbzVaq5pga5LrPc2vqoiJCMgAAQEVUqibZGKMRI0bI5XJJkk6dOqV77rmn1OoW77zzjv96iCo7swQcF+4BAABURKVC8vDhw72e/+Y3v/FrZ1A9qEkGAAConEqF5Llz51ZXP1CNqEkGAAConCpduIf6wUFNMgAAQKUQkoNACDXJAAAAlUJIDgLUJAMAAFQOITkIcMc9AACAyiEkBwFPTXIhNckAAAAVQkgOAp6Z5AJqkgEAACqEkBwEQh3FP2ZWtwAAAKgYQnIQYCYZAACgcgjJQSDk9EwyNckAAAAVQ0gOAqEO7rgHAABQGYTkIOBZJ7mgiHILAACAiiAkB4FQloADAACoFEJyEPDUJBe43TKGoAwAAHA+hOQgEHp6dQtjJMqSAQAAzo+QHAQ8M8mSVEhdMgAAwHkRkoOA3SbZiieTVcBUMgAAwHkRkoOAzWZTqN2zVjIzyQAAAOdDSA4SIadXuChghQsAAIDzIiQHiVDPXfe4NTUAAMB5EZKDRIidmWQAAICKIiQHiRDrhiLMJAMAAJwPITlIWBfusboFAADAeRGSg8SZC/eYSQYAADgfQnKQsC7coyYZAADgvAjJQcKqSabcAgAA4LxqNSSvWbNGN9xwg5KSkmSz2bRw4UKv/cYYTZkyRU2bNlV4eLhSU1O1fft2rzZHjhzRsGHDFB0drdjYWI0aNUonTpzwarNp0yZdeeWVCgsLU3Jysp566qlSfVmwYIHat2+vsLAwdenSRR988IHf329tCjldk0y5BQAAwPnVakjOyclRt27dNHv27HPuf+qpp/Tcc89pzpw5+uKLLxQZGam0tDSdOnXKajNs2DBt2bJFS5cu1aJFi7RmzRqNHj3a2p+dna1+/fqpefPmWr9+vZ5++mlNnTpV//jHP6w2n332mW677TaNGjVKGzZs0ODBgzV48GBt3ry5+t58DQu1VrdgJhkAAOB8Qmrz5AMGDNCAAQPOuc8Yo2effVaPPfaYbrrpJknSK6+8ooSEBC1cuFBDhw7Vd999p8WLF2vt2rW65JJLJEmzZs3SwIED9be//U1JSUl67bXXlJ+fr5dffllOp1OdOnXSxo0bNWPGDCtMz5w5U/3799fEiRMlSX/84x+1dOlSPf/885ozZ04NjET1s2aSuZkIAADAedXZmuRdu3YpMzNTqamp1raYmBj17NlT6enpkqT09HTFxsZaAVmSUlNTZbfb9cUXX1htrrrqKjmdTqtNWlqatm3bpqNHj1ptzj6Pp43nPOeSl5en7Oxsr0ddFsJMMgAAQIXV2ZCcmZkpSUpISPDanpCQYO3LzMxUfHy81/6QkBDFxcV5tTnXMc4+R1ltPPvPZdq0aYqJibEeycnJlX2LNerM6hbMJAMAAJxPnQ3Jdd3kyZN17Ngx6/HTTz/VdpfKZa2TzOoWAAAA51VnQ3JiYqIkaf/+/V7b9+/fb+1LTEzUgQMHvPYXFhbqyJEjXm3OdYyzz1FWG8/+c3G5XIqOjvZ61GWhrG4BAABQYXU2JLds2VKJiYlavny5tS07O1tffPGFUlJSJEkpKSnKysrS+vXrrTYrVqyQ2+1Wz549rTZr1qxRQUGB1Wbp0qVq166dGjZsaLU5+zyeNp7zBAJqkgEAACquVkPyiRMntHHjRm3cuFFS8cV6GzduVEZGhmw2m8aNG6c//elP+u9//6tvvvlGd955p5KSkjR48GBJUocOHdS/f3/dfffd+vLLL/Xpp59q7NixGjp0qJKSkiRJt99+u5xOp0aNGqUtW7bozTff1MyZMzVhwgSrHw888IAWL16s6dOna+vWrZo6darWrVunsWPH1vSQVJsQu+dmIswkAwAAnE+tLgG3bt069enTx3ruCa7Dhw/XvHnz9PDDDysnJ0ejR49WVlaWevfurcWLFyssLMx6zWuvvaaxY8eqb9++stvtGjJkiJ577jlrf0xMjJYsWaIxY8aoR48eaty4saZMmeK1lvIVV1yh119/XY899pgeffRRtWnTRgsXLlTnzp1rYBRqhufCvQJmkgEAAM6rVkPyNddcI2PKDm02m01PPvmknnzyyTLbxMXF6fXXXy/3PF27dtXHH39cbptbbrlFt9xyS/kdrses1S2YSQYAADivOluTDP+iJhkAAKDiCMlBwqpJJiQDAACcFyE5SHjKLYqMkZu1kgEAAMpFSA4SnplkSSqgLhkAAKBchOQg4TgrJFNyAQAAUD5CcpCw2WwK9Vy8R7kFAABAuQjJQSSEW1MDAABUCCE5iISyDBwAAECFEJKDSAg3FAEAAKgQQnIQ8axwwa2pAQAAykdIDiLWrampSQYAACgXITmIeG5NXcDqFgAAAOUiJAeRUDszyQAAABVBSA4iIaxuAQAAUCGE5CByptyCmWQAAIDyEJKDSKh1MxFmkgEAAMpDSA4iZ8otmEkGAAAoDyE5iJy5mQgzyQAAAOUhJAeRUOtmIswkAwAAlIeQHETO3EyEmWQAAIDyEJKDiFWTTLkFAABAuQjJQSTEWt2CcgsAAIDyEJKDSCg3EwEAAKgQQnIQsWaSuZkIAABAuQjJQYSZZAAAgIohJAcRa51kapIBAADKRUgOIp7VLQpY3QIAAKBchOQgEnq6JrnIbWQMQRkAAKAshOQg4plJllgrGQAAoDyE5CASYj8TklkrGQAAoGyE5CBis9msoMwKFwAAAGUjJAeZUAd33QMAADgfQnKQ8dQlU5MMAABQNkJykKHcAgAA4PwIyUHGKrfg1tQAAABlIiQHGc9MMjXJAAAAZSMkB5kzt6am3AIAAKAshOQgE+qgJhkAAOB8CMlBJoSaZAAAgPMiJAeZUFa3AAAAOC9CcpAJ4WYiAAAA50VIDjJOQjIAAMB5EZKDjDOk+EeeT0gGAAAoEyE5yHhmkvMLCckAAABlISQHGWsmmZAMAABQJkJykPGE5DxCMgAAQJkIyUHGKregJhkAAKBMhOQgQ7kFAADA+RGSgwwhGQAA4PwIyUHGE5IL3UZuN3fdAwAAOBdCcpDx1CRL1CUDAACUhZAcZBx2m0LsNkmUXAAAAJSFkByEuOseAABA+QjJQYi77gEAAJSPkByEWOECAACgfITkIMRd9wAAAMpHSA5C3HUPAACgfITkIES5BQAAQPkIyUGIkAwAAFA+QnIQotwCAACgfITkIMRMMgAAQPkIyUGIkAwAAFA+QnIQcnHHPQAAgHIRkoMQd9wDAAAoHyE5CJ25mUhRLfcEAACgbiIkByEn5RYAAADlIiQHIcotAAAAykdIDkKemeSCIiO3MbXcGwAAgLqHkByEPCFZkgoouQAAACiFkByEQux2OWw2SZRcAAAAnAshOUhxQxEAAICy1emQPHXqVNlsNq9H+/btrf2nTp3SmDFj1KhRI0VFRWnIkCHav3+/1zEyMjI0aNAgRUREKD4+XhMnTlRhYaFXm1WrVuniiy+Wy+VS69atNW/evJp4e7WKFS4AAADKVqdDsiR16tRJ+/btsx6ffPKJtW/8+PH6f//v/2nBggVavXq19u7dq5tvvtnaX1RUpEGDBik/P1+fffaZ5s+fr3nz5mnKlClWm127dmnQoEHq06ePNm7cqHHjxumuu+7SRx99VKPvs6axwgUAAEDZQmq7A+cTEhKixMTEUtuPHTumf/7zn3r99dd17bXXSpLmzp2rDh066PPPP9fll1+uJUuW6Ntvv9WyZcuUkJCg7t27649//KMmTZqkqVOnyul0as6cOWrZsqWmT58uSerQoYM++eQTPfPMM0pLS6vR91qTztxQhJAMAABQUp2fSd6+fbuSkpJ00UUXadiwYcrIyJAkrV+/XgUFBUpNTbXatm/fXhdeeKHS09MlSenp6erSpYsSEhKsNmlpacrOztaWLVusNmcfw9PGc4yy5OXlKTs72+tRn1CTDAAAULY6HZJ79uypefPmafHixXrxxRe1a9cuXXnllTp+/LgyMzPldDoVGxvr9ZqEhARlZmZKkjIzM70Csme/Z195bbKzs5Wbm1tm36ZNm6aYmBjrkZycXNW3W6OoSQYAAChbnS63GDBggPV9165d1bNnTzVv3lxvvfWWwsPDa7Fn0uTJkzVhwgTreXZ2dr0KytQkAwAAlK1OzySXFBsbq7Zt22rHjh1KTExUfn6+srKyvNrs37/fqmFOTEwstdqF5/n52kRHR5cbxF0ul6Kjo70e9QnlFgAAAGWrVyH5xIkT2rlzp5o2baoePXooNDRUy5cvt/Zv27ZNGRkZSklJkSSlpKTom2++0YEDB6w2S5cuVXR0tDp27Gi1OfsYnjaeYwQqyi0AAADKVqdD8kMPPaTVq1frxx9/1GeffaZf/vKXcjgcuu222xQTE6NRo0ZpwoQJWrlypdavX6+RI0cqJSVFl19+uSSpX79+6tixo+644w59/fXX+uijj/TYY49pzJgxcrlckqR77rlHP/zwgx5++GFt3bpVL7zwgt566y2NHz++Nt96tXNRbgEAAFCmOl2TvGfPHt122206fPiwmjRpot69e+vzzz9XkyZNJEnPPPOM7Ha7hgwZory8PKWlpemFF16wXu9wOLRo0SLde++9SklJUWRkpIYPH64nn3zSatOyZUu9//77Gj9+vGbOnKlmzZrppZdeCujl3yTKLQAAAMpTp0PyG2+8Ue7+sLAwzZ49W7Nnzy6zTfPmzfXBBx+Ue5xrrrlGGzZs8KmP9RXlFgAAAGWr0+UWqD6e1S24mQgAAEBphOQgRbkFAABA2QjJQYqQDAAAUDZCcpCybiZS5JYxppZ7AwAAULcQkoOUK+TMj76giJAMAABwNkJykHLYbbLbir+n5AIAAMAbITlI2Ww2r5ILAAAAnEFIDmJcvAcAAHBuhOQg5gnJeYVFtdwTAACAuoWQHMQotwAAADg3QnIQc4U6JEl5BYRkAACAsxGSg1j46ZB8Mp9yCwAAgLMRkoNYhLM4JOcSkgEAALwQkoOYJySfzC+s5Z4AAADULYTkIBbuCckFzCQDAACcjZAcxCKcIZKoSQYAACiJkBzEqEkGAAA4N0JyELNCckGR3G5Ty70BAACoOwjJQSws1CHb6e9zqUsGAACwEJKDmN1mUxhrJQMAAJRCSA5yLAMHAABQGiE5yHHxHgAAQGmE5CDHMnAAAAClEZKDHDcUAQAAKI2QHOSoSQYAACiNkBzkzoRkZpIBAAA8CMlBzlOTzIV7AAAAZxCSg1w4M8kAAAClEJKD3NlLwBnDrakBAAAkQnLQizh9x70iY5Rf6K7l3gAAANQNhOQgF+Kwy+ko/hhQcgEAAFCMkAzqkgEAAEogJIO1kgEAAEogJONMSOauewAAAJIIydCZtZIptwAAAChGSIZVk8wNRQAAAIoRkkFNMgAAQAmEZFhrJVNuAQAAUIyQDGqSAQAASiAkw+vW1AAAACAkQ2dCcn6RW4VF3JoaAACAkAw5Q+xy2GySKLkAAACQCMmQZLPZrGXgTuSxwgUAAAAhGZKkhhGhkqSjJ/NruScAAAC1j5AMSVJcpFOSdDSnoJZ7AgAAUPsIyZAkNTwdko8wkwwAAEBIRrG4iNMhOYeQDAAAQEiGpDPlFtm5BSwDBwAAgh4hGZKK10p2hdhlJB09SV0yAAAIboRkSCpeBs66eI+6ZAAAEOQIybA0pC4ZAABAEiEZZ/HMJBOSAQBAsCMkwxLHMnAAAACSCMk4iyckZ50skNuYWu4NAABA7SEkw9IgLEQOu01FbqPsXFa4AAAAwYuQDIvdZlPDiFBJ1CUDAIDgRkiGF+qSAQAACMkogdtTAwAAEJJRgnVDkRxqkgEAQPAiJMNLw7PWSjascAEAAIIUIRleGkY4FWK3Kb/IrQPH82q7OwAAALWCkAwvDrtNLRpHSpJ2HDhRy70BAACoHYRklNImPkqStP3ACUouAABAUCIko5QWjSLlsNt0LLdAh06wygUAAAg+hGSU4gyxq0WjCEnS9gPHa7k3AAAANY+QjHNqE99AEiUXAAAgOIXUdgdQN7VsXFxykXWyQIdz8tU4ylXbXQJQgw4cP6Wvdmdpw09HteXnbB08nqfDOfk6fqpA4U6HIp0hio0IVYvGkWrVOFLtEqN1cfNYNY0Jr+2uA4BfEJJxTs4Qu5rHReiHQznavv8EIRkIAj9n5erDb/bp/W/2aUNGVpnt8grdyjpZoJ+zcrVlb7bXvqSYMF1+USP1aR+vq9o2UUx4aDX3GgCqByEZZWoTH6UfDuXo233Z+sWFsQoLddR2lwBUQEZGhg4dOlShtgdyCpW+55Q+++mUth85c6dNm6QLY0LUtpFTbeJC1TjCoRiXXRGhNuUXGZ0qNMrKc2vv8UL9nF2oH7IK9GNWofYeO6V3Nvysdzb8LLtN6tDYqR5NXbokyaVuLRPVvHnzanrXAOBfhGSUqVV8lGJ2HdGx3AJ9tCVTN3ZLks1mq+1uAShHRkaG2nfooNyTJ8toYVNo42SFX9RDEe16y5XUztpjjFt5P23RyW2f6uT3n+nHE0e0phLntoWGyZXUTmEtL1Z4q0vlbHyhthzM15aD+Xpl03EVZn2j31zTRUMub6OLL2woh52/TwDUXYRklCnUYdegLk315rqf9OPhk1q7+6guaxFX290C6pSyZm0LioyO5bl17JRb2flu5RW6lXd6Bja/0CivyKjQLXkuiz37+lgjo5LXy5qz2thsksNmk90mOezF33u+HjyQKUebq9S3/82KjYuXJJ1ySycLbTpRaNPRPJsKjM3ryI1dRs0i3EqKcCu8eTupdztJv63y2JwozFdmrl2ZuXYdOCWFxDbVGxsP6Y2Nh9Qo0qm+HeKV2iFBl7dqpOgwyjIA1C2E5BJmz56tp59+WpmZmerWrZtmzZqlyy67rLa7VWuaNHCpT7smWvbdAX2+87BiwkLVNiGKGWUEjYIit46fKlR2boGOnyrU8VMFyj5VoOxThcrYd1B/nfGcikLC5YiMlSMiRo7IWNkjG8oRFlVLPY5Qo/5jtUOSjpy7RYjdpqaxYWrVJEqtm0Qp0lV9/xS0P/1117Yt+ufz0zXkd09o44FCHc7J11vr9uitdXtkt0ldLojRZS3j1PmCGHW5IEYtGkXKzkwzgFpESD7Lm2++qQkTJmjOnDnq2bOnnn32WaWlpWnbtm2Kj4+v7e7Vmk5JMdqbdUrf7svW4i2Z+irDpctaxunCuAiFOlhFEHWTMUZ5hW7l5BXqZH6RTuSdFXTzCpSdWxx4j58qtELvmTB8ZvupAne554nseUuZ+2wycjkkl90oxC45bMWPEJuRwyaVzIAlI+G5fhe16cysspHkNmeeuyUdO3JI+zN+ULP2v1CD2DgZSRFOh6JcIYoOC1VCtEuNo1w1HkBD7dLJbZ8qLSZTYy9rr+8O5uuLn09pQ2ae9p0o0td7junrPces9iF2qUmEQwmRDsWGORTltCvSabNm0G224rHwvA23kYqMVOQ2chudfpjT28587zbFr7Fbr7cpMiJcsbHRp2fkbbLZbNbsvN1us7YXuo0Ki9wqKDIqdLtVWGTOfH96X2GRUUGJdiF2uyJdDkU4HQoPDTn9fYiiwkLUwBWiBmEhahAWevpr8fdRrhDKUYBaRkg+y4wZM3T33Xdr5MiRkqQ5c+bo/fff18svv6xHHnmklntXu/q0a6IIp0Nf78nSgeN5WrRpnyQpJjxUsRGhCnXYFWK36dRxhxqm/o/mbczWkv1bSx3HqIw1l8+xuazVmctat/lcmz2bjh/PVm7uqXLbnt0/9+n93l+NFUw8X6Xif2g9Yab4e9s5tnl/b5NNNpsUHh6umOgG1mt0et+ZEGCzwoDOOq5Xm9MH92w/+7/qjSnZ5zPPZcw5txsjHcs+ptyTuaXea6ljnHO/91id7ex/8s8EQFs5+87ILyiS22ZXkduo0EiFbqMid3EoKnTrdCmDW7mFxSUNpwqN9fPzh7AQmyJCbYoMtVtfC3OP69NVy3XJ1f2UmJioCKfj9CNE4U6HwkLsNf6/LuuX/1evvftnDXjiH+r+iy41eu7yZB85KEn6zW9+U2qfo0FjhV3YVa6ktnImtFZofEsVhrq070SR9p0oqoHe5Uiq2IWONcnpkELtNoU6bKe/Fj8PsdusfXa7zQr9xb942axfwBzWPpv3frunffH2s0t5vP7+8Npe/H1hUZEcDof1d4GvbKV+JTxrXyX/yESEhys6usFZrz9zgFKHKvWL6Vlty/mltfQ+7w1n7y99zrL7U95xK9WfcgbtfH2Xzvwsz/nvqPHeZ7z2eb/+nO3OOmjJ8rKzX/fgde3q3P8eEZJPy8/P1/r16zV58mRrm91uV2pqqtLT00u1z8vLU15envX82LHiGZDs7OxSbavDiRMnJEl7tm9RXm5ZF+j4V6KkuBhpxwmHMnLsynfbdDRPOprl3S6qc18t/Oag9M3BGukXcD7u/FMyhafkzjsp96mTchfkyJ2XK5OXI5N3snh7fk7x17Me5vQ2k3dSZf/aJjlbxqiBq6MkKe/042iNvLPS9mfslCRl/vi9dkZG1FIvSvvx2w2SpEsH3KpmLduU0zJL5tQG5Z8KVb7NpTxbqArkUKEtRG45ZE7/A3/mazGbimfuPXvO/r746+nvzyrsNpJOHDuqvT9sk81ml+x2yWYv/t7zXLbi7202ybiLH0WFMu4iyV0k4z79fVFR8VdTKFNUvF1ut4y7SDa7Q7ZQl+yh4bKFOmULDS9+7oyQzRUhuzNcdldE8cMZIVuIU5J06vQDCAZ39WxaI/974slpFblRms1wOzVJ0t69e3XBBRfos88+U0pKirX94Ycf1urVq/XFF194tZ86daqeeOKJmu4mAAAAquinn35Ss2bNym3DTLKPJk+erAkTJljP3W63jhw5okaNGtXIf69mZ2crOTlZP/30k6Kjo6v9fPUZY1VxjFXFMVYVwzhVHGNVcYxVxTFW3owxOn78uJKSks7blpB8WuPGjeVwOLR//36v7fv371diYmKp9i6XSy6X913oYmNjq7OL5xQdHc2HvoIYq4pjrCqOsaoYxqniGKuKY6wqjrE6IyYmpkLtWJrgNKfTqR49emj58uXWNrfbreXLl3uVXwAAACDwMZN8lgkTJmj48OG65JJLdNlll+nZZ59VTk6OtdoFAAAAggMh+Sy33nqrDh48qClTpigzM1Pdu3fX4sWLlZCQUNtdK8Xlcunxxx8vVfKB0hirimOsKo6xqhjGqeIYq4pjrCqOsfIdq1sAAAAAJVCTDAAAAJRASAYAAABKICQDAAAAJRCSAQAAgBIIyfXU7Nmz1aJFC4WFhalnz5768ssva7tLNWrq1Kmy2Wxej/bt21v7T506pTFjxqhRo0aKiorSkCFDSt0oJiMjQ4MGDVJERITi4+M1ceJEFRYW1vRb8bs1a9bohhtuUFJSkmw2mxYuXOi13xijKVOmqGnTpgoPD1dqaqq2b9/u1ebIkSMaNmyYoqOjFRsbq1GjRunEiRNebTZt2qQrr7xSYWFhSk5O1lNPPVXdb83vzjdWI0aMKPU569+/v1ebYBiradOm6dJLL1WDBg0UHx+vwYMHa9u2bV5t/PVnbtWqVbr44ovlcrnUunVrzZs3r7rfnl9VZKyuueaaUp+re+65x6tNoI/Viy++qK5du1o3uEhJSdGHH35o7efzdMb5xorPUzUyqHfeeOMN43Q6zcsvv2y2bNli7r77bhMbG2v2799f212rMY8//rjp1KmT2bdvn/U4ePCgtf+ee+4xycnJZvny5WbdunXm8ssvN1dccYW1v7Cw0HTu3NmkpqaaDRs2mA8++MA0btzYTJ48uTbejl998MEH5ve//7155513jCTz7rvveu3/y1/+YmJiYszChQvN119/bW688UbTsmVLk5uba7Xp37+/6datm/n888/Nxx9/bFq3bm1uu+02a/+xY8dMQkKCGTZsmNm8ebP597//bcLDw83f//73mnqbfnG+sRo+fLjp37+/1+fsyJEjXm2CYazS0tLM3LlzzebNm83GjRvNwIEDzYUXXmhOnDhhtfHHn7kffvjBREREmAkTJphvv/3WzJo1yzgcDrN48eIafb9VUZGxuvrqq83dd9/t9bk6duyYtT8Yxuq///2vef/99833339vtm3bZh599FETGhpqNm/ebIzh83S2840Vn6fqQ0iuhy677DIzZswY63lRUZFJSkoy06ZNq8Ve1azHH3/cdOvW7Zz7srKyTGhoqFmwYIG17bvvvjOSTHp6ujGmOBzZ7XaTmZlptXnxxRdNdHS0ycvLq9a+16SSwc/tdpvExETz9NNPW9uysrKMy+Uy//73v40xxnz77bdGklm7dq3V5sMPPzQ2m838/PPPxhhjXnjhBdOwYUOvsZo0aZJp165dNb+j6lNWSL7pppvKfE2wjtWBAweMJLN69WpjjP/+zD388MOmU6dOXue69dZbTVpaWnW/pWpTcqyMKQ41DzzwQJmvCdaxatiwoXnppZf4PFWAZ6yM4fNUnSi3qGfy8/O1fv16paamWtvsdrtSU1OVnp5eiz2redu3b1dSUpIuuugiDRs2TBkZGZKk9evXq6CgwGuM2rdvrwsvvNAao/T0dHXp0sXrRjFpaWnKzs7Wli1bavaN1KBdu3YpMzPTa2xiYmLUs2dPr7GJjY3VJZdcYrVJTU2V3W7XF198YbW56qqr5HQ6rTZpaWnatm2bjh49WkPvpmasWrVK8fHxateune69914dPnzY2hesY3Xs2DFJUlxcnCT//ZlLT0/3OoanTX3+u63kWHm89tpraty4sTp37qzJkyfr5MmT1r5gG6uioiK98cYbysnJUUpKCp+ncpQcKw8+T9WDO+7VM4cOHVJRUVGpuwAmJCRo69attdSrmtezZ0/NmzdP7dq10759+/TEE0/oyiuv1ObNm5WZmSmn06nY2Fiv1yQkJCgzM1OSlJmZec4x9OwLVJ73dq73fvbYxMfHe+0PCQlRXFycV5uWLVuWOoZnX8OGDaul/zWtf//+uvnmm9WyZUvt3LlTjz76qAYMGKD09HQ5HI6gHCu3261x48apV69e6ty5syT57c9cWW2ys7OVm5ur8PDw6nhL1eZcYyVJt99+u5o3b66kpCRt2rRJkyZN0rZt2/TOO+9ICp6x+uabb5SSkqJTp04pKipK7777rjp27KiNGzfyeSqhrLGS+DxVJ0Iy6qUBAwZY33ft2lU9e/ZU8+bN9dZbbwXtH2b439ChQ63vu3Tpoq5du6pVq1ZatWqV+vbtW4s9qz1jxozR5s2b9cknn9R2V+q8ssZq9OjR1vddunRR06ZN1bdvX+3cuVOtWrWq6W7Wmnbt2mnjxo06duyY3n77bQ0fPlyrV6+u7W7VSWWNVceOHfk8VSPKLeqZxo0by+FwlLrKd//+/UpMTKylXtW+2NhYtW3bVjt27FBiYqLy8/OVlZXl1ebsMUpMTDznGHr2BSrPeyvv85OYmKgDBw547S8sLNSRI0eCfvwuuugiNW7cWDt27JAUfGM1duxYLVq0SCtXrlSzZs2s7f76M1dWm+jo6Hr3y29ZY3UuPXv2lCSvz1UwjJXT6VTr1q3Vo0cPTZs2Td26ddPMmTP5PJ1DWWN1LsH6eaoOhOR6xul0qkePHlq+fLm1ze12a/ny5V71ScHmxIkT2rlzp5o2baoePXooNDTUa4y2bdumjIwMa4xSUlL0zTffeAWcpUuXKjo62vovrEDUsmVLJSYmeo1Ndna2vvjiC6+xycrK0vr16602K1askNvttv7yTUlJ0Zo1a1RQUGC1Wbp0qdq1a1fvygcqY8+ePTp8+LCaNm0qKXjGyhijsWPH6t1339WKFStKlY/4689cSkqK1zE8berT323nG6tz2bhxoyR5fa6CYaxKcrvdysvL4/NUAZ6xOhc+T35U21cOovLeeOMN43K5zLx588y3335rRo8ebWJjY72uXA10Dz74oFm1apXZtWuX+fTTT01qaqpp3LixOXDggDGmePmgCy+80KxYscKsW7fOpKSkmJSUFOv1niVx+vXrZzZu3GgWL15smjRpEhBLwB0/ftxs2LDBbNiwwUgyM2bMMBs2bDC7d+82xhQvARcbG2vee+89s2nTJnPTTTedcwm4X/ziF+aLL74wn3zyiWnTpo3XsmZZWVkmISHB3HHHHWbz5s3mjTfeMBEREfVqWTNjyh+r48ePm4ceesikp6ebXbt2mWXLlpmLL77YtGnTxpw6dco6RjCM1b333mtiYmLMqlWrvJaZOnnypNXGH3/mPMtQTZw40Xz33Xdm9uzZ9W4ZqvON1Y4dO8yTTz5p1q1bZ3bt2mXee+89c9FFF5mrrrrKOkYwjNUjjzxiVq9ebXbt2mU2bdpkHnnkEWOz2cySJUuMMXyezlbeWPF5ql6E5Hpq1qxZ5sILLzROp9Ncdtll5vPPP6/tLtWoW2+91TRt2tQ4nU5zwQUXmFtvvdXs2LHD2p+bm2vuu+8+07BhQxMREWF++ctfmn379nkd48cffzQDBgww4eHhpnHjxubBBx80BQUFNf1W/G7lypVGUqnH8OHDjTHFy8D94Q9/MAkJCcblcpm+ffuabdu2eR3j8OHD5rbbbjNRUVEmOjrajBw50hw/ftyrzddff2169+5tXC6XueCCC8xf/vKXmnqLflPeWJ08edL069fPNGnSxISGhprmzZubu+++u9Qvo8EwVucaI0lm7ty5Vht//ZlbuXKl6d69u3E6neaiiy7yOkd9cL6xysjIMFdddZWJi4szLpfLtG7d2kycONFrXVtjAn+sfvvb35rmzZsbp9NpmjRpYvr27WsFZGP4PJ2tvLHi81S9bMYYU3Pz1gAAAEDdR00yAAAAUAIhGQAAACiBkAwAAACUQEgGAAAASiAkAwAAACUQkgEAAIASCMkAAABACYRkAAAAoARCMgAEuKlTp6p79+515jgAUB8QkgGgFmVmZur+++/XRRddJJfLpeTkZN1www1avny5387x0EMPeR1vxIgRGjx4sN+Ofy4EagD1XUhtdwAAgtWPP/6oXr16KTY2Vk8//bS6dOmigoICffTRRxozZoy2bt3ql/NERUUpKirKL8cCgGDBTDIA1JL77rtPNptNX375pYYMGaK2bduqU6dOmjBhgj7//HNJ0owZM9SlSxdFRkYqOTlZ9913n06cOGEdY968eYqNjdXChQvVpk0bhYWFKS0tTT/99JPV5uxZ3alTp2r+/Pl67733ZLPZZLPZtGrVKknSpEmT1LZtW0VEROiiiy7SH/7wBxUUFFTLe//mm2907bXXKjw8XI0aNdLo0aO93teqVat02WWXKTIyUrGxserVq5d2794tSfr666/Vp08fNWjQQNHR0erRo4fWrVtXLf0EELwIyQBQC44cOaLFixdrzJgxioyMLLU/NjZWkmS32/Xcc89py5Ytmj9/vlasWKGHH37Yq+3Jkyf15z//Wa+88oo+/fRTZWVlaejQoec870MPPaRf//rX6t+/v/bt26d9+/bpiiuukCQ1aNBA8+bN07fffquZM2fq//7v//TMM8/4941LysnJUVpamho2bKi1a9dqwYIFWrZsmcaOHStJKiws1ODBg3X11Vdr06ZNSk9P1+jRo2Wz2SRJw4YNU7NmzbR27VqtX79ejzzyiEJDQ/3eTwDBjXILAKgFO3bskDFG7du3L7fduHHjrO9btGihP/3pT7rnnnv0wgsvWNsLCgr0/PPPq2fPnpKk+fPnq0OHDvryyy912WWXeR0vKipK4eHhysvLU2Jiote+xx57zOtcDz30kN54441SobyqXn/9dZ06dUqvvPKK9QvC888/rxtuuEF//etfFRoaqmPHjun6669Xq1atJEkdOnSwXp+RkaGJEydaY9emTRu/9g8AJGaSAaBWGGMq1G7ZsmXq27evLrjgAjVo0EB33HGHDh8+rJMnT1ptQkJCdOmll1rP27dvr9jYWH333XeV6tObb76pXr16KTExUVFRUXrssceUkZFRqWNUxHfffadu3bp5zaD36tVLbrdb27ZtU1xcnEaMGKG0tDTdcMMNmjlzpvbt22e1nTBhgu666y6lpqbqL3/5i3bu3On3PgIAIRkAakGbNm1ks9nKvTjvxx9/1PXXX6+uXbvqP//5j9avX6/Zs2dLkvLz8/3an/T0dA0bNkwDBw7UokWLtGHDBv3+97/3+3kqau7cuUpPT9cVV1yhN998U23btrXqtKdOnaotW7Zo0KBBWrFihTp27Kh33323VvoJIHARkgGgFsTFxSktLU2zZ89WTk5Oqf1ZWVlav3693G63pk+frssvv1xt27bV3r17S7UtLCz0unBt27ZtysrK8ipROJvT6VRRUZHXts8++0zNmzfX73//e11yySVq06aNdaGcv3Xo0EFff/211/v+9NNPZbfb1a5dO2vbL37xC02ePFmfffaZOnfurNdff93a17ZtW40fP15LlizRzTffrLlz51ZLXwEEL0IyANSS2bNnq6ioSJdddpn+85//aPv27fruu+/03HPPKSUlRa1bt1ZBQYFmzZqlH374Qa+++qrmzJlT6jihoaG6//779cUXX2j9+vUaMWKELr/88lL1yB4tWrTQpk2btG3bNh06dEgFBQVq06aNMjIy9MYbb2jnzp167rnnqjw7m5ubq40bN3o9du7cqWHDhiksLEzDhw/X5s2btXLlSt1///264447lJCQoF27dmny5MlKT0/X7t27tWTJEm3fvl0dOnRQbm6uxo4dq1WrVmn37t369NNPtXbt2jJ/IQAAnxkAQK3Zu3evGTNmjGnevLlxOp3mggsuMDfeeKNZuXKlMcaYGTNmmKZNm5rw8HCTlpZmXnnlFSPJHD161BhjzNy5c01MTIz5z3/+Yy666CLjcrlMamqq2b17t3WOxx9/3HTr1s16fuDAAXPdddeZqKgoI8k618SJE02jRo1MVFSUufXWW80zzzxjYmJiyjxOeR5//HEjqdSjb9++xhhjNm3aZPr06WPCwsJMXFycufvuu83x48eNMcZkZmaawYMHm6ZNmxqn02maN29upkyZYoqKikxeXp4ZOnSoSU5ONk6n0yQlJZmxY8ea3Nxcn8YfAMpiM6aCV48AAOqcefPmady4ccrKyqrtrgBAQKHcAgAAACiBkAwAqDTPra7P9fj4449ru3sAUGWUWwAAKm3Hjh1l7rvgggsUHh5eg70BAP8jJAMAAAAlUG4BAAAAlEBIBgAAAEogJAMAAAAlEJIBAACAEgjJAAAAQAmEZAAAAKAEQjIAAABQwv8Hk7SWyK9LsLQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAIjCAYAAAB2/jgmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADkEklEQVR4nOzdeViN+f8/8OepdFpOnUirVkq2EjJ2ZQ0jxcwgTYTBTEy2MCHKbohiFrswjbKWD2ONLNmXjDVkkhkNY+kcWVrv3x++7p+jolIKz8d13dd17vu9ve4zXHNe3vf9fksEQRBAREREREREFU6togMgIiIiIiKiF5igERERERERVRJM0IiIiIiIiCoJJmhERERERESVBBM0IiIiIiKiSoIJGhERERERUSXBBI2IiIiIiKiSYIJGRERERERUSTBBIyIiIiIiqiSYoBEREZWDkJAQSCSS9zKWm5sb3NzcxPOEhARIJBJs2rTpvYzv5+cHGxub9zJWaWVmZuKbb76BqakpJBIJRo0aVdEhEREVigkaERHRW0RGRkIikYiHlpYWzM3N4e7ujkWLFuHx48dlMs6dO3cQEhKCpKSkMumvLFXm2Ipj1qxZiIyMxHfffYd169bB19f3jfXz8vKwevVquLm5oVq1apBKpbCxscHAgQNx+vTp9xT1m12+fBkhISFITU2t6FCIqAxJBEEQKjoIIiKiyiwyMhIDBw7EtGnTYGtri5ycHPz7779ISEjA3r17YWVlhW3btsHJyUlsk5ubi9zcXGhpaRV7nNOnT6Np06ZYvXo1/Pz8it0uOzsbAKCpqQngxQxau3btsHHjRnz55ZfF7qe0seXk5CA/Px9SqbRMxioPzZs3h4aGBo4cOfLWus+ePUOvXr2wa9cutG3bFh4eHqhWrRpSU1OxYcMGXLt2DWlpabCwsHgPkRdt06ZN+Oqrr3DgwAGVGVQi+rBpVHQAREREH4quXbvCxcVFPA8KCsL+/fvRvXt39OjRA1euXIG2tjYAQENDAxoa5fu/2adPn0JHR0dMzCpKlSpVKnT84rh37x7q1atXrLrjxo3Drl27sHDhwgKPQk6dOhULFy4shwjLlyAIeP78ufjnk4gqLz7iSERE9A7at2+P4OBg3Lp1C7/99pt4vbB30Pbu3YvWrVvDwMAAMpkMDg4OmDhxIoAXs15NmzYFAAwcOFB8nDIyMhLAi/fMGjRogDNnzqBt27bQ0dER277+DtpLeXl5mDhxIkxNTaGrq4sePXrg9u3bKnVsbGwKna17tc+3xVbYO2hPnjzB2LFjYWlpCalUCgcHB8yfPx+vP7gjkUgwYsQIxMbGokGDBpBKpahfvz527dpV+Bf+mnv37mHw4MEwMTGBlpYWGjZsiDVr1ojlL9/H++uvv7Bjxw4x9qIeC/z777+xdOlSdOrUqdD31NTV1REYGKgye3bu3Dl07doV+vr6kMlk6NChA44fP67Srqh3El8+PvtqPDY2NujevTuOHDmCzz77DFpaWqhZsybWrl2r0u6rr74CALRr1068r4SEBJU+du/eDRcXF2hra2Pp0qVwdXVFw4YNC713BwcHuLu7F1pGRO8PEzQiIqJ39PJ9pj179hRZ59KlS+jevTuysrIwbdo0hIWFoUePHkhMTAQA1K1bF9OmTQMADB06FOvWrcO6devQtm1bsY8HDx6ga9eucHZ2Rnh4ONq1a/fGuGbOnIkdO3ZgwoQJCAgIwN69e9GxY0c8e/asRPdXnNheJQgCevTogYULF6JLly5YsGABHBwcMG7cOIwZM6ZA/SNHjsDf3x99+/bFjz/+iOfPn+OLL77AgwcP3hjXs2fP4ObmhnXr1sHHxwfz5s2DXC6Hn58fIiIixNjXrVuH6tWrw9nZWYzdyMio0D537tyJ3Nzct76j9tKlS5fQpk0bnD9/HuPHj0dwcDD++usvuLm54cSJE8XqozA3btzAl19+iU6dOiEsLAxVq1aFn58fLl26BABo27YtAgICAAATJ04U76tu3bpiH8nJyfD29kanTp0QEREBZ2dn+Pr64s8//8TFixdVxjt16hSuXbuGr7/+utQxE1EZEYiIiOiNVq9eLQAQTp06VWQduVwuNGrUSDyfOnWq8Or/ZhcuXCgAEP77778i+zh16pQAQFi9enWBMldXVwGAsGTJkkLLXF1dxfMDBw4IAIQaNWoISqVSvL5hwwYBgBARESFes7a2FgYMGPDWPt8U24ABAwRra2vxPDY2VgAgzJgxQ6Xel19+KUgkEuHGjRviNQCCpqamyrXz588LAITFixcXGOtV4eHhAgDht99+E69lZ2cLLVq0EGQymcq9W1tbC59//vkb+xMEQRg9erQAQDh37txb6wqCIHh5eQmamppCSkqKeO3OnTuCnp6e0LZtW/Ha638eXnr5Z+uvv/5SiRWAcOjQIfHavXv3BKlUKowdO1a8tnHjRgGAcODAgQL9vuxj165dKtczMjIELS0tYcKECSrXAwICBF1dXSEzM7NY901E5YczaERERGVAJpO9cTVHAwMDAEBcXBzy8/NLNYZUKsXAgQOLXb9///7Q09MTz7/88kuYmZnhjz/+KNX4xfXHH39AXV1dnOF5aezYsRAEATt37lS53rFjR9SqVUs8d3Jygr6+Pm7evPnWcUxNTeHt7S1eq1KlCgICApCZmYmDBw+WOHalUgkAKt9bUfLy8rBnzx54eXmhZs2a4nUzMzP069cPR44cEfsrqXr16qFNmzbiuZGRERwcHN76nbzK1ta2wCOLcrkcnp6eWL9+vfi4aV5eHmJiYuDl5QVdXd1SxUtEZYcJGhERURnIzMx844/6Pn36oFWrVvjmm29gYmKCvn37YsOGDSVK1mrUqFGiBUHs7e1VziUSCezs7Mp9WfZbt27B3Ny8wPfx8vG7W7duqVy3srIq0EfVqlXx6NGjt45jb28PNTXVnzNFjVMc+vr6AFCsrRP+++8/PH36FA4ODgXK6tati/z8/ALv/BVXab+TV9na2hZ6vX///khLS8Phw4cBAPv27cPdu3eL/VgnEZUvJmhERETv6O+//4ZCoYCdnV2RdbS1tXHo0CHs27dPfA+oT58+6NSpE/Ly8oo1TnmswFfUZtrFjaksqKurF3pdqICdgOrUqQMAuHDhQpn2W9LvuSy+k6L+vLi7u8PExERc1Oa3336DqakpOnbsWOy+iaj8MEEjIiJ6R+vWrQOAt66Ap6amhg4dOmDBggW4fPkyZs6cif379+PAgQMAiv4RX1rXr19XORcEATdu3FBZcbFq1arIyMgo0Pb12aeSxGZtbY07d+4UmIW6evWqWF4WrK2tcf369QKzkO8yTteuXaGurq6yImdRjIyMoKOjg+Tk5AJlV69ehZqaGiwtLQG8+J4BFPiuSzPL91Jp/7yoq6ujX79+2LRpEx49eoTY2Fh4e3sXmRQS0fvFBI2IiOgd7N+/H9OnT4etrS18fHyKrPfw4cMC15ydnQEAWVlZACC+/1NYwlQaa9euVUmSNm3ahPT0dHTt2lW8VqtWLRw/flzc7BoAtm/fXuDRvJLE1q1bN+Tl5eGnn35Sub5w4UJIJBKV8d9Ft27d8O+//yImJka8lpubi8WLF0Mmk8HV1bXEfVpaWmLIkCHYs2cPFi9eXKA8Pz8fYWFh+Pvvv6Guro7OnTsjLi5O5bHRu3fv4vfff0fr1q3FRyZfvmN36NAhsd6TJ09UtgQoqXf58+Lr64tHjx5h2LBhyMzM5OqNRJUIN6omIiIqpp07d+Lq1avIzc3F3bt3sX//fuzduxfW1tbYtm0btLS0imw7bdo0HDp0CJ9//jmsra1x7949/PLLL7CwsEDr1q0BvPgRb2BggCVLlkBPTw+6urpo1qxZke8SvU21atXQunVrDBw4EHfv3kV4eDjs7OwwZMgQsc4333yDTZs2oUuXLujduzdSUlLw22+/qSzaUdLYPDw80K5dO0yaNAmpqalo2LAh9uzZg7i4OIwaNapA36U1dOhQLF26FH5+fjhz5gxsbGywadMmJCYmIjw8vFgLfRQmLCwMKSkpCAgIwJYtW9C9e3dUrVoVaWlp2LhxI65evYq+ffsCAGbMmCHub+fv7w8NDQ0sXboUWVlZ+PHHH8U+O3fuDCsrKwwePBjjxo2Duro6Vq1aBSMjI6SlpZUqTmdnZ6irq2Pu3LlQKBSQSqVo3749jI2N39q2UaNGaNCgATZu3Ii6deuicePGpYqBiMpBha4hSURE9AF4uRT6y0NTU1MwNTUVOnXqJERERKgs5/7S68uqx8fHC56enoK5ubmgqakpmJubC97e3sK1a9dU2sXFxQn16tUTNDQ0VJa1d3V1FerXr19ofEUts79+/XohKChIMDY2FrS1tYXPP/9cuHXrVoH2YWFhQo0aNQSpVCq0atVKOH36dIE+3xTb68vsC4IgPH78WBg9erRgbm4uVKlSRbC3txfmzZsn5Ofnq9QDIAwfPrxATEUt//+6u3fvCgMHDhSqV68uaGpqCo6OjoVuBVDcZfZfys3NFVasWCG0adNGkMvlQpUqVQRra2th4MCBBZbgP3v2rODu7i7IZDJBR0dHaNeunXD06NECfZ45c0Zo1qyZoKmpKVhZWQkLFiwocpn9wmIt7L/J8uXLhZo1awrq6uoqS+4X535//PFHAYAwa9asYn0nRPR+SAShAt7AJSIiIqIKFRERgdGjRyM1NbXQVSOJqGIwQSMiIiL6xAiCgIYNG8LQ0FBcpIaIKge+g0ZERET0iXjy5Am2bduGAwcO4MKFC4iLi6vokIjoNZxBIyIiIvpEpKamwtbWFgYGBvD398fMmTMrOiQieg0TNCIiIiIiokqC+6ARERERERFVEkzQiIiIiIiIKgkuEkL0gcvPz8edO3egp6cHiURS0eEQERER0WsEQcDjx49hbm4ONbU3z5ExQSP6wN25cweWlpYVHQYRERERvcXt27dhYWHxxjpM0KhChISEIDY2FklJSWXet5+fHzIyMhAbG1vmfVdGenp6AIAGwxZCXVO7gqMhIiIiqvwOzfB+r+MplUpYWlqKv9vehAkaFVtkZCRGjRqFjIyMAmUSiQRbt26Fl5dXsfoKDAzE999/L56/76Tqr7/+wqRJk5CQkICHDx+ievXqaNKkCebOnYs6deqIyxCfO3cOzs7OJeq7pN/Fu3r5WKO6pjbUpUzQiIiIiN5GX1+/QsYtzusoTNCoQshkMshksgoZOycnB506dYKDgwO2bNkCMzMz/P3339i5c2ehyScRERER0fvCVRypzCUkJEAikSA+Ph4uLi7Q0dFBy5YtkZycLNYJCQkRZ6ZCQkKwZs0axMXFQSKRQCKRICEhAcCL53R79+4NAwMDVKtWDZ6enkhNTRX7ycvLw5gxY2BgYABDQ0OMHz8eb9va79KlS0hJScEvv/yC5s2bw9raGq1atcKMGTPQvHlzAICtrS0AoFGjRpBIJHBzcwMAnDp1Cp06dUL16tUhl8vh6uqKs2fPin3b2NgAAHr27AmJRCKeA0BcXBwaN24MLS0t1KxZE6GhocjNzQXw4sXRkJAQWFlZQSqVwtzcHAEBAYXGn5WVBaVSqXIQERER0ceBCRqVm0mTJiEsLAynT5+GhoYGBg0aVGi9wMBA9O7dG126dEF6ejrS09PRsmVL5OTkwN3dHXp6ejh8+DASExMhk8nQpUsXZGdnAwDCwsIQGRmJVatW4ciRI3j48CG2bt36xriMjIygpqaGTZs2IS8vr9A6J0+eBADs27cP6enp2LJlCwDg8ePHGDBgAI4cOYLjx4/D3t4e3bp1w+PHjwG8SOAAYPXq1UhPTxfPDx8+jP79+2PkyJG4fPkyli5disjISMycORMAsHnzZixcuBBLly7F9evXERsbC0dHx0Jjmz17NuRyuXhwgRAiIiKijwcfcaRyM3PmTLi6ugIAfvjhB3z++ed4/vw5tLS0VOrJZDJoa2sjKysLpqam4vXffvsN+fn5WLFihfi87urVq2FgYICEhAR07twZ4eHhCAoKQq9evQAAS5Yswe7du98YV40aNbBo0SKMHz8eoaGhcHFxQbt27eDj44OaNWsCeJHEAYChoaFKTO3bt1fpa9myZTAwMMDBgwfRvXt3sZ2BgYFKu9DQUPzwww8YMGAAAKBmzZqYPn06xo8fj6lTpyItLQ2mpqbo2LEjqlSpAisrK3z22WeFxh8UFIQxY8aI5y9fOiUiIiKiDx9n0KjcODk5iZ/NzMwAAPfu3St2+/Pnz+PGjRvQ09MT31mrVq0anj9/jpSUFCgUCqSnp6NZs2ZiGw0NDbi4uIjnUVFRYluZTIbDhw8DAIYPH45///0XUVFRaNGiBTZu3Ij69etj7969b4zp7t27GDJkCOzt7SGXy6Gvr4/MzEykpaW99V6mTZumEsuQIUOQnp6Op0+f4quvvsKzZ89Qs2ZNDBkyBFu3bhUff3ydVCqFvr6+ykFEREREHwfOoFGx6evr48mTJ8jPz1fZYO/lwhpyuVylfpUqVcTPL2fA8vPziz1eZmYmmjRpgqioqAJlL2eq3qZHjx4qCVyNGjXEz3p6evDw8ICHhwdmzJgBd3d3zJgxA506dSqyvwEDBuDBgweIiIiAtbU1pFIpWrRoIT5y+aZ7CQ0NFWf6XqWlpQVLS0skJydj37592Lt3L/z9/TFv3jwcPHhQ5XskIiIioo8bEzQqNgcHB+Tm5iIpKQmNGzcWr79cJKN27dql7ltTU7PA+2CNGzdGTEwMjI2Ni5wlMjMzw4kTJ9C2bVsAQG5uLs6cOSPGp6enV6z9JiQSCerUqYOjR4+K8QAoEFNiYiJ++eUXdOvWDcCLRUzu37+vUqdKlSqF3ktycjLs7OyKjEFbW1tMGIcPH446dergwoULKt81EREREX3cmKBRsdWvXx+dO3fGoEGDEBYWhpo1ayI5ORmjRo1Cnz59VGanSsrGxga7d+9GcnIyDA0NIZfL4ePjg3nz5sHT0xPTpk2DhYUFbt26hS1btmD8+PGwsLDAyJEjMWfOHNjb26NOnTpYsGDBW5fKT0pKwtSpU+Hr64t69epBU1MTBw8exKpVqzBhwgQAgLGxMbS1tbFr1y5YWFhAS0sLcrkc9vb2WLduHVxcXKBUKjFu3Dhoa6vuPWZjY4P4+Hi0atUKUqkUVatWxZQpU9C9e3dYWVnhyy+/hJqaGs6fP4+LFy9ixowZiIyMRF5eHpo1awYdHR389ttv0NbWhrW1dbG/w0MzvPm4IxEREdEHju+gUYnExMTA1dUVw4YNQ/369REQEABPT0+sWLHinfodMmQIHBwc4OLiAiMjIyQmJkJHRweHDh2ClZUVevXqhbp162Lw4MF4/vy5mIiMHTsWvr6+GDBgAFq0aAE9PT307NnzjWNZWFjAxsYGoaGhaNasGRo3boyIiAiEhoZi0qRJAF68y7Zo0SIsXboU5ubm8PT0BACsXLkSjx49QuPGjeHr64uAgAAYGxur9B8WFoa9e/fC0tISjRo1AgC4u7tj+/bt2LNnD5o2bYrmzZtj4cKFYgJmYGCA5cuXo1WrVnBycsK+ffvwv//9D4aGhu/0vRIRERHRh0UivG3TKCIqMRsbG4waNQqjRo0q97GUSiXkcjkafr8E6lLttzf4P2fm9S/HqIiIiIjopZe/1xQKxVufeOIMGn30Xm6cXbVqVTx//lyl7NSpU+Lm2GXp1KlTGDp0aJn2SUREREQfPyZo9MnQ09MrsIn1ypUrYWVlVeZjGRkZQUdHp8z7JSIiIqKPGxM0+mQMGDAAq1atEs+fPXuG6OhocfPoVx05cgRt2rSBtrY2LC0tERAQgCdPngAA1q5dC5lMhuvXr4v1/f39UadOHTx9+hTAi0ccw8PDxfKMjAwMGzYMJiYm0NLSQoMGDbB9+3axfPPmzahfvz6kUilsbGwQFhZW1rdPRERERB8AJmj0yfD19cXhw4fFTaU3b94MGxubAsvYp6SkoEuXLvjiiy/w559/IiYmBkeOHMGIESMAAP3790e3bt3g4+OD3Nxc7NixAytWrEBUVFShs2b5+fno2rUrEhMT8dtvv+Hy5cuYM2cO1NXVAQBnzpxB79690bdvX1y4cAEhISEIDg5GZGRkofeRlZUFpVKpchARERHRx4HL7NMnw9jYGF27dkVkZCSmTJmCVatWYdCgQQXqzZ49Gz4+PuICH/b29li0aBFcXV3x66+/QktLC0uXLoWTkxMCAgKwZcsWhISEoEmTJoWOu2/fPpw8eRJXrlwR94qrWbOmWL5gwQJ06NABwcHBAF7sJ3f58mXMmzcPfn5+hcYXGhr6jt8GEREREVVGnEGjT8qgQYMQGRmJmzdv4tixY/Dx8SlQ5/z584iMjIRMJhMPd3d35Ofn46+//gIAVK1aFStXrsSvv/6KWrVq4YcffihyzKSkJFhYWBS5kfeVK1fQqlUrlWutWrXC9evXC2x4DQBBQUFQKBTicfv27ZJ8BURERERUiXEGjT4pXbt2xdChQzF48GB4eHgUus9YZmYmhg0bhoCAgAJlry4ocujQIairqyM9PR1PnjyBnp5eoWO+vpH1u5JKpZBKpWXaJxERERFVDpxBo0+KhoYG+vfvj4SEhEIfbwSAxo0b4/Lly7CzsytwaGpqAgCOHj2KuXPn4n//+x9kMpn4flphnJyc8Pfff+PatWuFltetWxeJiYkq1xITE1G7dm3xPTUiIiIi+jQwQaNPzvTp0/Hff//B3d290PIJEybg6NGjGDFiBJKSknD9+nXExcWJSdjjx4/h6+uLgIAAdO3aFVFRUYiJicGmTZsK7c/V1RVt27bFF198gb179+Kvv/7Czp07sWvXLgDA2LFjER8fj+nTp+PatWtYs2YNfvrpJwQGBpbPF0BERERElRYfcaRPjqamJqpXr15kuZOTEw4ePIhJkyahTZs2EAQBtWrVQp8+fQAAI0eOhK6uLmbNmgUAcHR0xKxZszBs2DC0aNECNWrUKNDn5s2bERgYCG9vbzx58gR2dnaYM2cOgBczdhs2bMCUKVMwffp0mJmZYdq0aYUuEPImh2Z4v3VneiIiIiKq3CSCIAgVHQQRlZ5SqYRcLodCoWCCRkRERFQJleT3Gh9xpEpNIpEgNja2yPKEhARIJBJkZGQAACIjI2FgYCCWh4SEwNnZuczjen0j6sqg7eT1aDJuLZqMW1vRoRARERFRKTFBowrzMrl6eZiYmOCLL77AzZs3i91Hy5YtkZ6eDrlcXmh5YGAg4uPjyypk0alTpzB06NAy75eIiIiIPm1M0KjCJScn486dO9i4cSMuXboEDw+PQvf/KoympiZMTU0hkUgKLZfJZIUupf+ujIyMoKOjU+b9EhEREdGnjQkaVThjY2OYmZmhbdu2mDJlCi5fvowbN26I5ffv30fPnj2ho6MDe3t7bNu2TSx7/RHH173+iKOfnx+8vLwQGhoKIyMj6Ovr49tvv0V2drZYx83NDSNGjMCIESMgl8tRvXp1BAcH49XXNV9/xFEikWDFihVFxgkAFy9eRNeuXSGTyWBiYgJfX1/cv39fLN+0aRMcHR2hra0NQ0NDdOzYEU+ePCnp10lEREREHzAmaFSpvNzU+dWEKTQ0FL1798aff/6Jbt26wcfHBw8fPiz1GPHx8bhy5QoSEhKwfv16bNmyBaGhoSp11qxZAw0NDZw8eRIRERFYsGABVqxY8cZ+3xRnRkYG2rdvj0aNGuH06dPYtWsX7t69i969ewMA0tPT4e3tjUGDBomx9erVC4Wt4ZOVlQWlUqlyEBEREdHHgQkaVRrp6emYP38+atSoAQcHB/G6n58fvL29YWdnh1mzZiEzMxMnT54s9TiamppYtWoV6tevj88//xzTpk3DokWLkJ+fL9axtLTEwoUL4eDgAB8fH3z//fdYuHDhG/t9U5w//fQTGjVqhFmzZqFOnTpo1KgRVq1ahQMHDuDatWtIT09Hbm4uevXqBRsbGzg6OsLf3x8ymazAOLNnz4ZcLhcPS0vLUn8XRERERFS5MEGjCmdhYQFdXV2Ym5vjyZMn2Lx5MzQ1NcVyJycn8bOuri709fVx7969Uo/XsGFDlffHWrRogczMTNy+fVu81rx5c5X32lq0aIHr16+/8d24N8V5/vx5HDhwADKZTDzq1KkDAEhJSUHDhg3RoUMHODo64quvvsLy5cvx6NGjQscJCgqCQqEQj1fjJiIiIqIPGzeqpgp3+PBh6Ovrw9jYGHp6egXKq1SponIukUhUZrsqizfFmZmZCQ8PD8ydO7dAOzMzM6irq2Pv3r04evQo9uzZg8WLF2PSpEk4ceIEbG1tVepLpVJIpdLyuxEiIiIiqjCcQaMKZ2tri1q1ahWanJWH8+fP49mzZ+L58ePHIZPJVB4VPHHihEqb48ePw97eHurq6qUas3Hjxrh06RJsbGxgZ2encujq6gJ4kdC1atUKoaGhOHfuHDQ1NbF169ZSjUdEREREHyYmaPTJyc7OxuDBg3H58mX88ccfmDp1KkaMGAE1tf//1yEtLQ1jxoxBcnIy1q9fj8WLF2PkyJGlHnP48OF4+PAhvL29cerUKaSkpGD37t0YOHAg8vLycOLECcyaNQunT59GWloatmzZgv/++w9169Yt9hiHZnjjzLz+ODOvf6njJCIiIqKKxUcc6ZPToUMH2Nvbo23btsjKyoK3tzdCQkJU6vTv3x/Pnj3DZ599BnV1dYwcOfKdNqY2NzdHYmIiJkyYgM6dOyMrKwvW1tbo0qUL1NTUoK+vj0OHDiE8PBxKpRLW1tYICwtD165d3/FuiYiIiOhDIhEKW8eb6CPl5+eHjIwMxMbGFlnHzc0Nzs7OKvuclURISAhiY2ORlJRUqvYlpVQqIZfLoVAooK+v/17GJCIiIqLiK8nvNT7iSOXu5WbSLw8TExN88cUXuHnz5nsbu6iNrEvq1fvQ19dH06ZNERcXp1InMDAQ8fHxZTJeSbSdvB5Nxq197+MSERERUdlhgkbvTXJyMu7cuYONGzfi0qVL8PDweOOy9e8qJyenXPpdvXo10tPTcfr0abRq1QpffvklLly4IJbLZDIYGhqWy9hERERE9HFjgkbvjbGxMczMzNC2bVtMmTIFly9fxo0bNxAZGQkDAwOVurGxsSr7kAFAXFwcGjduDC0tLdSsWROhoaHIzc0VyyUSCX799Vf06NEDurq6GDJkCNq1awcAqFq1qthfbGwssrKyEBAQAGNjY2hpaaF169Y4deoUgBezbm96vNHAwACmpqaoXbs2pk+fjtzcXBw4cEAsDwkJgbOzs0qblxtjS6VSmJmZYcSIEWJZWloaPD09IZPJoK+vj969e+Pu3bvF/l6JiIiI6OPBBI0qhLa2NoAXKyoWx+HDh9G/f3+MHDkSly9fxtKlSxEZGYmZM2eq1AsJCUHPnj1x4cIFhIaGYvPmzQBezN6lp6cjIiICADB+/Hhs3rwZa9aswdmzZ2FnZwd3d3c8fPiw2PeQm5uLlStXAoDKxtqv+/XXXzF8+HAMHToUFy5cwLZt22BnZwcAyM/Ph6enJx4+fIiDBw9i7969uHnzJvr06VNkf1lZWVAqlSoHEREREX0kBKJyduDAAQGA8OjRI0EQBOHOnTtCy5YthRo1aghZWVnC6tWrBblcrtJm69atwqt/PDt06CDMmjVLpc66desEMzMz8RyAMGrUqDeOLQiCkJmZKVSpUkWIiooSr2VnZwvm5ubCjz/++MZ7ASBoaWkJurq6gpqamgBAsLGxER48eCDWmTp1qtCwYUPx3NzcXJg0aVKh/e3Zs0dQV1cX0tLSxGuXLl0SAAgnT54stM3UqVMFAAWOht8vERoHrnlj/ERERET0/ikUCgGAoFAo3lqXM2j03lhYWEBXVxfm5uZ48uQJNm/e/MaZp1edP38e06ZNg0wmE48hQ4YgPT0dT58+Feu5uLi8ta+UlBTk5OSgVatW4rUqVargs88+w5UrVwAA3377rcpYr1q4cCGSkpKwc+dO1KtXDytWrEC1atUKHevevXu4c+cOOnToUGj5lStXYGlpqbJJdr169WBgYCDG8rqgoCAoFArxuH379lvvmYiIiIg+DNwHjd6bw4cPQ19fH8bGxtDT0xOvq6mpQXhtt4fXF/jIzMxEaGgoevXqVaBfLS0t8bOurm6ZxDpt2jQEBgYWWmZqago7OzvY2dlh9erV6NatGy5fvgxjY+MCdV8+ylmWpFIppFJpmfdLRERERBWPM2j03tja2qJWrVoqyRkAGBkZ4fHjx3jy5Il47fU9xBo3bozk5GQxMXr1UFMr+o/xyxm6V1eLrFWrFjQ1NZGYmChey8nJwalTp1CvXj0ALxY0eXWMonz22Wdo0qRJgXfhXtLT04ONjU2Ry+7XrVsXt2/fVpkFu3z5MjIyMsRYiIiIiOjTwQSNKlyzZs2go6ODiRMnIiUlBb///jsiIyNV6kyZMgVr165FaGgoLl26hCtXriA6OhqTJ09+Y9/W1taQSCTYvn07/vvvP2RmZkJXVxffffcdxo0bh127duHy5csYMmQInj59isGDB5c4/lGjRmHp0qX4559/Ci0PCQlBWFgYFi1ahOvXr+Ps2bNYvHgxAKBjx45wdHSEj48Pzp49i5MnT6J///5wdXUt1uOaRERERPRxYYJGFa5atWr47bff8Mcff8DR0RHr169HSEiISh13d3ds374de/bsQdOmTdG8eXMsXLgQ1tbWb+y7Ro0aCA0NxQ8//AATExNxefs5c+bgiy++gK+vLxo3bowbN25g9+7dqFq1aonj79KlC2xtbYucRRswYADCw8Pxyy+/oH79+ujevTuuX78O4MXWAHFxcahatSratm2Ljh07ombNmoiJiSlxHIdmeOPMvP4lbkdERERElYdEeP3lHyL6oCiVSsjlcigUCujr61d0OERERET0mpL8XuMMGtFHou3k9Wgybm1Fh0FERERE74AJGn0QBEHA8uXL0aJFC+jr60Mmk6F+/foYOXIkbty4UdHhERERERGVCSZoVOkJgoB+/fohICAA3bp1w549e3D58mWsXLkSWlpamDFjxjv1//qS/kREREREFYUJGlV6MTExiI6ORkxMDIKDg9G8eXNYWVmhefPmmDt3LlavXq1Sf8WKFahbty60tLRQp04d/PLLL2JZamoqJBIJYmJi4OrqCi0tLURFRcHPzw9eXl6YNWsWTExMYGBggGnTpiE3Nxfjxo1DtWrVYGFhUWCsCRMmoHbt2tDR0UHNmjURHByskvCFhITA2dkZ69atg42NDeRyOfr27YvHjx8DANauXQtDQ0NkZWWp9Ovl5QVfX9+y/iqJiIiIqJJjgkaV3vr16+Hg4IAePXoUWi6RSMTPUVFRmDJlCmbOnIkrV65g1qxZCA4Oxpo1a1Ta/PDDDxg5ciSuXLkCd3d3AMD+/ftx584dHDp0CAsWLMDUqVPRvXt3VK1aFSdOnMC3336LYcOG4e+//xb70dPTQ2RkJC5fvoyIiAgsX74cCxcuVBkrJSUFsbGx2L59O7Zv346DBw9izpw5AICvvvoKeXl52LZtm1j/3r172LFjBwYNGlTo/WZlZUGpVKocRERERPRxYIJGld61a9fg4OCgcm3UqFGQyWSQyWSwsLAQr0+dOhVhYWHo1asXbG1t0atXL4wePRpLly4t0P5lHTMzMwAvlvtftGgRHBwcMGjQIDg4OODp06eYOHEi7O3tERQUBE1NTRw5ckTsZ/LkyWjZsiVsbGzg4eGBwMBAbNiwQWWs/Px8REZGokGDBmjTpg18fX3Fjau1tbXRr18/lZm53377DVZWVnBzcyv0+5g9ezbkcrl4WFpalvxLJSIiIqJKiQkafZAmTZqEpKQkTJkyBZmZmQCAJ0+eICUlBYMHDxaTN5lMhhkzZiAlJUWlfWGbQNevXx9qav//r4SJiQkcHR3Fc3V1dRgaGuLevXvitZiYGLRq1QqmpqaQyWSYPHky0tLSVPq1sbGBnp6eeG5mZqbSx5AhQ7Bnzx5xo+vIyEj4+fmpzAy+KigoCAqFQjxu37791u+LiIiIiD4MGhUdANHb2NvbIzk5WeWakZERjIyMYGxsLF57magtX74czZo1U6mvrq6ucq6rq1tgnCpVqqicSySSQq/l5+cDAI4dOwYfHx+EhobC3d0dcrkc0dHRCAsLe2u/L/sAgEaNGqFhw4ZYu3YtOnfujEuXLmHHjh0Fv4j/I5VKIZVKiywnIiIiog8XEzSq9Ly9vdGvXz/ExcXB09OzyHomJiYwNzfHzZs34ePjU+5xHT16FNbW1pg0aZJ47datW6Xq65tvvkF4