Adult_data_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": "_60W-ApiNmrK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 **Exploratory Data Analysis (EDA) and Predictive Modeling Project**\n",
        "\n",
        "## 📌 **Project Overview**\n",
        "\n",
        "This project involves performing **Exploratory Data Analysis (EDA)** and predictive modeling on a dataset, which consists of demographic and employment information, to predict income levels and cluster individuals based on similarities in their characteristics. The project will guide through various stages, including data inspection, cleaning, visualization, and modeling.\n",
        "\n",
        "---\n",
        "\n",
        "## 🧩 **Objective**\n",
        "\n",
        "- Conduct **Exploratory Data Analysis (EDA)** to understand the dataset's structure and key patterns.\n",
        "- Handle missing values, duplicate entries, and outliers to prepare the data for analysis.\n",
        "- Implement predictive modeling to classify income levels using **Logistic Regression**.\n",
        "- Apply **K-Means Clustering** to group individuals based on their demographic and employment features.\n",
        "- Perform **Anomaly Detection** to identify and address any unusual data points.\n",
        "- **Feature Importance**: Evaluate which features contribute the most to the prediction task.\n",
        "- Apply **Natural Language Processing (NLP)** techniques for analyzing and processing textual data if applicable.\n",
        "\n",
        "---\n",
        "\n",
        "## 🛠 **Tools and Libraries Used**\n",
        "\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **NumPy**: For numerical operations on arrays and matrices.\n",
        "- **Matplotlib & Seaborn**: For data visualization (graphs, plots).\n",
        "- **Scikit-learn**: For machine learning (Logistic Regression, K-Means Clustering).\n",
        "- **SciPy**: For statistical operations (Z-Score calculation for outlier detection).\n",
        "- **XGBoost**: For advanced model evaluation and feature importance.\n",
        "- **NLTK / SpaCy**: For Natural Language Processing tasks (if applicable).\n",
        "\n",
        "---\n",
        "\n",
        "## ⚙ **Key Steps in the Project**\n",
        "\n",
        "### 📂 **1. Data Inspection and Cleaning**\n",
        "\n",
        "- **Data Loading**: Import the dataset for analysis.\n",
        "- **Missing Values**: Identify and handle missing values by filling with column means.\n",
        "- **Duplicate Values**: Detect and remove duplicate rows to ensure data integrity.\n",
        "- **Outlier Detection**: Use Z-score to identify and handle outliers.\n",
        "\n",
        "---\n",
        "\n",
        "### 📊 **2. Exploratory Data Analysis (EDA)**\n",
        "\n",
        "- **Visualizing Distributions**: Histograms with kernel density estimation (KDE) to visualize numerical features.\n",
        "- **Categorical Variable Analysis**: Bar charts for categorical variables.\n",
        "- **Correlation Analysis**: Heatmap to understand the relationships between numerical variables.\n",
        "\n",
        "---\n",
        "\n",
        "### 🧹 **3. Data Preprocessing**\n",
        "\n",
        "- **Feature Scaling**: Standardize numerical features using `StandardScaler` to normalize the data.\n",
        "- **One-Hot Encoding**: Transform categorical variables into numerical format for machine learning models.\n",
        "\n",
        "---\n",
        "\n",
        "### 🧠 **4. Predictive Modeling**\n",
        "\n",
        "- **Logistic Regression**: Implement logistic regression to predict income levels (binary classification).\n",
        "- **Model Evaluation**: Use accuracy, classification report, and confusion matrix to evaluate model performance.\n",
        "\n",
        "---\n",
        "\n",
        "### 🔢 **5. Clustering**\n",
        "\n",
        "- **K-Means Clustering**: Apply K-means clustering to group individuals based on their characteristics.\n",
        "- **Cluster Analysis**: Analyze and interpret the formed clusters.\n",
        "\n",
        "---\n",
        "\n",
        "### 🔍 **6. Anomaly Detection**\n",
        "\n",
        "- **Outlier Detection**: Use statistical techniques like Z-score or IQR to detect and remove outliers.\n",
        "- **Isolation Forest or DBSCAN**: Apply models like Isolation Forest or DBSCAN for anomaly detection in high-dimensional spaces.\n",
        "- **Visualizing Anomalies**: Use plots to highlight detected anomalies in the dataset.\n",
        "\n",
        "---\n",
        "\n",
        "### 📊 **7. Feature Importance**\n",
        "\n",
        "- **Model-Based Feature Importance**: Use models like XGBoost or Random Forest to calculate feature importance.\n",
        "- **SHAP Values**: Visualize the contribution of each feature using SHAP (Shapley Additive Explanations).\n",
        "- **Visual Representation**: Create bar plots to show the relative importance of different features.\n",
        "\n",
        "---\n",
        "\n",
        "### 🧠 **8. Natural Language Processing (NLP)**\n",
        "\n",
        "- **Text Preprocessing**: Tokenization, stemming, and lemmatization of text data if applicable.\n",
        "- **Sentiment Analysis**: Use NLP models like **BERT** or **DistilBERT** for sentiment analysis on any textual features.\n",
        "- **Text Classification**: Classify text data based on predefined categories using machine learning or deep learning methods.\n",
        "\n",
        "---\n",
        "\n",
        "## 📈 **Performance Evaluation**\n",
        "\n",
        "- **Accuracy Score**: Evaluate the model’s performance on the test set.\n",
        "- **Classification Report**: Detailed metrics (precision, recall, F1-score).\n",
        "- **Confusion Matrix**: Visualize the classification results.\n",
        "\n",
        "---\n",
        "\n",
        "## 💡 **Insights and Conclusions**\n",
        "\n",
        "- **Data Insights**: Interpret key patterns observed from the exploratory data analysis.\n",
        "- **Model Interpretation**: Discuss the logistic regression model’s results and its predictive accuracy.\n",
        "- **Clustering Insights**: Identify meaningful clusters and their implications.\n",
        "- **Anomaly Insights**: Discuss the impact of outliers and anomalies on model performance.\n",
        "- **Feature Importance Insights**: Identify the most important features for prediction and classification.\n",
        "\n",
        "---\n",
        "\n",
        "## 📚 **Learning Outcomes**\n",
        "\n",
        "- Gain hands-on experience with **Exploratory Data Analysis (EDA)** techniques.\n",
        "- Understand how to clean and preprocess data for machine learning.\n",
        "- Implement **Logistic Regression** and **K-Means Clustering** for predictive modeling and clustering tasks.\n",
        "- Apply **Anomaly Detection** techniques to identify and handle outliers.\n",
        "- Learn to compute and visualize **Feature Importance** to enhance model interpretability.\n",
        "- Apply **Natural Language Processing (NLP)** for analyzing textual data and extracting insights.\n",
        "- Evaluate model performance using various metrics and interpret results effectively.\n",
        "\n",
        "---\n",
        "\n",
        "## 📄 **Dataset Information**\n",
        "\n",
        "The dataset used in this project includes demographic and employment information such as age, education, marital status, and occupation, with the goal of predicting income levels (income >50K or <=50K). It also allows for clustering based on similarities in individual characteristics.\n",
        "\n",
        "---\n",
        "\n",
        "## 🚀 **Future Enhancements**\n",
        "\n",
        "- **Model Improvement**: Try other classification algorithms like Random Forest or SVM.\n",
        "- **Advanced Clustering**: Experiment with hierarchical clustering or DBSCAN for a different approach to grouping data.\n",
        "- **Hyperparameter Tuning**: Fine-tune model parameters using GridSearchCV for better performance.\n",
        "- **Deep Learning Models**: Experiment with deep learning models such as neural networks for better predictive accuracy.\n",
        "- **Advanced NLP**: Use more sophisticated NLP models like **GPT** or **BERT** for text-based prediction tasks.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "tdu6yQnLNosa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "enWmozO3RpUz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XJ0mm96ARqmd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_fz-HjeURqqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I-YSi4Qn6IQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "A5jAbZcrRsVz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f4OBLq-sz7Zq"
      },
      "outputs": [],
      "source": [
        "# Jupyter Notebook for Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Objective:\n",
        "# The purpose of this notebook is to explore and understand our dataset thoroughly."
      ],
      "metadata": {
        "id": "mEGHodIBz-rX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "6tjN48epRvhs"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jq6RFrPDRwez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANYNAmSwRwih"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CnFUGoV9z-ub"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "6UjqJ_jTRxk7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Import Necessary Libraries\n",
        "# We start by bringing in the tools we need to work with data: pandas for data manipulation, numpy for numerical operations, and seaborn and matplotlib for data visualization.\n"
      ],
      "metadata": {
        "id": "q_4tcv1Pz-xm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # This line imports the pandas library, which is used for data manipulation and analysis."
      ],
      "metadata": {
        "id": "xcfmadYYz-0r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # This line imports the numpy library, which provides support for numerical operations on arrays and matrices."
      ],
      "metadata": {
        "id": "3VH5XDTUz-3r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # This line imports the matplotlib library, which is used for creating visualizations."
      ],
      "metadata": {
        "id": "ApIixbDqz-6m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # This line imports the seaborn library, which is also used for data visualization with a focus on statistical graphics."
      ],
      "metadata": {
        "id": "SZMqLpscz-9p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lkXnd978SJiE"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xlXQ7CHSSKeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5E5Ci1SqSKiS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vF0C6RkAz_Ag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "fwvWwy_aSLOD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📂 **1. Data Inspection and Cleaning**\n",
        "\n",
        "### **Objective:**\n",
        "Data inspection and cleaning are essential steps in the data science process. These steps ensure that the dataset is well-prepared for further analysis and modeling by addressing missing values, duplicates, and outliers.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Data Loading**: Import the dataset using Pandas to inspect its contents and structure. Ensure that the dataset is loaded properly with the correct data types.\n",
        "  \n",
        "- **Missing Values**: Identify columns with missing values and handle them by either:\n",
        "  - Filling missing numerical values with the mean or median.\n",
        "  - Imputing categorical variables with the most frequent value.\n",
        "  - Dropping rows with too many missing values if necessary.\n",
        "\n",
        "- **Duplicate Values**: Check for any duplicate entries that could distort the analysis. Remove duplicate rows to ensure the dataset reflects unique records only.\n",
        "\n",
        "- **Outlier Detection**: Use statistical methods like Z-score or IQR (Interquartile Range) to identify and handle outliers. Outliers can skew results and affect model performance."
      ],
      "metadata": {
        "id": "-28lxiBlSNIr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Load the Data\n",
        "# Here, we load our dataset into the notebook. Think of it like bringing our data into our workspace so we can start working with it."
      ],
      "metadata": {
        "id": "brKuQfrr17Pm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/adult.data.csv')  # This line reads our dataset from a CSV file named 'data.csv' and stores it in a pandas DataFrame called 'df'."
      ],
      "metadata": {
        "id": "NZaznDRZ17TK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YcByBhem17V5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Inspect the Data\n",
        "# Now that we have our data, let's take a look at it to understand its structure and contents."
      ],
      "metadata": {
        "id": "yO9tXG4q17Y7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()  # This command shows us the first few rows of our dataset, giving us an initial glimpse of what the data looks like."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "CM4viswL17b2",
        "outputId": "d8595ac4-e081-48de-e43e-a565861bb92e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age         workclass  fnlwgt  education  education-num  \\\n",
              "0   39         State-gov   77516  Bachelors             13   \n",
              "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
              "2   38           Private  215646    HS-grad              9   \n",
              "3   53           Private  234721       11th              7   \n",
              "4   28           Private  338409  Bachelors             13   \n",
              "\n",
              "       marital-status         occupation   relationship   race     sex  \\\n",
              "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
              "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
              "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
              "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
              "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
              "\n",
              "   capital-gain  capital-loss  hours-per-week native-country salary  \n",
              "0          2174             0              40  United-States  <=50K  \n",
              "1             0             0              13  United-States  <=50K  \n",
              "2             0             0              40  United-States  <=50K  \n",
              "3             0             0              40  United-States  <=50K  \n",
              "4             0             0              40           Cuba  <=50K  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22572a33-54c4-4235-9b58-4502758f768e\" 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>marital-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>native-country</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</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>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=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-22572a33-54c4-4235-9b58-4502758f768e')\"\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-22572a33-54c4-4235-9b58-4502758f768e 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-22572a33-54c4-4235-9b58-4502758f768e');\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-864054ab-4ca2-4aa9-b43b-291d85ca5ef0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-864054ab-4ca2-4aa9-b43b-291d85ca5ef0')\"\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-864054ab-4ca2-4aa9-b43b-291d85ca5ef0 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\": 32561,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 17,\n        \"max\": 90,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          28,\n          73,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Without-pay\",\n          \"Self-emp-not-inc\",\n          \"?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105549,\n        \"min\": 12285,\n        \"max\": 1484705,\n        \"num_unique_values\": 21648,\n        \"samples\": [\n          128485,\n          469907,\n          235951\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          \"Bachelors\",\n          \"HS-grad\",\n          \"Some-college\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 16,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          13,\n          9,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital-status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Never-married\",\n          \"Married-civ-spouse\",\n          \"Married-AF-spouse\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Machine-op-inspct\",\n          \"?\",\n          \"Adm-clerical\"\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          \"Not-in-family\",\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          \"Black\",\n          \"Other\",\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\": 7385,\n        \"min\": 0,\n        \"max\": 99999,\n        \"num_unique_values\": 119,\n        \"samples\": [\n          3781,\n          15831\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 402,\n        \"min\": 0,\n        \"max\": 4356,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          419,\n          2051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          6,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native-country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 42,\n        \"samples\": [\n          \"El-Salvador\",\n          \"Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()  # This command provides us with general information about our dataset, such as the number of entries and data types of each column.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHZPGXo117ea",
        "outputId": "38629e00-42b9-475e-b367-1d6c6c27cff6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education-num   32561 non-null  int64 \n",
            " 5   marital-status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   sex             32561 non-null  object\n",
            " 10  capital-gain    32561 non-null  int64 \n",
            " 11  capital-loss    32561 non-null  int64 \n",
            " 12  hours-per-week  32561 non-null  int64 \n",
            " 13  native-country  32561 non-null  object\n",
            " 14  salary          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()  # This command generates summary statistics for numerical columns in our dataset, helping us understand the distribution and range of values."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "l06CosJq2Tuj",
        "outputId": "1165a00a-b7c6-469a-ea27-2e3e5f94cd9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
              "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
              "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
              "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
              "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
              "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
              "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
              "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
              "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
              "\n",
              "       hours-per-week  \n",
              "count    32561.000000  \n",
              "mean        40.437456  \n",
              "std         12.347429  \n",
              "min          1.000000  \n",
              "25%         40.000000  \n",
              "50%         40.000000  \n",
              "75%         45.000000  \n",
              "max         99.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9c5350f-d060-4d6f-bd46-2d327f2d9a47\" 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>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>32561.000000</td>\n",
              "      <td>3.256100e+04</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "      <td>32561.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.581647</td>\n",
              "      <td>1.897784e+05</td>\n",
              "      <td>10.080679</td>\n",
              "      <td>1077.648844</td>\n",
              "      <td>87.303830</td>\n",
              "      <td>40.437456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.640433</td>\n",
              "      <td>1.055500e+05</td>\n",
              "      <td>2.572720</td>\n",
              "      <td>7385.292085</td>\n",
              "      <td>402.960219</td>\n",
              "      <td>12.347429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.228500e+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>28.000000</td>\n",
              "      <td>1.178270e+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>37.000000</td>\n",
              "      <td>1.783560e+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>48.000000</td>\n",
              "      <td>2.370510e+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>90.000000</td>\n",
              "      <td>1.484705e+06</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>4356.000000</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-a9c5350f-d060-4d6f-bd46-2d327f2d9a47')\"\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-a9c5350f-d060-4d6f-bd46-2d327f2d9a47 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-a9c5350f-d060-4d6f-bd46-2d327f2d9a47');\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-3adbad27-5f16-4839-ba91-76797e06fe10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3adbad27-5f16-4839-ba91-76797e06fe10')\"\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-3adbad27-5f16-4839-ba91-76797e06fe10 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\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11498.327099385337,\n        \"min\": 13.640432553581341,\n        \"max\": 32561.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.58164675532078,\n          37.0,\n          32561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 486902.252790567,\n        \"min\": 12285.0,\n        \"max\": 1484705.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          189778.36651208502,\n          178356.0,\n          32561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education-num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11508.98951393426,\n        \"min\": 1.0,\n        \"max\": 32561.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.0806793403151,\n          10.0,\n          32561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35111.63595222616,\n        \"min\": 0.0,\n        \"max\": 99999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1077.6488437087312,\n          99999.0,\n          7385.292084840338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital-loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11366.935322921165,\n        \"min\": 0.0,\n        \"max\": 32561.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          87.303829734959,\n          4356.0,\n          402.9602186489998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours-per-week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11498.057671750134,\n        \"min\": 1.0,\n        \"max\": 32561.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          32561.0,\n          40.437455852092995,\n          45.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OMcFsFkF2T7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation:\n",
        "# The dataset contains 32,561 entries and 15 columns.\n",
        "# The columns consist of both numerical (int64) and categorical (object) data types.\n",
        "# There are no missing values in any of the columns."
      ],
      "metadata": {
        "id": "ppVz8Jf42T_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cdfgx-eM2UB9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Check for Missing Values\n",
        "# It's essential to ensure that our dataset does not contain any missing values, as they can affect the accuracy of our analysis."
      ],
      "metadata": {
        "id": "1PQqnpsG31Ws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()"
      ],
      "metadata": {
        "id": "D8LZGUKV31Zs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing Values:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ed5ar7h31cm",
        "outputId": "6f7757b5-303d-4fc7-e498-96bc10a4bde9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5Li8pBr3_DO",
        "outputId": "29f28814-363f-4f86-cdc6-fe161338dbf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age               0\n",
            "workclass         0\n",
            "fnlwgt            0\n",
            "education         0\n",
            "education-num     0\n",
            "marital-status    0\n",
            "occupation        0\n",
            "relationship      0\n",
            "race              0\n",
            "sex               0\n",
            "capital-gain      0\n",
            "capital-loss      0\n",
            "hours-per-week    0\n",
            "native-country    0\n",
            "salary            0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n8duNVgl3_HE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Check for Duplicate Values\n",
        "# Duplicate rows can introduce bias into our analysis, so we need to identify and remove them."
      ],
      "metadata": {
        "id": "sH1y3KLR3_J-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = df.duplicated().sum()"
      ],
      "metadata": {
        "id": "_3MpEbX13_M4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDuplicate Rows:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMMc_N-O3_Pm",
        "outputId": "d643c029-aff5-40b0-9ede-91d1226abe9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Duplicate Rows:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(duplicate_rows)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CA8ncEQ431fw",
        "outputId": "a250f19d-9117-40a7-f102-589fdbc63e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "3qKVtGxSSSR7"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jALQNOrvSTP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4M3n4pgd4Mt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zRJsYrFa4NDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "LFa-jtk1SVxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 **2. Exploratory Data Analysis (EDA)**\n",
        "\n",
        "### **Objective:**\n",
        "Exploratory Data Analysis (EDA) involves summarizing the dataset’s key characteristics and visualizing data distributions. This helps in identifying patterns, trends, and potential issues with the dataset.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Visualizing Distributions**: Use histograms and kernel density estimation (KDE) plots to visualize the distribution of numerical features. This step helps in understanding the central tendency and spread of the data.\n",
        "  \n",
        "- **Categorical Variable Analysis**: Use bar charts to visualize the distribution of categorical features. This is crucial for understanding the frequency of each category and detecting imbalances in the data.\n",
        "\n",
        "- **Correlation Analysis**: Create a heatmap to examine the relationships between numerical variables. This helps in understanding potential dependencies and correlations, which is useful for feature engineering."
      ],
      "metadata": {
        "id": "M9qvbWDeSWxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Visualize Numerical Variables\n",
        "# We want to understand the distribution of numerical variables in our dataset by creating histograms with a kernel density estimation curve."