eDj++ecfdOzYkY8tEhEREX2imKBRpde3b19s2bIFffv2RVBQENzd3WFiYoJbt24hJiZGZXYsNDQUAQEBkMvl6NKlC7KysnD69Gk8evQIY8aMKdO47O3tkZaWhujoaDRt2hQ7duzA1q1bS9VXv379EBgYiOXLl2Pt2tJtNn1ohvdbd6YnIiIiosqN76BRpfdyWfzw8HD88ccf6NChg7iQh6WlpcqiHd988w1WrFiB1atXw9HREa6uroiMjIStrW2Zx9WjRw+MHj0aI0aMgLOzM44ePYrg4OBS9SWXy/HFF19AJpPBy8urbAMlIiIiog+GRBAEoaKDIPqU+Pn5ISMjA7GxsSrXO3TogPr162PRokUl6k+pVEIul0OhUHAGjYiIiKgSKsnvNc6g0UdHEAQsW7YMzZo1g0wmg4GBAVxcXBAeHo6nT59WdHgFPHr0CFu3bkVCQgKGDx9e6n7aTl6PJuNK93gkEREREVUOfAeNPjq+vr7YsmULJk+ejJ9++glGRkY4f/48wsPDYWNjU+keIWzUqBEePXqEuXPnFtjvjYiIiIg+LZxBo4/Khg0bEBUVhfXr12PixIlo2rQpbGxs4Onpif3796Ndu3YAgFOnTqFTp06oXr065HI5XF1dcfbsWZW+JBIJVqxYgZ49e0JHRwf29vbYtm2bWJ6Xl4fBgwfD1tYW2tracHBwQEREhEofeXl5GDNmDAwMDGBoaIjx48fj9aeKlyxZAkdHR8yYMQOGhobo3r17gX3biIiIiOjTwASNPipRUVFwcHAodDl+iUQCuVwOAHj8+DEGDBiAI0eO4Pjx47C3t0e3bt3w+PFjlTahoaHo3bs3/vzzT3Tr1g0+Pj54+PAhACA/Px8WFhbYuHEjLl++jClTpmDixInYsGGD2D4sLAyRkZFYtWoVjhw5gocPHxZY6fHJkycYM2YMTp8+jfj4eKipqaFnz54qe6W9KisrC0qlUuUgIiIioo8DFwmhj0q9evVgb2+PuLi4ErXLz8+HgYEBfv/9d3Tv3h3Ai4Ru8uTJmD59OoAXiZRMJsPOnTvRpUuXQvsZMWIE/v33X2zatAkAYG5ujtGjR2PcuHEAgNzcXNja2qJJkyYFFgl56f79+zAyMsKFCxfQoEGDAuUhISEIDQ0tcL3h90ugLtXGmXn9S3TvRERERFS+uEgIfbKK++8Nd+/exZAhQ2Bvbw+5XA59fX1kZmYiLS1NpZ6Tk5P4WVdXF/r6+rh375547eeff0aTJk1gZGQEmUyGZcuWiX0oFAqkp6ejWbNmYn0NDQ24uLiojHH9+nV4e3ujZs2a0NfXh42NDQAUiOWloKAgKBQK8bh9+3ax7pmIiIiIKj8uEkIfldq1a+Pq1atvrTdgwAA8ePAAERERsLa2hlQqRYsWLZCdna1Sr0qVKirnEolEfPQwOjoagYGBCAsLQ4sWLaCnp4d58+bhxIkTJYrZw8MD1tbWWL58OczNzZGfn48GDRoUiOUlqVQKqVRaojGIiIiI6MPAGTT6qPTr1w/Xrl0r9BFHQRCgUCgAAImJiQgICEC3bt1Qv359SKVS3L9/v0RjJSYmomXLlvD390ejRo1gZ2ensriHXC6HmZmZSsKWm5uLM2fOiOcPHjxAcnIyJk+ejA4dOqBu3bp49OhRSW+biIiIiD4STNDoo9K7d2/06dMH3t7emDVrFk6fPo1bt25h+/bt6NixIw4cOAAAsLe3x7p163DlyhWcOHECPj4+0NbWLtFY9vb2OH36NHbv3o1r164hODgYp06dUqkzcuRIzJkzB7Gxsbh69Sr8/f2RkZEhlletWhWGhoZYtmwZbty4gf3792PMmDHv/D0QERER0YeJCRp9VCQSCX7//XcsWLAAsbGxcHV1hZOTE0JCQuDp6Ql3d3cAwMqVK/Ho0SM0btwYvr6+CAgIgLGxcYnGGjZsGHr16oU+ffqgWbNmePDgAfz9/VXqjB07Fr6+vhgwYID4GGTPnj3FcjU1NURHR+PMmTNo0KABRo8ejXnz5pXq3g/N8OYCIUREREQfOK7iSPSBK8mqQERERET0/nEVR6JKxM/PD15eXuK5m5sbRo0aVebjtJ28vsz7JCIiIqL3i6s4EhUhISEB7dq1w6NHj2BgYFBm/W7ZsqXA6pBERERERAATNKL3rlq1ahUdAhERERFVUnzEkaiYIiMjYWBggN27d6Nu3bqQyWTo0qUL0tPTxTp5eXkYM2YMDAwMYGhoiPHjxxfYPPv1RxzXrVsHFxcX6OnpwdTUFP369VPZDJuIiIiIPh1M0IhK4OnTp5g/fz7WrVuHQ4cOIS0tDYGBgWJ5WFgYIiMjsWrVKhw5cgQPHz7E1q1b39hnTk4Opk+fjvPnzyM2Nhapqanw8/Mrsn5WVhaUSqXKQUREREQfBz7iSFQCOTk5WLJkCWrVqgUAGDFiBKZNmyaWh4eHIygoCL169QIALFmyBLt3735jn4MGDRI/16xZE4sWLULTpk2RmZkJmUxWoP7s2bMRGhpaFrdDRERERJUMZ9CISkBHR0dMzgDAzMxMfBxRoVAgPT0dzZo1E8s1NDTg4uLyxj7PnDkDDw8PWFlZQU9PD66urgCAtLS0QusHBQVBoVCIx+3bt9/1toiIiIiokuAMGlEJvL76okQiKfCOWUk8efIE7u7ucHd3R1RUFIyMjJCWlgZ3d3dkZ2cX2kYqlUIqlZZ6TCIiIiKqvDiDRlRG5HI5zMzMcOLECfFabm4uzpw5U2Sbq1ev4sGDB5gzZw7atGmDOnXqcIEQIiIiok8YEzSiMjRy5EjMmTMHsbGxuHr1Kvz9/ZGRkVFkfSsrK2hqamLx4sW4efMmtm3bhunTp5dq7EMzvEsZNRERERFVFkzQiMrQ2LFj4evriwEDBqBFixbQ09NDz549i6xvZGSEyMhIbNy4EfXq1cOcOXMwf/789xgxEREREVUmEuFdXqAhKmN+fn7IyMhAbGxsuY4TEhKC2NhYJCUlles4bm5ucHZ2Rnh4eLmNoVQqIZfLoVAooK+vX27jEBEREVHplOT3GmfQ6L1JSEiARCIp9Pj333+L3c9ff/2Ffv36wdzcHFpaWrCwsICnpyeuXr1ajtFXfm0nr6/oEIiIiIjoHXEVR3rvkpOTC/zLgbGxcbHa5uTkoFOnTnBwcMCWLVtgZmaGv//+Gzt37nzju14fqry8PEgkEqip8d9SiIiIiD4F/NVH752xsTFMTU1VjuImIJcuXUJKSgp++eUXNG/eHNbW1mjVqhVmzJiB5s2bi/UmTJiA2rVrQ0dHBzVr1kRwcDBycnIK7XPPnj3Q0tIqkOCNHDkS7du3BwA8ePAA3t7eqFGjBnR0dODo6Ij161VnrJ48eYL+/ftDJpPBzMwMYWFhBcZ69OgR+vfvj6pVq0JHRwddu3bF9evXxfLIyEgYGBhg27ZtqFevHqRSaZH7oRERERHRx4cJGn1QjIyMoKamhk2bNiEvL6/Ienp6eoiMjMTly5cRERGB5cuXY+HChYXW7dChAwwMDLB582bxWl5eHmJiYuDj4wMAeP78OZo0aYIdO3bg4sWLGDp0KHx9fXHy5Emxzbhx43Dw4EHExcVhz549SEhIwNmzZ1XG8vPzw+nTp7Ft2zYcO3YMgiCgW7duKsnj06dPMXfuXKxYsQKXLl0qMLuYlZUFpVKpchARERHRx4EJGr13FhYWkMlk4lG/fv1it61RowYWLVqEKVOmoGrVqmjfvj2mT5+OmzdvqtSbPHkyWrZsCRsbG3h4eCAwMBAbNmwotE91dXX07dsXv//+u3gtPj4eGRkZ+OKLL8RxAwMD4ezsjJo1a+L7779Hly5dxD4zMzOxcuVKzJ8/Hx06dICjoyPWrFmD3Nxcsc/r169j27ZtWLFiBdq0aYOGDRsiKioK//zzj8qiKDk5Ofjll1/QsmVLODg4QEdHRyXe2bNnQy6Xi4elpWWxvz8iIiIiqtyYoNF7d/jwYSQlJYnHH3/8UWi9qKgolUTu8OHDAIDhw4fj33//RVRUFFq0aIGNGzeifv362Lt3r9g2JiYGrVq1gqmpKWQyGSZPnvzGRwV9fHyQkJCAO3fuiGN//vnnMDAwAPBiRm369OlwdHREtWrVIJPJsHv3brHPlJQUZGdno1mzZmKf1apVg4ODg3h+5coVaGhoqNQxNDSEg4MDrly5Il7T1NSEk5NTkbEGBQVBoVCIx+3bt4usS0REREQfFi4SQu+dra2tmPi8SY8ePVSSmRo1aoif9fT04OHhAQ8PD8yYMQPu7u6YMWMGOnXqhGPHjsHHxwehoaFwd3eHXC5HdHR0oe+EvdS0aVPUqlUL0dHR+O6777B161ZERkaK5fPmzUNERATCw8Ph6OgIXV1djBo1CtnZ2aX6Dt5EW1sbEomkyHKpVAqpVFrm4xIRERFRxWOCRpWWnp4e9PT03lpPIpGgTp06OHr0KADg6NGjsLa2xqRJk8Q6t27dems/Pj4+iIqKgoWFBdTU1PD555+LZYmJifD09MTXX38NAMjPz8e1a9dQr149AECtWrVQpUoVnDhxAlZWVgBeLAhy7do1uLq6AgDq1q2L3NxcnDhxAi1btgTwYvGR5ORksR8iIiIi+rTxEUd67+7du4d///1X5ShqhcXXJSUlwdPTE5s2bcLly5dx48YNrFy5EqtWrYKnpycAwN7eHmlpaYiOjkZKSgoWLVqErVu3vrVvHx8fnD17FjNnzsSXX36pMktlb2+PvXv34ujRo7hy5QqGDRuGu3fviuUymQyDBw/GuHHjsH//fly8eBF+fn4qq1Pa29vD09MTQ4YMwZEjR3D+/Hl8/fXXqFGjhhg7EREREX3aOING792r72W9dOzYMZVl8otiYWEBGxsbhIaGIjU1FRKJRDwfPXo0gBePRo4ePRojRoxAVlYWPv/8cwQHByMkJOSNfdvZ2eGzzz7DyZMnER4erlI2efJk3Lx5E+7u7tDR0cHQoUPh5eUFhUIh1pk3bx4yMzPh4eEBPT09jB07VqUcAFavXo2RI0eie/fuyM7ORtu2bfHHH3+gSpUqb733tzk0w/ud+yAiIiKiiiURBEGo6CCIqPSUSiXkcjkUCkWBDcCJiIiIqOKV5PcaH3EkKgE/Pz94eXm9sY6NjU2BGbj3oe3k9W+vRERERESVGhM0ov8jCAKWLVuGZs2aQSaTwcDAAC4uLggPD8fTp0+L3c+pU6cwdOjQcoyUiIiIiD5WTNCI/o+vry9GjRoFT09PHDhwAElJSQgODkZcXBz27NlT7H6MjIwKbC5NRERERFQcTNCIAGzYsAFRUVFYv349Jk6ciKZNm8LGxgaenp7Yv38/2rVrp1J//vz5MDMzg6GhIYYPH66yCuXrjzhKJBKsWLECPXv2hI6ODuzt7bFt2zaxPC8vD4MHD4atrS20tbXh4OCAiIiIcr9nIiIiIqp8mKARAYiKioKDg0Ohy91LJBLI5XLx/MCBA0hJScGBAwewZs0aREZGqmxqXZjQ0FD07t0bf/75J7p16wYfHx88fPgQwIs91SwsLLBx40ZcvnwZU6ZMwcSJE7Fhw4ZC+8rKyoJSqVQ5iIiIiOjjwASNCMD169cLXf6/MFWrVsVPP/2EOnXqoHv37vj8888RHx//xjZ+fn7w9vaGnZ0dZs2ahczMTJw8eRIAUKVKFYSGhsLFxQW2trbw8fHBwIEDi0zQZs+eDblcLh6WlpYlu1kiIiIiqrSYoBHhxQIhxVW/fn2oq6uL52ZmZrh3794b2zg5OYmfdXV1oa+vr9Lm559/RpMmTWBkZASZTIZly5YhLS2t0L6CgoKgUCjE4/bt28WOnYiIiIgqN25UTQSgdu3auHr1arHqvr6ptEQiQX5+fqnbREdHIzAwEGFhYWjRogX09PQwb948nDhxotC+pFIppFJpsWIlIiIiog8LZ9CIAPTr1w/Xrl1DXFxcgTJBEKBQKMpt7MTERLRs2RL+/v5o1KgR7OzskJKSUm7jEREREVHlxQSNCEDv3r3Rp08feHt7Y9asWTh9+jRu3bqF7du3o2PHjjhw4EC5jW1vb4/Tp09j9+7duHbtGoKDg3Hq1KlyG4+IiIiIKi8maER48cjh77//jgULFiA2Nhaurq5wcnJCSEgIPD094e7uXm5jDxs2DL169UKfPn3QrFkzPHjwAP7+/iXu59AM73KIjoiIiIjeJ4lQktURiKjSUSqVkMvlUCgU0NfXr+hwiIiIiOg1Jfm9xhk0onL0+qbV5ant5PXvZRwiIiIiKj9M0IgKIQgCli1bhmbNmkEmk8HAwAAuLi4IDw/H06dPKzo8IiIiIvpIMUEjKoSvry9GjRoFT09PHDhwAElJSQgODkZcXBz27NlT0eERERER0UeKCRrRazZs2ICoqCisX78eEydORNOmTWFjYwNPT0/s378f7dq1AwC4ublh1KhRKm29vLzg5+encu3x48fw9vaGrq4uatSogZ9//lmlfMGCBXB0dISuri4sLS3h7++PzMzMIuPLysqCUqlUOYiIiIjo48AEjeg1UVFRcHBwgKenZ4EyiUQCuVxeov7mzZuHhg0b4ty5c/jhhx8wcuRI7N27VyxXU1PDokWLcOnSJaxZswb79+/H+PHji+xv9uzZkMvl4mFpaVmieIiIiIio8mKCRvSa69evw8HBocz6a9WqFX744QfUrl0b33//Pb788kssXLhQLB81ahTatWsHGxsbtG/fHjNmzMCGDRuK7C8oKAgKhUI8bt++XWaxEhEREVHFYoJG9Jqy3nmiRYsWBc6vXLkinu/btw8dOnRAjRo1oKenB19fXzx48KDIxUikUin09fVVDiIiIiL6ODBBI3pN7dq1cfXq1bfWU1NTK5DM5eTklGis1NRUdO/eHU5OTti8eTPOnDkjvqOWnZ1dor6IiIiI6MPHBI3oNf369cO1a9cQFxdXoEwQBCgUCgCAkZER0tPTxbK8vDxcvHixQJvjx48XOK9bty4A4MyZM8jPz0dYWBiaN2+O2rVr486dO2V5O0RERET0AWGCRvSa3r17o0+fPvD29sasWbNw+vRp3Lp1C9u3b0fHjh1x4MABAED79u2xY8cO7NixA1evXsV3332HjIyMAv0lJibixx9/xLVr1/Dzzz9j48aNGDlyJADAzs4OOTk5WLx4MW7evIl169ZhyZIlpYr70AzvUt8zEREREVUOEqGsX7gh+gjk5+dj2bJlWLVqFS5dugQNDQ3Y29ujf//+GDJkCLS1tZGTk4ORI0ciJiYGGhoaGD16NI4fPw4DAwNERkYCAGxsbDBo0CBcvHgRO3bsgL6+PoKCghAQECCOtXDhQsybNw8ZGRlo27YtfHx80L9/fzx69AgGBgZvjVWpVEIul0OhUPB9NCIiIqJKqCS/15igEZWARCLB1q1b4eXlVa7juLm5wdnZGeHh4W+t+/IvfMPvlyBp0bByjYuIiIiISq4kCRofcaQPXkJCAiQSSaHHv//+W9HhEREREREVm0ZFB0BUVpKTkwv8i4SxsXEFRUNEREREVHKcQaOPhrGxMUxNTVUONbUXf8T9/Pzg5eWF+fPnw8zMDIaGhhg+fLjKsvjp6en4/PPPoa2tDVtbW/z++++wsbF542OGEyZMQO3ataGjo4OaNWsiODhYpc+QkBA4Oztj3bp1sLGxgVwuR9++ffH48WOxzpMnT9C/f3/IZDKYmZkhLCys7L8cIiIiIvogcAaNPhkHDhyAmZkZDhw4gBs3bqBPnz5wdnbGkCFDAAD9+/fH/fv3kZCQgCpVqmDMmDG4d+/eG/vU09NDZGQkzM3NceHCBQwZMgR6enoYP368WCclJQWxsbHYvn07Hj16hN69e2POnDmYOXMmAGDcuHE4ePAg4uLiYGxsjIkTJ+Ls2bNwdnYudMysrCxkZWWJ50ql8h2/GSIiIiKqLJig0UfDwsJC5dza2hqXLl0Sz6tWrYqffvoJ6urqqFOnDj7//HPEx8djyJAhuHr1Kvbt24dTp07BxcUFALBixQrY29u/cczJkyeLn21sbBAYGIjo6GiVBC0/Px+RkZHQ09MDAPj6+iI+Ph4zZ85EZmYmVq5cid9++w0dOnQAAKxZs6bAvbxq9uzZCA0NLea3QkREREQfEiZo9NE4fPiwmAQBQJUqVVTK69evD3V1dfHczMwMFy5cAPDi/TUNDQ00btxYLLezs0PVqlXfOGZMTAwWLVqElJQUZGZmIjc3t8B7cDY2NipxmZmZiTNzKSkpyM7ORrNmzcTyatWqwcHBocgxg4KCMGbMGPFcqVTC0tLyjXESERER0YeBCRp9NGxtbd+4b9jrCZtEIkF+fn6pxzt27Bh8fHwQGhoKd3d3yOVyREdHF3iHrKzHlUqlkEqlpW5PRERERJUXFwkhAuDg4IDc3FycO3dOvHbjxg08evSoyDZHjx6FtbU1Jk2aBBcXF9jb2+PWrVslGrdWrVqoUqUKTpw4IV579OgRrl27VvKbICIiIqIPHmfQ6KNx7949PH/+XOWaoaFhgRmswtSpUwcdO3bE0KFD8euvv6JKlSoYO3YstLW1IZFICm1jb2+PtLQ0REdHo2nTptixYwe2bt1aophlMhkGDx6McePGwdDQEMbGxpg0aZK4+iQRERERfVr4K5A+Gg4ODjAzM1M5zpw5U+z2a9euhYmJCdq2bYuePXuKKzJqaWkVWr9Hjx4YPXo0RowYAWdnZxw9ehTBwcEljnvevHlo06YNPDw80LFjR7Ru3RpNmjQpcT+HZniXuA0RERERVS4SQRCEig6CqDL6+++/YWlpiX379okrLFZGSqUScrkcCoWiwAIlRERERFTxSvJ7jTNoRP9n//792LZtG/766y8cPXoUffv2hY2NDdq2bVuu475tM+ziajt5/bsHQ0REREQVigkaEYDly5fjm2++Qc+ePVGzZk24urri0aNH4qbVZSEyMvKNq0wSEREREXGREPrkrVq1CqNGjcKiRYvg6uqKrKws/Pnnn7h48SKsra0rOjwiIiIi+oRwBo0+edu2bUPv3r0xePBg2NnZoX79+vD29sbMmTPFOvn5+Zg2bRosLCwglUrh7OyMXbt2ieUJCQmQSCTIyMgQryUlJUEikSA1NRUJCQkYOHAgFAoFJBIJJBIJQkJCxLpPnz7FoEGDoKenBysrKyxbtux93DoRERERVTJM0OiTZ2pqiuPHj79xD7OIiAiEhYVh/vz5+PPPP+Hu7o4ePXrg+vXrxRqjZcuWCA8Ph76+PtLT05Geno7AwECxPCwsDC4uLjh37hz8/f3x3XffITk5udC+srKyoFQqVQ4iIiIi+jgwQaNP3tSpU2FgYAAbGxs4ODjAz88PGzZsQH5+vlhn/vz5mDBhAvr27QsHBwfMnTsXzs7OxV7cQ1NTE3K5HBKJBKampjA1NYVMJhPLu3XrBn9/f9jZ2WHChAmoXr06Dhw4UGhfs2fPhlwuFw9LS8t3un8iIiIiqjyYoNEnz8zMDMeOHcOFCxcwcuRI5ObmYsCAAejSpQvy8/OhVCpx584dtGrVSqVdq1atcOXKlTKJwcnJSfz8Mom7d+9eoXWDgoKgUCjE4/bt22USAxERERFVPC4SQvR/GjRogAYNGsDf3x/ffvst2rRpg4MHDxZr02g1tRf/1vHqtoI5OTnFHvv1lSIlEonKDN6rpFIppFJpsfsmIiIiog8HZ9CIClGvXj0AwJMnT6Cvrw9zc3MkJiaq1ElMTBTrGRkZAQDS09PF8qSkJJX6mpqayMvLK8eoiYiIiOhDxxk0+uR99913MDc3R/v27WFhYYH09HTMmDEDRkZGaNGiBQBg3LhxmDp1KmrVqgVnZ2esXr0aSUlJiIqKAgDY2dnB0tISISEhmDlzJq5du4awsDCVcWxsbJCZmYn4+Hg0bNgQOjo60NHRKbP7ODTDu8z6IiIiIqKKwRk0+uR17NgRx48fx1dffYXatWvjiy++gJaWFuLj42FoaAgACAgIwJgxYzB27Fg4Ojpi165d2LZtG+zt7QG8eERx/fr1uHr1KpycnDB37lzMmDFDZZyWLVvi22+/RZ8+fWBkZIQff/zxvd8rEREREVVuEuHVl2bok+Pn54eMjAzExsYWu41EIsHWrVvh5eVVbnF9SFJTU2Fra4tz587B2dm50DoJCQlo164dHj16BAMDA0RGRmLUqFEq+6aVllKphFwuR8PvlyBp0bB37o+IiIiIytbL32sKhQL6+vpvrMsZtAr0cnNjiUQCNTU1yOVyNGrUCOPHj1d5l6k8RUREIDIyskRt0tPT0bVr1/IJqJhSU1PF704ikaBatWpwdXXF4cOHKzSu4urTpw+uXbtW0WEQERERUSXDBK0SSE5Oxp07d3Dq1ClMmDAB+/btQ4MGDXDhwoVyH1sul8PAwKBEbUxNTSvNKoL79u1Deno6Dh06BHNzc3Tv3h13796t6LDeSltbG8bGxhUdBhERERFVMkzQKgFjY2OYmpqidu3a6Nu3LxITE2FkZITvvvtOrJOfn49p06bBwsICUqkUzs7O2LVrl1j+ckZpw4YNaNOmDbS1tdG0aVNcu3YNp06dgouLC2QyGbp27Yr//vtPbOfn56fyqKKbmxsCAgIwfvx4VKtWDaampggJCVGJVyKRqDwS+ffff8Pb2xvVqlWDrq4uXFxccOLECQBASkoKPD09YWJiAplMhqZNm2Lfvn0q/dnY2GDWrFkYNGgQ9PT0YGVlhWXLlhXruzM0NISpqSkaNGiAiRMnQqlUimMDwMWLF9G1a1fIZDKYmJjA19cX9+/fV7nfESNGYMSIEZDL5ahevTqCg4NVlst//X4BiI8pvurq1ato2bIltLS00KBBAxw8eLDIuCMjIwskxv/73//QtGlTaGlpoXr16ujZs2exvgMiIiIi+ngwQauEtLW18e233yIxMVHcrDgiIgJhYWGYP38+/vzzT7i7u6NHjx64fv26StupU6di8uTJOHv2LDQ0NNCvXz+MHz8eEREROHz4MG7cuIEpU6a8cfw1a9ZAV1cXJ06cwI8//ohp06Zh7969hdbNzMyEq6sr/vnnH2zbtg3nz5/H+PHjxT28MjMz0a1bN8THx+PcuXPo0qULPDw8kJaWptJPWFgYXFxccO7cOfj7++O7775DcnJysb+zZ8+eYe3atQBeLGcPABkZGWjfvj0aNWqE06dPY9euXbh79y569+5d4H41NDRw8uRJREREYMGCBVixYkWxx35p3LhxGDt2LM6dO4cWLVrAw8MDDx48KFbbHTt2oGfPnujWrRvOnTuH+Ph4fPbZZ4XWzcrKglKpVDmIiIiI6CMhUIU5cOCAAEB49OhRgbKdO3cKAIQTJ04IgiAI5ubmwsyZM1XqNG3aVPD39xcEQRD++usvAYCwYsUKsXz9+vUCACE+Pl68Nnv2bMHBwUE8HzBggODp6Smeu7q6Cq1bty4wzoQJE8RzAMLWrVsFQRCEpUuXCnp6esKDBw+Kfd/169cXFi9eLJ5bW1sLX3/9tXien58vGBsbC7/++muRfby8X21tbUFXV1eQSCQCAKFJkyZCdna2IAiCMH36dKFz584q7W7fvi0AEJKTk8X7rVu3rpCfny/WmTBhglC3bt1C7/cluVwurF69WiWWOXPmiOU5OTmChYWFMHfuXEEQCv63Xr16tSCXy8X6LVq0EHx8fIq831dNnTpVAFDgaPj9kmK1JyIiIqL3S6FQCAAEhULx1rqcQaukhP97xE4ikUCpVOLOnTto1aqVSp1WrVrhypUrKtecnJzEzyYmJgAAR0dHlWsvZ+WK8mofAGBmZlZkm6SkJDRq1AjVqlUrtDwzMxOBgYGoW7cuDAwMIJPJcOXKlQIzaK+OKZFIYGpqKo758hFFmUyG+vXrq7SLiYnBuXPnsHnzZtjZ2SEyMhJVqlQBAJw/fx4HDhwQ28pkMtSpUwfAi0cvX2revDkkEol43qJFC1y/fr3Em0q/3DMNADQ0NODi4lLgv09RkpKS0KFDh2LVDQoKgkKhEI/bt2+XKE4iIiIiqry4UXUl9fKHvY2NTYnavUxOAIhJx+vXXj5+WJw+3tZGW1v7jX0FBgZi7969mD9/Puzs7KCtrY0vv/wS2dnZxR5zxYoVePbsWaH1LC0tYW9vD3t7e+Tm5qJnz564ePEipFIpMjMz4eHhgblz5xaIy8zM7I1xvx6L8NpuFDk5OcVuXxxv+x5fJZVKK80iLURERERUtjiDVgk9e/YMy5YtQ9u2bWFkZAR9fX2Ym5sjMTFRpV5iYiLq1atXQVG+4OTkhKSkJDx8+LDQ8sTERPj5+aFnz55wdHSEqakpUlNTSzRGjRo1YGdnBzs7O1hbWxdZ78svv4SGhgZ++eUXAEDjxo1x6dIl2NjYiO1fHrq6umK7VxcVAYDjx4/D3t4e6urqAAAjIyOVbQ+uX7+Op0+fFhj/+PHj4ufc3FycOXMGdevWLdY9Ojk5IT4+vlh1iYiIiOjjxQStErh37x7+/fdfXL9+HdHR0WjVqhXu37+PX3/9Vawzbtw4zJ07FzExMUhOTsYPP/yApKQkjBw5sgIjB7y9vWFqagovLy8kJibi5s2b2Lx5M44dOwYAsLe3x5YtW5CUlITz58+jX79+b53BKy2JRIKAgADMmTMHT58+xfDhw/Hw4UN4e3vj1KlTSElJwe7duzFw4ECVxxfT0tIwZswYJCcnY/369Vi8eLHK99q+fXv89NNPOHfuHE6fPo1vv/22wEweAPz888/YunUrrl69iuHDh+PRo0cYNGhQsWKfOnUq1q9fj6lTp+LKlSu4cOFCoTN/RERERPRxY4JWCTg4OMDc3BxNmjTBnDlz0LFjR1y8eFFldiwgIABjxozB2LFj4ejoiF27dmHbtm2wt7evwMhfrJi4Z88eGBsbo1u3bnB0dMScOXPE2acFCxagatWqaNmyJTw8PODu7o7GjRuXWzwDBgxATk4OfvrpJ3HWMS8vD507d4ajoyNGjRoFAwMDqKn9/z/6/fv3x7Nnz/DZZ59h+PDhGDlyJIYOHSqWh4WFwdLSEm3atEG/fv0QGBgIHR2dAmPPmTMHc+bMQcOGDXHkyBFs27YN1atXL1bcbm5u2LhxI7Zt2wZnZ2e0b98eJ0+eLNG9H5rhXaL6RERERFT5SITXX64h+oS4ubnB2dkZ4eHhFR1KqSmVSsjlcigUCujr61d0OERERET0mpL8XuMMGtF7lpCQAIlEgoyMjDLtt+3k9WXaHxERERG9f0zQiIrp9cQqMjISBgYGFRoTEREREX1cuMw+fdISEhIqOgQiIiIiIhFn0IhKISEhAQMHDoRCoYBEIoFEIkFISAgAYN26dXBxcYGenh5MTU3Rr1+/Ijf6fvLkCfT19bFp0yaV67GxsdDV1cXjx4/L+1aIiIiIqBJhgkZUCi1btkR4eDj09fWRnp6O9PR0BAYGAnixifX06dNx/vx5xMbGIjU1FX5+foX2o6uri759+2L16tUq11evXo0vv/wSenp6BdpkZWVBqVSqHERERET0ceAjjkSloKmpCblcDolEAlNTU5WyV/c+q1mzJhYtWoSmTZsiMzMTMpmsQF/ffPMNWrZsifT0dJiZmeHevXv4448/sG/fvkLHnj17NkJDQ8v2hoiIiIioUuAMGlEZO3PmDDw8PGBlZQU9PT24uroCeLEhdmE+++wz1K9fH2vWrAEA/Pbbb7C2tkbbtm0LrR8UFASFQiEet2/fLp8bISIiIqL3jgkaURl68uQJ3N3doa+vj6ioKJw6dQpbt24FAGRnZxfZ7ptvvkFkZCSAF483Dhw4EBKJpNC6UqkU+vr6KgcRERERfRyYoBGVkqamJvLy8lSuXb16FQ8ePMCcOXPQpk0b1KlTp8gFQl719ddf49atW1i0aBEuX76MAQMGlFfYRERERFSJMUEjKiUbGxtkZmYiPj4e9+/fx9OnT2FlZQVNTU0sXrwYN2/exLZt2zB9+vS39lW1alX06tUL48aNQ+fOnWFhYVHieA7N8C7NbRARERFRJcIEjaiUWrZsiW+//RZ9+vSBkZERfvzxRxgZGSEyMhIbN25EvXr1MGfOHMyfP79Y/Q0ePBjZ2dkqi4wQERER0adFIgiCUNFBEH0qIiMjMWrUKGRkZBQoW7duHUaPHo07d+5AKpVi69at8PLyemufSqUScrkcCoWC76MRERERVUIl+b3GGTSit0hISIBEIik0qbKxsUF4eHix++rTpw+uXbsmnoeEhMDJyQkpKSmYM2cOhg0bBk1NTaSnp6Nr164lirPt5PUlqk9ERERElQ8TNKL3SFtbG8bGxirX7t27hzp16sDU1BRBQUEAAFNTU0il0ooIkYiIiIgqEBM0ojK0YMECODo6QldXF5aWlvD390dmZqZYHhkZCQMDA/FzaGgo7t69i9zcXOzfvx+bNm0CAEgkEsTGxlbAHRARERFRRdKo6ACIPiZqampYtGgRbG1tcfPmTfj7+2P8+PH45ZdfCtTt06cPLl68iF27dmHfvn0AALlc/tYxsrKykJWVJZ4rlcqyuwEiIiIiqlBM0IiKqbCl758+fapyPmrUKPGzjY0NZsyYgW+//bbQBE1bWxsymQwaGhowNTUtdhyzZ89GaGho8QMnIiIiog8GEzSiYjp8+DD09PRUrrm5uamc79u3D7Nnz8bVq1ehVCqRm5uL58+f4+nTp9DR0SmTOIKCgjBmzBjxXKlUwtLSskz6JiIiIqKKxQSNqJhsbW3F98de0tD4/3+FUlNT0b17d3z33XeYOXMmqlWrhiNHjoj7m5VVgiaVSrmACBEREdFHigkaURk5c+YM8vPzERYWBjW1F+vvbNiw4Y1tNDU1kZeX9z7CIyIiIqIPAFdxJCojdnZ2yMnJweLFi3Hz5k2sW7cOS5YseWMbGxsb/PXXX0hKSsL9+/dVFv8gIiIiok8PEzSiMtKwYUMsWLAAc+fORYMGDRAVFYXZs2e/sc0XX3yBLl26oF27djAyMsL69aXfbPrQDO9StyUiIiKiykEiCIJQ0UEQUekplUrI5XIoFAro6+tXdDhERERE9JqS/F7jDBpRJRQSEgJnZ+cStWk7ufSzb0RERERUOTBBow9aQkICJBKJeJiYmOCLL77AzZs3y6TfjIyMsgmUiIiIiKgYmKDRRyE5ORl37tzBxo0bcenSJXh4eJR6dcScnJwyjo6IiIiIqHiYoNFHwdjYGGZmZmjbti2mTJmCy5cv48aNGwCAX3/9FbVq1YKmpiYcHBywbt06lbYSiQS//vorevToAV1dXQwZMgTt2rUDAFStWhUSiQR+fn4AXqy6GB4ertLe2dkZISEh4vnVq1fRunVraGlpoV69eti3bx8kEgliY2PFOhMmTEDt2rWho6ODmjVrIjg4mIkhEREREXEfNPr4aGtrAwCys7OxdetWjBw5EuHh4ejYsSO2b9+OgQMHwsLCQkzCgBfvfM2ZMwfh4eFQV1dHjx498MUXXyA5ORn6+vpin2+Tl5cHLy8vWFlZ4cSJE3j8+DHGjh1boJ6enh4iIyNhbm6OCxcuYMiQIdDT08P48ePfOkZWVpbKcvxKpbJYsRERERFR5ccEjT4q6enpmD9/PmrUqAEHBwd8++238PPzg7+/PwBgzJgxOH78OObPn6+SoPXr1w8DBw4Uz//66y8AL2bmDAwMij3+3r17kZKSgoSEBJiamgIAZs6ciU6dOqnUmzx5svjZxsYGgYGBiI6OLlaCNnv2bISGhhY7JiIiIiL6cPARR/ooWFhYQFdXF+bm5njy5Ak2b94MTU1NXLlyBa1atVKp26pVK1y5ckXlmouLS5nEkZycDEtLSzE5A4DPPvusQL2YmBi0atUKpqamkMlkmDx5MtLS0oo1RlBQEBQKhXjcvn27TGInIiIioorHGTT6KBw+fBj6+vowNjaGnp5eidvr6uoWq56amhpe3zqwpO+OHTt2DD4+PggNDYW7uzvkcjmio6MRFhZWrPZSqRRSqbREYxIRERHRh4EJGn0UbG1tC30UsW7dukhMTMSAAQPEa4mJiahXr94b+9PU1ASAAitBGhkZIT09XTxXKpXi45AA4ODggNu3b+Pu3bswMTEBAJw6dUqlj6NHj8La2hqTJk0Sr926destd0hEREREnwImaPRRGzduHHr37o1GjRqhY8eO+N///octW7Zg3759b2xnbW0NiUSC7du3o1u3btDW1oZMJkP79u0RGRkJDw8PGBgYYMqUKVBXVxfbderUCbVq1cKAAQPw448/4vHjx+L7ZhKJBABgb2+PtLQ0REdHo2nTptixYwe2bt36zvd6aIb3O/dBRERERBWL76DRR83LywsRERGYP38+6tevj6VLl2L16tVwc3N7Y7saNWogNDQUP/zwA0xMTDBixAgAL97/cnV1Rffu3fH555/Dy8sLtWrVEtupq6sjNjYWmZmZaNq0Kb755htxpkxLSwsA0KNHD4wePRojRoyAs7Mzjh49iuDg4PL5AoiIiIjogyIRXn+hhojKVGJiIlq3bo0bN26oJHNlRalUQi6XQ6FQQF9fv8z7JyIiIqJ3U5Lfa5xBIwKQkJAAiUSCjIyMd+5r69at2Lt3L1JTU7Fv3z4MHToUrVq1Kpfk7FVtJ68v1/6JiIiIqPzxHTSiMvb48WNMmDABaWlpqF69Ojp27FjsFRqJiIiI6NPGGTSiQjx48ADe3t6oUaMGdHR04OjoiPXrVWeo3NzcMGLECIwYMQJyuRzVq1dHcHAwfH19ce3aNTx//hyzZ8/GxYsXYWNjA1NTU/Tr1w/37t0T+3g5cxcfHw8XFxfo6OigZcuWSE5Oft+3TERERESVABM0okI8f/4cTZo0wY4dO3Dx4kUMHToUvr6+OHnypEq9NWvWQENDAydPnkRERAQWLFiAFStWiOU5OTmYPn06zp8/j9jYWKSmpsLPz6/AeJMmTUJYWBhOnz4NDQ0NDBo0qMjYsrKyoFQqVQ4iIiIi+jhwkRAivJjJateuHR49elTofmoA0L17d9SpUwfz588H8GIG7d69e7h06ZK4hP4PP/yAbdu24fLly4X2cfr0aTRt2hSPHz+GTCYTx923bx86dOgAAPjjjz/w+eef49mzZ+LKj68KCQlBaGhogesNv1+CpEXDSnP7RERERFSOuEgI0TvKy8vD9OnT4ejoiGrVqkEmk2H37t1IS0tTqde8eXMxOQOAFi1a4Pr16+IG12fOnIGHhwesrKygp6cHV1dXACjQj5OTk/jZzMwMAFQehXxVUFAQFAqFeNy+ffvdb5iIiIiIKgUuEkJUiHnz5iEiIgLh4eFwdHSErq4uRo0ahezs7GL38eTJE7i7u8Pd3R1RUVEwMjJCWloa3N3dC/RTpUoV8fPLhC8/P7/QfqVSKaRSaSnuioiIiIgqOyZoRIVITEyEp6cnvv76awAvkqVr166hXr16KvVOnDihcn78+HHY29tDXV0dV69exYMHDzBnzhxYWloCePGIIxERERFRUfiII1Eh7O3tsXfvXhw9ehRXrlzBsGHDcPfu3QL10tLSMGbMGCQnJ2P9+vVYvHgxRo4cCQCwsrKCpqYmFi9ejJs3b2Lbtm2YPn36+74VIiIiIvqAMEEjwv9/nFBD48Wk8uTJk9G4cWO4u7vDzc0Npqam8PLyKtCuf//+ePbsGT777DMMHz4cI0eOxNChQwEARkZGiIyMxMaNG1GvXj3MmTNHXGCkPBya4V1ufRMRERHR+8FVHIkAREdHY8iQIXj8+HGx27i5ucHZ2Rnh4eHlF1gxlGRVICIiIiJ6/7iKI1ExZWVl4fLly/jpp5/EZe7fRCKRIDY2ttzjcnNzw6hRo8p9HCIiIiKqXJig0ScjISEBEolEPExMTODm5oamTZtCV1cXixYtqugQiYiIiOgTx1Uc6ZOTnJwMPT09XL9+HUOHDoWNjQ3++OMPqKurl6ifhISE8gmQiIiIiD5ZnEGjT46xsTHMzMzQtm1bTJkyBZcvX8aNGzcAAL/++itq1aoFTU1NODg4YN26dW/sa8KECahduzZ0dHRQs2ZNBAcHIycnRywPCQmBs7Mz1q1bBxsbG8jlcvTt21flXbcnT56gf//+kMlkMDMzQ1hYWPncOBERERFVekzQ6JOmra0NAMjOzsbWrVsxcuRIjB07FhcvXsSwYcMwcOBAHDhwoMj2enp6iIyMxOXLlxEREYHly5dj4cKFKnVSUlIQGxuL7du3Y/v27Th48CDmzJkjlo8bNw4HDx5EXFwc9uzZg4SEBJw9e7bIMbOysqBUKlUOIiIiIvo4MEGjT1Z6ejrmz5+PGjVqwMHBAfPnz4efnx/8/f1Ru3ZtjBkzBr169Xrj0viTJ09Gy5YtYWNjAw8PDwQGBmLDhg0qdfLz8xEZGYkGDRqgTZs28PX1RXx8PAAgMzMTK1euxPz589GhQwc4OjpizZo1yM3NLXLM2bNnQy6Xi8fLTbCJiIiI6MPHBI0+ORYWFtDV1YW5uTmePHmCzZs3Q1NTE1euXEGrVq1U6rZq1QpXrlwpsq+YmBi0atUKpqamkMlkmDx5MtLS0lTq2NjYQE9PTzw3MzPDvXv3ALyYXcvOzkazZs3E8mrVqsHBwaHIMYOCgqBQKMTj9u3bJbp/IiIiIqq8uEgIfXIOHz4MfX19GBsbqyROJXXs2DH4+PggNDQU7u7ukMvliI6OLvAOWZUqVVTOJRKJuDF2aUilUkil0lK3JyIiIqLKizNo9MmxtbVFrVq1CiRndevWRWJiosq1xMRE1KtXr9B+jh49Cmtra0yaNAkuLi6wt7fHrVu3ShRLrVq1UKVKFZw4cUK89ujRI1y7dq1E/RARERHRx6FUM2gDBgzA4MGD0bZt27KOh6jCjBs3Dr1790ajRo3QsWNH/O9//8OWLVuwb9++Quvb29sjLS0N0dHRaNq0KXbs2IGtW7eWaEyZTIbBgwdj3LhxMDQ0hLGxMSZNmgQ1Nf7bCREREdGnqFS/AhUKBTp27Ah7e3vMmjUL//zzT1nHRfTeeXl5ISIiAvPnz0f9+vWxdOlSrF69Gm5uboXW79GjB0aPHo0RI0bA2dkZR48eRXBwcInHnTdvHtq0aQMPDw907NgRrVu3RpMmTd7xboiIiIjoQyQRBEEoTcP//vsP69atw5o1a3D58mV07NgRgwcPhqenZ4F3boio/CiVSsjlcigUCujr61d0OERERET0mpL8Xiv1c1RGRkYYM2YMzp8/jxMnTsDOzg6+vr4wNzfH6NGjcf369dJ2TVTmUlNTIZFIkJSUVO5jSSQSxMbGVopYiIiIiOjD8s4vuqSnp2Pv3r3Yu3cv1NXV0a1bN1y4cAH16tUrsGEvUWkIgoBly5ahWbNmkMlkMDAwgIuLC8LDw/H06dOKDq/ELC0tkZ6ejgYNGlR0KERERERUyZQqQcvJycHmzZvRvXt3WFtbY+PGjRg1ahTu3LmDNWvWYN++fdiwYQOmTZtW1vHSJ8jX1xejRo2Cp6cnDhw4gKSkJAQHByMuLg579uyp6PBKTF1dHaamptDQ4C4XRERERKSqVAmamZkZhgwZAmtra5w8eRKnT5/Gt99+q/I8Zbt27WBgYFBWcdInasOGDYiKisL69esxceJENG3aFDY2NvD09MT+/fvRrl07AEB+fj6mTZsGCwsLSKVSODs7Y9euXUX2GxkZWeDPZ2xsLCQSiXgeEhICZ2dnrFq1ClZWVpDJZPD390deXh5+/PFHmJqawtjYGDNnzizQf3p6Orp27QptbW3UrFkTmzZtEstef8QxLy8PgwcPhq2tLbS1teHg4ICIiIgiY8/KyoJSqVQ5iIiIiOjjUKp/wl+4cCG++uoraGlpFVnHwMAAf/31V6kDIwKAqKgoODg4wNPTs0CZRCKBXC4HAERERCAsLAxLly5Fo0aNsGrVKvTo0QOXLl2Cvb19qcdPSUnBzp07sWvXLqSkpODLL7/EzZs3Ubt2bRw8eBBHjx7FoEGD0LFjRzRr1kxsFxwcjDlz5iAiIgLr1q1D3759ceHCBdStW7fAGPn5+bCwsMDGjRthaGiIo0ePYujQoTAzM0Pv3r0L1J89ezZCQ0NLfU9EREREVHmVeAYtJycHAwcOxI0bN8ojHiIV169fh4ODw1vrzZ8/HxMmTEDfvn3h4OCAuXPnwtnZGeHh4e80fn5+PlatWoV69erBw8MD7dq1Q3JyMsLDw+Hg4ICBAwfCwcEBBw4cUGn31Vdf4ZtvvkHt2rUxffp0uLi4YPHixYWOUaVKFYSGhsLFxQW2trbw8fHBwIEDsWHDhkLrBwUFQaFQiMft27ff6R6JiIiIqPIo8QxalSpVYGVlhby8vPKIh0hFcXaBUCqVuHPnDlq1aqVyvVWrVjh//vw7jW9jYwM9PT3x3MTEBOrq6iobSZuYmODevXsq7Vq0aFHg/E2rNv78889YtWoV0tLS8OzZM2RnZ8PZ2bnQulKpFFKptOQ3Q0RERESVXqneQZs0aRImTpyIhw8flnU8RCpq166Nq1evlnm/ampqBZK/nJycAvVe39NPIpEUei0/P7/UsURHRyMwMBCDBw/Gnj17kJSUhIEDByI7O7vUfRIRERHRh6lUCdpPP/2EQ4cOwdzcHA4ODmjcuLHKQVRW+vXrh2vXriEuLq5AmSAI4mZ/5ubmSExMVClPTExEvXr1Cu3XyMgIjx8/xpMnT8RrZbkv2fHjxwucF/b+2cs4W7ZsCX9/fzRq1Ah2dnZISUkps1iIiIiI6MNRqkVCPD09VVa7IyovvXv3xtatW+Ht7Y3Jkyejc+fOMDIywoULF7Bw4UJ8//338PLywrhx4zB16lTUqlULzs7OWL16NZKSkhAVFVVov82aNYOOjg4mTpyIgIAAnDhxApGRkWUW98aNG+Hi4oLWrVsjKioKJ0+exMqVKwuta29vj7Vr12L37t2wtbXFunXrcOrUKdja2pZZPERERET0YShVghYSElLGYRAVTiKR4Pfff8eyZcuwatUqzJw5ExoaGrC3t0f//v3h7u4OAAgICIBCocDYsWNx79491KtXD9u2bStyBcdq1arht99+w7hx47B8+XJ06NABISEhGDp0aJnEHRoaiujoaPj7+8PMzAzr168vcjZv2LBhOHfuHPr06QOJRAJvb2/4+/tj586dZRILEREREX04JEJxVmF4Tc2aNXHq1CkYGhqqXM/IyEDjxo1x8+bNMguQ6EPj5+eHjIwMxMbGvpfxlEol5HK5+LgnEREREVUuJfm9VqoZtNTU1EJXcczKysLff/9dmi6J3klCQgLatWuHR48eVfgG6REREcVafZKIiIiI6HUlStC2bdsmft69e7e4STAA5OXlIT4+nu/N0Cfv1b8XREREREQlUaJVHL28vODl5QWJRIIBAwaI515eXujbty/27t2LsLCw8oqVqNgePHgAb29v1KhRAzo6OnB0dMT69etV6ri5ueH777/HqFGjULVqVZiYmGD58uV48uQJBg4cCD09PdjZ2am8C5aXl4fBgwfD1tYW2tracHBwQEREhEq/fn5+8PLyEs/z8/Px448/ws7ODlKpFFZWVpg5c6ZYPmHCBNSuXRs6OjqoWbMmgoODC13yn4iIiIg+fiWaQXu515OtrS1OnTqF6tWrl0tQRO/q+fPnaNKkCSZMmAB9fX3s2LEDvr6+qFWrFj777DOx3po1azB+/HicPHkSMTEx+O6777B161b07NkTEydOxMKFC+Hr64u0tDTo6OggPz8fFhYW2LhxIwwNDXH06FEMHToUZmZm6N27d6GxBAUFYfny5Vi4cCFat26N9PR0lb3d9PT0EBkZCXNzc1y4cAFDhgyBnp4exo8fX2h/WVlZyMrKEs+VSmUZfWtEREREVNFKtUgIUWVTnHfQunfvjjp16mD+/PkAXsyg5eXl4fDhwwBezI7J5XL06tULa9euBQD8+++/MDMzw7Fjx9C8efNC+x0xYgT+/fdfbNq0CYDqIiGPHz+GkZERfvrpJ3zzzTfFupf58+cjOjoap0+fLrQ8JCQEoaGhBa5zkRAiIiKiyqncFwkBgPj4eMTHx+PevXvizNpLq1atKm23RGUiLy8Ps2bNwoYNG/DPP/8gOzsbWVlZ0NHRUann5OQkflZXV4ehoSEcHR3FayYmJgCAe/fuidd+/vlnrFq1CmlpaXj27Bmys7Ph7OxcaBxXrlxBVlYWOnToUGSsMTExWLRoEVJSUpCZmYnc3Nw3/sUNCgrCmDFjxHOlUglLS8si6xMRERHRh6NE76C9FBoais6dOyM+Ph7379/Ho0ePVA6iijZv3jxERERgwoQJOHDgAJKSkuDu7o7s7GyVelWqVFE5l0gkKtdebsj+8h8hoqOjERgYiMGDB2PPnj1ISkrCwIEDC/T7kra29hvjPHbsGHx8fNCtWzds374d586dw6RJk4rsDwCkUin09fVVDiIiIiL6OJRqBm3JkiWIjIyEr69vWcdDVCYSExPh6emJr7/+GsCLBOvatWtFbhZdkn5btmwJf39/8VpKSkqR9e3t7aGtrY34+PhCH3E8evQorK2tMWnSJPHarVu33ilGIiIiIvpwlWoGLTs7Gy1btizrWIjKjL29Pfbu3YujR4/iypUrGDZsGO7evVsm/Z4+fRq7d+/GtWvXEBwcjFOnThVZX0tLCxMmTMD48eOxdu1apKSk4Pjx41i5cqXYX1paGqKjo5GSkoJFixZh69at7xwnEREREX2YSpWgffPNN/j999/LOhaiUnv5CKKGxotJ4cmTJ6Nx48Zwd3eHm5sbTE1NVZa+L61hw4ahV69e6NOnD5o1a4YHDx6ozKYVJjg4GGPHjsWUKVNQt25d9OnTR3ynrUePHhg9ejRGjBgBZ2dnHD16FMHBwe8cJxERERF9mEq1iuPIkSOxdu1aODk5wcnJqcB7PAsWLCizAImKIzo6GkOGDMHjx48rOpT3riSrAhERERHR+1fuqzj++eef4qp1Fy9eVCl7uagC0fuQlZWFlJQU/PTTT29cKbEySU1Nha2tLc6dO1fk6o9ERERE9GkqVYJ24MCBso6DPjGCIGDFihVYtWoVLl26hPz8fFhbW6Njx474/vvvYWdnV6x+du7cCV9fX7Rs2RKLFi0qtE5x9kgjIiIiIqoMSvUOGtG7EAQB/fr1Q0BAALp164Y9e/bg8uXLWLlyJbS0tDBjxoxi9+Xl5YXHjx9j9+7dsLKyKseoiYiIiIjKX6kStHbt2qF9+/ZFHkRvEhMTg+joaMTExCA4OBjNmzeHlZUVmjdvjrlz52L16tViXTc3N4waNUqlvZeXF/z8/MTzdevWwcXFBXp6ejA1NUW/fv3ERThSU1PRrl07AEDVqlUhkUjEtvn5+Zg9ezZsbW2hra2Nhg0bYtOmTWK/CQkJkEgk2L17Nxo1agRtbW20b98e9+7dw86dO1G3bl3o6+ujX79+ePr0qdhu165daN26NQwMDGBoaIju3bu/cSn+vLw8DB48WIzDwcEBERERRdbPysqCUqlUOYiIiIjo41CqRxxff28mJycHSUlJuHjxIgYMGFAWcdFHbP369XBwcECPHj0KLS/pe4w5OTmYPn06HBwccO/ePYwZMwZ+fn74448/YGlpic2bN+OLL75AcnIy9PX1xc2jZ8+ejd9++w1LliyBvb09Dh06hK+//hpGRkZwdXUV+w8JCcFPP/0EHR0d9O7dG71794ZUKsXvv/+OzMxM9OzZE4sXL8aECRMAAE+ePMGYMWPg5OSEzMxMTJkyBT179kRSUhLU1Ar+m0h+fj4sLCywceNGGBoa4ujRoxg6dCjMzMzQu3fvAvVnz56N0NDQEn1HRERERPRhKNUqjkUJCQlBZmYm5s+fX1Zd0keobt26qF27NuLi4sRro0aNwooVKwAABgYG+PvvvwG8mEFzdnZGeHi4WNfLywsGBgaIjIwstP/Tp0+jadOmePz4MWQyWaHvoGVlZaFatWrYt28fWrRoIbb95ptv8PTpU/z+++9iu3379okLkMyZMwdBQUFISUlBzZo1AQDffvstUlNTsWvXrkLjuX//PoyMjHDhwgU0aNCgWIuEjBgxAv/++6/KjN5LWVlZyMrKEs+VSiUsLS25iiMRERFRJVWSVRzL9B20r7/+GqtWrSrLLukTMWnSJCQlJWHKlCnIzMwsUdszZ87Aw8MDVlZW0NPTE2e/0tLSimxz48YNPH36FJ06dYJMJhOPl5tJv8rJyUn8bGJiAh0dHTE5e3nt5SOVAHD9+nV4e3ujZs2a0NfXh42NzVvj+fnnn9GkSRMYGRlBJpNh2bJlRdaXSqXQ19dXOYiIiIjo41CqRxyLcuzYMWhpaZVll/QRsre3R3Jysso1IyMjGBkZwdjYWOW6mpoaXp/kzcnJET8/efIE7u7ucHd3R1RUFIyMjJCWlgZ3d3dkZ2cXGcPLJHDHjh2oUaOGSplUKlU5f3WfP4lEUmDfP4lEIm6UDQAeHh6wtrbG8uXLYW5ujvz8fDRo0KDIeKKjoxEYGIiwsDC0aNECenp6mDdvHk6cOFFk/ERERET0cSpVgtarVy+Vc0EQkJ6ejtOnTyM4OLhMAqOPl7e3N/r164e4uDh4enq+sa6RkRHS09PF87y8PFy8eFFc+OPq1at48OAB5syZA0tLSwAvHnF8laamptj2pXr16kEqlSItLU3lfbN39eDBAyQnJ2P58uVo06YNAODIkSNvbJOYmIiWLVvC399fvPamRUWIiIiI6ONVqgRNLpernKupqcHBwQHTpk1D586dyyQw+nj17dsXW7ZsQd++fREUFAR3d3eYmJjg1q1biImJgbq6uli3ffv2GDNmDHbs2IFatWphwYIFyMjIEMutrKygqamJxYsX49tvv8XFixcxffp0lfGsra0hkUiwfft2dOvWDdra2tDT00NgYCBGjx6N/Px8tG7dGgqFAomJidDX1y/1YjdVq1aFoaEhli1bBjMzM6SlpeGHH354Yxt7e3usXbsWu3fvhq2tLdatW4dTp07B1ta2VDEQERER0YerVAnaq8ugE5WURCJBTEwMli9fjtWrV+PHH39ETk4OLCws0KFDByxYsECsO2jQIJw/fx79+/eHhoYGRo8eLc6eAS9m2CIjIzFx4kQsWrQIjRs3xvz581VWiKxRowZCQ0Pxww8/YODAgejfvz8iIyMxffp0GBkZYfbs2bh58yYMDAzQuHFjTJw4sdT3pqamhujoaAQEBKBBgwZwcHDAokWL4ObmVmSbYcOG4dy5c+jTpw8kEgm8vb3h7++PnTt3ljoOIiIiIvowvdMqjmfOnMGVK1cAAPXr10ejRo3KLDAiKp6SrApERERERO9fSX6vlWoG7d69e+jbty8SEhLEZcszMjLQrl07REdHw8jIqDTdEpULQRCwYsUKrFq1CpcuXUJ+fj6sra3RsWNHfP/997Czs6voEImIiIiIAJRymf3vv/8ejx8/xqVLl/Dw4UM8fPgQFy9ehFKpREBAQFnHSFRqgiCgX79+CAgIQLdu3bBnzx5cvnwZK1euhJaWFmbMmFFk2zetAklEREREVB5KlaDt2rULv/zyC+rWrSteq1evHn7++We+N0OVSkxMDKKjoxETE4Pg4GA0b94cVlZWaN68OebOnavyPqWfnx+8vLwwc+ZMmJubw8HBAQBw+/Zt9O7dGwYGBqhWrRo8PT2RmpqqMs6KFStQt25daGlpoU6dOvjll19Uyv/++294e3ujWrVq0NXVhYuLi8oy+nFxcWjcuDG0tLRQs2ZNhIaGIjc3t/y+GCIiIiKqlEr1iGN+fn6BvaCAF/tFvbofFFFFW79+PRwcHFQWDXmVRCJROY+Pj4e+vj727t0L4MWea+7u7mjRogUOHz4MDQ0NzJgxA126dMGff/4JTU1NREVFYcqUKfjpp5/QqFEjnDt3DkOGDIGuri4GDBiAzMxMuLq6okaNGti2bRtMTU1x9uxZ8e/K4cOH0b9/fyxatAht2rRBSkoKhg4dCgCYOnVqgZizsrKQlZUlniuVyjL5roiIiIioEhBKoUePHkLbtm2Ff/75R7z2999/C66uroKXl1dpuiQqF3Xq1BF69Oihcm3kyJGCrq6uoKurK9SoUUO8PmDAAMHExETIysoSr61bt05wcHAQ8vPzxWtZWVmCtra2sHv3bkEQBKFWrVrC77//rjLG9OnThRYtWgiCIAhLly4V9PT0hAcPHhQaY4cOHYRZs2apXFu3bp1gZmZWaP2pU6cKAAocCoXibV8HEREREVUAhUJR7N9rpVrF8fbt2+jRowcuXbokbg58+/ZtNGjQANu2bYOFhUUZppBEpVe3bl3Url0bcXFx4rX//vsPCoUCW7ZswaxZs8R91fz8/PDPP/+Is2cAMG7cOCxcuBBaWloq/T59+hQ///wz+vfvD5lMBm1tbaip/f8nhnNzcyGXy3H37l34+/vj0qVLOHjwYKExGhkZITMzU2X/t7y8PDx//hxPnjyBjo6OSv3CZtAsLS25iiMRERFRJVXuqzhaWlri7Nmz2LdvH65evQrgxQ/hjh07lqY7onJjb2+P5ORklWtGRkYwMjKCsbFxgfq6uroq55mZmWjSpAmioqIK1H2ZWAHA8uXL0axZM5XylwmXtrb2G2PMzMxEaGgoevXqVaDs9cQQAKRSKaRS6Rv7JCIiIqIPU4kStP3792PEiBE4fvw49PX10alTJ3Tq1AkAoFAoUL9+fSxZsgRt2rQpl2CJSsrb2xv9+vVDXFwcPD09S9y+cePGiImJgbGxcaH/2iGXy2Fubo6bN2/Cx8en0D6cnJywYsUKPHz4ENWqVSt0jOTkZC73T0REREQlW8UxPDwcQ4YMKfKH6rBhw7BgwYIyC47oXfXt2xdffvkl+vbti2nTpuHEiRNITU3FwYMHERMTo/JYYWF8fHxQvXp1eHp64vDhw/jrr7+QkJCAgIAA/P333wCA0NBQzJ49G4sWLcK1a9dw4cIFrF69Wvy74O3tDVNTU3h5eSExMRE3b97E5s2bcezYMQDAlClTsHbtWoSGhuLSpUu4cuUKoqOjMXny5PL9coiIiIio0ilRgnb+/Hl06dKlyPLOnTvjzJkz7xwUUVmRSCSIiYlBeHg4/vjjD3To0AEODg4YNGgQLC0tceTIkTe219HRwaFDh2BlZYVevXqhbt26GDx4MJ4/fy7+Q8U333yDFStWYPXq1XB0dISrqysiIyNha2sLANDU1MSePXtgbGyMbt26wdHREXPmzBGTQ3d3d2zfvh179uxB06ZN0bx5cyxcuBDW1tbl++UQERERUaVTokVCtLS0cPHixSIfxbpx4wYcHR3x7NmzMguQiN6sJC+dEhEREdH7V5LfayWaQatRowYuXrxYZPmff/4JMzOzknRJVK5SU1MhkUiQlJRU0aEQEREREb1ViRK0bt26ITg4GM+fPy9Q9uzZM0ydOhXdu3cvs+CIirJ582aoq6vjn3/+KbTc3t4eY8aMgaWlJdLT09GgQYNi9+3m5oZRo0aVUaRERERERMVXogRt8uTJePjwIWrXro0ff/wRcXFxiIuLw9y5c+Hg4ICHDx9i0qRJ5RUrkahHjx4wNDTEmjVrCpQdOnQIN27cwODBg6Gurg5TU1NoaJRqRwkiIiIioveqRAmaiYkJjh49igYNGiAoKAg9e/ZEz549MXHiRDRo0ABHjhyBiYlJecVKJKpSpQp8fX0RGRlZoGzVqlVo1qwZ6tevX+gjjhcvXkTXrl0hk8lgYmICX19f3L9/H8CLzaoPHjyIiIgISCQSSCQSpKamIiEhARKJBPHx8XBxcYGOjg5atmypssdaSkoKPD09YWJiAplMhqZNm2Lfvn0qsdnY2GDGjBniBtfW1tbYtm0b/vvvP3h6ekImk8HJyQmnT58ul++NiIiIiCq3EiVoAGBtbY0//vgD9+/fx4kTJ3D8+HHcv38ff/zxh7hqHdH7MHjwYFy/fh2HDh0Sr2VmZmLTpk0YPHhwoW0yMjLQvn17NGrUCKdPn8auXbtw9+5d9O7dGwAQERGBFi1aYMiQIUhPT0d6ejosLS3F9pMmTUJYWBhOnz4NDQ0NDBo0SGXsbt26IT4+HufOnUOXLl3g4eGBtLQ0lRgWLlyIVq1a4dy5c/j888/h6+uL/v374+uvv8bZs2dRq1Yt9O/fH0Wt35OVlQWlUqlyEBEREdFHQiD6gDVv3lwYMGCAeL5y5UpBR0dHUCqVgiAIwl9//SUAEM6dOycIgiBMnz5d6Ny5s0oft2/fFgAIycnJgiAIgqurqzBy5EiVOgcOHBAACPv27ROv7dixQwAgPHv2rMj46tevLyxevFg8t7a2Fr7++mvxPD09XQAgBAcHi9eOHTsmABDS09ML7XPq1KkCgAKHQqEoMg4iIiIiqjgKhaLYv9dKPINGVJkMGjQImzZtwuPHjwG8eLzxq6++gp6eXqH1z58/jwMHDkAmk4lHnTp1ALx4RPFtnJycxM8vVyy9d+8egBczaIGBgahbty4MDAwgk8lw5cqVAjNor/bx8pFgR0fHAtde9vu6oKAgKBQK8bh9+/Zb4yYiIiKiDwNXTqAPWt++fTF69Ghs2LABbdu2RWJiImbPnl1k/czMTHh4eGDu3LkFyoqzRUSVKlXEzxKJBACQn58PAAgMDMTevXsxf/582NnZQVtbG19++SWys7Pf2seb+n2dVCqFVCp9a6xERERE9OFhgkYfND09PXz11VdYtWoVUlJSULt2bbRp06bI+o0bN8bmzZthY2NT5MqOmpqayMvLK3EsiYmJ8PPzQ8+ePQG8SAZTU1NL3A8RERERfbr4iCN98AYPHoyjR49iyZIlKot2FGb48OF4+PAhvL29cerUKaSkpGD37t0YOHCgmJTZ2NjgxIkTSE1Nxf3794ucyXqdvb09tmzZgqSkJJw/fx79+vUrdlsiIiIiIoAJGn0EWrduDQcHByiVSvTv3/+Ndc3NzZGYmIi8vDx07twZjo6OGDVqFAwMDKCm9uKvQ2BgINTV1VGvXj0YGRkVeIesKAsWLEDVqlXRsmVLeHh4wN3dHY0bN37n+yMiIiKiT4dEEIpYy5uI3omfnx8yMjIQGxtbruMolUrI5XIoFAro6+uX61hEREREVHIl+b3GGTT6oL3cQLqw499//63o8IiIiIiISoSLhNBHITk5ucC/RhgbG1dQNGVDEATk5eUVuZgJEREREX18OINGHwVjY2OYmpqqHC/fKQNe7I9Wv359SKVSmJmZYcSIEQCA1NRUSCQSJCUliXUzMjIgkUiQkJAAAMjLy8PgwYNha2sLbW1tODg4ICIiQmX8vLw8jBkzBgYGBjA0NMT48ePx+tPDWVlZCAgIgLGxMbS0tNC6dWucOnVKLH85G7hz5040adIEUqkUR44cKeNvioiIiIgqMyZo9NH79ddfMXz4cAwdOhQXLlzAtm3bYGdnV+z2+fn5sLCwwMaNG3H58mVMmTIFEydOxIYNG8Q6YWFhiIyMxKpVq3DkyBE8fPgQW7duVeln/Pjx2Lx5M9asWYOzZ8/Czs4O7u7uePjwoUq9H374AXPmzMGVK1dUNrX+f+3dd1gVV/4/8PdF5FIuXBApoiAgSBOxgAaNYl2UiKgxNqKoRBONXYgSNWLssRKTuNEoRGPftRDN2oi4ig0VbCAaG+zKSoLKBQv1/P7w53wdKYGIUny/nmeevTPnM2fOOcHlfjgzZ57Lzc2FRqORbURERERUO/DeKaoVGjVqJNtv3Lgxrly5AgCYN28epk6diokTJ0rlXl5e5a67bt26mDNnjrRvZ2eHkydPYvv27RgwYAAAYOXKlQgLC0O/fv0AAH//+99x4MAB6ZxHjx5h9erViIqKQs+ePQEAa9euxaFDh7Bu3TqEhoZKsV9++SW6d+9eansWLlwoaw8RERER1R5M0KhWOHbsGAwNDaX9unXrAgAyMjJw9+5ddO3a9ZXq//bbb7F+/XqkpqbiyZMnyMvLQ4sWLQAAWVlZSE9PR9u2baV4bW1teHp6Src53rhxA/n5+Wjfvr2sjW3atEFycrLsWp6enmW2JSwsDFOmTJH2NRoNrK2tX6l/RERERFQ9MEGjWsHOzg7GxsbFjuvp6ZV53vPn1F58Xiw/P18Ws3XrVoSEhGDZsmXw9vaGoaEhlixZgtOnT796w0tgYGBQZrlSqYRSqXwt1yYiIiKiqsVn0KhWMzQ0hK2tLWJiYkosNzMzAwCkp6dLx15cMAQA4uLi0K5dO4wdOxYtW7aEg4MDbty4IZWr1Wo0aNBAlrAVFBTg3Llz0n6TJk2go6ODuLg46Vh+fj7i4+Ph6ur6Sn0kIiIiotqDM2hUK2RkZODp06eyY6ampqhbty7Cw8PxySefwNzcHD179kR2djbi4uIwfvx46Onp4Z133sGiRYtgZ2eHjIwMzJw5U1aPo6MjNmzYgAMHDsDOzg4bN25EfHw87OzspJiJEydi0aJFcHR0hLOzM5YvX46HDx9K5QYGBhgzZgxCQ0NRr1492NjY4KuvvsLjx48RHBz8WseGiIiIiGoOJmhUKzg5ORU7dvLkSbzzzjsICgrC06dPsWLFCoSEhKB+/fro37+/FLd+/XoEBwejdevWcHJywldffYW//e1vUvnHH3+MhIQEDBw4EAqFAoMHD8bYsWPxr3/9S4qZOnUq0tPTERQUBC0tLYwcORJ9+/ZFVlaWFLNo0SIUFRVh6NChyM7OhqenJw4cOAATE5PXNCpEREREVNMoxMsvayKiGkWj0UCtViMrK6vYy7qJiIiIqOpV5Psan0EjIiIiIiKqJpigEf0FsbGxUCgUsufMiIiIiIheFRM0otegsLAQRUVFVd0MIiIiIqphmKARVYKoqCgYGxsjOjoarq6uUCqVSE1NRXx8PLp374769etDrVbDx8cH58+fl52rUCjwww8/oG/fvtDX14ejoyOio6OrqCdEREREVJWYoBFVksePH2Px4sX44YcfcOXKFZibmyM7OxtBQUE4fvw4Tp06BUdHR/j5+SE7O1t27pw5czBgwABcvHgRfn5+CAwMxP3790u8Tm5uLjQajWwjIiIiotqBCRpRJcnPz8d3332Hdu3awcnJCfr6+ujSpQs+/PBDODs7w8XFBWvWrMHjx49x9OhR2bnDhw/H4MGD4eDggAULFiAnJwdnzpwp8ToLFy6EWq2WNmtr6zfRPSIiIiJ6A5igEVUSHR0dNG/eXHbs3r17GDVqFBwdHaFWq2FkZIScnBykpqbK4l48z8DAAEZGRsjIyCjxOmFhYcjKypK2tLS0yu8MEREREVUJvqiaqJLo6elBoVDIjgUFBSEzMxMRERFo3LgxlEolvL29kZeXJ4urW7eubF+hUJS6yIhSqYRSqazcxhMRERFRtcAEjeg1iouLw3fffQc/Pz8AQFpaGv74448qbhURERERVVdM0IheI0dHR2zcuBGenp7QaDQIDQ2Fnp5eVTeLiIiIiKopPoNG9BqtW7cODx48QKtWrTB06FBMmDAB5ubmVd0sIiIiIqqmFEIIUdWNIKK/TqPRQK1WIysrC0ZGRlXdHCIiIiJ6SUW+r3EGjagcYmNjoVAopM3CwgLvv/8+bt68WdVNIyIiIqJahAkaUQWkpKTg7t272LFjB65cuQJ/f38UFhb+pbry8/MruXVEREREVNMxQSOqAHNzczRo0AAdO3bEF198gaSkJPz2228AgD179qBVq1bQ1dWFvb095syZg4KCAulchUKB1atXo3fv3jAwMMD8+fMRFRUFY2Nj2TV2795dbLl+IiIiIno7MEEj+ouer8aYl5eHY8eOYdiwYZg4cSKSkpLw/fffIyoqCvPnz5edEx4ejr59++LSpUsYOXLkX7pubm4uNBqNbCMiIiKi2oEJGtFfkJ6ejqVLl6Jhw4ZwcnLCnDlzMH36dAQFBcHe3h7du3fH3Llz8f3338vOGzJkCEaMGAF7e3vY2Nj8pWsvXLgQarVa2qytrSujS0RERERUDTBBI6qARo0awcDAAFZWVnj06BH++c9/QkdHBxcuXMCXX34JlUolbaNGjUJ6ejoeP34sne/p6fnKbQgLC0NWVpa0paWlvXKdRERERFQ98EXVRBVw7NgxGBkZwdzcHIaGhtLxnJwczJkzB/369St2jq6urvTZwMBAVqalpYWX33TxZ4uHKJVKKJXKv9J8IiIiIqrmmKARVYCdnV2xRT0AoFWrVkhJSYGDg0OF6jMzM0N2djYePXokJW+JiYmV0FIiIiIiqomYoBFVgi+++AK9evWCjY0N+vfvDy0tLVy4cAGXL1/GvHnzSj2vbdu20NfXx+eff44JEybg9OnTiIqKenMNJyIiIqJqhc+gEVUCX19f7N27FwcPHoSXlxfeeecdrFixAo0bNy7zvHr16uGnn37CL7/8And3d2zZsgXh4eFvptFEREREVO0oxMsPwBBRjaLRaKBWq5GVlQUjI6Oqbg4RERERvaQi39c4g0Y1jkKhwO7du6u6GURERERElY4JGlULsbGxUCgUcHNzQ2FhoazM2NhY9lxWeno6evbs+YZbSERERET0+jFBo2rl5s2b2LBhQ5kxlpaWr3WZ+by8vNdWNxERERFRWZigUbUyfvx4zJ49G7m5uaXGvHyL43/+8x8MHjwY9erVg4GBATw9PXH69GkAwI0bNxAQEAALCwuoVCp4eXnh8OHDsvpsbW0xd+5cDBs2DEZGRhg9ejQAYNq0aWjatCn09fVhb2+PWbNmFXtH2bx586R3on300UeYPn06WrRoIZV36tQJkyZNkp3Tp08fDB8+XNrfuHEjPD09YWhoCEtLSwwZMgQZGRkVGDUiIiIiqi2YoFG1MmnSJBQUFGDVqlXlis/JyYGPjw/++9//Ijo6GhcuXMBnn32GoqIiqdzPzw8xMTFISEhAjx494O/vj9TUVFk9S5cuhYeHBxISEjBr1iwAgKGhIaKiopCUlISIiAisXbsWK1askM7ZtGkT5s+fj8WLF+PcuXOwsbHB6tWrK9zn/Px8zJ07FxcuXMDu3btx+/ZtWQL3stzcXGg0GtlGRERERLUD34NG1Yq+vj5mz56Nzz//HKNGjYJarS4zfvPmzfj9998RHx+PevXqAYDsZdEeHh7w8PCQ9ufOnYtdu3YhOjoa48aNk4536dIFU6dOldU9c+ZM6bOtrS1CQkKwdetWfPbZZwCAVatWITg4GCNGjADw7F1oBw8eRE5OToX6PHLkSOmzvb09vv76a3h5eSEnJwcqlapY/MKFCzFnzpwKXYOIiIiIagbOoFG1ExwcDFNTUyxevPhPYxMTE9GyZUspOXtZTk4OQkJC4OLiAmNjY6hUKiQnJxebQfP09Cx27rZt29C+fXtYWlpCpVJh5syZsvNSUlLQpk0b2Tkv75fHuXPn4O/vDxsbGxgaGsLHxwcAirXxubCwMGRlZUlbWlpaha9JRERERNUTEzSqdrS1tTF//nxERETg7t27Zcbq6emVWR4SEoJdu3ZhwYIFOHbsGBITE+Hu7l5sIRADAwPZ/smTJxEYGAg/Pz/s3bsXCQkJmDFjRoUXENHS0sLLrxp88Tm2R48ewdfXF0ZGRti0aRPi4+Oxa9cuAKUvVqJUKmFkZCTbiIiIiKh2YIJG1dIHH3wANze3P72Vr3nz5khMTMT9+/dLLI+Li8Pw4cPRt29fuLu7w9LSErdv3/7T6584cQKNGzfGjBkz4OnpCUdHR9y5c0cW4+TkhPj4eNmxl/fNzMyQnp4u7RcWFuLy5cvS/tWrV5GZmYlFixahQ4cOcHZ25gIhRERERG8xJmhUbS1atAjr16/Ho0ePSo0ZPHgwLC0t0adPH8TFxeHmzZv45z//iZMnTwIAHB0dsXPnTiQmJuLChQsYMmSItIBIWRwdHZGamoqtW7fixo0b+Prrr6WZrefGjx+PdevW4ccff8T169cxb948XLx4EQqFQorp0qUL9u3bh3379uHq1asYM2YMHj58KJXb2NhAR0cHq1atws2bNxEdHY25c+dWcKSIiIiIqLZggkbVVpcuXdClSxcUFBSUGqOjo4ODBw/C3Nwcfn5+cHd3x6JFi1CnTh0AwPLly2FiYoJ27drB398fvr6+aNWq1Z9eu3fv3pg8eTLGjRuHFi1a4MSJE9Lqjs8FBgYiLCwMISEhaNWqFW7duoXhw4dDV1dXihk5ciSCgoIwbNgw+Pj4wN7eHp07d5bKzczMEBUVhR07dsDV1RWLFi3C0qVLKzpURERERFRLKMTLD8gQkUx4eDh2796NxMTEP43t3r07LC0tsXHjxtffsP9Po9FArVYjKyuLz6MRERERVUMV+b7GGTSq9YYPHw6FQlHqZmtrW+b5ISEhiImJKXb88ePHWL58Oa5cuYKrV69i9uzZOHz4MIKCgl5TT4iIiIiotuMMGtV6WVlZePLkibTfoEEDREZGokePHgCAOnXqwMzMrML1PnnyBP7+/khISMDTp0/h5OSEmTNnol+/fpXW9vLgDBoRERFR9cYZNKIXqNVqWFpaShsAGBsbS/tLly5F06ZNoa+vD3t7e8yaNUu2FH54eDhatGgBALh8+TK0tLTw+++/Q09PD9u3b8eDBw/g7++P8+fPo1+/fpg3bx7effddAM9WbQwODoadnR309PTg5OSEiIgIWfuGDx+OPn36YOnSpWjQoAFMTU3x6aefytpARERERG8H7apuAFFVMzQ0RFRUFKysrHDp0iWMGjUKhoaG+Oyzz4rFurm5wdTUFEePHkX//v1x7Ngxaf+5o0ePolOnTgCAoqIiNGrUCDt27ICpqSlOnDiB0aNHo0GDBhgwYIB0zpEjR9CgQQMcOXIEv/32GwYOHIgWLVpg1KhRxdqQm5uL3NxcaV+j0VTiaBARERFRVeIMGr31Zs6ciXbt2sHW1hb+/v4ICQnB9u3bS4xVKBTo2LEjYmNjAQCxsbEYMWIEcnNzcfXqVeTn5+PEiRPw8fEBANStWxdz5syBp6cn7OzsEBgYiBEjRhSr38TEBN988w2cnZ3Rq1cvvPfeeyU+9wYACxcuhFqtljZra+vKGwwiIiIiqlJM0Oitt23bNrRv3x6WlpZQqVSYOXMmUlNTS4338fGRErSjR4+iS5cuUtIWHx+P/Px8tG/fXor/9ttv0bp1a5iZmUGlUmHNmjXF6ndzc5NeDQA8e06utBdWh4WFISsrS9rS0tJeofdEREREVJ0wQaO32smTJxEYGAg/Pz/s3bsXCQkJmDFjBvLy8ko9p1OnTkhKSsL169eRlJSEd999F506dUJsbCyOHj0KT09P6OvrAwC2bt2KkJAQBAcH4+DBg0hMTMSIESOK1V+3bl3ZvkKhKPWF2kqlEkZGRrKNiIiIiGoHPoNGb7UTJ06gcePGmDFjhnTszp07ZZ7j7u4OExMTzJs3Dy1atIBKpUKnTp2wePFiPHjwQHr+DADi4uLQrl07jB07Vjp248aNSu8HEREREdUOnEGjt5qjoyNSU1OxdetW3LhxA19//TV27dpV5jnPn0PbtGmTlIw1b94cubm5iImJkZ4/e17/2bNnceDAAVy7dg2zZs1CfHz86+wSEREREdVgTNDorda7d29MnjwZ48aNQ4sWLXDixAnMmjXrT8/z8fFBYWGhlKBpaWmhY8eOUCgUsufPPv74Y/Tr1w8DBw5E27ZtkZmZKZtNIyIiIiJ6EV9UTVTD8UXVRERERNUbX1RN9BpERUXB2Nj4tV/n9u3bUCgUSExMfO3XIiIiIqLqhQka1XqxsbFQKBR4+PBhVTeFiIiIiKhMTNDorVdYWFjqkvZERERERG8SEzR66zy/VTE6Ohqurq5QKpVITU1Fbm4uQkJC0LBhQxgYGKBt27bSC6lLcuPGDQQEBMDCwgIqlQpeXl44fPiwLMbW1hYLFizAyJEjYWhoCBsbG6xZs0YWc+bMGbRs2RK6urrw9PREQkLC6+g2EREREdUATNDorfT48WMsXrwYP/zwA65cuQJzc3OMGzcOJ0+exNatW3Hx4kV88MEH6NGjB65fv15iHTk5OfDz80NMTAwSEhLQo0cP+Pv7IzU1VRa3bNkyKfEaO3YsxowZg5SUFKmOXr16wdXVFefOnUN4eDhCQkLKbHtubi40Go1sIyIiIqLagQkavZXy8/Px3XffoV27dnBycsIff/yByMhI7NixAx06dECTJk0QEhKCd999F5GRkSXW4eHhgY8//hjNmjWDo6Mj5s6diyZNmiA6OloW5+fnh7Fjx8LBwQHTpk1D/fr1ceTIEQDA5s2bUVRUhHXr1sHNzQ29evVCaGhomW1fuHAh1Gq1tFlbW1fOoBARERFRldOu6gYQVQUdHR00b95c2r906RIKCwvRtGlTWVxubi5MTU1LrCMnJwfh4eHYt28f0tPTUVBQgCdPnhSbQXvxOgqFApaWlsjIyAAAJCcno3nz5tDV1ZVivL29y2x7WFgYpkyZIu1rNBomaURERES1BBM0eivp6elBoVBI+zk5OahTpw7OnTuHOnXqyGJVKlWJdYSEhODQoUNYunQpHBwcoKenh/79+yMvL08WV7duXdm+QqF4pUVJlEollErlXz6fiIiIiKovJmhEAFq2bInCwkJkZGSgQ4cO5TonLi4Ow4cPR9++fQE8S/Ju375doeu6uLhg48aNePr0qTSLdurUqQrVQURERES1B59BIwLQtGlTBAYGYtiwYdi5cydu3bqFM2fOYOHChdi3b1+J5zg6OmLnzp1ITEzEhQsXMGTIkArPjA0ZMgQKhQKjRo1CUlISfvnlFyxdurQyukRERERENRATNKL/LzIyEsOGDcPUqVPh5OSEPn36ID4+HjY2NiXGL1++HCYmJmjXrh38/f3h6+uLVq1aVeiaKpUKP//8My5duoSWLVtixowZWLx4cWV0h4iIiIhqIIUQQlR1I4jor9NoNFCr1cjKyoKRkVFVN4eIiIiIXlKR72ucQaNaydbWFitXrnylOsLDw9GiRQtpf/jw4ejTp88r1Vkez1+kTURERERvHyZoVKMMHz4cCoWi1M3W1rbSrhUSEoKYmJhKq4+IiIiI6M8wQaMaJSIiAunp6dIGPHt27Pl+fHx8pV1LpVKV+g40IiIiIqLXgQka1ShqtRqWlpbSBgDGxsbSvpmZmRT7+PFjjBw5EoaGhrCxscGaNWtkdU2bNg1NmzaFvr4+7O3tMWvWLOTn50vlL9/i+LL9+/fj3XffhbGxMUxNTdGrVy/cuHFDKr99+zYUCgV27tyJzp07Q19fHx4eHjh58qSsnqioKNjY2EBfXx99+/ZFZmZmmWOQm5sLjUYj24iIiIiodmCCRrXWsmXL4OnpiYSEBIwdOxZjxoxBSkqKVG5oaIioqCgkJSUhIiICa9euxYoVK8pd/6NHjzBlyhScPXsWMTEx0NLSQt++fYsttT9jxgyEhIQgMTERTZs2xeDBg1FQUAAAOH36NIKDgzFu3DgkJiaic+fOmDdvXpnXXbhwIdRqtbRZW1tXYFSIiIiIqDrjKo5UoykUCuzatavY4h22trbo0KEDNm7cCAAQQsDS0hJz5szBJ598UmJdS5cuxdatW3H27FkAz2bQdu/ejcTERADPnn97+PAhdu/eXeL5f/zxB8zMzHDp0iU0a9YMt2/fhp2dHX744QcEBwcDAJKSkuDm5obk5GQ4OztjyJAhyMrKkr1rbdCgQdi/fz8ePnxY4nVyc3ORm5sr7Ws0GlhbW3MVRyIiIqJqiqs4EgFo3ry59FmhUMDS0hIZGRnSsW3btqF9+/awtLSESqXCzJkzkZqaWu76r1+/jsGDB8Pe3h5GRkbSAiUv1/FiOxo0aAAAUjuSk5PRtm1bWby3t3eZ11UqlTAyMpJtRERERFQ7MEGjWqtu3bqyfYVCId1+ePLkSQQGBsLPzw979+5FQkICZsyYgby8vHLX7+/vj/v372Pt2rU4ffo0Tp8+DQDF6nixHQqFAgCK3QZJRERERAQA2lXdAKKqcOLECTRu3BgzZsyQjt25c6fc52dmZiIlJQVr165Fhw4dAADHjx+vcDtcXFykxO65U6dOVbgeIiIiIqodmKDRW8nR0RGpqanYunUrvLy8sG/fPuzatavc55uYmMDU1BRr1qxBgwYNkJqaiunTp1e4HRMmTED79u2xdOlSBAQE4MCBA9i/f3+F6yEiIiKi2oG3ONJbqXfv3pg8eTLGjRuHFi1a4MSJE5g1a1a5z9fS0sLWrVtx7tw5NGvWDJMnT8aSJUsq3I533nkHa9euRUREBDw8PHDw4EHMnDmzwvUQERERUe3AVRyJariKrApERERERG8eV3EkegWxsbFQKBQlLnNva2uLlStXvvE2EREREdHbgQkaERERERFRNcEEjegVpKamIiAgACqVCkZGRhgwYADu3bsnlYeHh6NFixbYuHEjbG1toVarMWjQIGRnZ0sx2dnZCAwMhIGBARo0aIAVK1agU6dOmDRpUhX0iIiIiIiqEhM0or+oqKgIAQEBuH//Po4ePYpDhw7h5s2bGDhwoCzuxo0b2L17N/bu3Yu9e/fi6NGjWLRokVQ+ZcoUxMXFITo6GocOHcKxY8dw/vz5Uq+bm5sLjUYj24iIiIioduAy+0SlaNSoUbFjjx8/lj7HxMTg0qVLuHXrFqytrQEAGzZsgJubG+Lj4+Hl5QXgWSIXFRUFQ0NDAMDQoUMRExOD+fPnIzs7Gz/++CM2b96Mrl27AgAiIyNhZWVVarsWLlyIOXPmVFo/iYiIiKj6YIJGVIpjx45JSdVznTp1kj4nJyfD2tpaSs4AwNXVFcbGxkhOTpYSNFtbW1k9DRo0QEZGBgDg5s2byM/PR5s2baRytVoNJyenUtsVFhaGKVOmSPsajUbWBiIiIiKquZigEZXCzs4OxsbGsmPa2hX/J1O3bl3ZvkKhQFFR0V9ul1KphFKp/MvnExEREVH1xWfQiP4iFxcXpKWlIS0tTTqWlJSEhw8fwtXVtVx12Nvbo27duoiPj5eOZWVl4dq1a5XeXiIiIiKq/jiDRvQXdevWDe7u7ggMDMTKlStRUFCAsWPHwsfHB56enuWqw9DQEEFBQQgNDUW9evVgbm6O2bNnQ0tLCwqF4jX3gIiIiIiqG86gEf1FCoUCe/bsgYmJCTp27Ihu3brB3t4e27Ztq1A9y5cvh7e3N3r16oVu3bqhffv2cHFxga6u7mtqORERERFVVwohhKjqRhDR/3n06BEaNmyIZcuWITg4+E/jNRoN1Go1srKyYGRk9AZaSEREREQVUZHva2/dDFpsbCwUCgUePnxYrvjbt29DoVAgMTHxtbbrdUhJSYGlpaXspchU/SQkJGDLli24ceMGTp8+DTMzMxQUFCAgIKCqm0ZEREREb1i1S9AKCwuxYsUKuLu7Q1dXFyYmJujZsyfi4uIqXFenTp0wadKkV2qPtbU10tPT0axZs1eqpzxKa29UVJRsNcHHjx8jLCwMTZo0ga6uLszMzODj44M9e/bIzgsLC8P48eNlS7wLIbB27Vp4e3vDyMgIKpUKbm5umDhxIn777bfX1bVXZmtri5UrVxY7Hh4ejhYtWkj7v//+O8aMGQMbGxsolUpYWlrC19cXcXFxyMvLQ/369WUviX7R3LlzYWFhgfz8/GJj/ldUJLlfunQpPDw80LNnT9jY2MDNzQ3169d/pesTERERUc1TrRI0IQQGDRqEL7/8EhMnTkRycjJiY2NhbW2NTp06Yffu3W+8TXXq1IGlpeVfWl79dfnkk0+wc+dOrFq1ClevXsX+/fvRv39/ZGZmSjGpqanYu3cvhg8fLh0TQmDIkCGYMGEC/Pz8cPDgQSQlJWHdunXQ1dXFvHnzqqA3lev9999HQkICfvzxR1y7dg3R0dHo1KkTMjMzoaOjgw8//BCRkZHFzhNCICoqCsOGDSu2LP7r1rJlS5w7dw45OTm4f/8+Tp48icTERFy5cuWNtoOIiIiIqgFRjWzdulUAENHR0cXK+vXrJ0xNTUVOTo4QQoigoCAREBAgi5k4caLw8fGRygHItlu3bokjR44IAOLBgwciJydHGBoaih07dsjq2bVrl9DX1xcajUbcunVLABAJCQlCCCGdf/jwYdG6dWuhp6cnvL29xdWrV2V1zJ07V5iZmQmVSiWCg4PFtGnThIeHR5n99/HxERMnTix2PDIyUqjVamlfrVaLqKioMutasmSJ8PT0lB3bsmWLACD27NlT4jlFRUVltiUgIEAEBQVJ+xs2bBCtW7cWKpVKWFhYiMGDB4t79+5J5c/Hav/+/aJFixZCV1dXdO7cWdy7d0/88ssvwtnZWRgaGorBgweLR48eldmfxo0bixUrVhQ7Pnv2bGlcHzx4IACI2NjYUuu5ePGiACCOHTsmO/68rcnJyUKI4mNemrVr1wpnZ2ehVCqFk5OT+Pbbb6Wyl3/+nv9sCiHEunXrhKurq9DR0RGWlpbi008/ldXbuXNnMXPmzD+9vhBCZGVlCQAiKyurXPFERERE9GZV5PtatZpB27x5M5o2bQp/f/9iZVOnTkVmZiYOHTpUrroiIiLg7e2NUaNGIT09Henp6bC2tpbFGBgYYNCgQcVmVCIjI9G/f3/ZrYEvmzFjBpYtW4azZ89CW1sbI0eOlMo2bdqE+fPnY/HixTh37hxsbGywevXqcrW7PCwtLfHLL7+U+WzZsWPHii31vmXLFjg5OaF3794lnlPRZd3z8/Mxd+5cXLhwAbt378bt27dlM3bPhYeH45tvvsGJEyeQlpaGAQMGYOXKldi8eTP27duHgwcPYtWqVRW6dklUKhVUKhV2796N3NzcEmPc3d3h5eWF9evXy45HRkaiXbt2cHZ2Lvf1Nm3ahC+++ALz589HcnIyFixYgFmzZuHHH38EAJw5cwYAcPjwYaSnp2Pnzp0AgNWrV+PTTz/F6NGjcenSJURHR8PBwUFWd5s2bXDs2LESr5ubmwuNRiPbiIiIiKh2qFYJ2rVr1+Di4lJi2fPj5X2Br1qtho6ODvT19WFpaQlLS0vUqVOnWNxHH32EAwcOID09HQCQkZGBX375RZZwlWT+/Pnw8fGBq6srpk+fjhMnTuDp06cAgFWrViE4OBgjRoxA06ZN8cUXX8Dd3b1c7S6PNWvW4MSJEzA1NYWXlxcmT55c7Bm9O3fuwMrKSnbs2rVrcHJykh2bNGmSlNg0atSoQu0YOXIkevbsCXt7e7zzzjv4+uuv8a9//Qs5OTmyuHnz5qF9+/Zo2bIlgoODcfToUaxevRotW7ZEhw4d0L9/fxw5cqRC1y6JtrY2oqKi8OOPP8LY2Bjt27fH559/josXL8rigoODsWPHDqmd2dnZ+Mc//vGn/81fNnv2bCxbtgz9+vWDnZ0d+vXrh8mTJ+P7778HAJiZmQEATE1NYWlpiXr16gF4Nh5Tp07FxIkT0bRpU3h5eRV79tDKygp37twp8boLFy6EWq2Wtpf/8EBERERENVe1StCAZ88CvUlt2rSBm5ubNOvx008/oXHjxujYsWOZ5zVv3lz63KBBAwDPkjvg2eqJbdq0KXad544dOyYlRSqVCps2bapQmzt27IibN28iJiYG/fv3x5UrV9ChQwfMnTtXinny5Em53qM1Y8YMJCYm4osvviiWWP2Zc+fOwd/fHzY2NjA0NISPjw+AZ8+/vejFsbKwsIC+vj7s7e1lx56P3aZNm2RjU9osUmnef/993L17F9HR0ejRowdiY2PRqlUrREVFSTGDBw9GYWEhtm/fDgDYtm0btLS0MHDgwBLrTE1NlbVpwYIFePToEW7cuIHg4GBZ2bx583Djxo1S25eRkYG7d++ia9euZfZDT08Pjx8/LrEsLCwMWVlZ0paWlvYno0JERERENUX1WfkCQNOmTZGcnFxi2fPjTZs2BQBoaWkVS+by8/P/0nU/+ugjfPvtt5g+fToiIyMxYsSIP73d78WFJJ7HFhUVlet6np6espX9LCwsAABGRkbIysoqFv/w4UOo1epi1+/QoQM6dOiAadOmYd68efjyyy8xbdo06OjooH79+njw4IHsHEdHR6SkpMiOmZmZwczMDObm5rLjfza+jx49gq+vL3x9fbFp0yaYmZkhNTUVvr6+yMvLK9bW5xQKRbFFOBQKhTR2vXv3Rtu2baWyhg0bVnhsdHV10b17d3Tv3h2zZs3CRx99hNmzZ0u3XxoZGaF///6IjIzEyJEjERkZiQEDBkClUhWrH3g2m/Xif6969epJyezatWtl7QVQ4kztc3p6eqWWvej+/fvSDNzLlEollEplueohIiIiopqlWs2gDRo0CNevX8fPP/9crGzZsmUwNTVF9+7dATxLLJ7flvjcy8uZ6+jooLCw8E+v++GHH+LOnTv4+uuvkZSUhKCgoL/eCQBOTk6Ij4+XHXtxX09PDw4ODtL2/Fk3JycnnD9/vlh958+flxLT0ri6uqKgoEC6zbJly5ZISkqSxQwePBgpKSnFluMvycvjW1hYiMuXL0v7V69eRWZmJhYtWoQOHTrA2dlZmgV7FYaGhrKxeZ7QODk54dy5c8Xiyzs2jx49kh0LDg7G8ePHsXfvXpw4caLMF0Jra2vL2lSvXj1YWFjAysoKN2/elJU5ODjAzs4OwLOfPwCyn0FDQ0PY2toiJiamzDZfvnwZLVu2LDOGiIiIiGqfajWDNmjQIOzYsQNBQUFYsmQJunbtCo1Gg2+//RbR0dHYsWMHDAwMAABdunTBkiVLsGHDBnh7e+Onn34q9qXW1tYWp0+fxu3bt6FSqaRngF5mYmKCfv36ITQ0FH/7298q/CzWy8aPH49Ro0bB09MT7dq1w7Zt23Dx4kXZbX0lGTNmDL755htMmDABH330EZRKJfbt24ctW7bIktZOnTph8ODB8PT0hKmpKZKSkvD555+jc+fO0pvJfX198dFHH6GwsFCa0Rk0aBB27tyJQYMGISwsDL6+vrCwsMCdO3ewbds22cxPly5dMGXKFOzbtw9NmjTB8uXLZS/3trGxgY6ODlatWoVPPvkEly9flt1iWdkmT56MDh06YP78+ejXrx8KCwuxZcsWnDx5Et999x0AIDMzEx988AFGjhyJ5s2bw9DQEGfPnsVXX31V7KXPHTt2hIODA4YNGwZnZ2e0a9euwm2aM2cOJkyYALVajR49eiA3Nxdnz57FgwcPMGXKFJibm0NPTw/79+9Ho0aNoKurC7VajfDwcHzyyScwNzdHz549kZ2djbi4OIwfP16q+9ixY691PImIiIiomnrdS0pWVH5+vliyZIlwc3MTOjo6wsjISPj6+orjx48Xi/3iiy+EhYWFUKvVYvLkyWLcuHGypcxTUlLEO++8I/T09EpcZv9FMTExAoDYvn277Hhpy+y/eH5CQoJU/3NffvmlqF+/vlCpVGLkyJFiwoQJ4p133vnT/p85c0Z0795dmJmZCbVaLdq2bSt27doli1mwYIHw9vYW9erVE7q6usLe3l5MmDBB/PHHH7JxtLKyEvv375edW1hYKP7+97+Ltm3bCgMDA6GjoyPs7e3FqFGjRFJSkhSXl5cnxowZI+rVqyfMzc3FwoULiy2zv3nzZmFrayuUSqXw9vYW0dHRfzpWJS1f/+JS+WU5cOCAaN++vTAxMRGmpqaiU6dO4ujRo1L506dPxfTp00WrVq2EWq0W+vr6wsnJScycOVM8fvy4WH0LFiwQAMRXX31VrKy8y+xv2rRJtGjRQujo6AgTExPRsWNHsXPnTql87dq1wtraWmhpacl+Nv/+978LJycnUbduXdGgQQMxfvx4qezEiRPC2Ni4xDaXhMvsExEREVVvFfm+phDiDa/KUU1t3LgRkydPxt27d6Vb0ypT9+7dYWlpiY0bN1Z63aV5PvN44MCBN3ZNenUDBw6Eh4cHPv/883LFazQaqNVqZGVlSTOoRERERFR9VOT7WrV6Bq0qPH78GDdu3MCiRYvw8ccfV0py9vjxYyxfvhxXrlzB1atXMXv2bBw+fPiVn22rqI8//hgdO3Ys831pb5PY2FgoFIpi28yZM6u6aZK8vDy4u7tj8uTJVd0UIiIiIqoCb/0MWnh4OObPn4+OHTtiz549pa7kVxFPnjyBv78/EhIS8PTpUzg5OWHmzJno169fJbSY/qrY2Fh07twZKSkpsr9cPF8i/0WFhYVQKBTQ0qr+f8PgDBoRERFR9cYZtAoIDw9Hfn4+YmJiKiU5A56t0nj48GFkZmbi0aNHOH/+PJOzasTc3Fx6ebmlpSVUKhWioqJgbGyM6OhouLq6QqlUIjU1FfHx8ejevTvq168PtVoNHx+fYittKhQK/PDDD+jbty/09fXh6OiI6OhoWcyVK1fQq1cvGBkZwdDQEB06dJC9L+2HH36Ai4sLdHV14ezsLC18QkRERERvl7c+QSN67vHjx1i8eDF++OEHXLlyBebm5sjOzkZQUBCOHz+OU6dOwdHREX5+fsVuG50zZw4GDBiAixcvws/PD4GBgbh//z4A4L///S86duwIpVKJX3/9FefOncPIkSNRUFAA4NnLub/44gvMnz8fycnJWLBgAWbNmiW9PP1lubm50Gg0so2IiIiIaoe3/hZHens8v8Xx+asanrtz5w5+/vlnjBgxAomJifDw8Ci1jqKiIhgbG2Pz5s3o1asXAEjPsT1fFv/Ro0dQqVT417/+hR49euDzzz/H1q1bkZKSUuwl3QDg4OCAuXPnYvDgwdKxefPm4ZdffsGJEyeKxYeHh2POnDnFjvMWRyIiIqLqqSK3OFar96ARvQnHjh2TXg4OPHsPHvDsxdLNmzeXxd67dw8zZ85EbGwsMjIyUFhYiMePHyM1NVUW9+J5BgYGMDIykl7cnZiYiA4dOpSYnD169Ag3btxAcHAwRo0aJR0vKCiAWq0usf1hYWGYMmWKtK/RaGBtbV3e7hMRERFRNcYEjd46dnZ2MDY2LnZcT08PCoVCdiwoKAiZmZmIiIhA48aNoVQq4e3tjby8PFncy8mXQqFAUVGRVG9pcnJyAABr165F27ZtZWUvvjj8RUqlEkqlstQ6iYiIiKjmYoJGVIa4uDh899138PPzAwCkpaXhjz/+qFAdzZs3x48//oj8/PxiiZyFhQWsrKxw8+ZNBAYGVlq7iYiIiKhm4iIhRGVwdHTExo0bkZycjNOnTyMwMLDMGbGSjBs3DhqNBoMGDcLZs2dx/fp1bNy4ESkpKQCeLTCycOFCfP3117h27RouXbqEyMhILF++/HV0iYiIiIiqMSZoRGVYt24dHjx4gFatWmHo0KGYMGECzM3NK1SHqakpfv31V+Tk5MDHxwetW7fG2rVrpdm0jz76CD/88AMiIyPh7u4OHx8fREVFwc7O7nV0iYiIiIiqMa7iSFTD8UXVRERERNUbX1RN9JooFArs3r37tV+nU6dOmDRp0mu/DhERERFVL0zQqEZau3YtPDw8oFKpYGxsjJYtW2LhwoWVVn94eDhatGhRafUREREREZUHV3GkGmf9+vWYNGkSvv76a/j4+CA3NxcXL17E5cuXq7ppRERERESvhDNoVONER0djwIABCA4OhoODA9zc3DB48GDMnz9fFrd+/Xq4ublBqVSiQYMGGDdunFSWmpqKgIAAqFQqGBkZYcCAAbh37x4AICoqCnPmzMGFCxegUCigUCgQFRVVYlumTZuGpk2bQl9fH/b29pg1axby8/Ol8uczcRs3boStrS3UajUGDRqE7OxsKebRo0cYNmwYVCoVGjRogGXLllXiaBERERFRTcIEjWocS0tLnDp1Cnfu3Ck1ZvXq1fj0008xevRoXLp0CdHR0XBwcAAAFBUVISAgAPfv38fRo0dx6NAh3Lx5EwMHDgQADBw4EFOnToWbmxvS09ORnp4ulb3M0NAQUVFRSEpKQkREBNauXYsVK1bIYm7cuIHdu3dj79692Lt3L44ePYpFixZJ5aGhoTh69Cj27NmDgwcPIjY2FufPny+1b7m5udBoNLKNiIiIiGoH3uJINc7s2bPRr18/2NraomnTpvD29oafnx/69+8PLa1nf3OYN28epk6diokTJ0rneXl5AQBiYmJw6dIl3Lp1C9bW1gCADRs2wM3NDfHx8fDy8oJKpYK2tjYsLS3LbMvMmTOlz7a2tggJCcHWrVvx2WefSceLiooQFRUFQ0NDAMDQoUMRExOD+fPnIycnB+vWrcNPP/2Erl27AgB+/PFHNGrUqNRrLly4EHPmzKnIkBERERFRDcEZNKpxGjRogJMnT+LSpUuYOHEiCgoKEBQUhB49eqCoqAgZGRm4e/eulPC8LDk5GdbW1lJyBgCurq4wNjZGcnJyhdqybds2tG/fHpaWllCpVJg5cyZSU1NlMba2tlJy9rz9GRkZAJ7NruXl5aFt27ZSeb169eDk5FTqNcPCwpCVlSVtaWlpFWozEREREVVfTNCoxmrWrBnGjh2Ln376CYcOHcKhQ4dw9OhR6OnpvZHrnzx5EoGBgfDz88PevXuRkJCAGTNmIC8vTxb3/IXUzykUChQVFf3l6yqVShgZGck2IiIiIqodmKBRreDq6grg2YIbhoaGsLW1RUxMTImxLi4uSEtLk808JSUl4eHDh1I9Ojo6KCwsLPOaJ06cQOPGjTFjxgx4enrC0dGxzOfiStKkSRPUrVsXp0+flo49ePAA165dq1A9RERERFQ78Bk0qnHGjBkDKysrdOnSBY0aNUJ6ejrmzZsHMzMzeHt7A3i2euInn3wCc3Nz9OzZE9nZ2YiLi8P48ePRrVs3uLu7IzAwECtXrkRBQQHGjh0LHx8feHp6Anh2W+KtW7eQmJiIRo0awdDQEEqlUtYOR0dHpKamYuvWrfDy8sK+ffuwa9euCvVFpVIhODgYoaGhMDU1hbm5OWbMmCE9S0dEREREbxd+C6Qap1u3bjh16hQ++OADNG3aFO+//z50dXURExMDU1NTAEBQUBBWrlyJ7777Dm5ubujVqxeuX78O4Nkthnv27IGJiQk6duyIbt26wd7eHtu2bZOu8f7776NHjx7o3LkzzMzMsGXLlmLt6N27NyZPnoxx48ahRYsWOHHiBGbNmlXh/ixZsgQdOnSAv78/unXrhnfffRetW7f+i6NDRERERDWZQgghqroRRNVBeHg4du/ejcTExEqt9/bt27Czs0NCQgJatGiB2NhYdO7cGQ8ePICxsfEr16/RaKBWq5GVlcXn0YiIiIiqoYp8X+MMGlUrQgisWbMGbdu2hUqlgrGxMTw9PbFy5Uo8fvy4qptXKdq1a4f09HSo1eqqbgoRERERVTNM0KhaGTp0KCZNmoSAgAAcOXIEiYmJmDVrlvQS59pAR0cHlpaWUCgUVd0UIiIiIqpmmKBRtbF9+3Zs2rQJW7Zsweeffw4vLy/Y2toiICAAv/76Kzp37gwAiI+PR/fu3VG/fn2o1Wr4+Pjg/PnzsroUCgW+//579OrVC/r6+nBxccHJkyfx22+/oVOnTjAwMEC7du1w48aNYu34/vvvYW1tDX19fQwYMABZWVlSWVFREb788ks0atQISqUSLVq0wP79+2XnnzlzBi1btoSuri48PT2RkJAgK4+NjYVCocDDhw8BAJmZmRg8eDAaNmwIfX19uLu7l/jMGxERERHVfkzQqNrYtGkTnJycEBAQUKxMoVBItwRmZ2cjKCgIx48fx6lTp+Do6Ag/Pz9kZ2fLzpk7dy6GDRuGxMREODs7Y8iQIfj4448RFhaGs2fPQgiBcePGyc757bffsH37dvz888/Yv38/EhISMHbsWKk8IiICy5Ytw9KlS3Hx4kX4+vqid+/e0gIkOTk56NWrF1xdXXHu3DmEh4cjJCSkzH4/ffoUrVu3xr59+3D58mWMHj0aQ4cOxZkzZ0qMz83NhUajkW1EREREVEsIomrCxcVF9O7du8LnFRYWCkNDQ/Hzzz9LxwCImTNnSvsnT54UAMS6deukY1u2bBG6urrS/uzZs0WdOnXEf/7zH+nYv/71L6GlpSXS09OFEEJYWVmJ+fPny67v5eUlxo4dK4QQ4vvvvxempqbiyZMnUvnq1asFAJGQkCCEEOLIkSMCgHjw4EGpfXrvvffE1KlTSyybPXu2AFBsy8rKKrU+IiIiIqo6WVlZ5f6+xhk0qjZEORcUvXfvHkaNGgVHR0eo1WoYGRkhJycHqampsrjmzZtLny0sLAAA7u7usmNPnz6VzUDZ2NigYcOG0r63tzeKioqQkpICjUaDu3fvon379rLrtG/fHsnJyQCA5ORkNG/eHLq6urI6ylJYWIi5c+fC3d0d9erVg0qlwoEDB4r157mwsDBkZWVJ24sv3CYiIiKimo0vqqZqo2nTprh69eqfxgUFBSEzMxMRERFo3LgxlEolvL29kZeXJ4urW7eu9Pn5ghwlHSsqKqqM5v9lS5YsQUREBFauXAl3d3cYGBhg0qRJxfrznFKpLPbSbCIiIiKqHTiDRtXGkCFDcO3aNezZs6dYmRBCWqwjLi4OEyZMgJ+fH9zc3KBUKvHHH39UShtSU1Nx9+5daf/UqVPQ0tKCk5MTjIyMYGVlhbi4ONk5cXFxcHV1BQC4uLjg4sWLePr0qayOssTFxSEgIAAffvghPDw8YG9vj2vXrlVKf4iIiIioZmGCRtXGgAEDMHDgQAwePBgLFizA2bNncefOHezduxfdunXDkSNHAACOjo7YuHEjkpOTcfr0aQQGBkJPT69S2qCrq4ugoCBcuHABx44dw4QJEzBgwABYWloCAEJDQ7F48WJs27YNKSkpmD59OhITEzFx4kQAz5JMhUKBUaNGISkpCb/88guWLl1a5jUdHR1x6NAhnDhxAsnJyfj4449x7969SukPEREREdUsvMWRqg2FQoHNmzdjzZo1WL9+PebPnw9tbW04Ojpi2LBh8PX1BQCsW7cOo0ePRqtWrWBtbY0FCxb86UqJ5eXg4IB+/frBz88P9+/fR69evfDdd99J5RMmTEBWVhamTp2KjIwMuLq6Ijo6Go6OjgAAlUqFn3/+GZ988glatmwJV1dXLF68GO+//36p15w5cyZu3rwJX19f6OvrY/To0ejTp49seX8iIiIiejsoRHlXZiCiakmj0UCtViMrKwtGRkZV3RwiIiIieklFvq/xFse30O3bt6FQKJCYmPjar2Vra4uVK1e+Uh1RUVEwNjaulPaU15scIyIiIiKi55ig1XDDhw+HQqEodbO1tS12jrW1NdLT09GsWbM33+BK8vvvv6N///4wMTGBkZEROnXqhJSUFFlMWeOiUCgQHh5eNY0nIiIiIioFn0Gr4SIiIrBo0SJpv0GDBoiMjESPHj0AAHXq1JHF5+XlQUdHR1r0oqaaNm0azp49i71798LS0hLnz58vFpOeni593rZtG7744gtZEqdSqd5IW4mIiIiIyoszaDWcWq2GpaWltAGAsbGxtO/l5YW5c+di2LBhMDIywujRo4vdvhcbGwuFQoGYmBh4enpCX18f7dq1kyUzN27cQEBAACwsLKBSqeDl5YXDhw/L2pKRkQF/f3/o6enBzs4OmzZtKtbe5cuXS+/6sra2xtixY5GTkyOLiYqKgo2NDfT19dG3b19kZmYWq0dLSwvt2rVD+/bt0aRJE3zwwQdwcnKSxbw4Lmq1GgqFQto3NzfH8uXL0ahRIyiVSrRo0QL79+8vdp2rV6+iXbt20NXVRbNmzXD06FGprLCwEMHBwbCzs4Oenh6cnJwQEREhO7+goAATJkyAsbExTE1NMW3aNAQFBaFPnz5STG5uLiZMmABzc3Po6uri3XffRXx8fLG2EBEREVHtxwTtLbB06VJ4eHggISEBs2bNKjVuxowZWLZsGc6ePQttbW2MHDlSKsvJyYGfnx9iYmKQkJCAHj16wN/fH6mpqVLM8OHDkZaWhiNHjuAf//gHvvvuO2RkZMiuoaWlha+//hpXrlzBjz/+iF9//RWfffaZVH769GkEBwdj3LhxSExMROfOnTFv3rxibQ0ICMA//vGPEpOq8oiIiMCyZcuwdOlSXLx4Eb6+vujduzeuX78uiwsNDcXUqVORkJAAb29v+Pv7SwljUVERGjVqhB07diApKQlffPEFPv/8c2zfvl06f/Hixdi0aRMiIyMRFxcHjUaD3bt3y67x2Wef4Z///Cd+/PFHnD9/Hg4ODvD19cX9+/dLbHtubi40Go1sIyIiIqJaQlCtAkDs2rVL2m/cuLHo06ePLObWrVsCgEhISBBCCHHkyBEBQBw+fFiK2bdvnwAgnjx5Uuq13NzcxKpVq4QQQqSkpAgA4syZM1J5cnKyACBWrFhRah07duwQpqam0v7gwYOFn5+fLGbgwIFCrVZL+1euXBEqlUosXLhQNGzYUGzfvl0qO3v2rAAgfv/9d1kdkZGRsjqsrKzE/PnzZTFeXl5i7NixQoj/G6NFixZJ5fn5+aJRo0Zi8eLFpfbn008/Fe+//760b2FhIZYsWSLtFxQUCBsbGxEQECCEECInJ0fUrVtXbNq0SYrJy8sTVlZW4quvvirxGrNnzxYAim1ZWVmltouIiIiIqk5WVla5v69xBu0t4OnpWa645s2bS58bNGgAANIMWE5ODkJCQuDi4gJjY2OoVCokJydLM2jJycnQ1tZG69atpTqcnZ2Lrb54+PBhdO3aFQ0bNoShoSGGDh2KzMxMPH78WKqnbdu2snO8vb1l++Hh4ejZsyemT5+O6OhofPLJJ/j73/8OALh06RKcnZ1Rv379Uvup0Whw9+5dtG/fXna8ffv2SE5OLvXa2tra8PT0lMV8++23aN26NczMzKBSqbBmzRppTLKysnDv3j20adNGiq9Tp45sjG7cuIH8/HxZW+rWrYs2bdoUa8tzYWFhyMrKkra0tLRS+0pERERENQsXCXkLGBgYlCuubt260meFQgHg2W18ABASEoJDhw5h6dKlcHBwgJ6eHvr374+8vLxyt+P27dvo1asXxowZg/nz56NevXo4fvw4goODkZeXB319/XLVc/HiRQQFBQEAWrVqhejoaPj6+uKPP/7A/v37MWLEiHK36VVs3boVISEhWLZsGby9vWFoaIglS5bg9OnTr/W6SqUSSqXytV6DiIiIiKoGZ9CoXOLi4jB8+HD07dsX7u7usLS0xO3bt6VyZ2dnFBQU4Ny5c9KxlJQUPHz4UNo/d+4cioqKsGzZMrzzzjto2rQp7t69K7uOi4tLsQTn1KlTsv2GDRvi2LFj0n779u2xa9cuzJ07Fzdu3MC4cePK7IuRkRGsrKwQFxdXrI+urq6lXvt5/1xcXKT4du3aYezYsWjZsiUcHBxw48YNKV6tVsPCwkK24EdhYaFsxckmTZpAR0dH1pb8/HzEx8cXawsRERER1X6cQaNycXR0xM6dO+Hv7w+FQoFZs2ZJs2sA4OTkhB49euDjjz/G6tWroa2tjUmTJkFPT0+KcXBwQH5+PlatWgV/f3/ExcVJtyY+N2HCBLRv3x5Lly5FQEAADhw4UGwhkNDQUPj5+eHTTz/FmDFjkJ+fj6NHj0JHRwe///47fv75ZwwcOLDM/oSGhmL27Nlo0qQJWrRogcjISCQmJhZbefLbb7+Fo6MjXFxcsGLFCjx48EBaPMXR0REbNmzAgQMHYGdnh40bNyI+Ph52dnbS+ePHj8fChQvh4OAAZ2dnrFq1Cg8ePJBmKA0MDDBmzBiEhoaiXr16sLGxwVdffYXHjx8jODi4Av+FiIiIiKg24Awalcvy5cthYmKCdu3awd/fH76+vmjVqpUsJjIyElZWVvDx8UG/fv0wevRomJubS+UeHh5Yvnw5Fi9ejGbNmmHTpk1YuHChrI533nkHa9euRUREBDw8PHDw4EHMnDlTFtOjRw/ExMTg0qVLaN++Pbp06YKUlBScOXMGc+bMwfDhw3HixIky+zNhwgRMmTIFU6dOhbu7O/bv34/o6Gg4OjrK4hYtWoRFixbBw8MDx48fR3R0tPR828cff4x+/fph4MCBaNu2LTIzMzF27FjZ+dOmTcPgwYMxbNgweHt7Q6VSwdfXF7q6urJrvP/++xg6dChatWqF3377DQcOHICJicmf/FchIiIiotpGIYQQVd0IouoqKioKkyZNkt2q+SqKiopgYmICS0tL2XvmXoVGo4FarUZWVhaMjIwqpU4iIiIiqjwV+b7GGTSqMhs3boSBgQF+++032fG7d+/CxMQE33zzTRW1rPLcuXMHa9euxbVr13Dp0iWMGTMGjx8/xoYNG6q6aURERERUDTFBoyozdOhQ+Pr6Yvjw4bLn2UaNGoXWrVvj008/rcLWVQ4tLS1ERUXBy8sL7du3x6VLlxATE1PsVQJERERERAATNKpi33//Pa5du4bly5cDeHZLYVxcHCIjI9G5c2dMmjRJFt+nTx8MHz5c2k9PT8d7770HPT092NnZYfPmzbC1tcXKlSulmNTUVAQEBEClUsHIyAgDBgzAvXv3pPILFy6gc+fOMDQ0hJGREVq3bo2zZ8+W2N4bN24gICAAFhYWUKlU8PLywuHDh6Xyzz//XJZ8WVtbIy4uDra2tggJCcGJEyewfv169OnTR4rZv38/3n33XRgbG8PU1BS9evWSrQZJRERERG8PJmhUpczMzLBmzRrMmjULhw4dwuTJkxEREQFra+tynT9s2DDcvXsXsbGx+Oc//4k1a9ZIL9cGnj3zFRAQgPv37+Po0aM4dOgQbt68KVvlMTAwEI0aNUJ8fDzOnTuH6dOny94J96KcnBz4+fkhJiYGCQkJ6NGjB/z9/aWXUwcGBuLMmTOyBOvKlSu4ePEihgwZUmKdjx49wpQpU3D27FnExMRAS0sLffv2lc0qvig3NxcajUa2EREREVHtwGX2qcr16dMHAwYMkJKd5y+h/jNXr17F4cOHER8fD09PTwDADz/8IFuJ8flqj7du3ZKSvg0bNsDNzQ3x8fHw8vJCamoqQkND4ezsDADFVnJ8kYeHBzw8PKT9uXPnYteuXYiOjsa4cePg5uYGDw8PbN68GbNmzQIAbNq0CW3btoWDg0OJdb7//vuy/fXr18PMzAxJSUlo1qxZsfiFCxdizpw55RkiIiIiIqphOING1cLz96q9vKR+WVJSUqCtrS1b7t/BwUG2PH1ycjKsra1lM3Kurq4wNjZGcnIyAGDKlCn46KOP0K1bNyxatKjM2wtzcnIQEhICFxcXGBsbQ6VSITk5WZpBA57Nom3evBkAIITAli1bEBgYWGqd169fx+DBg2Fvbw8jIyPY2toCgKzOF4WFhSErK0va0tLSyhglIiIiIqpJmKBRtaCtrS37X+DZAhsvvwUiPz+/0q8dHh6OK1eu4L333sOvv/4KV1dX7Nq1q8TYkJAQ7Nq1CwsWLMCxY8eQmJgId3d35OXlSTGDBw9GSkoKzp8/jxMnTiAtLa3MF2f7+/vj/v37WLt2LU6fPo3Tp08DgKzOFymVShgZGck2IiIiIqodmKBRtWVmZob09HRpv7CwEJcvX5b2nZycUFBQgISEBOnYb7/9hgcPHkj7Li4uSEtLk80yJSUl4eHDh3B1dZWONW3aFJMnT8bBgwfRr18/REZGltimuLg4DB8+HH379oW7uzssLS1x+/ZtWUyjRo3g4+ODTZs2YdOmTejevbvshd0vyszMREpKCmbOnImuXbvCxcVF1n4iIiIierswQaNqq0uXLti3bx/27duHq1evYsyYMbIXRjs7O6Nbt24YPXo0zpw5g4SEBIwePRp6enpQKBQAgG7dusHd3R2BgYE4f/48zpw5g2HDhsHHxweenp548uQJxo0bh9jYWNy5cwdxcXGIj4+Hi4tLiW1ydHTEzp07kZiYiAsXLmDIkCElLuYRGBiIrVu3YseOHWXe3mhiYgJTU1OsWbMGv/32G3799VdMmTLl1QaOiIiIiGosJmhUbY0cORJBQUFSQmVvb4/OnTvLYjZs2AALCwt07NgRffv2xahRo2BoaAhdXV0AgEKhwJ49e2BiYoKOHTuiW7dusLe3x7Zt2wAAderUQWZmJoYNG4amTZtiwIAB6NmzZ6mLcCxfvhwmJiZo164d/P394evrK3sG7rn+/fsjMzMTjx8/li2p/zItLS1s3boV586dQ7NmzTB58mQsWbLkL44YEREREdV0CvHyQz5ENdh//vMfWFtb4/Dhw+jatWtVN+eN0Gg0UKvVyMrK4vNoRERERNVQRb6vcZl9qtF+/fVX5OTkwN3dHenp6fjss89ga2uLjh07Vkr9nTp1QosWLWQvviYiIiIiel14iyNVW7GxsVAoFLLnzp6ztbXFypUrkZ+fj88//xxubm7o27cvzMzMEBsbW+qLpomIiIiIqjPOoFGN5uvrC19f36puRpny8/OZMBIRERFRuXAGjWqF1NRUBAQEQKVSwcjICAMGDMC9e/ek8vDwcLRo0QIbN26Era0t1Go1Bg0ahOzsbCnm0aNHGDZsGFQqFRo0aIBly5YVu45CocDu3btlx4yNjREVFQUAuH37NhQKBbZt2wYfHx/o6upi06ZNyMzMxODBg9GwYUPo6+vD3d0dW7ZskdXzj3/8A+7u7tDT04OpqSm6deuGR48eVd4gEREREVG1xwSNaryioiIEBATg/v37OHr0KA4dOoSbN28Wezn0jRs3sHv3buzduxd79+7F0aNHsWjRIqk8NDQUR48exZ49e3Dw4EHExsbi/Pnzf6lN06dPx8SJE5GcnAxfX188ffoUrVu3xr59+3D58mWMHj0aQ4cOxZkzZwAA6enpGDx4MEaOHInk5GTExsaiX79+xV7UDQC5ubnQaDSyjYiIiIhqB97iSNVeo0aNih17/Pix9DkmJgaXLl3CrVu3YG1tDeDZ8vtubm6Ij4+Hl5cXgGeJXFRUFAwNDQEAQ4cORUxMDObPn4+cnBysW7cOP/30k7T6448//ljitctj0qRJ6Nevn+xYSEiI9Hn8+PE4cOAAtm/fjjZt2iA9PR0FBQXo168fGjduDABwd3cvse6FCxeW+hoAIiIiIqrZmKBRtXfs2DEpqXquU6dO0ufk5GRYW1tLyRkAuLq6wtjYGMnJyVKCZmtrK6unQYMGyMjIAPBsdi0vLw9t27aVyuvVqwcnJ6e/1GZPT0/ZfmFhIRYsWIDt27fjv//9L/Ly8pCbmwt9fX0AgIeHB7p27Qp3d3f4+vrib3/7G/r37w8TE5NidYeFhcleZq3RaGR9JyIiIqKaiwkaVXt2dnYwNjaWHdPWrviP7ssLdSgUChQVFVWoDoVCUey2w/z8/GJxBgYGsv0lS5YgIiICK1euhLu7OwwMDDBp0iTk5eUBePbC7EOHDuHEiRM4ePAgVq1ahRkzZuD06dOws7OT1aVUKqFUKivUbiIiIiKqGfgMGtV4Li4uSEtLQ1pamnQsKSkJDx8+hKura7nqaNKkCerWrYvTp09Lxx48eIBr167J4szMzJCeni7tX79+XXa7ZWni4uIQEBCADz/8EB4eHrC3ty9Wt0KhQPv27TFnzhwkJCRAR0cHu3btKlf7iYiIiKh24Awa1XjdunWDu7s7AgMDsXLlShQUFGDs2LHw8fEpdqthaVQqFYKDgxEaGgpTU1OYm5tjxowZ0NKS/w2jS5cu+Oabb+Dt7Y3CwkJMmzatXEvoOzo64h//+AdOnDgBExMTLF++HPfu3ZMSyNOnTyMmJgZ/+9vfYG5ujtOnT+P333+Hi4tLxQeEiIiIiGosJmhU4ykUCuzZswfjx49Hx44doaWlhR49emDVqlUVqmfJkiXIycmBv78/DA0NMXXqVGRlZclili1bhhEjRqBDhw6wsrJCREQEzp0796d1z5w5Ezdv3oSvry/09fUxevRo9OnTR6rfyMgI//73v7Fy5UpoNBo0btwYy5YtQ8+ePSvUByIiIiKq2RSipHW8iajG0Gg0UKvVyMrKgpGRUVU3h4iIiIheUpHva3wGjYiIiIiIqJpggkY1UmxsLBQKBR4+fFiszNbWFitXrnzjbSIiIiIielVM0IiqUElL9BMRERHR24sJGtVqt2/fhkKhQGJionTs4cOHUCgUiI2NBfB/s3ExMTHw9PSEvr4+2rVrh5SUFFld8+bNg7m5OQwNDfHRRx9h+vTpaNGihVQeHx+P7t27o379+lCr1fDx8cH58+dldSgUCqxevRq9e/eGgYEB5s2bBwcHByxdulQWl5iYCIVCgd9++61Yn3Jzc6HRaGQbEREREdUOTNCI/r8ZM2Zg2bJlOHv2LLS1tTFy5EipbNOmTZg/fz4WL16Mc+fOwcbGBqtXr5adn52djaCgIBw/fhynTp2Co6Mj/Pz8kJ2dLYsLDw9H3759cenSJQQHB2PkyJGIjIyUxURGRqJjx45wcHAo1s6FCxdCrVZLm7W1dSWOAhERERFVJSZoVKM1atQIKpVKtqWmpv6luubPnw8fHx+4urpi+vTpOHHiBJ4+fQoAWLVqFYKDgzFixAg0bdoUX3zxBdzd3WXnd+nSBR9++CGcnZ3h4uKCNWvW4PHjxzh69KgsbsiQIRgxYgTs7e1hY2OD4cOHIyUlBWfOnAHw7LbHzZs3yxLEF4WFhSErK0vaXnxBNxERERHVbEzQqEY7duwYEhMTZZuVldVfqqt58+bS5wYNGgAAMjIyAAApKSlo06aNLP7l/Xv37mHUqFFwdHSEWq2GkZERcnJyiiWML78828rKCu+99x7Wr18PAPj555+Rm5uLDz74oMR2KpVKGBkZyTYiIiIiqh34omqq0ezs7GBsbCw7pq39fz/WWlrP/gbx4uv+SluYo27dutJnhUIBACgqKip3W4KCgpCZmYmIiAg0btwYSqUS3t7eyMvLk8UZGBgUO/ejjz7C0KFDsWLFCkRGRmLgwIHQ19cv97WJiIiIqHbgDBrVamZmZgCA9PR06diLC4aUl5OTE+Lj42XHXt6Pi4vDhAkT4OfnBzc3NyiVSvzxxx/lqt/Pzw8GBgZYvXo19u/fX+rtjURERERUu3EGjWo1PT09vPPOO1i0aBHs7OyQkZGBmTNnVrie8ePHY9SoUfD09ES7du2wbds2XLx4Efb29lKMo6MjNm7cCE9PT2g0GoSGhkJPT69c9depUwfDhw9HWFgYHB0d4e3tXeE2EhEREVHNxxk0qvXWr1+PgoICtG7dGpMmTcK8efMqXEdgYCDCwsIQEhKCVq1a4datWxg+fDh0dXWlmHXr1uHBgwdo1aoVhg4digkTJsDc3Lzc1wgODkZeXh5GjBhR4fYRERERUe2gEC8+nENE5da9e3dYWlpi48aNlVLfsWPH0LVrV6SlpcHCwqLc52k0GqjVamRlZXHBECIiIqJqqCLf13iLI9GfiI2NRefOnTFv3jz06dMHderUwZYtW3D48GEcOnTolevPzc3F77//jvDwcHzwwQcVSs6IiIiIqHbhLY5E5XTw4EF07NgRrVu3xs8//4x//vOf6Nat2yvXu2XLFjRu3BgPHz7EV199VQktJSIiIqKaigkaUTnt2bMHmZmZePToEc6fP49+/foBeLaaY/fu3VG/fn2o1Wr4+Pjg/PnzsnNTU1MREBAAlUoFIyMjDBgwAPfu3QMADB8+HOfPn4eRkRGcnZ1hZGSE1q1b4+zZs2+8j0RERERUtZigEb2i7OxsBAUF4fjx4zh16hQcHR3h5+eH7OxsAM/epRYQEID79+/j6NGjOHToEG7evImBAwdKdQQGBqJRo0aIj4/HuXPnMH36dNl72V6Um5sLjUYj24iIiIioduAzaESvqEuXLrL9NWvWwNjYGEePHkWvXr0QExODS5cu4datW7C2tgYAbNiwAW5uboiPj4eXlxdSU1MRGhoKZ2dnAM+W7C/NwoULMWfOnNfXISIiIiKqMpxBI3pF9+7dw6hRo+Do6Ai1Wg0jIyPk5OQgNTUVAJCcnAxra2spOQMAV1dXGBsbIzk5GQAwZcoUfPTRR+jWrRsWLVqEGzdulHq9sLAwZGVlSVtaWtrr7SARERERvTFM0IheUVBQEBITExEREYETJ04gMTERpqamyMvLK3cd4eHhuHLlCt577z38+uuvcHV1xa5du0qMVSqVMDIykm1EREREVDswQSN6RXFxcZgwYQL8/Pzg5uYGpVKJP/74Qyp3cXFBWlqabKYrKSkJDx8+hKurq3SsadOmmDx5Mg4ePIh+/fohMjLyjfaDiIiIiKoen0EjKqdLly7B0NBQ2lcoFPDw8ICjoyM2btwIT09PaDQahIaGQk9PT4rr1q0b3N3dERgYiJUrV6KgoABjx46Fj48PPD098eTJE4SGhqJ///6ws7PDf/7zH8THx+P999+vim4SERERURVigkZUTh07dpTt16lTBwUFBVi3bh1Gjx6NVq1awdraGgsWLEBISIgUp1AosGfPHowfPx4dO3aElpYWevTogVWrVkn1ZGZmYtiwYbh37x7q16+Pfv36lXshECEEAHA1RyIiIqJq6vn3tOff28qiEOWJIqJq6+bNm2jSpElVN4OIiIiI/kRaWhoaNWpUZgxn0IhquHr16gF49jJstVpdxa2pmTQaDaytrZGWlsZFV/4Cjt+r4xi+Oo7hq+H4vTqO4aup7eMnhEB2djasrKz+NJYJGlENp6X1bK2f50v801/HVTFfDcfv1XEMXx3H8NVw/F4dx/DV1ObxK+8f0rmKIxERERERUTXBBI2IiIiIiKiaYIJGVMMplUrMnj0bSqWyqptSY3EMXw3H79VxDF8dx/DVcPxeHcfw1XD8/g9XcSQiIiIiIqomOINGRERERERUTTBBIyIiIiIiqiaYoBEREREREVUTTNCIiIiIiIiqCSZoRDXct99+C1tbW+jq6qJt27Y4c+ZMVTfpjVu4cCG8vLxgaGgIc3Nz9OnTBykpKbKYp0+f4tNPP4WpqSlUKhXef/993Lt3TxaTmpqK9957D/r6+jA3N0doaCgKCgpkMbGxsWjVqhWUSiUcHBwQFRX1urtXJRYtWgSFQoFJkyZJxziGZfvvf/+LDz/8EKamptDT04O7uzvOnj0rlQsh8MUXX6BBgwbQ09NDt27dcP36dVkd9+/fR2BgIIyMjGBsbIzg4GDk5OTIYi5evIgOHTpAV1cX1tbW+Oqrr95I/163wsJCzJo1C3Z2dtDT00OTJk0wd+5cvLiWGcdQ7t///jf8/f1hZWUFhUKB3bt3y8rf5Hjt2LEDzs7O0NXVhbu7O3755ZdK729lK2v88vPzMW3aNLi7u8PAwABWVlYYNmwY7t69K6vjbR4/4M9/Bl/0ySefQKFQYOXKlbLjb/sYlkgQUY21detWoaOjI9avXy+uXLkiRo0aJYyNjcW9e/equmlvlK+vr4iMjBSXL18WiYmJws/PT9jY2IicnBwp5pNPPhHW1tYiJiZGnD17VrzzzjuiXbt2UnlBQYFo1qyZ6Natm0hISBC//PKLqF+/vggLC5Nibt68KfT19cWUKVNEUlKSWLVqlahTp47Yv3//G+3v63bmzBlha2srmjdvLiZOnCgd5xiW7v79+6Jx48Zi+PDh4vTp0+LmzZviwIED4rfffpNiFi1aJNRqtdi9e7e4cOGC6N27t7CzsxNPnjyRYnr06CE8PDzEqVOnxLFjx4SDg4MYPHiwVJ6VlSUsLCxEYGCguHz5stiyZYvQ09MT33///Rvt7+swf/58YWpqKvbu3Stu3bolduzYIVQqlYiIiJBiOIZyv/zyi5gxY4bYuXOnACB27dolK39T4xUXFyfq1KkjvvrqK5GUlCRmzpwp6tatKy5duvTax+BVlDV+Dx8+FN26dRPbtm0TV69eFSdPnhRt2rQRrVu3ltXxNo+fEH/+M/jczp07hYeHh7CyshIrVqyQlb3tY1gSJmhENVibNm3Ep59+Ku0XFhYKKysrsXDhwipsVdXLyMgQAMTRo0eFEM9+0datW1fs2LFDiklOThYAxMmTJ4UQz37JaGlpif/9739SzOrVq4WRkZHIzc0VQgjx2WefCTc3N9m1Bg4cKHx9fV93l96Y7Oxs4ejoKA4dOiR8fHykBI1jWLZp06aJd999t9TyoqIiYWlpKZYsWSIde/jwoVAqlWLLli1CCCGSkpIEABEfHy/F/Otf/xIKhUL897//FUII8d133wkTExNpPJ9f28nJqbK79Ma99957YuTIkbJj/fr1E4GBgUIIjuGfefnL8ZscrwEDBoj33ntP1p62bduKjz/+uFL7+DqVlVw8d+bMGQFA3LlzRwjB8XtZaWP4n//8RzRs2FBcvnxZNG7cWJagcQxLxlsciWqovLw8nDt3Dt26dZOOaWlpoVu3bjh58mQVtqzqZWVlAQDq1asHADh37hzy8/NlY+Xs7AwbGxtprE6ePAl3d3dYWFhIMb6+vtBoNLhy5YoU82Idz2Nq03h/+umneO+994r1k2NYtujoaHh6euKDDz6Aubk5WrZsibVr10rlt27dwv/+9z9Z39VqNdq2bSsbP2NjY3h6ekox3bp1g5aWFk6fPi3FdOzYETo6OlKMr68vUlJS8ODBg9fdzdeqXbt2iImJwbVr1wAAFy5cwPHjx9GzZ08AHMOKepPjVVv/Xb8sKysLCoUCxsbGADh+5VFUVIShQ4ciNDQUbm5uxco5hiVjgkZUQ/3xxx8oLCyUfRkGAAsLC/zvf/+rolZVvaKiIkyaNAnt27dHs2bNAAD/+9//oKOjI/1Sfe7Fsfrf//5X4lg+LysrRqPR4MmTJ6+jO2/U1q1bcf78eSxcuLBYGcewbDdv3sTq1avh6OiIAwcOYMyYMZgwYQJ+/PFHAP/X/7L+vf7vf/+Dubm5rFxbWxv16tWr0BjXVNOnT8egQYPg7OyMunXromXLlpg0aRICAwMBcAwr6k2OV2kxtWk8nz59imnTpmHw4MEwMjICwPErj8WLF0NbWxsTJkwosZxjWDLtqm4AEVFl+vTTT3H58mUcP368qptSo6SlpWHixIk4dOgQdHV1q7o5NU5RURE8PT2xYMECAEDLli1x+fJl/P3vf0dQUFAVt65m2L59OzZt2oTNmzfDzc0NiYmJmDRpEqysrDiGVKXy8/MxYMAACCGwevXqqm5OjXHu3DlERETg/PnzUCgUVd2cGoUzaEQ1VP369VGnTp1iq+jdu3cPlpaWVdSqqjVu3Djs3bsXR44cQaNGjaTjlpaWyMvLw8OHD2XxL46VpaVliWP5vKysGCMjI+jp6VV2d96oc+fOISMjA61atYK2tja0tbVx9OhRfP3119DW1oaFhQXHsAwNGjSAq6ur7JiLiwtSU1MB/F//y/r3amlpiYyMDFl5QUEB7t+/X6ExrqlCQ0OlWTR3d3cMHToUkydPlmZ0OYYV8ybHq7SY2jCez5OzO3fu4NChQ9LsGcDx+zPHjh1DRkYGbGxspN8rd+7cwdSpU2FrawuAY1gaJmhENZSOjg5at26NmJgY6VhRURFiYmLg7e1dhS1784QQGDduHHbt2oVff/0VdnZ2svLWrVujbt26srFKSUlBamqqNFbe3t64dOmS7BfF81/Gz794e3t7y+p4HlMbxrtr1664dOkSEhMTpc3T0xOBgYHSZ45h6dq3b1/s1Q7Xrl1D48aNAQB2dnawtLSU9V2j0eD06dOy8Xv48CHOnTsnxfz6668oKipC27ZtpZh///vfyM/Pl2IOHToEJycnmJiYvLb+vQmPHz+Glpb8a0mdOnVQVFQEgGNYUW9yvGrrv+vnydn169dx+PBhmJqayso5fmUbOnQoLl68KPu9YmVlhdDQUBw4cAAAx7BUVb1KCRH9dVu3bhVKpVJERUWJpKQkMXr0aGFsbCxbRe9tMGbMGKFWq0VsbKxIT0+XtsePH0sxn3zyibCxsRG//vqrOHv2rPD29hbe3t5S+fMl4v/2t7+JxMREsX//fmFmZlbiEvGhoaEiOTlZfPvtt7ViifjSvLiKoxAcw7KcOXNGaGtri/nz54vr16+LTZs2CX19ffHTTz9JMYsWLRLGxsZiz5494uLFiyIgIKDEJc9btmwpTp8+LY4fPy4cHR1ly00/fPhQWFhYiKFDh4rLly+LrVu3Cn19/Rq5RPzLgoKCRMOGDaVl9nfu3Cnq168vPvvsMymGYyiXnZ0tEhISREJCggAgli9fLhISEqRVBt/UeMXFxQltbW2xdOlSkZycLGbPnl0jljgva/zy8vJE7969RaNGjURiYqLsd8uLqwm+zeMnxJ//DL7s5VUcheAYloQJGlENt2rVKmFjYyN0dHREmzZtxKlTp6q6SW8cgBK3yMhIKebJkydi7NixwsTEROjr64u+ffuK9PR0WT23b98WPXv2FHp6eqJ+/fpi6tSpIj8/XxZz5MgR0aJFC6GjoyPs7e1l16htXk7QOIZl+/nnn0WzZs2EUqkUzs7OYs2aNbLyoqIiMWvWLGFhYSGUSqXo2rWrSElJkcVkZmaKwYMHC5VKJYyMjMSIESNEdna2LObChQvi3XffFUqlUjRs2FAsWrTotfftTdBoNGLixInCxsZG6OrqCnt7ezFjxgzZl2GOodyRI0dK/P++oKAgIcSbHa/t27eLpk2bCh0dHeHm5ib27dv32vpdWcoav1u3bpX6u+XIkSNSHW/z+Anx5z+DLyspQXvbx7AkCiGEeBMzdURERERERFQ2PoNGRERERERUTTBBIyIiIiIiqiaYoBEREREREVUTTNCIiIiIiIiqCSZoRERERERE1QQTNCIiIiIiomqCCRoREREREVE1wQSNiIiIiIiommCCRkREREREVE0wQSMiIqJa5fbt21AoFEhMTKzqphARVRgTNCIiIiIiomqCCRoRERFVqqKiInz11VdwcHCAUqmEjY0N5s+fDwC4dOkSunTpAj09PZiammL06NHIycmRzu3UqRMmTZokq69Pnz4YPny4tG9ra4sFCxZg5MiRMDQ0hI2NDdasWSOV29nZAQBatmwJhUKBTp06vba+EhFVNiZoREREVKnCwsKwaNEizJo1C0lJSdi8eTMsLCzw6NEj+Pr6wsTEBPHx8dixYwcOHz6McePGVfgay5Ytg6enJxISEjB27FiMGTMGKSkpAIAzZ84AAA4fPoz09HTs3LmzUvtHRPQ6aVd1A4iIiKj2yM7ORkREBL755hsEBQUBAJo0aYJ3330Xa9euxdOnT7FhwwYYGBgAAL755hv4+/tj8eLFsLCwKPd1/Pz8MHbsWADAtGnTsGLFChw5cgROTk4wMzMDAJiamsLS0rKSe0hE9HpxBo2IiIgqTXJyMnJzc9G1a9cSyzw8PKTkDADat2+PoqIiafarvJo3by59VigUsLS0REZGxl9vOBFRNcEEjYiIiCqNnp7eK52vpaUFIYTsWH5+frG4unXryvYVCgWKiope6dpERNUBEzQiIiKqNI6OjtDT00NMTEyxMhcXF1y4cAGPHj2SjsXFxUFLSwtOTk4AADMzM6Snp0vlhYWFuHz5coXaoKOjI51LRFTTMEEjIiKiSqOrq4tp06bhs88+w4YNG3Djxg2cOnUK69atQ2BgIHR1dREUFITLly/jyJEjGD9+PIYOHSo9f9alSxfs27cP+/btw9WrVzFmzBg8fPiwQm0wNzeHnp4e9u/fj3v37iErK+s19JSI6PVggkZERESVatasWZg6dSq++OILuLi4YODAgcjIyIC+vj4OHDiA+/fvw8vLC/3790fXrl3xzTffSOeOHDkSQUFBGDZsGHx8fGBvb4/OnTtX6Pra2tr4+uuv8f3338PKygoBAQGV3UUiotdGIV6+0ZuIiIiIiIiqBGfQiIiIiIiIqgkmaERERERERNUEEzQiIiIiIqJqggkaERERERFRNcEEjYiIiIiIqJpggkZERERERFRNMEEjIiIiIiKqJpigERERERERVRNM0IiIiIiIiKoJJmhERERERETVBBM0IiIiIiKiauL/AdZziwN4RdBiAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "9MwOfKRatt8W"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SFe5D35Ytu5m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oMC4n3H7Gq5Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qw3gkRL5Gq8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "TRztv85Wtvdu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ❓ Check for Missing Values\n",
        "\n",
        "**Why?**  \n",
        "Missing values can introduce bias and affect model performance if not handled appropriately. We use `df.isnull().sum()` to check the count of missing values in each column."
      ],
      "metadata": {
        "id": "CxdlI1eTtwom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Check for Missing Values\n",
        "# We check for missing values in the dataset.\n",
        "# Missing values can affect the analysis, so it's important to handle them appropriately."
      ],
      "metadata": {
        "id": "f58sYWQaGrA8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()"
      ],
      "metadata": {
        "id": "hufJrwVlHuz8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "7Mq2EfpLtyau"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xirJmX5ptzbe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ivbnrj6dtzeo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OvlOnccJHu30"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "sOi9x4emt0HG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🛠️ Handle Missing Values\n",
        "\n",
        "**Why?**  \n",
        "Handling missing values is essential for maintaining the integrity of the dataset.\n",
        "\n",
        "- For **categorical variables**: Fill missing values with the mode (most frequent value) using `df[column].fillna(mode)`.\n",
        "- For **numerical variables**: Fill missing values with the mean using `df[column].fillna(mean)`.\n",
        "\n",
        "This approach ensures the dataset remains comprehensive without introducing significant bias."
      ],
      "metadata": {
        "id": "gC-Qdb0Pt0we"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Handle Missing Values\n",
        "# Depending on the nature of missing values and the dataset, we decide on a strategy to handle them.\n",
        "# Common strategies include filling missing values with the mean or median for numerical variables\n",
        "# and mode for categorical variables, or dropping rows or columns with missing values.\n",
        "# We choose the strategy based on the distribution and importance of each column.\n",
        "# Here, we'll consider the mean for numerical variables and mode for categorical variables."
      ],
      "metadata": {
        "id": "LcW4Z4IOHu6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    if df[column].dtype == 'object':\n",
        "        df[column].fillna(df[column].mode()[0], inplace=True)\n",
        "    else:\n",
        "        df[column].fillna(df[column].mean(), inplace=True)\n"
      ],
      "metadata": {
        "id": "cARMEu1fHu9Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "vvvtnaeJt4WH"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Re7y6OdBt5RJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AdBkp7Pft5Ue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eAiQsAriHvAf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "trADWxNnt6CW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 Check for Duplicate Values\n",
        "\n",
        "**Why?**  \n",
        "Duplicate entries can distort the analysis by over-representing certain data points. We use `df.duplicated().sum()` to check for duplicate rows."
      ],
      "metadata": {
        "id": "FhxxCWIlt6--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Check for Duplicate Values\n",
        "# We check for duplicate rows in the dataset.\n",
        "# Duplicate values can skew analysis results, so it's important to identify and remove them."
      ],
      "metadata": {
        "id": "S9yx2opzHvCi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = df.duplicated().sum()"
      ],
      "metadata": {
        "id": "kH1vWR14IAEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "k1aGLUtct-8-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PBQj9UFKt_-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GKEYqQnKuACU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DqI3n8DxIAHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "9mt8r2LEuAog"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ✂️ Remove Duplicate Values\n",
        "\n",
        "**Why?**  \n",
        "Removing duplicate rows prevents skewing of results and ensures a clean dataset. We use `df.drop_duplicates(inplace=True)` to eliminate any duplicate entries."
      ],
      "metadata": {
        "id": "TQoI2wTiuBcu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Remove Duplicate Values\n",
        "# If duplicate rows are found, we remove them from the dataset."
      ],
      "metadata": {
        "id": "C_bszIXgIAKk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "PbHt5YPTIAM8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "5d7b-Lc-uEnk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pevdMLypuFPm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vv6_TE0juFTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8YrGsIOGIAPh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "ViT6OKpiuGFH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 Check Data Distribution\n",
        "\n",
        "Visualize the distribution of all numerical features using histograms. This helps us detect skewness and potential outliers in the dataset.\n",
        "\n",
        "- `df.hist()`: Creates histograms for each numerical column to visualize their distribution.\n",
        "- `plt.show()`: Displays the plots."
      ],
      "metadata": {
        "id": "3AcQ-X33uGxn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 9. Check Data Distribution\n",
        "# We check the distribution of data in each column.\n",
        "# This helps identify skewness, outliers, or other distribution characteristics."
      ],
      "metadata": {
        "id": "51cpFDKVIASK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=30, figsize=(20,15), density=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "id": "ovESVD0PIAVX",
        "outputId": "a6fa0684-0dd1-4e94-9e7d-77df807af107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "v2Zy4Go4uSxO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z6YNPyJCIWxk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zF4Tjx04IW1m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tb0kKxR5uVI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Ntp4db6puVcW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔬 Check Correlations\n",
        "\n",
        "**Why?**  \n",
        "Understanding the correlation between features helps identify multicollinearity and determine which features may be more influential.\n",
        "\n",
        "- We compute the correlation matrix using `df.corr()`.\n",
        "- A heatmap visualization (`sns.heatmap()`) displays the strength of relationships between features."
      ],
      "metadata": {
        "id": "7kjGhe28uWam"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 10. Check Correlations\n",
        "# We check the correlation between different variables in the dataset.\n",
        "# This helps identify relationships between variables, which is useful for analysis and modeling.\n",
        "# We visualize the correlation matrix using a heatmap for better understanding."
      ],
      "metadata": {
        "id": "99SituNgIXMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "K0Z4IpLdIXPx",
        "outputId": "16a331d5-67a0-4768-920e-a1bd2ac65b45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-f96464a2691e>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "FnIc324MuczP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4EbDTg8Oud1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U2ZpSksUIXS8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fZwA5NkbIXWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "c8nOiYAoue4J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔢 Handle Categorical Variables\n",
        "\n",
        "**Why?**  \n",
        "Machine learning algorithms require numerical inputs. We use **One-Hot Encoding** via `pd.get_dummies()` to convert categorical variables into numerical format.\n",
        "\n",
        "- This process creates new binary columns for each category while removing one category to avoid the **dummy variable trap** (`drop_first=True`)."
      ],
      "metadata": {
        "id": "9Zb6fXKJufaH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 11. Handle Categorical Variables\n",
        "# If the dataset contains categorical variables, we need to encode them into numerical values.\n",
        "# This is necessary for machine learning algorithms to process categorical data.\n",
        "# We use techniques like one-hot encoding or label encoding to convert categorical variables."
      ],
      "metadata": {
        "id": "84Je7DbIIlb2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "m4lOu5RKIles"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "7qNUV5jzuhQX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q35mKZQ7uiMR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5exm6TjCuiZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Azoh5q1jIlh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "S6wjQeuPui8m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚨 Outlier Detection\n",
        "\n",
        "**Why?**  \n",
        "Outliers can significantly affect the model's performance by skewing the results. We detect outliers using the **Interquartile Range (IQR)** method:\n",
        "\n",
        "- **IQR Formula**: \\( \\text{IQR} = Q3 - Q1 \\)\n",
        "- Data points below \\( Q1 - 1.5 \\times \\text{IQR} \\) or above \\( Q3 + 1.5 \\times \\text{IQR} \\) are considered outliers."
      ],