      ],
      "metadata": {
        "id": "lG5G7dUw3Jlv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.select_dtypes(include=np.number).columns:\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": "EvdMfzg63JoR",
        "outputId": "7126a00e-44a6-470f-a671-eb5dee2efa42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHHCAYAAABeLEexAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABneklEQVR4nO3deXhU5d3G8e+Zmcxk30lCICwiIIIrVEQBUVAE3Kp9K0oVfVG6gBZxpVVRtFIREbFUqlXQFpfq61a1VAoiVAERQRQQ2ROzAtn3ZOa8f0xmYFiTMMlMMvfnuuaCOeeZmd8hgdw82zFM0zQRERERCWGWQBcgIiIiEmgKRCIiIhLyFIhEREQk5CkQiYiISMhTIBIREZGQp0AkIiIiIU+BSEREREKeApGIiIiEPAUiERERCXkKRCLt3COPPIJhGK3yWcOGDWPYsGHe5ytWrMAwDN5+++1W+fxbbrmFbt26tcpnNVd5eTm33XYbaWlpGIbBlClTjtm2vr6e++67j4yMDCwWC9dcc02TPqs1v/YibZ0CkUgbsmjRIgzD8D7Cw8NJT09n5MiRzJs3j7KyMr98Tk5ODo888ggbN270y/v5UzDX1hhPPPEEixYt4te//jV/+9vfuOmmm47Z9uWXX+app57iZz/7Ga+88gp33XVXK1baPH/+859ZtGhRoMsQaTJboAsQkaabMWMG3bt3p66ujry8PFasWMGUKVOYM2cOH3zwAWeeeaa37YMPPsgDDzzQpPfPycnh0UcfpVu3bpx99tmNft0nn3zSpM9pjuPV9uKLL+JyuVq8hpOxfPlyzj//fKZPn96otp06deKZZ55phcr8489//jPJycnccsstgS5FpEkUiETaoFGjRjFgwADv82nTprF8+XKuuOIKrrrqKrZu3UpERAQANpsNm61l/6pXVlYSGRmJ3W5v0c85kbCwsIB+fmMUFBRw+umnN7ptfHx8yxYkIoCGzETajUsuuYSHHnqIvXv38ve//917/GjzSJYuXcrgwYOJj48nOjqa3r1787vf/Q5wz/v5yU9+AsCtt97qHZ7zDIMMGzaMfv36sX79eoYOHUpkZKT3tYfPIfJwOp387ne/Iy0tjaioKK666iqysrJ82nTr1u2ovQqHvueJajvaHKKKigruvvtuMjIycDgc9O7dm9mzZ2Oapk87wzCYPHky7733Hv369cPhcNC3b1+WLFly9D/wwxQUFDBhwgRSU1MJDw/nrLPO4pVXXvGe98yn2r17Nx999JG39j179hzxXnv27MEwDD799FM2b97sbbtixQrvudmzZ/PCCy/Qo0cPHA4HP/nJT1i3bt1xa7z22ms599xzfY5deeWVGIbBBx984D22du1aDMPgX//6l/fYpk2buOiii4iIiKBz5848/vjjLFy40OcaunXrxubNm/nss8+8NR/t+0EkGKmHSKQduemmm/jd737HJ598wu23337UNps3b+aKK67gzDPPZMaMGTgcDnbs2MHnn38OQJ8+fZgxYwYPP/wwEydOZMiQIQBccMEF3vc4cOAAo0aNYuzYsfziF78gNTX1uHX94Q9/wDAM7r//fgoKCpg7dy4jRoxg48aN3p6sxmhMbYcyTZOrrrqKTz/9lAkTJnD22Wfz73//m3vvvZfs7OwjhqL++9//8s477/Cb3/yGmJgY5s2bx3XXXUdmZiZJSUnHrKuqqophw4axY8cOJk+eTPfu3Xnrrbe45ZZbKC4u5re//S19+vThb3/7G3fddRedO3fm7rvvBqBDhw5HvF+HDh3429/+xh/+8AfKy8uZOXOm9/qrqqoAeO211ygrK+OXv/wlhmEwa9Ysrr32Wnbt2nXMnrIhQ4bw/vvvU1paSmxsLKZp8vnnn2OxWFi1ahVXXXUVAKtWrcJisXDhhRcCkJ2dzcUXX4xhGEybNo2oqCj++te/4nA4fN5/7ty53HHHHURHR/P73/8e4ITfGyJBwxSRNmPhwoUmYK5bt+6YbeLi4sxzzjnH+3z69OnmoX/Vn3nmGRMw9+3bd8z3WLdunQmYCxcuPOLcRRddZALmggULjnruoosu8j7/9NNPTcDs1KmTWVpa6j3+j3/8wwTMZ5991nusa9eu5vjx40/4nserbfz48WbXrl29z9977z0TMB9//HGfdj/72c9MwzDMHTt2eI8Bpt1u9zn2zTffmID53HPPHfFZh5o7d64JmH//+9+9x2pra81BgwaZ0dHRPtfetWtXc8yYMcd9P4+LLrrI7Nu3r8+x3bt3m4CZlJRkFhYWeo+///77JmD+85//9B47/Gvv+bP7+OOPTdM0zU2bNpmA+T//8z/mwIEDve2uuuoqn++hO+64wzQMw9ywYYP32IEDB8zExEQTMHfv3u093rdvX5+vl0hboSEzkXYmOjr6uKvNPHNS3n///WZPQHY4HNx6662Nbn/zzTcTExPjff6zn/2Mjh078vHHHzfr8xvr448/xmq1cuedd/ocv/vuuzFN02dICGDEiBH06NHD+/zMM88kNjaWXbt2nfBz0tLSuOGGG7zHwsLCuPPOOykvL+ezzz7zw9X4uv7660lISPA+9/SWHa/Wc845h+joaFauXAm4e4I6d+7MzTffzNdff01lZSWmafLf//7X+34AS5YsYdCgQT6T2BMTExk3bpyfr0okcBSIRNqZ8vJyn/BxuOuvv54LL7yQ2267jdTUVMaOHcs//vGPJoWjTp06NWkCdc+ePX2eG4bBqaeeetT5M/60d+9e0tPTj/jz6NOnj/f8obp06XLEeyQkJFBUVHTCz+nZsycWi+8/qcf6HH84vFZPODperVarlUGDBrFq1SrAHYiGDBnC4MGDcTqdrFmzhi1btlBYWOgTiPbu3cupp556xPsd7ZhIW6VAJNKO/Pjjj5SUlBz3B1VERAQrV67kP//5DzfddBObNm3i+uuv59JLL8XpdDbqc5oy76exjrWBYGNr8ger1XrU4+ZhE7CDQXNrHTx4MOvWraO6utobiOLj4+nXrx+rVq3yhqVDA5FIKFAgEmlH/va3vwEwcuTI47azWCwMHz6cOXPmsGXLFv7whz+wfPlyPv30U+DY4aS5tm/f7vPcNE127NjhsyIsISGB4uLiI157eO9KU2rr2rUrOTk5Rwwhfv/9997z/tC1a1e2b99+RC+bvz/HH4YMGUJtbS2vv/462dnZ3uAzdOhQbyDq1auXz2Torl27smPHjiPe62jHtDO2tFUKRCLtxPLly3nsscfo3r37ced2FBYWHnHMMzekpqYGgKioKICjBpTmePXVV31Cydtvv01ubi6jRo3yHuvRowdr1qyhtrbWe+zDDz88Ynl+U2obPXo0TqeTP/3pTz7Hn3nmGQzD8Pn8kzF69Gjy8vJ48803vcfq6+t57rnniI6O5qKLLvLL5/jDwIEDCQsL48knnyQxMZG+ffsC7qC0Zs0aPvvssyN6h0aOHMnq1at9dgcvLCxk8eLFR7x/VFSU375vRFqTlt2LtEH/+te/+P7776mvryc/P5/ly5ezdOlSunbtygcffEB4ePgxXztjxgxWrlzJmDFj6Nq1KwUFBfz5z3+mc+fODB48GHCHk/j4eBYsWEBMTAxRUVEMHDiQ7t27N6vexMREBg8ezK233kp+fj5z587l1FNP9dka4LbbbuPtt9/m8ssv5+c//zk7d+7k73//u88k56bWduWVV3LxxRfz+9//nj179nDWWWfxySef8P777zNlypQj3ru5Jk6cyF/+8hduueUW1q9fT7du3Xj77bf5/PPPmTt37nHndLW2yMhI+vfvz5o1a7x7EIG7h6iiooKKioojAtF9993H3//+dy699FLuuOMO77L7Ll26UFhY6NMr1L9/f55//nkef/xxTj31VFJSUrjkkkta9RpFmiWga9xEpEk8y+49D7vdbqalpZmXXnqp+eyzz/os7/Y4fOn1smXLzKuvvtpMT0837Xa7mZ6ebt5www3mDz/84PO6999/3zz99NNNm83ms8z9aEvBPY617P711183p02bZqakpJgRERHmmDFjzL179x7x+qefftrs1KmT6XA4zAsvvND86quvjnjP49V2+LJ70zTNsrIy86677jLT09PNsLAws2fPnuZTTz1lulwun3aAOWnSpCNqOtZ2AIfLz883b731VjM5Odm02+3mGWeccdStAfy17P6pp546oj1gTp8+3fv88K+9x7333msC5pNPPulz/NRTTzUBc+fOnUe8ZsOGDeaQIUNMh8Nhdu7c2Zw5c6Y5b948EzDz8vK87fLy8swxY8aYMTExJqAl+NJmGKYZhLMFRUQk6E2ZMoW//OUvlJeXH3OSt0hboTlEIiJyQp4dsj0OHDjA3/72NwYPHqwwJO2C5hCJiMgJDRo0iGHDhtGnTx/y8/N56aWXKC0t5aGHHgp0aSJ+oUAkIiInNHr0aN5++21eeOEFDMPg3HPP5aWXXmLo0KGBLk3ELzSHSEREREKe5hCJiIhIyFMgEhERkZCnOUSN4HK5yMnJISYmRtvSi4iItBGmaVJWVkZ6evoRN18+nAJRI+Tk5JCRkRHoMkRERKQZsrKy6Ny583HbKBA1gmfb/aysLGJjYwNcjYiIiDRGaWkpGRkZjbp9jgJRI3iGyWJjYxWIRERE2pjGTHfRpGoREREJeQpEIiIiEvIUiERERCTkKRCJiIhIyFMgEhERkZCnQCQiIiIhT4FIREREQp4CkYiIiIQ8BSIREREJeQpEIiIiEvIUiERERCTkKRCJiIhIyFMgEhERkZCnQCQiIiIhzxboAqR9cblcZGdne5936tQJi0W5W0REgpsCkfhVdnY2T7+7htikFEoPFHD3T88nIyMj0GWJiIgclwKR+F1sUgoJKR0DXYaIiEijaSxDREREQl5AA9HKlSu58sorSU9PxzAM3nvvvWO2/dWvfoVhGMydO9fneGFhIePGjSM2Npb4+HgmTJhAeXm5T5tNmzYxZMgQwsPDycjIYNasWS1wNSIiItJWBTQQVVRUcNZZZzF//vzjtnv33XdZs2YN6enpR5wbN24cmzdvZunSpXz44YesXLmSiRMnes+XlpZy2WWX0bVrV9avX89TTz3FI488wgsvvOD36xEREZG2KaBziEaNGsWoUaOO2yY7O5s77riDf//734wZM8bn3NatW1myZAnr1q1jwIABADz33HOMHj2a2bNnk56ezuLFi6mtreXll1/GbrfTt29fNm7cyJw5c3yCk4iIiISuoJ5D5HK5uOmmm7j33nvp27fvEedXr15NfHy8NwwBjBgxAovFwtq1a71thg4dit1u97YZOXIk27Zto6io6KifW1NTQ2lpqc9DRERE2q+gDkRPPvkkNpuNO++886jn8/LySElJ8Tlms9lITEwkLy/P2yY1NdWnjee5p83hZs6cSVxcnPehZeMiIiLtW9AGovXr1/Pss8+yaNEiDMNo1c+eNm0aJSUl3kdWVlarfr6IiIi0rqANRKtWraKgoIAuXbpgs9mw2Wzs3buXu+++m27dugGQlpZGQUGBz+vq6+spLCwkLS3N2yY/P9+njee5p83hHA4HsbGxPg8RERFpv4I2EN10001s2rSJjRs3eh/p6ence++9/Pvf/wZg0KBBFBcXs379eu/rli9fjsvlYuDAgd42K1eupK6uzttm6dKl9O7dm4SEhNa9KBEREQlKAV1lVl5ezo4dO7zPd+/ezcaNG0lMTKRLly4kJSX5tA8LCyMtLY3evXsD0KdPHy6//HJuv/12FixYQF1dHZMnT2bs2LHeJfo33ngjjz76KBMmTOD+++/nu+++49lnn+WZZ55pvQsVERGRoBbQQPTVV19x8cUXe59PnToVgPHjx7No0aJGvcfixYuZPHkyw4cPx2KxcN111zFv3jzv+bi4OD755BMmTZpE//79SU5O5uGHH9aSexEREfEKaCAaNmwYpmk2uv2ePXuOOJaYmMhrr7123NedeeaZrFq1qqnliYiISIgI2jlEIiIiIq1FgUhERERCngKRiIiIhDwFIhEREQl5CkQiIiIS8hSIREREJOQpEImIiEjIUyASERGRkKdAJCIiIiFPgUhERERCXkBv3SHtm+lykZOTA0CnTp2wWJS/RUQkOOknlLSY0qL9/HX5Fp5+dw3Z2dmBLkdEROSY1EMkfuFyucjOziYnJ8fnhr0xiR2Ijo4JYGUiIiInpkAkfpGdnc3T766hrGg/cWkZJAa6IBERkSZQIBK/iU1KwTxxMxERkaCjOUQiIiIS8hSIREREJOQpEImIiEjIUyASERGRkKdAJCIiIiFPgUhERERCngKR+NW2Mhtf74d6pyvQpYiIiDSa9iGSk3LoDtU19S6+L7MDsGRzHqfbwBrg+kRERBpDgUhOyqE7VFfHZQAGADv3VVAfaedcR2DrExERaQwNmclJi01KITohmaJq9/PoMBMD2FtpI78qoKWJiIg0igKR+E1hrfvXU2Kgd5r7hq7FNQEsSEREpJE0ZCZ+YZpQ1BB+EhxQG+GeS1ReH8CiREREGkmBSPyiymlQ4zQwMIm3Q01EGAAVdQEuTEREpBE0ZCZ+UVTn/laKDTOxWiDOE4jUQyQiIm2AApH4RVGt+1spIcwJQFykOxBVOw3qXWbA6hIREWkMBSLxi+KGHqIEu3tDxnCbhTDDHYTKarVJo4iIBDcFIjlpLtP0BqL4MHf4MQyDSJv792W16iESEZHgpkAkJ62k2oXTNLAZJjG2g+Enyur+fXmNeohERCS4KRDJSStt6AGKsYNhHDweZfMMmamHSEREgpsCkZy0ijp3D1DkYZs4RFk9Q2bqIRIRkeCmQCQnraKhByjysDu5HuwhUiASEZHgpkAkJ83TQxRxeA9RQyAqrzW19F5ERIKaApGctMq6hh6iwwJRuMXEYpiYQEFZbesXJiIi0kgBDUQrV67kyiuvJD09HcMweO+997zn6urquP/++znjjDOIiooiPT2dm2++mZycHJ/3KCwsZNy4ccTGxhIfH8+ECRMoLy/3abNp0yaGDBlCeHg4GRkZzJo1qzUuL2RUNASiw3uIDAOiGo5llyoQiYhI8ApoIKqoqOCss85i/vz5R5yrrKzk66+/5qGHHuLrr7/mnXfeYdu2bVx11VU+7caNG8fmzZtZunQpH374IStXrmTixIne86WlpVx22WV07dqV9evX89RTT/HII4/wwgsvtPj1hYJap4uq+qP3EMEhgahEgUhERIJXQG/uOmrUKEaNGnXUc3FxcSxdutTn2J/+9CfOO+88MjMz6dKlC1u3bmXJkiWsW7eOAQMGAPDcc88xevRoZs+eTXp6OosXL6a2tpaXX34Zu91O37592bhxI3PmzPEJTtI8+8vdd2+1YGK3wOHTp6PDgCrILqlp9dpEREQaq03NISopKcEwDOLj4wFYvXo18fHx3jAEMGLECCwWC2vXrvW2GTp0KHa73dtm5MiRbNu2jaKiolatvz3KbwhEEVbTZw8iD0+v0Z59pWRlZeFyacWZiIgEnzYTiKqrq7n//vu54YYbiI2NBSAvL4+UlBSfdjabjcTERPLy8rxtUlNTfdp4nnvaHK6mpobS0lKfhxxdftnBQHQ0joal+Nvyynn63TVkZ2e3VmkiIiKN1iYCUV1dHT//+c8xTZPnn3++xT9v5syZxMXFeR8ZGRkt/pltVUG5e25Q5AkCkdMSRmxSylHbiIiIBFrQByJPGNq7dy9Lly719g4BpKWlUVBQ4NO+vr6ewsJC0tLSvG3y8/N92niee9ocbtq0aZSUlHgfWVlZ/rykdqWxPUQ1ztaqSEREpOmCOhB5wtD27dv5z3/+Q1JSks/5QYMGUVxczPr1673Hli9fjsvlYuDAgd42K1eupK6uzttm6dKl9O7dm4SEhKN+rsPhIDY21uchR3dwDtHR5wbZG77DalxgmtqcUUREglNAA1F5eTkbN25k48aNAOzevZuNGzeSmZlJXV0dP/vZz/jqq69YvHgxTqeTvLw88vLyqK11D9P06dOHyy+/nNtvv50vv/ySzz//nMmTJzN27FjS09MBuPHGG7Hb7UyYMIHNmzfz5ptv8uyzzzJ16tRAXXa74hkyO1EPERjqJRIRkaAV0GX3X331FRdffLH3uSekjB8/nkceeYQPPvgAgLPPPtvndZ9++inDhg0DYPHixUyePJnhw4djsVi47rrrmDdvnrdtXFwcn3zyCZMmTaJ///4kJyfz8MMPa8m9H5imSZ5nyMx29EBkMSDMMKkzDarr1UMkIiLBKaCBaNiwYccdRmnMEEtiYiKvvfbacduceeaZrFq1qsn1yfGVVtdT5bmPmeXYXyu7xaTOqUAkIiLBK6jnEElwyy2pAtzDYrbjfCc5GobTapwKRCIiEpwUiKTZcordgSgq7PjfRo6G3iP1EImISLBSIJJmyy6uBiDKfpQtqg9hVyASEZEgp0AkzZbb0EMUecIeIvevCkQiIhKsFIik2fJL3TdsjbQ1sodIc4hERCRIKRBJsxWUuYfMIsOOH4g0h0hERIKdApE0W0FDD1HECXqIvKvMFIhERCRIKRBJs+U3sodIQ2YiIhLsFIikWarrnBRXNuxS3chl9zX1Ji7dz0xERIKQApE0y74y93CZ3Wp4b+B6LJ7zJlBarRuaiYhI8FEgkmbxTKhOigrDMI4/ZGYxIKyhl6i4qr7FaxMREWkqBSJpFs+S++Soxt0Oz3PX+yIFIhERCUIKRNIs+aXuHqLkyLBGtfdszqgeIhERCUYKRNIsB3uIGhmIGnqIijWHSEREgpACkTTLwTlEjRsys3sCUaV6iEREJPgoEEmTuVwuMgtKALDWlmM2Yin9wR4iBSIREQk+CkTSZNnZ2WzLKwXgi293UlFRfsLXaA6RiIgEMwUiaZYal/tbJyEutlHttcpMRESCmQKRNFlNvYtal/v34dbG7TztHTJTIBIRkSCkQCRNdqDCfcsOm8XgBPd19ToYiLTKTEREgo8CkTTZ/gp3L0+Uw8YJNqn28ty+o7SmvlGTsEVERFqTApE02f6GHqIoT7dPI3gCkdMFFbXqJRIRkeCiQCRNtr/hLvfR9sbtQQRgtYC1oTepuLK2JcoSERFpNgUiabIDDUNmkY7GByIAR0MiKm4IVCIiIsFCgUiazDNkFt3EQGS3KRCJiEhwUiCSJjs4qbrxc4jg0JVmGjITEZHgokAkTbavwh1omtpD5BkyK1IPkYiIBBkFImkS0zQpKG/mkFlDICrRpGoREQkyCkTSJMWVddTUu/cRam4PkeYQiYhIsGnaTzRpd1wuF9nZ2QB06tQJi+X4GTm3pBqAcKuBzdq0PO3wTKquUiASEZHgokAUojxBKCcnh9fW7sXA4O6fnk9GRsZxX5dbUgVAZFgjt6g+xMEeIg2ZiYhIcFEgClHZ2dk8/e4ayor2E5eWQXR0TKNel9PQQxRlb3ogsmvITEREgpQCUQiLTUqhqXcVyy329BA1ffrZwWX3CkQiIhJcNKlamsQzhyhKQ2YiItKOKBBJk+QUn8QcokN2qtYd70VEJJgoEEmT5JV6eoia/q3jmUNU7zJ1x3sREQkqCkTSaKZpntSQmc1iHDKxWsNmIiISPBSIpNEOVNRSW+/CoHlDZgBx4e55/FppJiIiwSSggWjlypVceeWVpKenYxgG7733ns950zR5+OGH6dixIxEREYwYMYLt27f7tCksLGTcuHHExsYSHx/PhAkTKC8v92mzadMmhgwZQnh4OBkZGcyaNaulL61dyi129w4lRtqwGM0LRDHh7qVmCkQiIhJMAhqIKioqOOuss5g/f/5Rz8+aNYt58+axYMEC1q5dS1RUFCNHjqS6utrbZty4cWzevJmlS5fy4YcfsnLlSiZOnOg9X1paymWXXUbXrl1Zv349Tz31FI888ggvvPBCi19fe5PTsCljSnRYs98jtmHtve54LyIiwSSg+xCNGjWKUaNGHfWcaZrMnTuXBx98kKuvvhqAV199ldTUVN577z3Gjh3L1q1bWbJkCevWrWPAgAEAPPfcc4wePZrZs2eTnp7O4sWLqa2t5eWXX8Zut9O3b182btzInDlzfIKTnJhnD6KUGDvQvEnRsQ09RLrjvYiIBJOgnUO0e/du8vLyGDFihPdYXFwcAwcOZPXq1QCsXr2a+Ph4bxgCGDFiBBaLhbVr13rbDB06FLvd7m0zcuRItm3bRlFRUStdTfuQ27DCLCXqJHqIGuYQ6Y73IiISTII2EOXl5QGQmprqczw1NdV7Li8vj5SUFJ/zNpuNxMREnzZHe49DP+NwNTU1lJaW+jzk4BwivwyZqYdIRESCSNAGokCaOXMmcXFx3seJbnjaVrhcLrKyssjKyiInJ6fJmyPm+mMOkWdStW7fISIiQSRoA1FaWhoA+fn5Psfz8/O959LS0igoKPA5X19fT2FhoU+bo73HoZ9xuGnTplFSUuJ9ZGVlnfwFBQHPDV1fXLmLF5Z8TUVF+YlfdIgcTw9RTPMCkely4ap2f2ZRhYbMREQkeARtIOrevTtpaWksW7bMe6y0tJS1a9cyaNAgAAYNGkRxcTHr16/3tlm+fDkul4uBAwd626xcuZK6uoM9EkuXLqV3794kJCQc