      "metadata": {
        "id": "99d7ssSkujxA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 12. Outlier Detection\n",
        "# Outliers can significantly impact analysis and statistical models.\n",
        "# We use methods like the IQR score or Z-score to detect and handle outliers.\n",
        "# Here, we remove outliers based on the IQR score."
      ],
      "metadata": {
        "id": "E83R_vFWIllB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = df.quantile(0.25)"
      ],
      "metadata": {
        "id": "TNOa9wA3KTl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q3 = df.quantile(0.75)"
      ],
      "metadata": {
        "id": "-adwzBdWNup6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "poTiVMqoIAX-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DMyQzRr4HvIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify outliers based on the IQR score\n",
        "outlier_condition = ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)"
      ],
      "metadata": {
        "id": "j9mtZJp9QZ5W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "bUcjr_FouodB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ezShU4cgupRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nqJVgm_ZQZ8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dlhPYPcOQaDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "Y3yhstgUuqLf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📏 Feature Scaling\n",
        "\n",
        "**Why?**  \n",
        "Scaling standardizes the features to ensure they contribute equally to the model's performance. We use **Standardization** via `StandardScaler()`:\n",
        "\n",
        "- **Standardization Formula**: \\( z = \\frac{x - \\text{mean}}{\\text{std}} \\)\n",
        "- This technique centers the data around 0 with a standard deviation of 1."
      ],
      "metadata": {
        "id": "6aasm2lNuq63"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 13. Feature Scaling\n",
        "# If the dataset contains features with different scales, we need to scale them to a standard scale.\n",
        "# This ensures that all features contribute equally to the analysis."
      ],
      "metadata": {
        "id": "IELGrTwEQaGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "GxTqCj8_QaJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "93ENgx3RSQ0k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)"
      ],
      "metadata": {
        "id": "i-kKfvGuSajW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "EPyAbv9vuweZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S2Xh5G0vuxbQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Frc_fXkCSavZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NuIJBun1SayP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "98vfRztCuyS5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📦 Save the Preprocessed Dataset\n",
        "\n",
        "We save the cleaned and scaled dataset to a CSV file (`preprocessed_data.csv`). This allows us to use the preprocessed data for various machine learning tasks without repeating the preprocessing steps."
      ],
      "metadata": {
        "id": "HK3BuyDjuzCx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 14. Final Dataset\n",
        "# After preprocessing, we have a clean and standardized dataset ready for analysis and modeling.\n",
        "# This dataset can now be used for various machine learning tasks such as classification or regression."
      ],
      "metadata": {
        "id": "fdnRePoHQxVD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BGa1zO_IQxX8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the preprocessed data to a CSV file\n",
        "df_scaled.to_csv('preprocessed_data.csv', index=False)"
      ],
      "metadata": {
        "id": "tDOPYw5_RMjh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "wn-CZNv5u1BX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AZaQ3CvdStP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ACFTHlK_StfZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Pg4jHqSARMmY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "LV7zhXu3u2j_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POSSIBLE TASKS FROM THE DATASET"
      ],
      "metadata": {
        "id": "lo5eM3M5SyXV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PREDICTIVE MODELING"
      ],
      "metadata": {
        "id": "F2gvjYdLT4dZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZceMIOvXvA74"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 Random Forest Classification\n",
        "\n",
        "- **Objective**: To classify whether an individual's income is greater than 50K.\n",
        "- **Approach**: We split the dataset into training and testing sets, then use the **RandomForestClassifier** for prediction."
      ],
      "metadata": {
        "id": "D3pCBVLnvBXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries"
      ],
      "metadata": {
        "id": "XUnMX9HFU7et"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "DdY73R0aRMtQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "veacucZUT-Ua"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UnKhrz29T-X-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df contains your preprocessed dataset and 'Target' is the column containing target labels"
      ],
      "metadata": {
        "id": "K7BoFHgtVQmB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Target_ >50K.'])  # Features"
      ],
      "metadata": {
        "id": "nbPGHd4-VTJ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Target_ >50K.']  # Target labels"
      ],
      "metadata": {
        "id": "LzsglCVYVuSQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eX5AtVvnVTWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "# Assuming X contains input features and y contains target labels\n",
        "# We split the data into 80% training and 20% testing sets"
      ],
      "metadata": {
        "id": "facWkFouUS1S"
      },
      "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)"
      ],
      "metadata": {
        "id": "Zg_H8xt2UXuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fivvUMB0UX40"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the unique values of the target variable\n",
        "print(y_train.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9ZU19uwWxRQ",
        "outputId": "90fbb465-0ca4-4e83-85c9-f0bb8a32c7c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.55622641  1.79782905]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yMi_2S5VWxUu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define thresholds to categorize the target variable"
      ],
      "metadata": {
        "id": "LZDwDlNKWxnS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold_low = -1  # Define the lower threshold for the \"low\" category"
      ],
      "metadata": {
        "id": "aHIU6j-vXU14"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold_high = 1  # Define the upper threshold for the \"high\" category"
      ],
      "metadata": {
        "id": "m9Z4mGexXZmJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KUzXI2gYXZp8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert continuous target variable into categorical"
      ],
      "metadata": {
        "id": "9BFmQnzyXZs1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_categorical = pd.cut(y_train, bins=[float('-inf'), threshold_low, threshold_high, float('inf')],\n",
        "                              labels=['low', 'medium', 'high'])\n"
      ],
      "metadata": {
        "id": "mOyoKFcvXZxR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1WV4TIPeXmj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train RandomForestClassifier with the categorical target variable"
      ],
      "metadata": {
        "id": "l6Y9Kml0Xmwx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "hFd4kaX5XZ0k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X_train, y_train_categorical)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "hbIYIrJGXU4u",
        "outputId": "e9c69018-b8ae-4bd6-a8d0-7f30088a2208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "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>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j4fbg9rnRMwl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict labels for test set"
      ],
      "metadata": {
        "id": "udp1BqMNSvY_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "ZdCUWATmSvb3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "4jxnOStQvE6D"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EifSCbUEvF54"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YYxc_OOZSve-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SwRjo0e-SviN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "1hSOl-vovGiG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 Linear Regression\n",
        "\n",
        "- **Objective**: To predict continuous values based on input features.\n",
        "- **Approach**: We apply **Linear Regression** to understand relationships between features and the target variable."
      ],
      "metadata": {
        "id": "oBApTEP-vJ1n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# regression"
      ],
      "metadata": {
        "id": "NqL-dAYzhmHo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "bpJvh-BSglbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sFX19vKjgle1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming X contains input features and y contains target values"
      ],
      "metadata": {
        "id": "GqSwzWcFgliH"
      },
      "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)"
      ],
      "metadata": {
        "id": "AJ-iVsuWgllP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BOYAlRL_hCJD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize and train a Linear Regression model"
      ],
      "metadata": {
        "id": "chebybVphCWf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "WwK6ucTPhIc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Y5Lp-jHNhIfx",
        "outputId": "f225f544-f940-43cd-ba8d-7dae8992fc91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fZV0VNkPhIlU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict values for test set"
      ],
      "metadata": {
        "id": "ueNwdnaKhIpc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "f27KUjaChItj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "48G8yGmwvOTV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zRfziuAdhIxE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e3LOS7kwhVDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0nks80lPvP2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "8kxbbFN-vQIP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📈 K-Means Clustering\n",
        "\n",
        "- **Objective**: To group similar data points based on their attributes.\n",
        "- **Approach**: We use **K-Means** with 3 clusters to categorize the dataset into distinct groups."
      ],
      "metadata": {
        "id": "C8oG_0d1vQ0Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# clustering"
      ],
      "metadata": {
        "id": "xFjehEzNhZgx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "4RlIfd2bhVIz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "06qn5EWWhVNO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming X contains input features"
      ],
      "metadata": {
        "id": "ynFeYDcJhVRf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=3)"
      ],
      "metadata": {
        "id": "TPj4LQAleQwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.fit(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "BdsdUuXhh4dp",
        "outputId": "09de65ab-4293-4a7f-ff32-926a98c334a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zKQe5AM8h4oi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get cluster labels for each data point"
      ],
      "metadata": {
        "id": "ICqThy3-h427"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_labels = kmeans.labels_"
      ],
      "metadata": {
        "id": "zQ3C9BHTh_Ri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "YzTvZxD6vVWg"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9PVLRo56vWJg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g3--Krj6h_a-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yzQ3Ev21iSGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "wmPGyKO5vWzg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🛠️ Feature Engineering\n",
        "\n",
        "- **Objective**: To create new features that can enhance model performance.\n",
        "- **Example**: We create a new feature `Total_Capital` by adding `Capital_Gain` and `Capital_Loss`. This captures the total capital information in a single feature."
      ],
      "metadata": {
        "id": "a9BzRj8mvXq9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FEATURE ENGINEERING"
      ],
      "metadata": {
        "id": "Uc37Klc7eSNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df contains the dataset\n",
        "# Example feature engineering steps:\n",
        "# Creating a new feature Total_Capital by adding Capital_Gain and Capital_Loss"
      ],
      "metadata": {
        "id": "61q1vpuEejch"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total_Capital'] = df['Capital_Gain'] + df['Capital_Loss']"
      ],
      "metadata": {
        "id": "wCzrMNjKeRFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sKh2YwhAf5PX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Preprocessed Data to a CSV file"
      ],
      "metadata": {
        "id": "0_IyyvvTf5o4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('preprocessed_data.csv', index=False)"
      ],
      "metadata": {
        "id": "Dzo9HS9sSvlE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "-gJwhEQAvaFY"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uGNqlwrcvbxo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IKVyC7wFgOEu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZOl9lZ-QgOIg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📝 Final Notes\n",
        "\n",
        "- The project involves a comprehensive analysis of the adult income dataset with various preprocessing steps to prepare data for machine learning tasks.\n",
        "- Different models such as **Random Forest, Linear Regression,** and **K-Means Clustering** provide a holistic approach to understanding and predicting income classifications.\n",
        "- Saving the preprocessed data ensures reproducibility and efficient use of resources in subsequent analysis stages."
      ],
      "metadata": {
        "id": "-XjAU6CdvcVY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "---"
      ],
      "metadata": {
        "id": "BChOy98BvhUi"
      }
    }
  ]
}