9bMdDgexsbE+j/YiNimFhJSORCckN+l1LpdJfunJDZmVFu1nzbZsAApKmhbGREREWlJAA1F5eTkbN25k48aNgHsi9caNG8nMzMQwDKZMmcLjjz/OBx98wLfffsvNN99Meno611xzDQB9+vTh8ssv5/bbb+fLL7/k888/Z/LkyYwdO5b09HQAbrzxRux2OxMmTGDz5s28+eabPPvss0ydOjVAV902ZRdXUe8ysVstJEU2f8gsriFcllbr1h0iIhI8Arrs/quvvuLiiy/2PveElPHjx7No0SLuu+8+KioqmDhxIsXFxQwePJglS5YQHh7ufc3ixYuZPHkyw4cPx2KxcN111zFv3jzv+bi4OD755BMmTZpE//79SU5O5uGHH9aS+ybasc/do9M9OQqrpXmbMgLY3VOIFIhERCSoBDQQDRs27LgTew3DYMaMGcyYMeOYbRITE3nttdeO+zlnnnkmq1atanadAjsL3IGoR0rUSb2PvaFPsrSmHtM0MZq547WIiIg/Be0cIgkuOxt6iDo4XM1aoebhCUROF7rjvYiIBI2A9hBJ27GjoYdo844sftycT1xaBonNeB+rBawGOE33He+jHfoWFBGRwFMPkTTKzn0VAKQmxzd5hdrhHFb3MFlRhfYiEhGR4KBAJCdUWFFLYcO+QXGOk/+WcdgaApFu3yEiIkFCgUhOyDNclhYThu0kVph5eHuIFIhERCRIKBDJCXkmVHdNCD9By8ZpuJ2Z7mcmIiJBQ4FITsjTQ9Q1weGX99OQmYiIBBsFohDmMmFvhZXyE3TU+D0QeSdVKxCJiEhwUCAKYVnlsLHEwac5UFBx7D2B/D5k5u0h0pCZiIgEBwWiEFZQ5f61zmWwdHcVa/eWHdGmqtZJdrG7od97iDRkJiIiQUKBKESZJuxz37yemDATpwkP/3sv5TX1Pu127ivHNCEu3EplUUGzd6g+lCcQaVK1iIgECwWiEFXhNKh2GlgwGZYOsXaDyjoXH2zM8Wn31Z5CAGyuWl5Y8jUVFeUn/dmeIbNCzSESEZEgoUAUog7UuL/08XYXYRbomRgGwBvrMn3affCNOyB1T4o46R2qPQ72ECkQiYhIcFAgClH7a92bASXbXQD0SHBvurjpxxI255QAkFVYydeZxRhAtzj/3XPM00NUUeuktt7lt/cVERFpLgWiEGSaJvsbeoiSHe7VZeE2gyHdYwF4c10WAP/c5O4dOqdTFJFh/vtWsVvAs+G1eolERCQYKBCFoNLqeqpdFgxMEsIO9tBc2dd9//p3N2Szr6zGO59oeM94v36+YRjEhrt7qLT0XkREgoECUQj6sagSgEQH2A75DujfOZpuSZGUVddzydMr+D6vjDCrwbAecX6vIS7cPQSnidUiIhIMFIhCUE6xe7198mH7LFoMgxduHsDpHWMpq3Yvv/9JRjTlhf5Zbn8oTw+RhsxERCQYKBCFoNJq9zBVTNiR53qlxvD+5Au5d2Rv+qREEFa+z2/L7Q8V53D3EGnITEREgoECUQiqaNh8MfwYC8fCrBYmXXwqL/xPT7p2TPbbcvtDxUV45hCph0hERAJPgSgEVdS4V5Y1ZJKAiG1IY7rBq4iIBAMFohBT5zSpdbpXlh2rh6g1xGmVmYiIBJFmBaJdu3b5uw5pJZV17snRNsPEj1sLNZkmVYuISDBp1o/EU089lYsvvpi///3vVFdX+7smaUGVDTtDh1v9u2qsqTzL7jWHSEREgkGzAtHXX3/NmWeeydSpU0lLS+OXv/wlX375pb9rkxbg6SEKtwRLINKQmYiIBF6zAtHZZ5/Ns88+S05ODi+//DK5ubkMHjyYfv36MWfOHPbt2+fvOsVPPIEoIsA9RAd3qlYPkYiIBN5JzSKx2Wxce+21vPXWWzz55JPs2LGDe+65h4yMDG6++WZyc3P9Vaf4SWV9Qw9RwIfM3IGopKoOpyuwtYiIiJxUIPrqq6/4zW9+Q8eOHZkzZw733HMPO3fuZOnSpeTk5HD11Vf7q07xk6q64JhDFNuwMaNpQmmVhs1ERCSwmrXwes6cOSxcuJBt27YxevRoXn31VUaPHo3F4s5X3bt3Z9GiRXTr1s2ftYofHGsOkelykZPjvplrp06dvF/LlmKzGsSE2yirrqewspaEKHuLfp6IiMjxNCsQPf/88/zv//4vt9xyCx07djxqm5SUFF566aWTKk78zzNkdvgcotKi/fx1eTlRUZnc/dPzycjIaPFaEiLtlFXXa+m9iIgEXLMC0fbt20/Yxm63M378+Oa8vbQQp8ukqu7Yc4hiEjsQHR3TavUkRIaRWQhFFRoyExGRwGrWuMjChQt56623jjj+1ltv8corr5x0UaHO5XKRlZVFVlYWLpfLb+9bVFWPCRiAI8DL7gHiI93DZFppJiIigdasQDRz5kySk4+84WdKSgpPPPHESRcV6rKzs3n63TU8/e4asrOz/fa++xt6YiIdViyG39622RIb5g0V6n5mIiISYM0aMsvMzKR79+5HHO/atSuZmZknXZRAbFKK399zX7k7EEXZA3gTs0MkR7sD0f7ymgBXIiIioa5ZPUQpKSls2rTpiOPffPMNSUlJJ12UtIz9lfUARDsCG4g8K9ps9VUA7CtTIBIRkcBq1k/GG264gTvvvJOYmBiGDh0KwGeffcZvf/tbxo4d69cCxX/2N/QQRTtsEMApRJ4VbZUx7pVs2QdKA1eMiIgIzQxEjz32GHv27GH48OHYbO63cLlc3HzzzZpDFMT2Ncwhigq3QVVga4lJ7EBUXBL8mENhQ8+ViIhIoDQrENntdt58800ee+wxvvnmGyIiIjjjjDPo2rWrv+sTP/JMqo52BD4QAUTaPTd4VSASEZHAOqnJJL169aJXr17+qkVa2L5Dhsz8t5i/+SLt7vuZFVfX43SZWINh6ZuIiISkZk2qdjqdvPTSS9x4442MGDGCSy65xOfhL06nk4ceeoju3bsTERFBjx49eOyxxzDNgxNgTNPk4YcfpmPHjkRERDBixIgjNo4sLCxk3LhxxMbGEh8fz4QJEygvL/dbnW2FZ2gqqiGIBFpEmLsOl6m9iEREJLCa1UP029/+lkWLFjFmzBj69euHYbTM/+yffPJJnn/+eV555RX69u3LV199xa233kpcXBx33nknALNmzWLevHm88sordO/enYceeoiRI0eyZcsWwsPDARg3bhy5ubksXbqUuro6br31ViZOnMhrr73WInUHo3qni9IaJwARditlAa4HwGIxcFihxuleep8c7Qh0SSIiEqKaFYjeeOMN/vGPfzB69Gh/1+Pjiy++4Oqrr2bMmDEAdOvWjddff50vv/wScPcOzZ07lwcffJCrr74agFdffZXU1FTee+89xo4dy9atW1myZAnr1q1jwIABADz33HOMHj2a2bNnk56e3qLXEAxcLhdbduz1Pg+3BUcPEUCEzUKN08X+slpIC3Q1IiISqpo1ZGa32zn11FP9XcsRLrjgApYtW8YPP/wAuPc5+u9//8uoUaMA2L17N3l5eYwYMcL7mri4OAYOHMjq1asBWL16NfHx8d4wBDBixAgsFgtr16496ufW1NRQWlrq82jLsrOzmfvhegDsFhNLEM3VCbe5a9HmjCIiEkjNCkR33303zz77rM9cnpbwwAMPMHbsWE477TTCwsI455xzmDJlCuPGjQMgLy8PgNTUVJ/Xpaames/l5eWRkuK767PNZiMxMdHb5nAzZ84kLi7O+2iNO7+3NFt0IgBBMn3IS4FIRESCQbOGzP773//y6aef8q9//Yu+ffsSFhbmc/6dd97xS3H/+Mc/WLx4Ma+99hp9+/Zl48aNTJkyhfT0dMaPH++XzziaadOmMXXqVO/z0tLSNh+Kapzu8OpoVgRuORENgWifApGIiARQswJRfHw8P/3pT/1dyxHuvfdeby8RwBlnnMHevXuZOXMm48ePJy3NPekkPz+fjh07el+Xn5/P2WefDUBaWhoFBQU+71tfX09hYaH39YdzOBw4HO1rgm91vTsQnaiHyHNbDYCcnJwW7wX09hCVaZWZiIgETrMC0cKFC/1dx1FVVlZisfh2aVitVlwu9y463bt3Jy0tjWXLlnkDUGlpKWvXruXXv/41AIMGDaK4uJj169fTv39/AJYvX47L5WLgwIGtch3BoLE9RJ7banTsUk32ji3EpbVsz1iEhsxERCQINHtjxvr6elasWMHOnTu58cYbiYmJIScnh9jYWKKjo/1S3JVXXskf/vAHunTpQt++fdmwYQNz5szhf//3fwEwDIMpU6bw+OOP07NnT++y+/T0dK655hoA+vTpw+WXX87tt9/OggULqKurY/LkyYwdOzYkVph51DSyhwjct9VISOlIyYGCEzc+SZpDJCIiwaBZgWjv3r1cfvnlZGZmUlNTw6WXXkpMTAxPPvkkNTU1LFiwwC/FPffcczz00EP85je/oaCggPT0dH75y1/y8MMPe9vcd999VFRUMHHiRIqLixk8eDBLlizx7kEEsHjxYiZPnszw4cOxWCxcd911zJs3zy81thWeITNHkE2qjghTIBIRkcBr9saMAwYM4JtvviEpKcl7/Kc//Sm3336734qLiYlh7ty5zJ0795htDMNgxowZzJgx45htEhMTQ2oTxqOpbhgyswfZpGpPD9GB8lpcruDaEkBEREJHswLRqlWr+OKLL7Db7T7Hu3XrRnZ2tl8KE/+qCdIeonCrOwDVu0yKq+pIjLKf4BUiIiL+16z+ApfLhdPpPOL4jz/+SExMzEkXJf7n7SEKskBktRjENKQ0DZuJiEigNCsQXXbZZT7DWIZhUF5ezvTp01v8dh7SdKZpHuwhCrIhM4DESHdH5f4yBSIREQmMZg2ZPf3004wcOZLTTz+d6upqbrzxRrZv305ycjKvv/66v2uUk1Rdb9LQQRR0Q2YACRE29hbVaHNGEREJmGYFos6dO/PNN9/wxhtvsGnTJsrLy5kwYQLjxo0jIiLC3zXKSSqpqgfAgok1COcse3uIyrU5o4iIBEaz9yGy2Wz84he/8Gct0kKKq92ByG4xMYzgSkSmy4Xd5a5vX1l1gKsREZFQ1axA9Oqrrx73/M0339ysYqRllFS5J8AH4/yh0qL9bD/gAqLJzC8KdDkiIhKimr0P0aHq6uqorKzEbrcTGRmpQBRkijw9RFYTCK4eIoC4mCiogcLK+kCXIiIiIapZfQZFRUU+j/LycrZt28bgwYM1qToIeeYQ2S0te6PW5gpvmOhdUFEX2EJERCRk+W0QpWfPnvzxj388ovdIAq+k2j1kFqyBqGFONQXlCkQiIhIYfp1VYrPZyMnJ8edbih8UN/QQOYI0EEU0BKLyGicVNRo2ExGR1tesOUQffPCBz3PTNMnNzeVPf/oTF154oV8KE/8p8a4yC3AhxxBmcT/qXJBbUsWpKdrtXEREWlezAtE111zj89wwDDp06MAll1zC008/7Y+6xI+CvYcIICrMQnGNi5ziagUiERFpdc0KRC6Xy991SAsK9jlEAJFhBsU1kFeivYhERKT1BekgivhTcZCvMgN3IALIKakKcCUiIhKKmtVDNHXq1Ea3nTNnTnM+QvzE6TIpbQM9RFFh7myeW6weIhERaX3NCkQbNmxgw4YN1NXV0bt3bwB++OEHrFYr5557rrddsN0mIhSVVNXhiUHBOqkaIEo9RCIiEkDNCkRXXnklMTExvPLKKyQkJADuzRpvvfVWhgwZwt133+3XIqX5Civcd5C3W8ASxPk00u4uLldziEREJACa1Wfw9NNPM3PmTG8YAkhISODxxx/XKrMgc6DhDvLhtiBOQxwcMtOkahERCYRmBaLS0lL27dt3xPF9+/ZRVlZ20kWJ/xyoaCuByF1feU09pdXasVpERFpXswLRT3/6U2699VbeeecdfvzxR3788Uf+7//+jwkTJnDttdf6u0Y5CQfK3UNmwR6IbBaDWIf7pmaaWC0iIq2tWXOIFixYwD333MONN95IXZ37f/M2m40JEybw1FNP+bVAOTltpYcIoEN0GKU1TnJKquidps0ZRUSk9TQrEEVGRvLnP/+Zp556ip07dwLQo0cPoqKi/FqcnLy2MocIICU6jJ0HqtVDJCIire6kFmLn5uaSm5tLz549iYqKwjSDd5+bUHWgYZWZw9o2AhG472cmIiLSmpoViA4cOMDw4cPp1asXo0ePJjc3F4AJEyZoyX2QaVs9RHZAS+9FRKT1NSsQ3XXXXYSFhZGZmUlkZKT3+PXXX8+SJUv8VpycPM8cooi2EIhi1EMkIiKB0aw5RJ988gn//ve/6dy5s8/xnj17snfvXr8UJv7hWWXmsBlQE+BiTiDVM2SmOUQiItLKmtVDVFFR4dMz5FFYWIjD4TjposQ/6p0uiqvcqwDbxpCZOxDllFRpPpqIiLSqZgWiIUOG8Oqrr3qfG4aBy+Vi1qxZXHzxxX4rTk5OUWUdpgkGbWdStcWA6joX+8qCvDtLRETalWYNmc2aNYvhw4fz1VdfUVtby3333cfmzZspLCzk888/93eN0kyeFWax4VYsbeBGu2FWC+nxEfxYVMXewkpSYsMDXZKIiISIZvUQ9evXjx9++IHBgwdz9dVXU1FRwbXXXsuGDRvo0aOHv2uUZipsWGGWENGs3BsQXZPcQ7F7D1QGuBIREQklTf5JWVdXx+WXX86CBQv4/e9/3xI1iZ/sb1hhlhDZdgJRl8RIPucA3+7OZWCKSadOnbBYTmq7LBERkRNq8k+asLAwNm3a1BK1iJ95VpjFh7edQBRvdU8C/2x7IU+/u4bs7OwAVyQiIqGgWf/1/sUvfsFLL73k71rEzwrbYA9Rpzj3KsUq00psUkqAqxERkVDRrJ+U9fX1vPzyy/znP/+hf//+R9zDbM6cOX4pLtSZLhc5OTkAzRo62t8whyg+3EZ9ddtYtdUpzr1bdUlVHRAW2GJERCRkNCkQ7dq1i27duvHdd99x7rnnAvDDDz/4tDHawGqmtqK0aD9/XV5OVFQmd//0fDIyMpr0eu+QWaSN/UG+16En/Bl1LsC99L7Wqb2IRESkdTQpEPXs2ZPc3Fw+/fRTwH2rjnnz5pGamtoixQnEJHYgOjqmWa/1DplF2Njvz6JagCf8uWqrcVgyqHEZlNW6Al2WiIiEiCYFosN3D/7Xv/5FRUWFXwsS//Hcxyw+whrgShonJrEDzpoqog5ATQ2U1aqHSEREWsdJrWdujdsrZGdn84tf/IKkpCQiIiI444wz+Oqrr3xqePjhh+nYsSMRERGMGDGC7du3+7xHYWEh48aNIzY2lvj4eCZMmEB5eXmL1x5o+xuGzNrSPkQADXfwoKxGPUQiItI6mhSIDMM4Yo5QS84ZKioq4sILLyQsLIx//etfbNmyhaeffpqEhARvm1mzZjFv3jwWLFjA2rVriYqKYuTIkVRXH5w0M27cODZv3szSpUv58MMPWblyJRMnTmyxuoNBbb2Lsup6oO0FoqiGcjVkJiIiraXJQ2a33HKL9wau1dXV/OpXvzpildk777zjl+KefPJJMjIyWLhwofdY9+7dfeqZO3cuDz74IFdffTUAr776Kqmpqbz33nuMHTuWrVu3smTJEtatW8eAAQMAeO655xg9ejSzZ88mPT3dL7UGG8/8IavFINrRNobMPKIaeojKNWQmIiKtpEk9ROPHjyclJYW4uDji4uL4xS9+QXp6uve55+EvH3zwAQMGDOB//ud/SElJ4ZxzzuHFF1/0nt+9ezd5eXmMGDHCeywuLo6BAweyevVqAFavXk18fLw3DAGMGDECi8XC2rVrj/q5NTU1lJaW+jzaGs9wWWKUvU3cx+xQnh6iUvUQiYhIK2lSD9GhPTWtYdeuXTz//PNMnTqV3/3ud6xbt44777wTu93O+PHjycvLAzhilVtqaqr3XF5eHikpvhv82Ww2EhMTvW0ON3PmTB599NEWuKLW4+khSoqyB7iSpvP0EFXWmdQ6FYpERKTlBfVNolwuF+eeey5PPPEE55xzDhMnTuT2229nwYIFLfq506ZNo6SkxPvIyspq0c9rCZ473SdFt71A5LBAmNXdq5VbWhvgakREJBQEdSDq2LEjp59+us+xPn36kJmZCUBaWhoA+fn5Pm3y8/O959LS0igoKPA5X19fT2FhobfN4RwOB7GxsT6PtmZfWUMginIEuJKmMwyIj3QHuazitrHDtoiItG1BHYguvPBCtm3b5nPshx9+oGvXroB7gnVaWhrLli3zni8tLWXt2rUMGjQIgEGDBlFcXMz69eu9bZYvX47L5WLgwIGtcBWBkVPsXmXXMT48wJU0T2JDINpTqEAkIiItL6jXY991111ccMEFPPHEE/z85z/nyy+/5IUXXuCFF14A3Ev+p0yZwuOPP07Pnj3p3r07Dz30EOnp6VxzzTWAu0fp8ssv9w611dXVMXnyZMaOHdtuV5gB5BRXAdApPiLAlTRPYsPcp71FCkQiItLygjoQ/eQnP+Hdd99l2rRpzJgxg+7duzN37lzGjRvnbXPfffdRUVHBxIkTKS4uZvDgwSxZsoTw8IM9I4sXL2by5MkMHz4ci8XCddddx7x58wJxSa0mp8QdiNLjIoC2Nw/nYCAK8puwiYhIuxDUgQjgiiuu4IorrjjmecMwmDFjBjNmzDhmm8TERF577bWWKC9o5TYMmaXHR4Cz7QaiPUU1mKapmwaLiEiLCuo5RNI81XVO733M2uqQWVxEGAZQVecit0S9RCIi0rIUiNohz/yhKLuV2DZ22w4Pq8Ug1uHuFdpR0P7vOyciIoGlQNQO5RwyXNaWh5riHO5vz+0KRCIi0sIUiNohTw9RehsdLvPwBCL1EImISEtTIGqHsttLIAr3BKKyAFciIiLtnQJRO3RwD6K2uSmjh3qIRESktSgQtUPePYjaeA9RbBgYQFFlHftKqwJdjoiItGMKRO3QoZOq27LKkgOEG04AVm/ZE9hiRESkXWuba7JDlMvlIjs72/u8U6dOWCy+mdY0Te8cora6B9GhYsMtVFW5N2gUERFpKQpEQc40YW1ODUWf/sg9w0yeeW8tsUkplB4o4O6fnk9GRoZP+wMVtdTWuzAMSI1t23OIAOLskF8Fuw5oc0YREWk5CkRBrrQOth2oY9uBQs7rEkNsUgoJKR2P2d4zoTolxoHd1vZHROPdd/Dgh32aQyQiIi2n7f/EbOeyKw7+ftG6fEzTPG779rIHkUe8w/3rjv1V1DtdgS1GRETaLQWiIJdbefD3Ow9Uk1XqPG777HYyodojygZhFqh1muzcV3HiF4iIiDSDAlEQq6g3KKk1MICr+yYCsKmg9ri9RNlF7gQVY6kjKysLl6tt96oYBiRGuL9Nv8suCXA1IiLSXikQBbHcaisAqVFWbhuYRoTNQmG1i6yiY8+n2Zlb6P41v5Sn313jsyqtrUoMd/85fJejQCQiIi1DgSiI5Va5g0CXWCvxETZG9IoHYNe+Y+/c7Jl83DmtA7FJKS1eY2vw9BBtzi4NcCUiItJeKRAFqao6F4V17i9PRpx7MeCgrjEA7DlQedRhs4KyanLL6gBIjXVgulzk5OSQk5NzwsnYwSzJE4hySnC52u51iIhI8NKy+yC1r9IFGMTZTaLC3IGgf+doLAaUVNVRWhN2xGs2ZBYDEB9uwWGzklu0n78uL8dVW01cWsYR7duKWIcFh82gotbJngMVnNIhOtAliYhIO6MeoiBVWe+eDB11SGSNtFtJjXIPo2WX1R/xGk8g6hBx8Msak9iB6ITkliu0FVgMg1OT3KvmvsvRsJmIiPifAlGQqqpzDw01zCf26hzjPvBj2ZHL7zdkFgGQHGk94lxb16uDOxBt1kozERFpAQpEQaqq/uiBqFOMu8sov8JJRe3BUFTvdLHpR3dY6NAOA1HvhkD0zY/FgS1ERETaJQWiIOXtITpsllesw0J8RBgmsC7r4Gqz7/PKqKpzEm23EOcwWrHS1tE3LRJwDwvW1B9/c0oREZGmUiAKUpXH6CEC6JYcBcC/txV5j23IKgagT2okhtH+AlHXBAfJ0Q5q6l1sbJgrJSIi4i8KREHqWENmAGd0igPgv7tL2dIwyXjDXnc46psa2ToFtiLT5SI3N5cz08IBWL3rQIArEhGR9kaBKAjVu0yqjxOIEqPsdGvYm2jesu2UVdexdrd7h+p+aVGtVmdrKS3az1+Xb+HAvn0ArFEgEhERP9M+REGouMqzpN7EcYz50WemhLG3pJ4lm/P4oaCM7OIqYsNt9E2LZNPeViu11cQkdiAqLIpvSir5OrOY6jon4WHtb/K4iIgEhnqIgtCBCvdu0w6LybGmA8WHWxnWwz10tmtfBR1iHCy+7Xyij5Wg2oEYu0FSpI3aehdfZxad+AUiIiKNpEAUhPZXunuIjjZcdqhbz0slNtzGaWkxvPubCzijc1wrVBc4hmFwbif3LtVrdhUGuBoREWlPNGQWhDw9ROEWEzj2irHuieF8MW04UXZru1xZdjRnd4pi6fZi1uw8AJcGuhoREWkvFIiC0AFvD5F7YrXnJq3AETdpjXaE1pfQ00O0IauI8pr6kLt+ERFpGRoyC0KHziGCg6usXljyNRUV5cd7abvXKc7OKclR1DlNlm3ND3Q5IiLSTigQBaHDe4igfdyk1R8Mw2D0GR0B+GhTboCrERGR9kKBKAj5ziGSw3kC0Yof9lFeU3+C1iIiIiemQBSEPD1EDqsC0dH0To0iI95Obb2Lf/x3Cy6XK9AliYhIG6dAFGRM06TQM2SmHqKjysnJIaK+AoAXV+0mOzs7wBWJiEhbp0AUZIoq66h3uYNQU3qIXC4XWVlZ5OTkHLESrT3qlRYLQH6NjYpaZ4CrERGRtk5rloNMQVk1AHYrWJuwtVB2djZPv7uGsqL9xKVlkNhC9QXSodsPxDsM4iPDKK6sY8XOEk7rEeDiRESkTVMPUZApKK0BINLWuC/NoT1D7X0l2qHbD1RWVnB6R3cv0YdbtGu1iIicnDYViP74xz9iGAZTpkzxHquurmbSpEkkJSURHR3NddddR36+7/40mZmZjBkzhsjISFJSUrj33nuprw/O1UkFZe5AFBHWuO4hT89QqOxRdGjoO71jLAbwXV4lW3NKyMrKIisrS5OsRUSkydrMkNm6dev4y1/+wplnnulz/K677uKjjz7irbfeIi4ujsmTJ3Pttdfy+eefA+B0OhkzZgxpaWl88cUX5ObmcvPNNxMWFsYTTzwRiEs5Ls+QWYSt8eNlsUkptP9ZQ0eKctjIiLWSWerkpRVbqd+fBcDdPz2fjIyMAFcnIiJtSZvoISovL2fcuHG8+OKLJCQkeI+XlJTw0ksvMWfOHC655BL69+/PwoUL+eKLL1izZg0An3zyCVu2bOHvf/87Z599NqNGjeKxxx5j/vz51NbWBuqSjml/mbumpgSiUNYzMQyAJduKiUzoQGxSSoArEhGRtqhNBKJJkyYxZswYRowY4XN8/fr11NXV+Rw/7bTT6NKlC6tXrwZg9erVnHHGGaSmpnrbjBw5ktLSUjZv3nzUz6upqaG0tNTn0VqKK92ByKFA1ChpkQYdIi2U1zjZW1wX6HJERKSNCvohszfeeIOvv/6adevWHXEuLy8Pu91OfHy8z/HU1FTy8vK8bQ4NQ57znnNHM3PmTB599FE/VN90JVXuH+p2q8HxxsGOd8PXUFJefIBYp4t9RPP9/hp6JNoDXZKIiLRBQd1DlJWVxW9/+1sWL15MeHh4q33utGnTKCkp8T6ysrJa7bM9gchxgjX3uuHrQb1SogCTAzUGxdWaUC0iIk0X1IFo/fr1FBQUcO6552Kz2bDZbHz22WfMmzcPm81GamoqtbW1FBcX+7wuPz+ftLQ0ANLS0o5YdeZ57mlzOIfDQWxsrM+jtRR7e4hO3La9L7NvrAgbpIW7N2fcXqhhMxERabqgDkTDhw/n22+/ZePGjd7HgAEDGDdunPf3YWFhLFu2zPuabdu2kZmZyaBBgwAYNGgQ3377LQUFBd42S5cuJTY2ltNPP73Vr+lEfIbMpNG6Rrq3UdhZXEdNvXqJRESkaYJ6DlFMTAz9+vXzORYVFUVSUpL3+IQJE5g6dSqJiYnExsZyxx13MGjQIM4//3wALrvsMk4//XRuuukmZs2aRV5eHg8++CCTJk3C4XC0+jUdj2maPkNmuiFF46U6XERYTaqcBqt2lXBq90BXJCIibUlQ9xA1xjPPPMMVV1zBddddx9ChQ0lLS+Odd97xnrdarXz44YdYrVYGDRrEL37xC26++WZmzJgRwKqPrrrORW1D74Z6iJrGMKBrjPv3H2jnahERaaKg7iE6mhUrVvg8Dw8PZ/78+cyfP/+Yr+natSsff/xxC1d28jy9Q1YDwixQFeB62ppuMbCtGDZkV7BrXzmndIgOdEkiItJGtPkeovbEE4iiHVYMQz1ETRVpg/QY92z0N9a13spAERFp+xSIgohnU8YYRyOWmMlR9WrYufrt9T9SU69ZWCIi0jgKREHE00MUG97mRjKDRqcYK8lRNgoralm6Jf/ELxAREUGBKKh49iBSD1HzWQyDMX0SAXj9y8wAVyMiIm2FAlEQKVUg8osxfRIxDPh8xwH27K8IdDkiItIGKBAFkYNDZgpEzWW6XJjl+zkvw70GX5OrRUSkMRSIgkhxpXqITpbnHm9m2X4A3l6f5d3bSURE5FgUiIJIiYbM/CImsQO9OiWSGGFjf3kty7ZqcrWIiByfAlEQUSDyH4thMLpPAgCvaXK1iIicgAJRENEqM/+68nT3arNV2/eTeaCyUa9xuVxkZWWRlZWFy6WhNhGRUKFAFERKtQ+RX6XHORjSMxmA2R9906iQk52dzdPvruHpd9eQnZ3dGmWKiEgQUCAKIhoy8787h/cE4J9bDvDoW2sbFXJik1KITUpp6dJERCSIKBAFCdM0tey+BfykWyLnd43BxGBHTUygyxERkSClQBQkymvqcbpMQD1E/nb7wDQA9pTUs2N/VYCrERGRYKRAFCQ8exDZbRYcNn1ZTpbpcpGTk0NWVhbRdUV0jXOHzDkrs6l3arK0iIj40uzdIFFcWQNAjN1CTk4OpmkGuKK2zb1BYzkdu1STvWMLpyVlkFNm8G1uJc+v2MkdDXOLREREQD1EQWNnVh4A9U4XLyz5moqK8gBX1PbFJHYgIaUj0QnJRIXBeekOAOYu287GrOLAFiciIkFFgShIlFbXAxAZbic6ITnA1bRP3WMtDOpkx+ky+fXf1+vGryIi4qVAFCTKapwAhIdpQnVLKSs+gKMsm2ibi9ySan7+l9XsKCgLdFkiIhIEFIiChDcQaUJ1i0pMSmbUqdGckhROQVkNP1uwmv9b/6PmbImIhDj99A0SnkDkUA9Ri4sIszDvmlM4q3McxZV13P3WN9zw4hqyCht3ew8REWl/FIiCRFm1eohaU1y4jbd+dQH3X34a4WEW1uwqZPSzq/jP9uJAlyYiIgGgn75BQj1ErcezR1F+bjZX9LDz798OYUDXBMpq6nn0k0y+ya8NdIkiItLKtA9RkCj1TqpWRm1ph+5RVLIvjxsHdeOp0Z15e2si81fsZFNBLekxCqYiIqFEP32DhLeHyKYfxK3Bs0cRFgt/Xb6FZ99fy9i+UVzWKx4T+Dyrmuo67WgtIhIqFIiCRFnDPkTqIWp9MYkdiElIJicnh+t7WoiwGZTWmrywNi/QpYmISCvRkFmQKKk+uA9RTYBrCUWeYTRXbTXnJGXwRb7BO5v2c+M5u0iOCqNTp05YLAqrIiLtlf6FDwJ1TheVDcMz2pgxcGISOxCdkExaJCTanThNeOTjnTz97hqys7MDXZ6IiLQgBaIg4LnTPaA73QeJ7pHuIcwdJU6iEzsEuBoREWlp+ukbBIor3cu87VawGEaAqxGAjhFOHBaTihonWaXOQJcjIiItTIEoCBRXuXuIHFaFoWBhNaBbjPv32w7UHb+xiIi0eQpEQaCowt1DpEAUXLrHggHkVTjJLKoOdDkiItKCFIiCgHqIglOkDbomRQLwyQ/FgS1GRERalAJREPDMIXLYFIiCzWlpsQAs/aEY0zQDXI2IiLQUBaIgUNSwysyuHqKgc0qHKGwWyCmt5evM4kCXIyIiLUSBKAh4lt1ryCz4hFktdIl171/6/kbtRSQi0l4pEAUB75CZ9mQMSt3j3YHow0251Dl1fzMRkfZIgSgIeHuINIcoKHWMtpIQYaOwopb/bt8f6HJERKQFBHUgmjlzJj/5yU+IiYkhJSWFa665hm3btvm0qa6uZtKkSSQlJREdHc11111Hfn6+T5vMzEzGjBlDZGQkKSkp3HvvvdTX17fmpRxXUaWW3Qczi2EwvGccAG+tzwpwNSIi0hKCOhB99tlnTJo0iTVr1rB06VLq6uq47LLLqKio8La56667+Oc//8lbb73FZ599Rk5ODtdee633vNPpZMyYMdTW1vLFF1/wyiuvsGjRIh5++OFAXNJRFWtSddAb0ycRgKVb8tlfrtvvioi0N0F9t/slS5b4PF+0aBEpKSmsX7+eoUOHUlJSwksvvcRrr73GJZdcAsDChQvp06cPa9as4fzzz+eTTz5hy5Yt/Oc//yE1NZWzzz6bxx57jPvvv59HHnkEu90eiEvzUVylZffBzHS5iKwtok9KBFsLqvi/9T/yy4t6BLosERHxo6DuITpcSUkJAImJ7v+tr1+/nrq6OkaMGOFtc9ppp9GlSxdWr14NwOrVqznjjDNITU31thk5ciSlpaVs3ry5Fas/uuo6J9UNd7rXkFlwKi3az1+Xb8FRUwzAm+uytCeRiEg702YCkcvlYsqUKVx44YX069cPgLy8POx2O/Hx8T5tU1NTycvL87Y5NAx5znvOHU1NTQ2lpaU+j5biGS6zWiCszXw1Qk9MYgf6dEokIszCrv0VrN1dGOiSRETEj9rMj+BJkybx3Xff8cYbb7T4Z82cOZO4uDjvIyMjo8U+yzOhOtZhw9Cd7oNamNVgRM94AF78dCtZWVlkZWXhcmkpvohIW9cmAtHkyZP58MMP+fTTT+ncubP3eFpaGrW1tRQXF/u0z8/PJy0tzdvm8FVnnueeNoebNm0aJSUl3kdWVsutLPIGonBtQtQWXNMvCYBl24uZs3QHT7+7huxsbdgoItLWBXUgMk2TyZMn8+6777J8+XK6d+/uc75///6EhYWxbNky77Ft27aRmZnJoEGDABg0aBDffvstBQUF3jZLly4lNjaW008//aif63A4iI2N9Xm0lJKGITMForahV4cIzu8SAxj8UG4jNikl0CWJiIgfBPUqs0mTJvHaa6/x/vvvExMT453zExcXR0REBHFxcUyYMIGpU6eSmJhIbGwsd9xxB4MGDeL8888H4LLLLuP000/npptuYtasWeTl5fHggw8yadIkHA5HIC8POHgfs1iHFdBE3bZg/E9SWJNZxtbcUnrHRAa6HBER8YOgDkTPP/88AMOGDfM5vnDhQm655RYAnnnmGSwWC9dddx01NTWMHDmSP//5z962VquVDz/8kF//+tcMGjSIqKgoxo8fz4wZM1rrMo7Ls+Q+NtwG1AW2GDku0+UiJyeHRCAtykJehYvN+/Q1ExFpD4I6EDVmaXN4eDjz589n/vz5x2zTtWtXPv74Y3+W5jeeVWZx4VZw6odrMHMvvy/HVVtNz7gM8ioMthfVkVNaS8tNuxcRkdYQ1HOIQkFRhbuHKMYR1NlUGsQkdiA6IZnkcMhIiMBlwp+/yA10WSIicpIUiAKsuOqQHiJpMwwDhvbqgAF8trOENbsOBLokERE5CQpEAVasZfdtVnK0g54J7p69h97ZSF29M8AViYhIcykQBVixd9m9hszaolPspdhwsX1/NX9asinQ5YiISDMpEAWY77J7aWscVuiX5N5h/C9rctl7oCLAFYmISHMoEAWQaZqUVGnIrK3rHgOpUVaq603u/79NuFzaT0pEpK1RIAqgilondU73D884DZm1WYYBg9LDcFhhza5CFn6+O9AliYhIEykQBZBnyb3dZsFh041d2zKzopBuYWUAPPGvrXy1pzDAFYmISFOoWyKAUmId/HPyYMpr6jGMykCXIyfp9LRoaops7Cmp59eLv+aF604hOSoMgE6dOmGx6P8fIiLBSoEogBw2K2d0jgMgK0uBqK0zDBjUyYFhtbK7sIYJi79jVK9Yyg/kc+OgbqSnpysYiYgEKf3LLOJHYVaDP4zqRrTDSmGdlXX7DEzDwl+Xb+Hpd9eQnZ0d6BJFROQoFIhE/Cwj3sETo7piMeCHgnK2loURk9iB2KSUQJcmIiLHoEAk0gLO6RTNoE4OALaXh7GnLMAFiYjIcSkQibSQHglhnNctEYAN+yG3vD7AFYmIyLEoEIm0oPNPSaRzRD0mBiv2VpNZVB3okkRE5CgUiET8yHS5yMnJIScnB9M0MQyDs+NrSXKY1LngkU8yqa7TTWBFRIKNlt2L+FFp0X7+urwcV201cWkZJAJWA85LgeU5sH1/NTM/3spt57q3W9AyfBGR4KBAJOJnMYkdcNZU+RyLsMGFncNZvreaV1bv5fvva+gY4eTun55PRkZGgCoVEREP/ddUpJV0jrUx9uxkADaUOrDGJAe4IhER8VAgEmlFE89P47SUCGqdsDKrmnqXGeiSREQEBSKRVhVmtfDIZV0Is8C+ShcLv8wPdEkiIoICkUir8axAMyoOMDDdvWnjq+sL+PfmvABXJiIiCkQircS9Am0LLyz5mpSwak5LCgNg6psb+SFfW1mLiASSApFIK4pJ7EB0gnsy9YCOds7tFEVFrZPbXvmKglJt2igiEigKRCIBYjEMHh3Zlc4JEWQWVnLDi2soKFMoEhEJBAUikQCKj7Cx+LaBdIwLZ+e+Cm58cS25JVUnfqGIiPiVApFIgHgmWVsqC3l6TAYdomzsKCjnyuf+y7o9hYEuT0QkpGinapEA8dzmo2OXarJ3bKG3LZIaWwL7y2sZ+8Jq7r/8NCYMPgWrxQh0qSIi7Z56iEQCKCaxAwkpHYlOSCYlOYkhyTWkhlXjdMETH3/PT59bwepvd+ByuQJdqohIu6ZAJBJEbBa4oJODs+JqseJiU24l4xZ/z4x31lNV6wx0eSIi7ZYCkUiQMQzoFlXPiM4GGYkRuDBY9FUBI+Z8xtvrf8Sp232IiPidApFIkIoKg5+e3YmhGXaSIixkF1dxz1vfMGL2Mt7b8CP1Tg2jiYj4iwKRSBAzDIMEZzFnheVxqq0Im+Fid2ENU978houfXsFfV+1if3lNoMsUEWnztMpMpA2IT+pATHQVp9kMsqrD2FVcT1ZhFY9/tJWZ/9rKgM4xXHpGJwafmkxkXQmm6R5Ws1jc/+fp1KmT9/ciInIkBSKRNsRuhW62EpLCy8kxreS6YilxhrE2s4y1md+721hMYqkiwe6kZ6cUbJUHuPfa88nIyAhw9SIiwUuBSKQNik/qQExNFac7bESldmXLnlwsYXY25VZQUw/7iWR/NWzfWUWELYrIz3MYf1EcfTrGBrp0EZGgpEAk0sYlRtnp28HO7UNPoc7p4qmlO9m7r4TC+jCKaq1U1bt4Y+N+3ti4itPSYvjpOZ0Y1juFXqnRGIbvpo8ul4vs7GxAw2wiEloUiETaAc9tQACSIyzYo+uxOsJI73YKX36zmT2lJvvrHXyfV8bMf33PzH99T3K0g1OSo0iLCyc+MoyIMCu1VeV8ue1HbLi45qxUBvTsRN9TuyoYiUi7F1KBaP78+Tz11FPk5eVx1lln8dxzz3HeeecFtCbP/8hzcnK8E2FFmspzGxBXbTVxaQfnClktBh3DnXSOi6CqqordxU4OuCIocdrYX15zjBVqdgC+WVUCq0pIjPiB09Lj6JUaQ6/UGE7pEEVqbDgdYhxEhlmxWAz1LIlImxcygejNN99k6tSpLFiwgIEDBzJ37lxGjhzJtm3bSElJCVhd2dnZPP3uGsqK9hOXlkFiwCqRti4msQPOmqpjnrdb4PSO0ThrqigvL6TWFs25PVKpD4sm70AxG38spbq6GmtENDV1LsqcFirrDQqr6vli5wG+2HngqO/rsBnYLVDvdGIzID1hO0mxkSQ6TDrG2OnbLZUuSVFkJESSHG0/YphORCQYhEwgmjNnDrfffju33norAAsWLOCjjz7i5Zdf5oEHHghobbFJKahvSFpTXJI7PG35YScdu5xCceY2zkrLwFljxeoAZ00NVkcENVWV7CurxhbfkR/z91GBg2ozjHos1DTcSaSm3sTdz+TuFdq2rwr2HRLM1uZ5fxsRZqVzQgTJEQbJUWFERUVhmuA0TVymidNlUl5RQb3TxGWCiUmYxcBus2C3WkiIjcERZsFusxBmMaiuLCfMatAhMQFHmBWbxaCspBiHzaBLx1RiIsKIctiItFsBcLpMTJOGz3JRkJ+PAaSlpWGzWYm224gOt+mGuiIhKCQCUW1tLevXr2fatGneYxaLhREjRrB69eoAViYSWJ6by5YcKDjqeZsFOqckkHFKFzqYRVgddpw1VVSUl1NfW0NkSmdSOndn7/atmGHh1NbUUFlVTW2dEzMqkfIaJ2W1LmotDirrXFTVOdleUM527ycUNrHiY7XPPcqxvU14320+z6LsFqLtVqIdVpJiIoiNsBMbYSPGYcMAysrLcZmecAXu6GbgMk0w3UOVdqs7yCXFx2K3Wd0hzuoOjSbu4fLikoY9o0zAcA89un9rEBsbi2FYMAywGGBg4O5cMyktKcXEJCYmDpdpUlxSgst0v6e7HoOomBgMoLy8DIsBcXFxWAyj4QEGUFpagsUwSIiP8w5zGgaYpklJSQmmy4XR8JqEhHgsFgtGQxvDMBp+734/9+ebOBuCrcs0qXe6OFBUjNPprsticX+W1WLBajGwWtzvbbUYWBt+dX/+wT+jwqIiXC7T/admGCTEJ4BhcPgMA9N0UVxc7H2ekJDgrfFwhuEOxcVFxQ1t47EY7ut3HeV9LEfp1Tyip9M0KSoqAiAxMcHn/KFNPRW5TBfFDe0TEhKxHBbCj9WTeqyofqyO16P9CZimi8LCg3+XEhMTMYyDw9xmw3/RPX/Gpvd1vn/oB88f1v6I17koKirCNA9+XTznzUP+3Dp2SOLqczof4wpbXkgEov379+N0OklNTfU5npqayvfff39E+5qaGmpqDs6tKCkpAaC0tNTvtZWVlbEvew/lRQew2MMJs1gozM3CYg/HVVt91F9Ptk1b+AzVGByvP1GbivxMqsIs1O/PxGIPx1pbTZw9HFd9NZaqcpIb2qZ17saPu36gqLKWmnoDS2wy1fUu6mpriYmJo6z4ABarFdNZT2RMLKazvuG5E6w2ouISKT6wj5q6epxOF1htuJwm1ohITJeLuvp6wsKjqK6sxGIPx+kyqXOZOLHgworLsHj/lTYO6Y/1/sPsCSQNPzzKaqCsoc12RKQ1JEZYubiHf7cG8fzcbswc3ZAIRE01c+ZMHn300SOOa2M7ERGRlpEFxM1smfcuKysjLi7uuG1CIhAlJydjtVrJz8/3OZ6fn09aWtoR7adNm8bUqVO9z10ud/diUlLSSU8ILS0tJSMjg6ysLGJjQ2OTvFC8ZgjN69Y165rbs1C87rZ+zaZpUlZWRnp6+gnbhkQgstvt9O/fn2XLlnHNNdcA7pCzbNkyJk+efER7h8OBw+HwORYfH+/XmmJjY9vkN9fJCMVrhtC8bl1zaAjFa4bQvO62fM0n6hnyCIlABDB16lTGjx/PgAEDOO+885g7dy4VFRXeVWciIiISukImEF1//fXs27ePhx9+mLy8PM4++2yWLFlyxERrERERCT0hE4gAJk+efNQhstbkcDiYPn36EUNy7VkoXjOE5nXrmkNDKF4zhOZ1h9I1G6buFyEiIiIhTjccEhERkZCnQCQiIiIhT4FIREREQp4CkYiIiIQ8BaIWMH/+fLp160Z4eDgDBw7kyy+/PG77t956i9NOO43w8HDOOOMMPv7441aq1H+acs0vvvgiQ4YMISEhgYSEBEaMGHHCP6Ng1dSvtccbb7yBYRjejULbkqZec3FxMZMmTaJjx444HA569erV5r7Hm3rNc+fOpXfv3kRERJCRkcFdd91FdXV1K1V78lauXMmVV15Jeno6hmHw3nvvnfA1K1as4Nxzz8XhcHDqqaeyaNGiFq/Tn5p6ze+88w6XXnopHTp0IDY2lkGDBvHvf/+7dYr1k+Z8nT0+//xzbDYbZ599dovV19oUiPzszTffZOrUqUyfPp2vv/6as846i5EjR1JQcPS7iX/xxRfccMMNTJgwgQ0bNnDNNddwzTXX8N1337Vy5c3X1GtesWIFN9xwA59++imrV68mIyODyy67jOzs7Fau/OQ09bo99uzZwz333MOQIUNaqVL/aeo119bWcumll7Jnzx7efvtttm3bxosvvkinTp1aufLma+o1v/baazzwwANMnz6drVu38tJLL/Hmm2/yu9/9rpUrb76KigrOOuss5s+f36j2u3fvZsyYMVx88cVs3LiRKVOmcNttt7WpgNDUa165ciWXXnopH3/8MevXr+fiiy/myiuvZMOGDS1cqf809Zo9iouLufnmmxk+fHgLVRYgpvjVeeedZ06aNMn73Ol0munp6ebMmTOP2v7nP/+5OWbMGJ9jAwcONH/5y1+2aJ3+1NRrPlx9fb0ZExNjvvLKKy1VYotoznXX19ebF1xwgfnXv/7VHD9+vHn11Ve3QqX+09Rrfv75581TTjnFrK2tba0S/a6p1zxp0iTzkksu8Tk2depU88ILL2zROlsKYL777rvHbXPfffeZffv29Tl2/fXXmyNHjmzBylpOY675aE4//XTz0Ucf9X9BraAp13z99debDz74oDl9+nTzrLPOatG6WpN6iPyotraW9evXM2LECO8xi8XCiBEjWL169VFfs3r1ap/2ACNHjjxm+2DTnGs+XGVlJXV1dSQmJrZUmX7X3OueMWMGKSkpTJgwoTXK9KvmXPMHH3zAoEGDmDRpEqmpqfTr148nnngCp9PZWmWflOZc8wUXXMD69eu9w2q7du3i448/ZvTo0a1ScyC09X/H/MHlclFWVtam/h1rjoULF7Jr1y6mT58e6FL8LqR2qm5p+/fvx+l0HnE7kNTUVL7//vujviYvL++o7fPy8lqsTn9qzjUf7v777yc9Pf2If1CDWXOu+7///S8vvfQSGzdubIUK/a8517xr1y6WL1/OuHHj+Pjjj9mxYwe/+c1vqKuraxP/oDbnmm+88Ub279/P4MGDMU2T+vp6fvWrX7WpIbOmOta/Y6WlpVRVVRERERGgylrP7NmzKS8v5+c//3mgS2kx27dv54EHHmDVqlXYbO0vPqiHSALqj3/8I2+88Qbvvvsu4eHhgS6nxZSVlXHTTTfx4osvkpycHOhyWo3L5SIlJYUXXniB/v37c/311/P73/+eBQsWBLq0FrNixQqeeOIJ/vznP/P111/zzjvv8NFHH/HYY48FujRpIa+99hqPPvoo//jHP0hJSQl0OS3C6XRy44038uijj9KrV69Al9Mi2l/EC6Dk5GSsViv5+fk+x/Pz80lLSzvqa9LS0prUPtg055o9Zs+ezR//+Ef+85//cOaZZ7ZkmX7X1OveuXMne/bs4corr/Qec7lcANhsNrZt20aPHj1atuiT1JyvdceOHQkLC8NqtXqP9enTh7y8PGpra7Hb7S1a88lqzjU/9NBD3HTTTdx2220AnHHGGVRUVDBx4kR+//vfY7G0v/+HHuvfsdjY2HbfO/TGG29w22238dZbb7WpXu6mKisr46uvvmLDhg3ee4K6XC5M08Rms/HJJ59wySWXBLjKk9P+/mYGkN1up3///ixbtsx7zOVysWzZMgYNGnTU1wwaNMinPcDSpUuP2T7YNOeaAWbNmsVjjz3GkiVLGDBgQGuU6ldNve7TTjuNb7/9lo0bN3ofV111lXdVTkZGRmuW3yzN+VpfeOGF7Nixwxv+AH744Qc6duwY9GEImnfNlZWVR4QeTyA02+mtI9v6v2PN9frrr3Prrbfy+uuvM2bMmECX06JiY2OP+DfsV7/6Fb1792bjxo0MHDgw0CWevABP6m533njjDdPhcJiLFi0yt2zZYk6cONGMj4838/LyTNM0zZtuusl84IEHvO0///xz02azmbNnzza3bt1qTp8+3QwLCzO//fbbQF1CkzX1mv/4xz+adrvdfPvtt83c3Fzvo6ysLFCX0CxNve7DtcVVZk295szMTDMmJsacPHmyuW3bNvPDDz80U1JSzMcffzxQl9BkTb3m6dOnmzExMebrr79u7tq1y/zkk0/MHj16mD//+c8DdQlNVlZWZm7YsMHcsGGDCZhz5swxN2zYYO7du9c0TdN84IEHzJtuusnbfteuXWZkZKR57733mlu3bjXnz59vWq1Wc8mSJYG6hCZr6jUvXrzYtNls5vz5833+HSsuLg7UJTRZU6/5cO1tlZkCUQt47rnnzC5duph2u90877zzzDVr1njPXXTRReb48eN92v/jH/8we/XqZdrtdrNv377mRx991MoVn7ymXHPXrl1N4IjH9OnTW7/wk9TUr/Wh2mIgMs2mX/MXX3xhDhw40HQ4HOYpp5xi/uEPfzDr6+tbueqT05RrrqurMx955BGzR48eZnh4uJmRkWH+5je/MYuKilq/8Gb69NNPj/p31HOd48ePNy+66KIjXnP22WebdrvdPOWUU8yFCxe2et0no6nXfNFFFx23fVvQnK/zodpbIDJMs5324YqIiIg0kuYQiYiISMhTIBIREZGQp0AkIiIiIU+BSEREREKeApGIiIiEPAUiERERCXkKRCIiIhLyFIhEpM0xTZOJEyeSmJiIYRhs3LjxhK8xDIP33nuvxWsTkaZZuXIlV155Jenp6c3+e2qaJrNnz6ZXr144HA46derEH/7whya9hwKRiLQ5S5YsYdGiRXz44Yfk5ubSr1+/QJdEt27dmDt3bqDLEGlzKioqOOuss5g/f36z3+O3v/0tf/3rX5k9ezbff/89H3zwAeedd16T3kN3uxeRNmfnzp107NiRCy64INCliMhJGjVqFKNGjTrm+ZqaGn7/+9/z+uuvU1xcTL9+/XjyyScZNmwYAFu3buX555/nu+++o3fv3gB07969yXWoh0hE2pRbbrmFO+64g8zMTAzDoFu3bgwbNow777yT++67j8TERNLS0njkkUeO+R4/+9nPmDx5svf5lClTMAyD77//HoDa2lqioqL4z3/+A0BZWRnjxo0jKiqKjh078swzzzBs2DCmTJkCwLBhw9i7dy933XUXhmFgGEaLXb9IqJk8eTKrV6/mjTfeYNOmTfzP//wPl19+Odu3bwfgn//8J6eccgoffvgh3bt3p1u3btx2220UFhY26XMUiESkTXn22WeZMWMGnTt3Jjc3l3Xr1gHwyiuvEBUVxdq1a5k1axYzZsxg6dKlR32Piy66iBUrVniff/bZZyQnJ3uPrVu3jrq6Om8P1NSpU/n888/54IMPWLp0KatWreLrr7/2vv6dd96hc+fOzJgxg9zcXHJzc1vm4kVCTGZmJgsXLuStt95iyJAh9OjRg3vuuYfBgwezcOFCAHbt2sXevXt56623ePXVV1m0aBHr16/nZz/7WZM+S0NmItKmxMXFERMTg9VqJS0tzXv8zDPPZPr06QD07NmTP/3pTyxbtoxLL730iPcYNmwYv/3tb9m3bx82m40tW7bw0EMPsWLFCn71q1+xYsUKfvKTnxAZGUlZWRmvvPIKr732GsOHDwdg4cKFpKene98vMTERq9VKTEyMT00icnK+/fZbnE4nvXr18jleU1NDUlISAC6Xi5qaGl599VVvu5deeon+/fuzbds27zDaiSgQiUi7cOaZZ/o879ixIwUFBUdt269fPxITE/nss8+w2+2cc845XHHFFd5JnZ999pl3fsKuXbuoq6vzmaAZFxfX6H9kRaT5ysvLsVqtrF+/HqvV6nMuOjoacP9dt9lsPqGpT58+gLuHSYFIREJKWFiYz3PDMHC5XEdtaxgGQ4cOZcWKFTgcDoYNG8aZZ55JTU0N3333HV988QX33HNPa5QtIsdxzjnn4HQ6KSgoYMiQIUdtc+GFF1JfX8/OnTvp0aMHAD/88AMAXbt2bfRnaQ6RiIQkzzyiFStWMGzYMCwWC0OHDuWpp56ipqaGCy+8EIBTTjmFsLAw71wlgJKSEu8/uB52ux2n09mq1yDSHpSXl7Nx40bvfmK7d+9m48aNZGZm0qtXL8aNG8fNN9/MO++8w+7du/nyyy+ZOXMmH330EQAjRozg3HPP5X//93/ZsGED69ev55e//CWXXnrpEUNtx6NAJCIhadiwYWzZsoXNmzczePBg77HFixczYMAAoqKiAIiJiWH8+PHce++9fPrpp2zevJkJEyZgsVh8VpN169aNlStXkp2dzf79+wNyTSJt0VdffcU555zDOeecA7gXMZxzzjk8/PDDgHvO3s0338zdd99N7969ueaaa1i3bh1dunQBwGKx8M9//pPk5GSGDh3KmDFj6NOnD2+88UaT6tCQmYiEpDPOOIP4+Hh69erlnYswbNgwnE6nd/6Qx5w5c/jVr37FFVdcQWxsLPfddx9ZWVmEh4d728yYMYNf/vKX9OjRg5qaGkzTbM3LEWmzhg0bdty/L2FhYTz66KM8+uijx2yTnp7O//3f/51UHYapv7UiIk1SUVFBp06dePrpp5kwYUKgyxERP1APkYjICWzYsIHvv/+e8847j5KSEmbMmAHA1VdfHeDKRMRfFIhERBph9uzZbNu2DbvdTv/+/Vm1ahXJycmBLktE/ERDZiIiIhLytMpMREREQp4CkYiIiIQ8BSIREREJeQpEIiIiEvIUiERERCTkKRCJiIhIyFMgEhERkZCnQCQiIiIhT4FIREREQt7/A+QKYxKO/22GAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAHHCAYAAACiOWx7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABhZklEQVR4nO3dd3wT9f8H8FfSNGm694IuoJSW0SKzgAyplCFDHKBsURwgS1FRQVQQASlLpOJPWYKCCogsqew9CoVSoEAptEAH0L3T5H5/lOZLaIFrmzZpeT0fjzwgd5/cvS/pePVzn/ucRBAEAURERET0WFJDF0BERERUGzA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBHVsBkzZkAikdTIvrp27YquXbtqn+/btw8SiQR//vlnjex/5MiR8Pb2rpF9VVZOTg7efPNNuLq6QiKRYOLEidW2r4c/D2Pk7e2NkSNHGroMIqPE0ERUBStXroREItE+zMzM4O7ujtDQUCxevBjZ2dl62c/t27cxY8YMREVF6WV7+mTMtYnxzTffYOXKlXj33XexZs0aDBs2zNAlVbsjR45gxowZyMjIMHQpRLWKzNAFENUFX331FXx8fKBSqZCcnIx9+/Zh4sSJCAsLw5YtW9CiRQtt288//xyffPJJhbZ/+/ZtfPnll/D29kZQUJDo1+3atatC+6mMx9X2008/QaPRVHsNVbFnzx60b98eX3zxhaFLqTFHjhzBl19+iZEjR8LW1lZnXWxsLKRS/j1NVB6GJiI96NWrF1q3bq19PnXqVOzZswcvvPAC+vXrh4sXL0KpVAIAZDIZZLLq/dbLy8uDubk55HJ5te7nSUxNTQ26fzFSU1MREBBg6DKMhkKhMHQJREaLf04QVZPnnnsO06ZNw40bN/Drr79ql5c3pikiIgKdOnWCra0tLC0t4efnh08//RRAyTikNm3aAABGjRqlPRW4cuVKACXjZJo1a4bIyEh07twZ5ubm2tc+agyNWq3Gp59+CldXV1hYWKBfv35ITEzUafOosS0PbvNJtZU3pik3NxcffPABPDw8oFAo4Ofnh++++w6CIOi0k0gkGDduHDZv3oxmzZpBoVCgadOm2LlzZ/lv+ENSU1MxevRouLi4wMzMDIGBgVi1apV2fen4rvj4eGzbtk1b+/Xr1x+73V9//RWtWrWCUqmEvb09Bg8eXOa9A4Dly5ejYcOGUCqVaNu2LQ4ePFimTenp3Yf3WVrbvn37dJYfP34cvXv3hp2dHSwsLNCiRQssWrRIu/7cuXMYOXIkGjRoADMzM7i6uuKNN97AvXv3tG1mzJiBKVOmAAB8fHzKHHd5n/u1a9fwyiuvwN7eHubm5mjfvj22bdtWbs0bNmzArFmzUL9+fZiZmaF79+64evXqY9/TUt7e3njhhRdw6NAhtG3bFmZmZmjQoAFWr16t0+5R4wLLez9Lt7lv3z60bt0aSqUSzZs31763GzduRPPmzWFmZoZWrVrhzJkzomqlpxNDE1E1Kh0f87jTZDExMXjhhRdQWFiIr776CvPnz0e/fv1w+PBhAIC/vz+++uorAMCYMWOwZs0arFmzBp07d9Zu4969e+jVqxeCgoKwcOFCdOvW7bF1zZo1C9u2bcPHH3+M8ePHIyIiAiEhIcjPz6/Q8Ymp7UGCIKBfv35YsGABevbsibCwMPj5+WHKlCmYPHlymfaHDh3Ce++9h8GDB2Pu3LkoKCjASy+9pBMCypOfn4+uXbtizZo1GDJkCObNmwcbGxuMHDlSGzL8/f2xZs0aODo6IigoSFu7k5PTI7c7a9YsDB8+HL6+vggLC8PEiROxe/dudO7cWWd80M8//4y3334brq6umDt3Ljp27FhuMK2IiIgIdO7cGRcuXMCECRMwf/58dOvWDVu3btVpc+3aNYwaNQpLlizB4MGD8fvvv6N3797aUDpw4EC89tprAIAFCxY88bhTUlLQoUMH/Pvvv3jvvfcwa9YsFBQUoF+/fti0aVOZ9t9++y02bdqEDz/8EFOnTsWxY8cwZMgQ0cd59epVvPzyy3j++ecxf/582NnZYeTIkYiJianI21Vmm6+//jr69u2L2bNnIz09HX379sXatWsxadIkDB06FF9++SXi4uLw6quvGv0pZTIggYgqbcWKFQIA4eTJk49sY2NjI7Rs2VL7/IsvvhAe/NZbsGCBAEC4c+fOI7dx8uRJAYCwYsWKMuu6dOkiABDCw8PLXdelSxft87179woAhHr16glZWVna5Rs2bBAACIsWLdIu8/LyEkaMGPHEbT6uthEjRgheXl7a55s3bxYACDNnztRp9/LLLwsSiUS4evWqdhkAQS6X6yw7e/asAEBYsmRJmX09aOHChQIA4ddff9UuKyoqEoKDgwVLS0udY/fy8hL69Onz2O0JgiBcv35dMDExEWbNmqWzPDo6WpDJZNrlRUVFgrOzsxAUFCQUFhZq2y1fvlwAoPPelX79xMfH62yz9HPau3evIAiCUFxcLPj4+AheXl5Cenq6TluNRqP9f15eXpm6f/vtNwGAcODAAe2yefPmlbtfQSj7uU+cOFEAIBw8eFC7LDs7W/Dx8RG8vb0FtVqtU7O/v7/OcS9atEgAIERHR5fZV3n7frjW1NRUQaFQCB988IF22cPfQ6XKez9Lt3nkyBHtsn///VcAICiVSuHGjRva5T/++KPO+070MPY0EVUzS0vLx15FVzoQ9++//670X7gKhQKjRo0S3X748OGwsrLSPn/55Zfh5uaG7du3V2r/Ym3fvh0mJiYYP368zvIPPvgAgiBgx44dOstDQkLQsGFD7fMWLVrA2toa165de+J+XF1dtT0qQMn4qvHjxyMnJwf79++vcO0bN26ERqPBq6++irt372ofrq6u8PX1xd69ewEAp06dQmpqKt555x2dMWUjR46EjY1NhfcLAGfOnEF8fDwmTpxYZuD2g6epSsfNAUBBQQHu3r2L9u3bAwBOnz5dqX1v374dbdu2RadOnbTLLC0tMWbMGFy/fh0XLlzQaT9q1Cid43722WcB4ImfWamAgADtawDAyckJfn5+ol//qG0GBwdrn7dr1w5AySl0T0/PMsursi+q2xiaiKpZTk6OTkB52KBBg9CxY0e8+eabcHFxweDBg7Fhw4YKBah69epVaNC3r6+vznOJRIJGjRo9cTxPVd24cQPu7u5l3g9/f3/t+gc9+AutlJ2dHdLT05+4H19f3zJXgT1qP2JcuXIFgiDA19cXTk5OOo+LFy8iNTVVZ9sPv8empqZo0KBBhfcLAHFxcQCAZs2aPbZdWloaJkyYABcXFyiVSjg5OcHHxwcAkJmZWal937hxA35+fmWWi/3M7OzsAED7meXk5CA5OVn7uHPnzmNfX7qNJ33mj/PwNkvDq4eHR7nLq7Ivqtt49RxRNbp58yYyMzPRqFGjR7ZRKpU4cOAA9u7di23btmHnzp1Yv349nnvuOezatQsmJiZP3M+DPQz68qgJONVqtaia9OFR+xEeGjReEzQaDSQSCXbs2FFuXZaWlhXe5uPe48p49dVXceTIEUyZMgVBQUGwtLSERqNBz549a2yczpM+s++++w5ffvmldrmXl5dOWBfzmVf0fXvUNo3p64tqB4Ymomq0Zs0aAEBoaOhj20mlUnTv3h3du3dHWFgYvvnmG3z22WfYu3cvQkJC9D6D+JUrV3SeC4KAq1ev6swnZWdnV+7khzdu3NDpMalIbV5eXvjvv/+QnZ2t09t06dIl7Xp98PLywrlz56DRaHR6m6qyn4YNG0IQBPj4+KBx48aP3TdQ8h4/99xz2uUqlQrx8fEIDAzULivthXn4fX6496b0FOX58+cREhJS7n7T09Oxe/dufPnll5g+fbp2+cOfNVDxzyw2NrbM8sq+l8OHD9c51VeZwP/g+/bg6crK9CASVQRPzxFVkz179uDrr7+Gj4/PY68eSktLK7OsdJLIwsJCAICFhQWAsr9cK2v16tU646z+/PNPJCUloVevXtplDRs2xLFjx1BUVKRdtnXr1jJXgFWktt69e0OtVuP777/XWb5gwQJIJBKd/VdF7969kZycjPXr12uXFRcXY8mSJbC0tESXLl0qvM2BAwfCxMQEX375ZZmeCEEQtFf0tW7dGk5OTggPD9d571auXFnmPSoNQwcOHNAuU6vVWL58uU67Z555Bj4+Pli4cGGZbZTWUtpr8nBtCxcuLHMsFf3MTpw4gaNHj2qX5ebmYvny5fD29q7wHFcNGjRASEiI9tGxY8cKvR4o/33Lzc3VmVKCqDqwp4lID3bs2IFLly6huLgYKSkp2LNnDyIiIuDl5YUtW7bAzMzska/96quvcODAAfTp0wdeXl5ITU3FDz/8gPr162v/Im/YsCFsbW0RHh4OKysrWFhYoF27dtrxKhVlb2+PTp06YdSoUUhJScHChQvRqFEjvPXWW9o2b775Jv7880/07NkTr776KuLi4vDrr7/qDMyuaG19+/ZFt27d8Nlnn+H69esIDAzErl278Pfff2PixIlltl1ZY8aMwY8//oiRI0ciMjIS3t7e+PPPP3H48GEsXLjwsWPMHqVhw4aYOXMmpk6diuvXr2PAgAGwsrJCfHw8Nm3ahDFjxuDDDz+EqakpZs6cibfffhvPPfccBg0ahPj4eKxYsaLMmKamTZuiffv2mDp1KtLS0mBvb4/ff/8dxcXFOu2kUimWLVuGvn37IigoCKNGjYKbmxsuXbqEmJgY/Pvvv7C2tkbnzp0xd+5cqFQq1KtXD7t27UJ8fHyZY2nVqhUA4LPPPsPgwYNhamqKvn37asPUgz755BP89ttv6NWrF8aPHw97e3usWrUK8fHx+Ouvvwwye3iPHj3g6emJ0aNHY8qUKTAxMcEvv/wCJycnJCQk1Hg99BQxzEV7RHVD6SXOpQ+5XC64uroKzz//vLBo0SKdS9tLPXy59O7du4X+/fsL7u7uglwuF9zd3YXXXntNuHz5ss7r/v77byEgIECQyWQ6l/h36dJFaNq0abn1PWrKgd9++02YOnWq4OzsLCiVSqFPnz46l16Xmj9/vlCvXj1BoVAIHTt2FE6dOlVmm4+r7eEpBwSh5HL1SZMmCe7u7oKpqang6+srzJs3T+fSeUEomXJg7NixZWp61FQID0tJSRFGjRolODo6CnK5XGjevHm50yKInXKg1F9//SV06tRJsLCwECwsLIQmTZoIY8eOFWJjY3Xa/fDDD4KPj4+gUCiE1q1bCwcOHCj3vYuLixNCQkIEhUIhuLi4CJ9++qkQERFR7qXvhw4dEp5//nnByspKsLCwEFq0aKEz/cLNmzeFF198UbC1tRVsbGyEV155Rbh9+7YAQPjiiy90tvX1118L9erVE6RSqc5l+uW9v3FxccLLL78s2NraCmZmZkLbtm2FrVu36rQp/dr6448/dJbHx8c/ckqKhz3qsyjvfYuMjBTatWsnyOVywdPTUwgLC3vklAPlbbO8r6/SWufNm/fEWunpJBEEjngjIiIiehKOaSIiIiISgaGJiIiISASGJiIiIiIRGJqIiIiIRGBoIiIiIhKBoYmIiIhIBE5uqScajQa3b9+GlZWV3m95QURERNVDEARkZ2fD3d39iZO1MjTpye3bt8vcMZuIiIhqh8TERNSvX/+xbRia9KT0tgyJiYmwtrY2cDVEREQkRlZWFjw8PETdXomhSU9KT8lZW1szNBEREdUyYobWcCA4ERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERiSAzdAFERHXNuElTcCcju8xyJ1srfL9gngEqIiJ9YGgiItKzOxnZaP/65DLLj60LM0A1RKQvPD1HREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAYNTQcOHEDfvn3h7u4OiUSCzZs366wXBAHTp0+Hm5sblEolQkJCcOXKFZ02aWlpGDJkCKytrWFra4vRo0cjJydHp825c+fw7LPPwszMDB4eHpg7d26ZWv744w80adIEZmZmaN68ObZv36734yUiIqLay6ChKTc3F4GBgVi6dGm56+fOnYvFixcjPDwcx48fh4WFBUJDQ1FQUKBtM2TIEMTExCAiIgJbt27FgQMHMGbMGO36rKws9OjRA15eXoiMjMS8efMwY8YMLF++XNvmyJEjeO211zB69GicOXMGAwYMwIABA3D+/PnqO3giIiKqVSSCIAiGLgIAJBIJNm3ahAEDBgAo6WVyd3fHBx98gA8//BAAkJmZCRcXF6xcuRKDBw/GxYsXERAQgJMnT6J169YAgJ07d6J37964efMm3N3dsWzZMnz22WdITk6GXC4HAHzyySfYvHkzLl26BAAYNGgQcnNzsXXrVm097du3R1BQEMLDw0XVn5WVBRsbG2RmZsLa2lpfbwsR1UKDRr3zyBv2rl8h7mcKEdWMivz+NtoxTfHx8UhOTkZISIh2mY2NDdq1a4ejR48CAI4ePQpbW1ttYAKAkJAQSKVSHD9+XNumc+fO2sAEAKGhoYiNjUV6erq2zYP7KW1Tuh8iIiIimaELeJTk5GQAgIuLi85yFxcX7brk5GQ4OzvrrJfJZLC3t9dp4+PjU2Ybpevs7OyQnJz82P2Up7CwEIWFhdrnWVlZFTk8IiIiqmWMtqfJ2M2ePRs2Njbah4eHh6FLIiIiompktKHJ1dUVAJCSkqKzPCUlRbvO1dUVqampOuuLi4uRlpam06a8bTy4j0e1KV1fnqlTpyIzM1P7SExMrOghEhERUS1itKHJx8cHrq6u2L17t3ZZVlYWjh8/juDgYABAcHAwMjIyEBkZqW2zZ88eaDQatGvXTtvmwIEDUKlU2jYRERHw8/ODnZ2dts2D+yltU7qf8igUClhbW+s8iIiIqO4yaGjKyclBVFQUoqKiAJQM/o6KikJCQgIkEgkmTpyImTNnYsuWLYiOjsbw4cPh7u6uvcLO398fPXv2xFtvvYUTJ07g8OHDGDduHAYPHgx3d3cAwOuvvw65XI7Ro0cjJiYG69evx6JFizB58v+ubJkwYQJ27tyJ+fPn49KlS5gxYwZOnTqFcePG1fRbQkREREbKoAPBT506hW7dummflwaZESNGYOXKlfjoo4+Qm5uLMWPGICMjA506dcLOnTthZmamfc3atWsxbtw4dO/eHVKpFC+99BIWL16sXW9jY4Ndu3Zh7NixaNWqFRwdHTF9+nSduZw6dOiAdevW4fPPP8enn34KX19fbN68Gc2aNauBd4GIiIhqA6OZp6m24zxNRFSK8zQR1R51Yp4mIiIiImPC0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIYdWhSq9WYNm0afHx8oFQq0bBhQ3z99dcQBEHbRhAETJ8+HW5ublAqlQgJCcGVK1d0tpOWloYhQ4bA2toatra2GD16NHJycnTanDt3Ds8++yzMzMzg4eGBuXPn1sgxEhERUe1g1KFpzpw5WLZsGb7//ntcvHgRc+bMwdy5c7FkyRJtm7lz52Lx4sUIDw/H8ePHYWFhgdDQUBQUFGjbDBkyBDExMYiIiMDWrVtx4MABjBkzRrs+KysLPXr0gJeXFyIjIzFv3jzMmDEDy5cvr9HjJSIiIuMlM3QBj3PkyBH0798fffr0AQB4e3vjt99+w4kTJwCU9DItXLgQn3/+Ofr37w8AWL16NVxcXLB582YMHjwYFy9exM6dO3Hy5Em0bt0aALBkyRL07t0b3333Hdzd3bF27VoUFRXhl19+gVwuR9OmTREVFYWwsDCdcEVERERPL6PuaerQoQN2796Ny5cvAwDOnj2LQ4cOoVevXgCA+Ph4JCcnIyQkRPsaGxsbtGvXDkePHgUAHD16FLa2ttrABAAhISGQSqU4fvy4tk3nzp0hl8u1bUJDQxEbG4v09PRqP04iIiIyfkbd0/TJJ58gKysLTZo0gYmJCdRqNWbNmoUhQ4YAAJKTkwEALi4uOq9zcXHRrktOToazs7POeplMBnt7e502Pj4+ZbZRus7Ozq5MbYWFhSgsLNQ+z8rKqsqhEhERkZEz6p6mDRs2YO3atVi3bh1Onz6NVatW4bvvvsOqVasMXRpmz54NGxsb7cPDw8PQJREREVE1MurQNGXKFHzyyScYPHgwmjdvjmHDhmHSpEmYPXs2AMDV1RUAkJKSovO6lJQU7TpXV1ekpqbqrC8uLkZaWppOm/K28eA+HjZ16lRkZmZqH4mJiVU8WiIiIjJmRh2a8vLyIJXqlmhiYgKNRgMA8PHxgaurK3bv3q1dn5WVhePHjyM4OBgAEBwcjIyMDERGRmrb7NmzBxqNBu3atdO2OXDgAFQqlbZNREQE/Pz8yj01BwAKhQLW1tY6DyIiIqq7jDo09e3bF7NmzcK2bdtw/fp1bNq0CWFhYXjxxRcBABKJBBMnTsTMmTOxZcsWREdHY/jw4XB3d8eAAQMAAP7+/ujZsyfeeustnDhxAocPH8a4ceMwePBguLu7AwBef/11yOVyjB49GjExMVi/fj0WLVqEyZMnG+rQiYiIyMgY9UDwJUuWYNq0aXjvvfeQmpoKd3d3vP3225g+fbq2zUcffYTc3FyMGTMGGRkZ6NSpE3bu3AkzMzNtm7Vr12LcuHHo3r07pFIpXnrpJSxevFi73sbGBrt27cLYsWPRqlUrODo6Yvr06ZxugIiIiLQkwoPTa1OlZWVlwcbGBpmZmTxVR/SUGzTqHbR/vWxP9bF1YVi/ItwAFRHRo1Tk97dRn54jIiIiMhYMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCDJDF0BERNVj3KQpuJORXWa5k60Vvl8wzwAVEdVuDE1ERHXUnYxstH99cpnlx9aFGaAaotqPp+eIiIiIRGBoIiIiIhKBoYmIiIhIBIYmIiIiIhEYmoiIiIhEYGgiIiIiEoGhiYiIiEgEhiYiIiIiERiaiIiIiERgaCIiIiISgaGJiIiISASGJiIiIiIRGJqIiIiIRGBoIiIiIhKBoYmIiIhIBIYmIiIiIhEYmoiIiIhEYGgiIiIiEoGhiYiIiEgEhiYiIiIiERiaiIiIiERgaCIiIiISgaGJiIiISASGJiIiIiIRGJqIiIiIRKhUaLp27Zq+6yAiIiIyapUKTY0aNUK3bt3w66+/oqCgQN81ERERERmdSoWm06dPo0WLFpg8eTJcXV3x9ttv48SJE/qujYiIiMhoVCo0BQUFYdGiRbh9+zZ++eUXJCUloVOnTmjWrBnCwsJw584dfddJREREZFBVGgguk8kwcOBA/PHHH5gzZw6uXr2KDz/8EB4eHhg+fDiSkpL0VScRERGRQVUpNJ06dQrvvfce3NzcEBYWhg8//BBxcXGIiIjA7du30b9/f33VSURERGRQssq8KCwsDCtWrEBsbCx69+6N1atXo3fv3pBKSzKYj48PVq5cCW9vb33WSkRERGQwlQpNy5YtwxtvvIGRI0fCzc2t3DbOzs74+eefq1QcERERkbGoVGi6cuXKE9vI5XKMGDGiMpsnIiIiMjqVGtO0YsUK/PHHH2WW//HHH1i1alWViyIiIiIyNpUKTbNnz4ajo2OZ5c7Ozvjmm2+qXBQRERGRsalUaEpISICPj0+Z5V5eXkhISKhyUURERETGplKhydnZGefOnSuz/OzZs3BwcKhyUQ+6desWhg4dCgcHByiVSjRv3hynTp3SrhcEAdOnT4ebmxuUSiVCQkLKjLlKS0vDkCFDYG1tDVtbW4wePRo5OTk6bc6dO4dnn30WZmZm8PDwwNy5c/V6HERERFS7VSo0vfbaaxg/fjz27t0LtVoNtVqNPXv2YMKECRg8eLDeiktPT0fHjh1hamqKHTt24MKFC5g/fz7s7Oy0bebOnYvFixcjPDwcx48fh4WFBUJDQ3XuiTdkyBDExMQgIiICW7duxYEDBzBmzBjt+qysLPTo0QNeXl6IjIzEvHnzMGPGDCxfvlxvx0JERES1W6Wunvv6669x/fp1dO/eHTJZySY0Gg2GDx+u1zFNc+bMgYeHB1asWKFd9uBpQUEQsHDhQnz++efaiTRXr14NFxcXbN68GYMHD8bFixexc+dOnDx5Eq1btwYALFmyBL1798Z3330Hd3d3rF27FkVFRfjll18gl8vRtGlTREVFISwsTCdcERER0dOrUj1Ncrkc69evx6VLl7B27Vps3LgRcXFx2tChL1u2bEHr1q3xyiuvwNnZGS1btsRPP/2kXR8fH4/k5GSEhIRol9nY2KBdu3Y4evQoAODo0aOwtbXVBiYACAkJgVQqxfHjx7VtOnfurFN7aGgoYmNjkZ6errfjISIiotqrUj1NpRo3bozGjRvrq5Yyrl27hmXLlmHy5Mn49NNPcfLkSYwfP147B1RycjIAwMXFRed1Li4u2nXJyclwdnbWWS+TyWBvb6/T5uGB7aXbTE5O1jkdWKqwsBCFhYXa51lZWVU8WiIiIjJmlQpNarUaK1euxO7du5GamgqNRqOzfs+ePXopTqPRoHXr1tpTfi1btsT58+cRHh5u8IkzZ8+ejS+//NKgNRAREVHNqdTpuQkTJmDChAlQq9Vo1qwZAgMDdR764ubmhoCAAJ1l/v7+2mkNXF1dAQApKSk6bVJSUrTrXF1dkZqaqrO+uLgYaWlpOm3K28aD+3jY1KlTkZmZqX0kJiZW5hCJiIiolqhUT9Pvv/+ODRs2oHfv3vquR0fHjh0RGxurs+zy5cvw8vICUDIo3NXVFbt370ZQUBCAktNkx48fx7vvvgsACA4ORkZGBiIjI9GqVSsAJT1hGo0G7dq107b57LPPoFKpYGpqCgCIiIiAn59fuafmAEChUEChUOj9mImIiMg4VXogeKNGjfRdSxmTJk3CsWPH8M033+Dq1atYt24dli9fjrFjxwIAJBIJJk6ciJkzZ2LLli2Ijo7G8OHD4e7ujgEDBgAo6Znq2bMn3nrrLZw4cQKHDx/GuHHjMHjwYLi7uwMAXn/9dcjlcowePRoxMTFYv349Fi1ahMmTJ1f7MRIREVHtUKnQ9MEHH2DRokUQBEHf9eho06YNNm3ahN9++w3NmjXD119/jYULF2LIkCHaNh999BHef/99jBkzBm3atEFOTg527twJMzMzbZu1a9eiSZMm6N69O3r37o1OnTrpzMFkY2ODXbt2IT4+Hq1atcIHH3yA6dOnc7oBIiIi0qrU6blDhw5h79692LFjB5o2bao9pVVq48aNeikOAF544QW88MILj1wvkUjw1Vdf4auvvnpkG3t7e6xbt+6x+2nRogUOHjxY6TqJiIiobqtUaLK1tcWLL76o71qIiIiIjFalQtODM3QTERERPQ0qNaYJKLls/7///sOPP/6I7OxsAMDt27fL3AiXiIiIqC6oVE/TjRs30LNnTyQkJKCwsBDPP/88rKysMGfOHBQWFiI8PFzfdRIREREZVKUnt2zdujXS09OhVCq1y1988UXs3r1bb8URERERGYtK9TQdPHgQR44cKXNzXm9vb9y6dUsvhREREREZk0r1NGk0GqjV6jLLb968CSsrqyoXRURERGRsKhWaevTogYULF2qfSyQS5OTk4Isvvqj2W6sQERERGUKlTs/Nnz8foaGhCAgIQEFBAV5//XVcuXIFjo6O+O233/RdIxEREZHBVSo01a9fH2fPnsXvv/+Oc+fOIScnB6NHj8aQIUN0BoYTERER1RWVCk0AIJPJMHToUH3WQkRERGS0KhWaVq9e/dj1w4cPr1QxRERERMaqUqFpwoQJOs9VKhXy8vIgl8thbm7O0ERERER1TqWunktPT9d55OTkIDY2Fp06deJAcCIiIqqTKn3vuYf5+vri22+/LdMLRURERFQXVHogeLkbk8lw+/ZtfW6SiIjIqI2bNAV3MrLLLHeytcL3C+YZoCKqLpUKTVu2bNF5LggCkpKS8P3336Njx456KYyIiKg2uJORjfavTy6z/Ni6MANUQ9WpUqFpwIABOs8lEgmcnJzw3HPPYf78+fqoi4iIiMioVCo0aTQafddBREREZNT0NhCciIiIqC6rVE/T5Mllz90+SlgYz+kSERFR7Vep0HTmzBmcOXMGKpUKfn5+AIDLly/DxMQEzzzzjLadRCLRT5VEREREBlap0NS3b19YWVlh1apVsLOzA1Ay4eWoUaPw7LPP4oMPPtBrkURERESGVqkxTfPnz8fs2bO1gQkA7OzsMHPmTF49R0RERHVSpUJTVlYW7ty5U2b5nTt3kJ1ddoIvIiIiotquUqHpxRdfxKhRo7Bx40bcvHkTN2/exF9//YXRo0dj4MCB+q6RiIiIyOAqNaYpPDwcH374IV5//XWoVKqSDclkGD16NObN45TxREREVPdUKjSZm5vjhx9+wLx58xAXFwcAaNiwISwsLPRaHBEREZGxqNLklklJSUhKSoKvry8sLCwgCIK+6iIiIiIyKpUKTffu3UP37t3RuHFj9O7dG0lJSQCA0aNHc7oBIiIiqpMqFZomTZoEU1NTJCQkwNzcXLt80KBB2Llzp96KIyIiIjIWlRrTtGvXLvz777+oX7++znJfX1/cuHFDL4URERERGZNK9TTl5ubq9DCVSktLg0KhqHJRRERERMamUqHp2WefxerVq7XPJRIJNBoN5s6di27duumtOCIiIiJjUanTc3PnzkX37t1x6tQpFBUV4aOPPkJMTAzS0tJw+PBhfddIREREZHCV6mlq1qwZLl++jE6dOqF///7Izc3FwIEDcebMGTRs2FDfNRIREREZXIV7mlQqFXr27Inw8HB89tln1VETERERkdGpcE+Tqakpzp07Vx21EBERERmtSp2eGzp0KH7++Wd910JERERktCo1ELy4uBi//PIL/vvvP7Rq1arMPefCwsL0UhwRERGRsahQaLp27Rq8vb1x/vx5PPPMMwCAy5cv67SRSCT6q46IiIjISFQoNPn6+iIpKQl79+4FUHLblMWLF8PFxaVaiiMiIiIyFhUa0yQIgs7zHTt2IDc3V68FERERERmjSg0EL/VwiCIiIiKqqyoUmiQSSZkxSxzDRERERE+DCo1pEgQBI0eO1N6Ut6CgAO+8806Zq+c2btyovwqJiIiIjECFQtOIESN0ng8dOlSvxRAREREZqwqFphUrVlRXHURERERGrUoDwYmIiIieFgxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkQq0KTd9++y0kEgkmTpyoXVZQUICxY8fCwcEBlpaWeOmll5CSkqLzuoSEBPTp0wfm5uZwdnbGlClTUFxcrNNm3759eOaZZ6BQKNCoUSOsXLmyBo6IiIiIaotaE5pOnjyJH3/8ES1atNBZPmnSJPzzzz/4448/sH//fty+fRsDBw7Urler1ejTpw+Kiopw5MgRrFq1CitXrsT06dO1beLj49GnTx9069YNUVFRmDhxIt588038+++/NXZ8REREZNxqRWjKycnBkCFD8NNPP8HOzk67PDMzEz///DPCwsLw3HPPoVWrVlixYgWOHDmCY8eOAQB27dqFCxcu4Ndff0VQUBB69eqFr7/+GkuXLkVRUREAIDw8HD4+Ppg/fz78/f0xbtw4vPzyy1iwYIFBjpeIiIiMT60ITWPHjkWfPn0QEhKiszwyMhIqlUpneZMmTeDp6YmjR48CAI4ePYrmzZvDxcVF2yY0NBRZWVmIiYnRtnl426GhodptEBEREVXohr2G8Pvvv+P06dM4efJkmXXJycmQy+WwtbXVWe7i4oLk5GRtmwcDU+n60nWPa5OVlYX8/Hwolcoy+y4sLERhYaH2eVZWVsUPjoiIiGoNo+5pSkxMxIQJE7B27VqYmZkZuhwds2fPho2Njfbh4eFh6JKIiIioGhl1aIqMjERqaiqeeeYZyGQyyGQy7N+/H4sXL4ZMJoOLiwuKioqQkZGh87qUlBS4uroCAFxdXctcTVf6/EltrK2ty+1lAoCpU6ciMzNT+0hMTNTHIRMREZGRMurQ1L17d0RHRyMqKkr7aN26NYYMGaL9v6mpKXbv3q19TWxsLBISEhAcHAwACA4ORnR0NFJTU7VtIiIiYG1tjYCAAG2bB7dR2qZ0G+VRKBSwtrbWeRAREVHdZdRjmqysrNCsWTOdZRYWFnBwcNAuHz16NCZPngx7e3tYW1vj/fffR3BwMNq3bw8A6NGjBwICAjBs2DDMnTsXycnJ+PzzzzF27FgoFAoAwDvvvIPvv/8eH330Ed544w3s2bMHGzZswLZt22r2gImIiMhoGXVoEmPBggWQSqV46aWXUFhYiNDQUPzwww/a9SYmJti6dSveffddBAcHw8LCAiNGjMBXX32lbePj44Nt27Zh0qRJWLRoEerXr4//+7//Q2hoqCEOiYiIiIxQrQtN+/bt03luZmaGpUuXYunSpY98jZeXF7Zv3/7Y7Xbt2hVnzpzRR4lERERUBxn1mCYiIiIiY8HQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0ERFVE0EQcCUlG9fv5UIQBEOXQ0RVJDN0AUREddX521nYcykVAOBmYwY7UzsDV0REVcGeJiKiapCaXYD9l+8AACQSICmzABftOiA2OdvAlRFRZTE0ERHpWbFEhu3RyVBrBPg4WuCNDj6oZ6uEIJFi+YFrhi6PiCqJoYmISM9SlN7IzFfBykyGHgEusDSToVMjRwDAlrO3kJJVYOAKiagyGJqIiPQszcwdANDW2x5mpiYAAFcbM1gV3YNKLWDlkesGrI6IKouhiYhIj+Lv5iLP1AYSCdDQyVJnnWteHABg7bEbyC0sNkR5RFQFDE1ERHq0PToJAOBhZw6l3ERnnV1hCnwcLZBVUIxNZ24ZojwiqgKGJiIiPdp2riQ0+TpbllknAfBqaw8AwH8XU2qyLCLSA4YmIiI9uX43FxeSsgBBU+bUXKnnmjgDAI7G3UN+kbomyyOiKmJoIiLSk233T83ZFN0tc2quVGMXS9SzVaKwWIMjcXdrsjwiqiKGJiIiPdkfWzKZpX1h0iPbSCQSdGviBADa2cKJqHYw6tA0e/ZstGnTBlZWVnB2dsaAAQMQGxur06agoABjx46Fg4MDLC0t8dJLLyElRXesQEJCAvr06QNzc3M4OztjypQpKC7WvXJl3759eOaZZ6BQKNCoUSOsXLmyug+PiOqQwmI1om5mAACsi+49tm3pKbq9l1J5TzqiWsSoQ9P+/fsxduxYHDt2DBEREVCpVOjRowdyc3O1bSZNmoR//vkHf/zxB/bv34/bt29j4MCB2vVqtRp9+vRBUVERjhw5glWrVmHlypWYPn26tk18fDz69OmDbt26ISoqChMnTsSbb76Jf//9t0aPl4hqr+ibmSgq1sDRUg6FOvexbYMbOEIhk+J2ZgFiU3hbFaLawqhv2Ltz506d5ytXroSzszMiIyPRuXNnZGZm4ueff8a6devw3HPPAQBWrFgBf39/HDt2DO3bt8euXbtw4cIF/Pfff3BxcUFQUBC+/vprfPzxx5gxYwbkcjnCw8Ph4+OD+fPnAwD8/f1x6NAhLFiwAKGhoTV+3ERU+5y8ng4AaO1lj/S4x7dVyk3QoaED9sbewZ5LqWjial0DFRJRVRl1T9PDMjMzAQD29vYAgMjISKhUKoSEhGjbNGnSBJ6enjh69CgA4OjRo2jevDlcXFy0bUJDQ5GVlYWYmBhtmwe3UdqmdBtERE9y6noaAKC1t52o9qWn6PbdHwdFRMav1oQmjUaDiRMnomPHjmjWrBkAIDk5GXK5HLa2tjptXVxckJycrG3zYGAqXV+67nFtsrKykJ+fX249hYWFyMrK0nkQ0dNJoxFw6kZJT1Mbb3tRr+l4/150UQkZKFBx6gGi2qDWhKaxY8fi/Pnz+P333w1dCoCSQeo2Njbah4eHh6FLIiIDuXonB5n5KihNTRDgLu5Um4+jBVytzVCk1iDyfuAiIuNWK0LTuHHjsHXrVuzduxf169fXLnd1dUVRUREyMjJ02qekpMDV1VXb5uGr6UqfP6mNtbU1lEpluTVNnToVmZmZ2kdiYmKVjpGIaq+T90/NtfS0hamJuB+rEokEwQ0dAIDzNRHVEkYdmgRBwLhx47Bp0ybs2bMHPj4+OutbtWoFU1NT7N69W7ssNjYWCQkJCA4OBgAEBwcjOjoaqan/mw8lIiIC1tbWCAgI0LZ5cBulbUq3UR6FQgFra2udBxE9nU6VDgIXeWquVHCDktB0NO7xUxQQkXEw6qvnxo4di3Xr1uHvv/+GlZWVdgySjY0NlEolbGxsMHr0aEyePBn29vawtrbG+++/j+DgYLRv3x4A0KNHDwQEBGDYsGGYO3cukpOT8fnnn2Ps2LFQKBQAgHfeeQfff/89PvroI7zxxhvYs2cPNmzYgG3bthns2Imo9ijtaWojchB4qdKepnM3M5FbWAwLhVH/SCZ66hl1T9OyZcuQmZmJrl27ws3NTftYv369ts2CBQvwwgsv4KWXXkLnzp3h6uqKjRs3atebmJhg69atMDExQXBwMIYOHYrhw4fjq6++0rbx8fHBtm3bEBERgcDAQMyfPx//93//x+kGiOiJ7uYU4mZ6PiQSIMjDtkKv9bA3R307JYo1gjZ4EZHxMuo/a8TMlGtmZoalS5di6dKlj2zj5eWF7du3P3Y7Xbt2xZkzZypcIxE93aJvlUyF4uNoASsz0wq/PriBA/6IvImjcffQ1c9Z3+URkR4ZdU8TEZGxO3+zJDS1qGdTqdd3aHR/XNM1jmsiMnYMTUREVXDufk9T8/q2lXp9cIOS+ZrO38pEZp5KX2URUTVgaCIiqoLo+z1NzSvZ0+RqY4aGThbQCPqbemD9yQQM/b/juGXhi7s5hXrZJhEZ+ZgmIiJjlppdgOSsAkgkQFORk1qW51lfJ8TdycXBq3fRq7lbpbdTrNZg5raLWHnkeskCyyZYezwBbb3ttVfqUd0wbtIU3MnQvdmzk60Vvl8wz0AVPR0YmoiIKun8/VNzjZwsqzRdwLO+jlh55DoOXal8T5MgCBi37gx2xpRMzTK0vSe27j+JDIULTlxPg6eDOerZlj9ZL9U+dzKy0f71yTrLjq0LM1A1Tw+eniMiqqRzpafm6lfu1Fypdg0cIJNKkJCWhxv3ciu1ja3nkrAzJhlymRQ/DHkGMwc0h1/GCQS4lfSARVxIgUqtqVKdRE87hiYiokqq6nimUpYKGZ7xLJkY82AlepvyiorxzfaLAICxXRuh9wOn+Dr7OsJSIUNmvoozjxNVEUMTEVEllc7R1KKKPU0A0Mm35Cq6ypyiW7r3KpIyC1DfTom3uzTQWacwNcFzTUrmfzp7MwN5RcVVrpXoacXQRERUCSlZBUjNLoRUAgS4VT00PXs/NB2Juwu15skT+5ZKTMvDTwfiAQDTXgiAmalJmTY+jhZwtlJAIwAXk7LLrCcicRiaiIgqoXQ8k6+zFZTyskGlolrUt4W1mQxZBcU4k5Au+nXL9sehSK1Bx0YO6BHg8sh2pacQo29lQnwkI6IHMTQREVVC9M0MAFUfBF7KRCrRnkbbei5J1GuSMvPx56mbAICJIY0hkUge2baxixXkJlJk5quQJXesesFETyGGJiKiStDneKZS/YPqAQC2nruNYhFXuv24/xqK1Bq087FHG2/7x7aVy6Twc7UCAKQqPateLNFTiKGJiKiCBEHQhqZmVbxy7kGdfB1hZ26KuzlFOPKEK93uZBfitxMJAID3n/MVtf3SU3TpCjfc40zhRBXG0EREVEFJmQW4m1MEE6lEOw+SPpiaSNGnRcl0AX9H3X5s2/D9cSgs1iDIwxYdG4mb7dvJSgFnKwUEiRS7LqRUuV6ipw1nBCciqqDSXqbGLlblXq1WFf2D6uHXYwn4NyYZs1TNyt1+Yloe1hy9AQCY/PzjxzI9rJGzJVKzC7HzfDJea6vf03Tl3doD4O09qO5gaCIiqqDSSS1b6PHUXKlWnnaoZ6vErYx8RFxIQd9A9zJtvtsViyK1Bp0aOaJzY6cKbb+RkyWOxN3Dkbi7yMxXwUZpqq/Sy721B8Dbe1DdwdNzREQVdK50PJMeB4GXkkoleLFlyYDw73bFokCl1lkffTNTe+ruk15NKrx9Ows5lMXZUKkF7L2UWvWCiZ4iDE1ERBUgCIJ2uoHq6GkCgLe7NICrtRlu3MvDwv+uaJdn5qvw8V/nAAAvtqxX6UHodgUlUxrsPJ9c9WKfcsVqDdTQ7ylaMl4MTUREFXArIx/peSqYmkjQxM2qWvZhZWaKr/o3BQD8dPAajsbdQ2a+CiNXnMCFpCw4WMjxYahfpbdvX1gSmvZdTkV+kfoJrak8giBg4+mbeObrCJxy7okNpxJx+kY6NBWYzZ1qH45pIiKqgNLxTI1drKCQVV8PQ4+mrujVzBU7zifjtZ+OQSIBBAGwUZpizeh2qGerrPS2zYuzUN9OiZvp+dh/+Q56NnPVY+V1X15RMSatj8K/MfevQJRIkZRZgKTMAmTkq9DNz6lCg/Op9mBPExFRBZy9qf9JLR/lq/7N0NXPCWamUggCYKWQYc3otghwr9o0BxIAoU1LgtJ/Fzn1QEXN3n4J/8akwNREgimhfgi8uxudGpXMsh59KxNRiRmGLZCqDXuaiKjG1eZL00/fvy9cSw+7at+Xk5UCK0e1hVojICEtD1ZmMjhaKvSy7e7+zvj5UDz2XEqFWiPARMqeETGOXbuHNcdKpnv4vxFt0KWxEw6szkMrLztIABy8ehcHrtyFvYXcsIVStWBoIqIaV1svTS9Wa3Du/iDwlp62NbZfE6kEPo4Wet1mG297WJvJkJZbhKjEdLTyevxtWAjIL1JrB+K/1tYTXR6a7qGlpy3S8ooQczsL+y/fgS8YROsanp4jIhLpUnI2ClQaWJnJ0NDJ0tDlVImpiRRd/UpuEBxxgVMPiLF071XcuJcHNxszTO1ddroHiUSCZ30dYWYqRXqeCneUHgaokqoTQxMRkUhn7p+aC/KwhbQOnM4KCXABAOzmuKYnyipQYdWR6wCAL/oGwNqs/ElBFTIT7c2Tb1o05tWJdQxPzxERiXQmIQMA8Ixn9Y9nqgldGjtBJpXgSmoObtzLhZeDfk8B1oTyxsdVx9i4NUdvILuwGI1dLNEj4PFXG7aoZ4OoxAxkFyix8sh1vNu1oV5rIcNhTxMRkUjaQeA1OJ6pOtkoTdHWp6RX5L+LtfMUXen4uAcf5V1kUBUFKjVWHI4HALzTpeETexllJlK0b1ByE+WfDl4rM6s71V4MTUREIqTlFuH6vTwANXPlXE3p7l9yim5XDGcHf5Q/TiXibk4R6tkqy70XYHmauFhBrs5DWm4RNp+5Vc0VUk1haCIiEqF0PFNDJwvYmOvvJreG1uP+uKaT19NwN6fQwNUYH7VGwE8HS3qZ3u7SAKYm4n5tSqUSuOaVvO7nQ/EQBM4UXhdwTBPRU6Q2z49kaHVtPFMpD3tzNK9ng+hbmdgVk4LX23kauiSjsi82FQlpebBRmuKVVhW7Gs4pPwH3HFrgSmoODly5W2aKAqp9GJqIniK1dX4kYxB5o3Q8U90KTQDQs5krom9lYsf5JIamh6w6WjKR5aA2HlDKK3bbHJlQjFfbeGDF4ev4+VA8Q1MdwNNzRERPkF+k1oamdg3q3iSQve7fe+5o3D1k5qkMXI3xuHYnBwcu34FEAgxt51WpbYzq4AOJBDhw+Q6upOh3gDrVPIYmIqInOHUjDUVqDdxszNBAzzNzG4MGTpZo4mqFYo2ACM7ZpFV6u5Tn/Jzh6WBeqW14Ophrx439cv8KPKq9GJqIiJ7g0NW7AICOjRzr7N3re97vbdp5PsnAlRiH3MJi/Bl5EwAwvIN3lbY1ulMDAMDG07dwj4PtazWGJiKiJzh8PzSV3sm+LurVzA0AcODyXaTnFhm4GsPbHHUL2QXF8HG0wLNV/NzbeNuhRX0bFBZrsPZ4gp4qJENgaCIieoy03JIbsAJAh0YOBq6m+vi5WqGpuzWK1Bpsjnq65xUSBAGrj5ScmhvW3qvKt8yRSCQY3ckHALD66A0UFnOyy9qKoYmI6DGOxt2DIABNXK3gbGVm6HKq1eA2JZfU/34i8ameV+h4fBpiU7JhLjfBS63q62WbvZu7wdXaDHdzCrHp9NMdSmszhiYiosd4cDxTXdcvqB4UMiliU7Jx9mamocsxmNVHrwMAXmxZDzZK/UxkamoixZvPlvQ2Ld13FSq1Ri/bpZrF0ERE9AiCIDwV45lK2ShN0bt5ydim9SefzrE3SZn5+Dem5ArC4cHeet32kHZecLSUIzEtH5t4a5VaiZNbEj2AM2bTgy4lZyMhLQ9yEyna+NS9+ZnKM6iNBzaduYUtUbfxWZ8AWCqerl8TvxyKh1ojoJ2PPfxcrfS6baXcBGM6N8A32y9h6d6rGNiyHmQib8tCxuHp+m4gegLOmE0P2nL2NgCgq5/TUxMe2vnYo4GjBa7dzcXqo9fxXtdGhi6pxtzLKcSvx0p62N7p2rBa9jG0vRd+3H8NN+7lYeOZW3i1dcVuzUKG9XT8FCAiqiBBEPDP/dDUP6iegaupORKJBO93b4RJ688ifF8chrT1qlM3KH6c/zsUj3yVGi3q26BrNd3yxFwuw9tdSnqb5uy4hBB/F9hbyKtlX/pUXi/809gDz35BIqJynE7IwM30fFjITdDd39nQ5dSofoH10MTVClkFxVi2P87Q5dSI9NwirD5yHQAw/jnfap3EdGQHH/i5WOFebhG+3nqh2vajT6W98A8+yhvKUNexp4nIgDiGynhtuT9XUY+mrjAzrdiNWms7E6kEU0L9MHrVKaw4HI9RHb3hYl23p1v46eA15BapEeBmXe0hWS6TYs7LLTDwh8PYdOYW+gW6o1uTpyuY11YMTUQGxDFUxqlYrcG26JLbifQLdDdwNYbxXBNntPKyQ+SNdHyw4SxWjmpTZwctX07Jxk8HrwEAJoZUby9TqSAPW4zu5IOfDsZj8oYo/PpmOzR1t6n2/VLV1M3vACKiKtgbewd3c4pgZ26KTr51f6qB8kgkEswc0AzmchMcunq31pxGqiiNRsAnf52DSi0gxN8Zz9+/uW5NmPy8HwI9bJGep8LrPx1HtMi5sQqL1SiQKlFUzLmeahp7moiozqvIaVBBELBkzxUAwKutPWBaR3tXxPB3s8aCQUF4e00kVh29AQdLBcZ2awSTKt5WxJj8evwGTidkwFIhw9cDmtXoDZmVchOsGd0WI385gdMJGXjlxyN4tbUHhgd7o76dEqYmUiSk5SE2ORuxydm4nJKN2JRsxN/NhdopBGf3x0Euk6K+rRJ+rlbQsB+k2jE0EVGdV5HToPti7+DczUwoTU3wVucGNVGeUQtt6oopoX6Y928swiIuI+JCCqb2aoJnvOxq/VivI1fvYua2iwCAj3r6wc1GWeM1WJuZYvXodnh7zSkcvnoPq4/ewOqjN574OomggSCRoqhYg2t3c3Htbi5MHbtj/ckEvNzKo9YHW2Md78nQRER0nyAIWLi7pJdpWLAXHC0VBq7IOLzXtSHsLeSYvf0iom9l4vX/Ow65iRQNnS2hkEkhk0ogM5Hgom17pEUnwVIhg625KTzszGFrpNMVnE5Ix5urT6GoWIPnA1wwtJ2XwWqxVMjw6+h2OBp3D8sPXsOBy3eguX/rP4VMCl8XS/i5WMPP1RKNXazQxNUa4ydORKtXJyIjrwhXUnNwKTkbOYVm+PivaKw5dgPfvRKIJq7WBjumqjLW8Z4MTURE9+2NTcXZxAyYmUrx1rPsZSolkUjwWltPhPi74Lt/Y/HfxRTcyy3CxaQs3YYKJ2Sl5ugsslTIYGPhh9sZ+XC3rfmenIcVqzVYdyIB83bGIq9IjU6NHLHktZaQGrhnRiKRoEMjR3Ro5Ai1RkBeUTHyVWo4WCjK7TWSoOQqPGdrMzhbm6F9Awds2fI30p0Ccf5WFvp9fxif9GyCkR28DX5sdQlDExkFY+2KJf3IyCtC9K1MXErKRm5RMW5a+CEqMQNOlgo4WsmhkBn+NE9qdgE++SsaADCsvRecrNjL9DAnKwXmvNwCgiDgxr08xN/NRbFGgFqjQbFGwIJlP6Ne6x7IKSxGanYhkjILkFNYjBzLxug0Zw/6Brrj/ed80cjZUu+1CYKAIrUGGg1QJFUgObMAxRoN1BoBao2AuzlFOHk9DVuibiM2peRnTVsfeywf3sroTjOaSCWwMjOFlZn4XjoTqQRuedfw1wcT8NGfZ7E39g6+2noBe2NTMf+VQDjX8SkjagpDExkFY+2KpcorUKmx83wyfj+ZgGPX0nRXWjbGrct3AABSCeDlYIEmBhzIWqzW4P11Z5CaXYjGLpaY9Hxjg9RRW0gkEng7WsDb0UJn+ZqCW2jpaad9XqwuGW9z6PR5ZMsd8XfUbfxz9jb6BbpjfHdfNHCqfHi6nZGPI3H3cM06ENeOJyAzX4Ui9f2ryZx6oP3s3Y98rY3SFB/2aIzX23nV+rE/D3OyUuCXkW3w67EbmLntIg5euYvQhQfw7UstENrU1dDl1XoMTURPiQKVGpmmDohKzEBGXhHyi9SApOQv1CyLxvg76hYC69vCy8G8SlcQXUzKwvqTidh05hYy81Xa5V4O5mjmbgNbc1NE7N0PS89mSM0uRE5hMeLv5iL+bi5MHZ/D/x28htfaesKihu71VqzWYMY/MTgenwYLuQmWDW0Fczl/NOqDzESKxi5WSEs/imkz52DR7iuIuJCCzVG3seV+eBrV0QeBHraitnftTg52nE/GvzHJOFd6eb7SE8gp1G0oCDCVSSGVSCCTSiCVSmCpkOEZTzu08bZD/6B6sKsFty6pLIlEgmHB3ghu6IDxv0XhQlIW3l4TidfaemDaCwH8+q4CvnNEdVhmvgr/nk/GtugkHI+/hwL7Drh0v4dHh6UfJvweBQCoZ6tEcEMHdGjogI6NHEXNBJ2WW4Qd55Ow4WQizj4w10w9WyVebe2BV1rX1xnPcnXLUrQP7KF97aXkLFxIykJuoRIzt13E93uvYlQHH4zo4AVb8+r75aaSyjHs5xM4eu0eAGDOyy3QsAq9H/RozerZ4KfhrXH+ViYW/ncF/10sCU+bo26jRX0bPO/vgk6+jvBxtIC1mSlUGg1SswpxKTkbx67dw8Erd3A55X/jpSSSkgkiky+eROvgzrA1l8PKTAaZVILjvy3A+hXhBjxa49DI2QqbxnZA2K7LWH7wGn47kYhDV+/iwx5+6NvCnWOdKoGhiZ5aGo2A5KwCXL+Xi7TcIhSoNEg188DV1ByYmUphq5TDQmFSo/O26EOBSo3dF1Pxd9Qt7Iu9879TFgBM1QXwdHWArbkcFvKScRzFGgFXzp+Bh18LRN/KxK2MfPwZeRN/Rt4EADR0skBwQwf4uVrDy94cFgoTqDXAnexCXErOwqnr6Tgef097tY+piQTPB7hgcBtPdGzk+MTTH/YWcnRo6Ii2PvbY/vdGaHyCceNeHhb8dxnLD8Th9XaeePPZBnq9jUduYTHO3czEOYduKL52D+ZyE8x5qQVeaPF0zv5dk5rVs8H/jSgJT78cisfWc0kln8XNTMyPuAyg5JRt6dfTg2TSksHSPZu64vkAFzhZKTBo1Bo0cOpdw0dReyhkJpja2x9d/JzwwYazSEzLx4TfoxC+/xpea+uBPs3d4MCrREVjaKJy1cWB2flFapxJTMep6+k4eT0Np2+kI7dIrdvIJgjx92+fAQByEykcreQQLP2xIzoJLT3t4GpTtV/e2QUqJKTl4VZ6Pu6Y1ce5mxkASk6TKWQmsFTIUCQ1Q7FaI/q2FZn5KpyIT8OO6CT8G5Osc1yNXSzRP6geQvxdMG3qFAT3KDt2TH3kLNa/9y7yiopx6no6DsfdxdG4e4i+lYm4O7mIu5P7xBoC3KzxYst6ePGZepW6VF8mlcI5PwFrJ3+C7eeT8cPeq7iUnI2fDsZj5ZHr6OrnjIEt6+HZxk6wrMCpu6wCFXJkNriUnIW72UW4mZGH1KxCCAAglcPX2RI/DHkGvi5WFa6ZKq9ZPRuEDQrCp338sfN8Mg5duYsT19OQllukDUxymRSe9uZo62OP9g0c0KWxE2yUxjmFgbHr0NAR/03ugp8PxWP5gWu4mJSF6X/H4Mt/LqChkwWauFrDyUoBM9OSnzl5RWrkFaqRW1SMvCI1LtgFI/5EAlRqDVTqkg9IY/8sRq04gcYuVghwt4a/mzUaOFrU2dvtAAxNZSxduhTz5s1DcnIyAgMDsWTJErRt29bQZdW4ujAw+052ISJvpJWEpBvpiLmVieKH/nyVSSWob6eEs7UZlKYmOBt9HkpnL+QVqZFVUDKw9HZGAWDRCO+uPQ0AcLMxQ0tPWwR52KKRsyU87c3hYKGAUm4CmVSCPJUaWfkqJKblIzEtDzfScpGQlo+Ee7lISMtDet7/xvnApiWuxZZzuszpeTT+fAecrczgZmsGd1slHC3ksFGaQmFqgmK1gNyiYiSmlVzBFJuSDeGBQ6tnq0T/IHf0C3LXmavlSX1m5nIZOjd2QufGTgCAzDwVjsXfw8n4NFy/X39hsQYmEgmslKZocv+H5XNNnOFhb16hz+dRZCZS9At0R98WbtgXewc/7LuKk9fTEXEhBREXUmAilaCZuzWauFrD3VYJewtTSCQSqDUC7uUU4m5uEe5mF+JuTiES0vJxN6cQcOiMmJgUnf242ZjB4sYh7Jg1rU7/kDd2jpYKDG3vhaHtS+ZJKirWICOvCKYmUtiam9a6nl5jZqGQYXx3Xwxt74WNp2/i76jbiL6VicspOTqnPssld0R29kNjx0xtsTf2DvY+8DNMLpPCz8UKzevboHm9kkdjFyvIZU/+HhMEAdmFxUjNKkSmqQMuJWchr1CNfJUaakGARiMg26x+ZQ5dbxiaHrB+/XpMnjwZ4eHhaNeuHRYuXIjQ0FDExsbC2Zl3oNaH7AIVkjMLkJRZoP03KTMfl2zb4eqxG8gtKgaEkoGMEgmgcgxBl3l7YaM0hY3SFNb3/7W9/2/ppcLFGgGZ+Sqk5xbhRloert3Jwc30/DL7d7U2Qxsfe7TxtkNrL3s0drHU+YU5aNQvaN+7EwBArRGQnleE1KxCnI08DseGLXApOauk5uhkbI9OrvT7YG8hR307Ja7HXYVTfW/tMRSqNCWXaBcUQQMpkrMKkJxVgDMJGU/cZgNHCzzr64h+QfXwjKetXn7Z2JibIrSpq0GuupFIJOjWxBndmjgjNjkbG8/cxPboJCSm5ePszUydsVNPYqougLODHezMTeFuq0R9OyWszExx7HISA5ORKZ17iKqPvYUcbz7bAG8+2wDJmQW4mJSFS8nZyMgvQqGq5HS+udwEFgpZyb9yGZb//AsCuvSD3EQKUxMJBABndv2JYaNG41JyFi4mZeNSUhZyi9SIvpWJ6Fv/+/40NZGgnq0S9eyUsDOXw1Ihg4lUApVag9xCNVKzC5CaXYiUrAIU3N8/7Dvg0kN/6ACAg9ypJt6iR2JoekBYWBjeeustjBo1CgAQHh6Obdu24ZdffsEnn3xi4OqqTqXWIKegGDmFxcgqUCErv+TfzHwVsvJVyCooRla+CoXFGlyzboGsC8kQBEBAyRgDE4kEqVbN8NU/F2Aqk8BUKoWpiRQyEwnk9/+VmUiRX1R8f5sl/6blFpX88r8/Z0u5FM5AblHZ5SZK3LiXV6njlUgAPxcrtPKyQ+v7Iam+nVJ0mDCRSuBoqYCjpQJZ+6KxfsJY5BYWI/pWJs4kZODczQxcv5eHm2l5yH7ouEp/SHg6WMDTXgkvewt42Cvhef/f0vlXBo36Fe17dyiz76PrwvD9ooVIyizA7Yx83MrIR3peETLzVShUaWAqk0JpaoL6dkp42JmjRX2bOv2Lxs/VClN7+WNqL3/cysjHqetpuHEvD7cz8pGZr4JGECCVSGBvIS/5zKwUcLSQw91WCR8nC7z17vtoX85pSaKnnauNGVxtzNCtyeM7Bv76IQk+D00xYVuUqu0hBErGiSam5+H8rSxE38rE+fvhKTNfhev38nBd5M9yKzMZVDkZcHR0grnCBOamMpiYSGAikSAt6lTFD1KPGJruKyoqQmRkJKZOnapdJpVKERISgqNHjxqsriNxd/HD3jhcunwFRar7v5glJSnfVCZDo4YNAAEQIGhPzwgo6eYs1gjIKShGdmExsgtU/0vwYii9cCep7JgmmPvgl8PxVTomazMZ3G2VcLUxg5uNGVytldj81wYEdukNC4UMUokEGqHkeM7t+BUzPvsYmfkq7SMj739Br7BYAwECTKRS2ChlsFXK4WlvDk8Hc/i7Wet9/IOFQob2DRzQvoGDzvJitQZ5KjXUagHmChPITaRV6umRANqZfsVejv20qGerRL2geoYug4geIpVK4OVgAS8HC/Rp4Qag5HfR7cwC3EzLw830kj9ycguLoRYEmJqU/PHnbK2Ai7UZnK0UcLYyg1JugkGj3kH70HKGiBxPKrOsJjE03Xf37l2o1Wq4uLjoLHdxccGlS5fKtC8sLERh4f/O72ZmlnRFZmVllWlbFQnJ93AgJgGA4v4DJakIAFTA3Us3K7xNhakUlnIZrJUyWJmZwlopg7XCFFZKGawUJae8Nv6zDZ4tgiGBBJCUfOFrNEBizHH0Ce0BlUaDYrVwf1Dg//5frBFgZmoCG6UprBQyWCllsFHK4WptBmcbBVyszMqdf2f/iji4mAklBwWUpAYJIM+7g8b2MpR8qVbwFgyqfGSpyp6ie+xLiopQkFv23L6qqEjUZ2sCoDAPKHxiS/3sr6Jqen/GUkdd35+x1GGI4y5vn3X9fX7UPo11f1ZSwN/RFP6OT/ojVg1VQS5UBTX7npZuTxDKuWTzYQIJgiAIt27dEgAIR44c0Vk+ZcoUoW3btmXaf/HFFwLud+rwwQcffPDBBx+1+5GYmPjErMCepvscHR1hYmKClBTdgWcpKSlwdS07CHbq1KmYPPl/XYcajQZpaWlwcHCoM1d7ZGVlwcPDA4mJibC2rr13yxaLx1u38XjrNh5v3VddxywIArKzs+Hu/uR52hia7pPL5WjVqhV2796NAQMGACgJQrt378a4cePKtFcoFFAodOeisbW1rYFKa561tfVT800J8HjrOh5v3cbjrfuq45htbGxEtWNoesDkyZMxYsQItG7dGm3btsXChQuRm5urvZqOiIiInl4MTQ8YNGgQ7ty5g+nTpyM5ORlBQUHYuXNnmcHhRERE9PRhaHrIuHHjyj0d9zRSKBT44osvypyGrKt4vHUbj7du4/HWfcZwzBJBEHONHREREdHTjfcPICIiIhKBoYmIiIhIBIYmIiIiIhEYmoiIiIhEYGgiHbNnz0abNm1gZWUFZ2dnDBgwALGxsYYuq8Z8++23kEgkmDhxoqFLqVa3bt3C0KFD4eDgAKVSiebNm+PUKcPePby6qNVqTJs2DT4+PlAqlWjYsCG+/vprcfeZqgUOHDiAvn37wt3dHRKJBJs3b9ZZLwgCpk+fDjc3NyiVSoSEhODKlSuGKVYPHne8KpUKH3/8MZo3bw4LCwu4u7tj+PDhuH37tuEKrqInfb4PeueddyCRSLBw4cIaq0/fxBzvxYsX0a9fP9jY2MDCwgJt2rRBQkJCjdTH0EQ69u/fj7Fjx+LYsWOIiIiASqVCjx49kJuba+jSqt3Jkyfx448/okWLFoYupVqlp6ejY8eOMDU1xY4dO3DhwgXMnz8fdnZ2hi6tWsyZMwfLli3D999/j4sXL2LOnDmYO3culixZYujS9CI3NxeBgYFYunRpuevnzp2LxYsXIzw8HMePH4eFhQVCQ0NRUFBQw5Xqx+OONy8vD6dPn8a0adNw+vRpbNy4EbGxsejXr58BKtWPJ32+pTZt2oRjx46JuhWIMXvS8cbFxaFTp05o0qQJ9u3bh3PnzmHatGkwMzOrmQL1cbNbqrtSU1MFAML+/fsNXUq1ys7OFnx9fYWIiAihS5cuwoQJEwxdUrX5+OOPhU6dOhm6jBrTp08f4Y033tBZNnDgQGHIkCEGqqj6ABA2bdqkfa7RaARXV1dh3rx52mUZGRmCQqEQfvvtNwNUqF8PH295Tpw4IQAQbty4UTNFVaNHHe/NmzeFevXqCefPnxe8vLyEBQsW1Hht1aG84x00aJAwdOhQwxQkCAJ7muixMjMzAQD29vYGrqR6jR07Fn369EFISIihS6l2W7ZsQevWrfHKK6/A2dkZLVu2xE8//WTosqpNhw4dsHv3bly+fBkAcPbsWRw6dAi9evUycGXVLz4+HsnJyTpf1zY2NmjXrh2OHj1qwMpqTmZmJiQSSZ29N6hGo8GwYcMwZcoUNG3a1NDlVCuNRoNt27ahcePGCA0NhbOzM9q1a/fYU5b6xtBEj6TRaDBx4kR07NgRzZo1M3Q51eb333/H6dOnMXv2bEOXUiOuXbuGZcuWwdfXF//++y/effddjB8/HqtWrTJ0adXik08+weDBg9GkSROYmpqiZcuWmDhxIoYMGWLo0qpdcnIyAJS5FZSLi4t2XV1WUFCAjz/+GK+99lqdvantnDlzIJPJMH78eEOXUu1SU1ORk5ODb7/9Fj179sSuXbvw4osvYuDAgdi/f3+N1MDbqNAjjR07FufPn8ehQ4cMXUq1SUxMxIQJExAREVFz58QNTKPRoHXr1vjmm28AAC1btsT58+cRHh6OESNGGLg6/duwYQPWrl2LdevWoWnTpoiKisLEiRPh7u5eJ4+XSqhUKrz66qsQBAHLli0zdDnVIjIyEosWLcLp06chkUgMXU6102g0AID+/ftj0qRJAICgoCAcOXIE4eHh6NKlS7XXwJ4mKte4ceOwdetW7N27F/Xr1zd0OdUmMjISqampeOaZZyCTySCTybB//34sXrwYMpkMarXa0CXqnZubGwICAnSW+fv719jVJzVtypQp2t6m5s2bY9iwYZg0adJT0bPo6uoKAEhJSdFZnpKSol1XF5UGphs3biAiIqLO9jIdPHgQqamp8PT01P78unHjBj744AN4e3sbujy9c3R0hEwmM+jPL/Y0kQ5BEPD+++9j06ZN2LdvH3x8fAxdUrXq3r07oqOjdZaNGjUKTZo0wccffwwTExMDVVZ9OnbsWGYaicuXL8PLy8tAFVWvvLw8SKW6fx+amJho/2qty3x8fODq6ordu3cjKCgIAJCVlYXjx4/j3XffNWxx1aQ0MF25cgV79+6Fg4ODoUuqNsOGDSszDjM0NBTDhg3DqFGjDFRV9ZHL5WjTpo1Bf34xNJGOsWPHYt26dfj7779hZWWlHfdgY2MDpVJp4Or0z8rKqsx4LQsLCzg4ONTZcVyTJk1Chw4d8M033+DVV1/FiRMnsHz5cixfvtzQpVWLvn37YtasWfD09ETTpk1x5swZhIWF4Y033jB0aXqRk5ODq1evap/Hx8cjKioK9vb28PT0xMSJEzFz5kz4+vrCx8cH06ZNg7u7OwYMGGC4oqvgccfr5uaGl19+GadPn8bWrVuhVqu1P8Ps7e0hl8sNVXalPenzfTgUmpqawtXVFX5+fjVdql486XinTJmCQYMGoXPnzujWrRt27tyJf/75B/v27auZAg123R4ZJQDlPlasWGHo0mpMXZ9yQBAE4Z9//hGaNWsmKBQKoUmTJsLy5csNXVK1ycrKEiZMmCB4enoKZmZmQoMGDYTPPvtMKCwsNHRperF3795yv2dHjBghCELJtAPTpk0TXFxcBIVCIXTv3l2IjY01bNFV8LjjjY+Pf+TPsL179xq69Ep50uf7sNo+5YCY4/3555+FRo0aCWZmZkJgYKCwefPmGqtPIgh1ZFpcIiIiomrEgeBEREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCQCQxMRGY19+/ZBIpEgIyPD0KXA29sbCxcuNHQZRGREGJqI6Km2cuVK2Nralll+8uRJjBkzpuYLIiKjxXvPERGVw8nJydAlEJGRYU8TEVUbjUaD2bNnw8fHB0qlEoGBgfjzzz+167dv347GjRtDqVSiW7duuH79us7rZ8yYgaCgIJ1lCxcuhLe3t86yX375BU2bNoVCoYCbmxvGjRunXRcWFobmzZvDwsICHh4eeO+995CTkwOg5HTgqFGjkJmZCYlEAolEghkzZgAoe3ouISEB/fv3h6WlJaytrfHqq68iJSWlTK1r1qyBt7c3bGxsMHjwYGRnZz/2PfL29sY333yDN954A1ZWVvD09NS5eXJ5pyyjoqIgkUi071dpb9nWrVvh5+cHc3NzvPzyy8jLy8OqVavg7e0NOzs7jB8/Hmq1+rH1ENGjMTQRUbWZPXs2Vq9ejfDwcMTExGDSpEkYOnQo9u/fj8TERAwcOBB9+/ZFVFQU3nzzTXzyyScV3seyZcswduxYjBkzBtHR0diyZQsaNWqkXS+VSrF48WLExMRg1apV2LNnDz766CMAQIcOHbBw4UJYW1sjKSkJSUlJ+PDDD8vsQ6PRoH///khLS8P+/fsRERGBa9euYdCgQTrt4uLisHnzZmzduhVbt27F/v378e233z7xGObPn4/WrVvjzJkzeO+99/Duu+8iNja2Qu9DXl4eFi9ejN9//x07d+7Evn378OKLL2L79u3Yvn071qxZgx9//FEntBJRBdXYrYGJ6KlSUFAgmJubC0eOHNFZPnr0aOG1114Tpk6dKgQEBOis+/jjjwUAQnp6uiAIgvDFF18IgYGBOm0WLFggeHl5aZ+7u7sLn332mei6/vjjD8HBwUH7fMWKFYKNjU2Zdg/eLX7Xrl2CiYmJkJCQoF0fExMjABBOnDihrdXc3FzIysrStpkyZYrQrl27x9bj5eUlDB06VPtco9EIzs7OwrJlywRB+N9d30vfE0EQhDNnzggAhPj4eO0xABCuXr2qbfP2228L5ubmQnZ2tnZZaGio8Pbbbz+2HiJ6NPY0EVG1uHr1KvLy8vD888/D0tJS+1i9ejXi4uJw8eJFtGvXTuc1wcHBFdpHamoqbt++je7duz+yzX///Yfu3bujXr16sLKywrBhw3Dv3j3k5eWJ3s/Fixfh4eEBDw8P7bKAgADY2tri4sWL2mXe3t6wsrLSPndzc0NqaioAYO3atTrvw8GDB7XtWrRoof2/RCKBq6ur9nVimZubo2HDhtrnLi4u8Pb2hqWlpc6yim6XiP6HA8GJqFqUjhvatm0b6tWrp7NOoVBg/PjxT9yGVCqFIAg6y1Qqlfb/SqXysa+/fv06XnjhBbz77ruYNWsW7O3tcejQIYwePRpFRUUwNzcXeziimJqa6jyXSCTQaDQAgH79+umExAffk8e9Tiot+dv2wffhwffgcdt43HaJqOIYmoioWgQEBEChUCAhIQFdunQps97f3x9btmzRWXbs2DGd505OTkhOToYgCJBIJABKBkGXsrKygre3N3bv3o1u3bqV2UdkZCQ0Gg3mz5+vDR8bNmzQaSOXy584ONrf3x+JiYlITEzU9jZduHABGRkZCAgIeOxrH6z1wV4osUqv4ktKSoKdnR0A3feAiGoOQxMRVQsrKyt8+OGHmDRpEjQaDTp16oTMzEwcPnwY1tbWeOeddzB//nxMmTIFb775JiIjI7Fy5UqdbXTt2hV37tzB3Llz8fLLL2Pnzp3YsWMHrK2ttW1mzJiBd955B87OzujVqxeys7Nx+PBhvP/++2jUqBFUKhWWLFmCvn374vDhwwgPD9fZh7e3N3JycrB7924EBgbC3Ny8TA9USEgImjdvjiFDhmDhwoUoLi7Ge++9hy5duqB169bV9h4CQKNGjeDh4YEZM2Zg1qxZuHz5MubPn1+t+ySi8nFMExFVm6+//hrTpk3D7Nmz4e/vj549e2Lbtm3w8fGBp6cn/vrrL2zevBmBgYEIDw/HN998o/N6f39//PDDD1i6dCkCAwNx4sSJMle3jRgxAgsXLsQPP/yApk2b4oUXXsCVK1cAAIGBgQgLC8OcOXPQrFkzrF27FrNnz9Z5fYcOHfDOO+9g0KBBcHJywty5c8sch0Qiwd9//w07Ozt07twZISEhaNCgAdavX6/nd6wsU1NT/Pbbb7h06RJatGiBOXPmYObMmdW+XyIqSyI8PGCAiIiIiMpgTxMRERGRCAxNRERERCIwNBERERGJwNBEREREJAJDExEREZEIDE1EREREIjA0EREREYnA0EREREQkAkMTERERkQgMTUREREQiMDQRERERicDQRERERCTC/wO90EvVhlFevwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAHHCAYAAABwaWYjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABiPElEQVR4nO3deVxU5f4H8M/sDOAALoAom/u+YSKulfxE5VamlXqt0CiXsFS8arao2S1M07TcqntTK8vl3rJyjUDzpoSK4oKKu5gyoCkzqMgy8/z+sDk5ggoIc2b083695nWbc75zznPOUPO5z3nOcxRCCAEiIiIiKjel3A0gIiIicjUMUEREREQVxABFREREVEEMUEREREQVxABFREREVEEMUEREREQVxABFREREVEEMUEREREQVxABFREREVEEMUEQuYvr06VAoFA7Z18MPP4yHH35Yer9161YoFAr85z//ccj+hw0bhpCQEIfsq7KuXLmCF198Ef7+/lAoFBg3bpzcTbJz+vRpKBQKLFu2rEq3GxISgmHDhlXpNsvr1r9LIjkxQBHJYNmyZVAoFNLLzc0NAQEBiIqKwkcffYT8/Pwq2c/58+cxffp0pKenV8n2qpIzt6083nvvPSxbtgyjR4/Gl19+ieeee07uJt3Vhg0bMH36dLmbQXRfUMvdAKIH2YwZMxAaGori4mIYjUZs3boV48aNw9y5c/HDDz+gTZs2Uu2bb76J1157rULbP3/+PN5++22EhISgXbt25f7cTz/9VKH9VMad2vbZZ5/BarVWexvuRXJyMjp37oxp06bJ3ZQyBQcHo6CgABqNRlq2YcMGLFy40GVDlCP+LonKiwGKSEZ9+/ZFx44dpfdTpkxBcnIy/va3v+Hxxx/H4cOHodfrAQBqtRpqdfX+K3vt2jW4u7tDq9VW637u5uYffWeVm5uLFi1ayN2M27L1bN5P5P67JLoZL+EROZlHH30Ub731Fs6cOYOvvvpKWl7WGKjExER069YN3t7e8PT0RNOmTfH6668DuDFu6aGHHgIADB8+XLpcaBsT8/DDD6NVq1ZIS0tDjx494O7uLn32dmNNLBYLXn/9dfj7+8PDwwOPP/44zp49a1dzuzEyN2/zbm0rawzU1atXMWHCBAQGBkKn06Fp06b44IMPIISwq1MoFBgzZgzWrl2LVq1aQafToWXLlti0aVPZJ/wWubm5iI2NhZ+fH9zc3NC2bVssX75cWm8bD3bq1CmsX79eavvp06fvuN2vvvoKnTp1gru7O3x8fNCjRw+7HpXvv/8e0dHRCAgIgE6nQ8OGDfHOO+/AYrGUOo+2761Lly7Q6/UIDQ3FkiVL7OpuHQM1bNgwLFy4UDpHtpfNBx98gC5duqBWrVrQ6/UICwu75zFvW7duRceOHeHm5oaGDRvik08+KfPveOnSpXj00Ufh6+sLnU6HFi1aYPHixaW2d7uxeatXr8a7776L+vXrw83NDb169cLx48fvqe1Ed8MeKCIn9Nxzz+H111/HTz/9hJdeeqnMmoyMDPztb39DmzZtMGPGDOh0Ohw/fhzbt28HADRv3hwzZszA1KlTMWLECHTv3h0A0KVLF2kbf/zxB/r27YvBgwfj2WefhZ+f3x3b9e6770KhUGDy5MnIzc3FvHnzEBkZifT0dKmnrDzK07abCSHw+OOPY8uWLYiNjUW7du2wefNmTJw4EefOncOHH35oV//rr7/i22+/xcsvv4waNWrgo48+wsCBA5GVlYVatWrdtl0FBQV4+OGHcfz4cYwZMwahoaFYs2YNhg0bhry8PIwdOxbNmzfHl19+ifHjx6N+/fqYMGECAKBOnTq33e7bb7+N6dOno0uXLpgxYwa0Wi1SU1ORnJyM3r17A7gxLs7T0xPx8fHw9PREcnIypk6dCrPZjNmzZ9tt7/Lly+jXrx+eeeYZDBkyBKtXr8bo0aOh1WrxwgsvlNmGkSNH4vz580hMTMSXX35Zav38+fPx+OOPY+jQoSgqKsLKlSvx9NNPY926dYiOjr7tsd3O3r170adPH9StWxdvv/02LBYLZsyYUeZ5Wrx4MVq2bInHH38carUaP/74I15++WVYrVbExcXddV8zZ86EUqnEP/7xD5hMJsyaNQtDhw5FampqhdtNVG6CiBxu6dKlAoDYtWvXbWu8vLxE+/btpffTpk0TN/8r++GHHwoA4sKFC7fdxq5duwQAsXTp0lLrevbsKQCIJUuWlLmuZ8+e0vstW7YIAKJevXrCbDZLy1evXi0AiPnz50vLgoODRUxMzF23eae2xcTEiODgYOn92rVrBQDxz3/+067uqaeeEgqFQhw/flxaBkBotVq7Zfv27RMAxMcff1xqXzebN2+eACC++uoraVlRUZGIiIgQnp6edsceHBwsoqOj77g9IYQ4duyYUCqV4sknnxQWi8VundVqlf752rVrpT47cuRI4e7uLq5fvy4ts31vc+bMkZYVFhaKdu3aCV9fX1FUVCSEEOLUqVOlzm9cXJy43X/2b91/UVGRaNWqlXj00Uftlt/u+73VY489Jtzd3cW5c+ekZceOHRNqtbpUG8o69qioKNGgQQO7Zbf7u2zevLkoLCyUls+fP18AEAcOHLhrO4kqi5fwiJyUp6fnHe/G8/b2BnDj0k9lB1zrdDoMHz683PXPP/88atSoIb1/6qmnULduXWzYsKFS+y+vDRs2QKVS4dVXX7VbPmHCBAghsHHjRrvlkZGRaNiwofS+TZs2MBgMOHny5F334+/vjyFDhkjLNBoNXn31VVy5cgW//PJLhdu+du1aWK1WTJ06FUql/X9yb76UdXMPXn5+Pi5evIju3bvj2rVrOHLkiN3n1Go1Ro4cKb3XarUYOXIkcnNzkZaWVuE23rr/y5cvw2QyoXv37tizZ0+Ft2WxWPDzzz+jf//+CAgIkJY3atQIffv2veO+TSYTLl68iJ49e+LkyZMwmUx33d/w4cPtxkfZejTv9n0T3QsGKCIndeXKFbuwcqtBgwaha9euePHFF+Hn54fBgwdj9erVFQpT9erVq9DA3MaNG9u9VygUaNSo0V3H/9yrM2fOICAgoNT5aN68ubT+ZkFBQaW24ePjg8uXL991P40bNy4VdG63n/I4ceIElErlXQecZ2Rk4Mknn4SXlxcMBgPq1KmDZ599FgBKhYiAgAB4eHjYLWvSpAkAVPq7WLduHTp37gw3NzfUrFkTderUweLFi+8YYCwWC4xGo92rqKgIubm5KCgoQKNGjUp9pqxl27dvR2RkJDw8PODt7Y06depI4/HKE6Bu/b59fHwA4K7fN9G94BgoIif0+++/w2QylfljY6PX67Ft2zZs2bIF69evx6ZNm7Bq1So8+uij+Omnn6BSqe66n4qMWyqv2032abFYytWmqnC7/YhbBpw7i7y8PPTs2RMGgwEzZsxAw4YN4ebmhj179mDy5MnVPqXD//73Pzz++OPo0aMHFi1ahLp160Kj0WDp0qX4+uuvb/u5s2fPIjQ01G7Zli1b0LRp03Lv+8SJE+jVqxeaNWuGuXPnIjAwEFqtFhs2bMCHH35YrmN3te+b7g8MUEROyDbINyoq6o51SqUSvXr1Qq9evTB37ly89957eOONN7BlyxZERkZW+czlx44ds3svhMDx48ft5qvy8fFBXl5eqc+eOXMGDRo0kN5XpG3BwcH4+eefkZ+fb9cLZbu0FRwcXO5t3W0/+/fvh9VqteuFupf9NGzYEFarFYcOHbrtXFxbt27FH3/8gW+//RY9evSQlp86darM+vPnz+Pq1at2vVBHjx4FgDvO4H67c/7f//4Xbm5u2Lx5M3Q6nbR86dKlt90WAPj7+yMxMdFuWdu2bWEwGODm5lbmnXC3Lvvxxx9RWFiIH374wa4nacuWLXfcN5HceAmPyMkkJyfjnXfeQWhoKIYOHXrbukuXLpVaZvuBLiwsBADpB7asQFMZX3zxhd24rP/85z/Izs62G9fSsGFD/PbbbygqKpKWrVu3rtR0BxVpW79+/WCxWLBgwQK75R9++CEUCkWZ42oqo1+/fjAajVi1apW0rKSkBB9//DE8PT3Rs2fPCm+zf//+UCqVmDFjRqneFFsPia0H5eYek6KiIixatKjMbZaUlOCTTz6xq/3kk09Qp04dhIWF3bYttzvnKpUKCoXCbsqE06dPY+3atXc8Njc3N0RGRtq9fHx8oFKpEBkZibVr1+L8+fNS/fHjx0uNVyvr2E0m013DG5Hc2ANFJKONGzfiyJEjKCkpQU5ODpKTk5GYmIjg4GD88MMPd5wIccaMGdi2bRuio6MRHByM3NxcLFq0CPXr10e3bt0A3Agz3t7eWLJkCWrUqAEPDw+Eh4eXuuxSXjVr1kS3bt0wfPhw5OTkYN68eWjUqJHdVAsvvvgi/vOf/6BPnz545plncOLECXz11Vd2g7or2rbHHnsMjzzyCN544w2cPn0abdu2xU8//YTvv/8e48aNK7XtyhoxYgQ++eQTDBs2DGlpaQgJCcF//vMfbN++HfPmzbvjmLTbadSoEd544w2888476N69OwYMGACdToddu3YhICAACQkJ6NKlC3x8fBATE4NXX30VCoUCX3755W0vQQUEBOD999/H6dOn0aRJE6xatQrp6en49NNP7zgJqS1cvfrqq4iKioJKpcLgwYMRHR2NuXPnok+fPvj73/+O3NxcLFy4EI0aNcL+/fsrfMzAjXnLfvrpJ3Tt2hWjR4+WAnCrVq3sHt/Tu3dvaLVaPPbYYxg5ciSuXLmCzz77DL6+vsjOzq7UvokcQr4bAIkeXLZpDGwvrVYr/P39xf/93/+J+fPn290ub3PrNAZJSUniiSeeEAEBAUKr1YqAgAAxZMgQcfToUbvPff/996JFixbS7eO229p79uwpWrZsWWb7bne7+DfffCOmTJkifH19hV6vF9HR0eLMmTOlPj9nzhxRr149odPpRNeuXcXu3btLbfNObbt1GgMhhMjPzxfjx48XAQEBQqPRiMaNG4vZs2fbTQUgxI1pDOLi4kq1qby33+fk5Ijhw4eL2rVrC61WK1q3bl3mVAvlncbA5vPPPxft27cXOp1O+Pj4iJ49e4rExERp/fbt20Xnzp2FXq8XAQEBYtKkSWLz5s0CgNiyZYtUZ/vedu/eLSIiIoSbm5sIDg4WCxYssNtfWdMYlJSUiFdeeUXUqVNHKBQKu7+nf//736Jx48ZCp9OJZs2aiaVLl5b6m7Mdd3nOoxA3/kbbt28vtFqtaNiwofjXv/4lJkyYINzc3OzqfvjhB9GmTRvh5uYmQkJCxPvvvy8+//xzAUCcOnXK7tjL+rtcs2bNXY+dqKophOAoOyIiV/Hwww/j4sWLOHjwoNxNqZT+/fsjIyOj1Hg6IlfDMVBERFQtCgoK7N4fO3YMGzZsKPMxQUSuhmOgiIioWjRo0ADDhg1DgwYNcObMGSxevBharRaTJk2Su2lE94wBioiIqkWfPn3wzTffwGg0QqfTISIiAu+9916pCVmJXBHHQBERERFVEMdAEREREVUQAxQRERFRBXEMVBWxWq04f/48atSoUeWPzyAiIqLqIYRAfn4+AgICSj1I/E4YoKrI+fPnERgYKHcziIiIqBLOnj2L+vXrl7ueAaqK2B7xcPbsWRgMBplbQ0REROVhNpsRGBhY4Uc1MUBVEdtlO4PBwABFRETkYio6/IaDyImIiIgqiAGKiIiIqIIYoIiIiIgqiAGKiIiIqIIYoIiIiIgqiAGKiIiIqIIYoIiIiIgqiAGKiIiIqIIYoIiIiIgqiAGKiIiIqIIYoIiIiIgqiAGKiIiIqIIYoIiIiIgqSC13A+jOsk0F2P+7CV56DTo3qCV3c4iIiAgy90BZLBa89dZbCA0NhV6vR8OGDfHOO+9ACCHVCCEwdepU1K1bF3q9HpGRkTh27Jjddi5duoShQ4fCYDDA29sbsbGxuHLlil3N/v370b17d7i5uSEwMBCzZs0q1Z41a9agWbNmcHNzQ+vWrbFhw4bqOfAKSD15CSO/TMPCLcflbgoRERH9SdYA9f7772Px4sVYsGABDh8+jPfffx+zZs3Cxx9/LNXMmjULH330EZYsWYLU1FR4eHggKioK169fl2qGDh2KjIwMJCYmYt26ddi2bRtGjBghrTebzejduzeCg4ORlpaG2bNnY/r06fj000+lmh07dmDIkCGIjY3F3r170b9/f/Tv3x8HDx50zMm4DXetCgBwtbBE1nYQERHRTYSMoqOjxQsvvGC3bMCAAWLo0KFCCCGsVqvw9/cXs2fPltbn5eUJnU4nvvnmGyGEEIcOHRIAxK5du6SajRs3CoVCIc6dOyeEEGLRokXCx8dHFBYWSjWTJ08WTZs2ld4/88wzIjo62q4t4eHhYuTIkeU6FpPJJAAIk8lUrvry+vXYBRE8eZ3oPfeXKt0uERERVf73W9YeqC5duiApKQlHjx4FAOzbtw+//vor+vbtCwA4deoUjEYjIiMjpc94eXkhPDwcKSkpAICUlBR4e3ujY8eOUk1kZCSUSiVSU1Olmh49ekCr1Uo1UVFRyMzMxOXLl6Wam/djq7Ht51aFhYUwm812r+og9UAVsQeKiIjIWcg6iPy1116D2WxGs2bNoFKpYLFY8O6772Lo0KEAAKPRCADw8/Oz+5yfn5+0zmg0wtfX1269Wq1GzZo17WpCQ0NLbcO2zsfHB0aj8Y77uVVCQgLefvvtyhx2hXjqbnxF14os1b4vIiIiKh9Ze6BWr16NFStW4Ouvv8aePXuwfPlyfPDBB1i+fLmczSqXKVOmwGQySa+zZ89Wy37c/wxQHANFRETkPGTtgZo4cSJee+01DB48GADQunVrnDlzBgkJCYiJiYG/vz8AICcnB3Xr1pU+l5OTg3bt2gEA/P39kZuba7fdkpISXLp0Sfq8v78/cnJy7Gps7+9WY1t/K51OB51OV5nDrhCPPy/hFZZYUWKxQq3i1F1ERERyk/XX+Nq1a1Aq7ZugUqlgtVoBAKGhofD390dSUpK03mw2IzU1FREREQCAiIgI5OXlIS0tTapJTk6G1WpFeHi4VLNt2zYUFxdLNYmJiWjatCl8fHykmpv3Y6ux7Ucu7tq/Mu5VXsYjIiJyCrIGqMceewzvvvsu1q9fj9OnT+O7777D3Llz8eSTTwIAFAoFxo0bh3/+85/44YcfcODAATz//PMICAhA//79AQDNmzdHnz598NJLL2Hnzp3Yvn07xowZg8GDByMgIAAA8Pe//x1arRaxsbHIyMjAqlWrMH/+fMTHx0ttGTt2LDZt2oQ5c+bgyJEjmD59Onbv3o0xY8Y4/LzcTKtWQvtnr9M1DiQnIiJyDtV0V2C5mM1mMXbsWBEUFCTc3NxEgwYNxBtvvGE33YDVahVvvfWW8PPzEzqdTvTq1UtkZmbabeePP/4QQ4YMEZ6ensJgMIjhw4eL/Px8u5p9+/aJbt26CZ1OJ+rVqydmzpxZqj2rV68WTZo0EVqtVrRs2VKsX7++3MdSXdMYCCFE27c3i+DJ68SxnPy7FxMREVG5Vfb3WyHETdN+U6WZzWZ4eXnBZDLBYDBU6ba7zkzGubwCfB/XFW0Dvat020RERA+yyv5+c0SyC+BcUERERM6FAcoF2KYyuFbIQeRERETOgAHKBXjq2ANFRETkTBigXIBtKgPORk5EROQcGKBcgG0yTc5GTkRE5BwYoFzAX49zYQ8UERGRM2CAcgG2HihOpElEROQcGKBcgIetB4oBioiIyCkwQLkADy0v4RERETkTBigX4K7jIHIiIiJnwgDlAjw4jQEREZFTYYByARwDRURE5FwYoFwA54EiIiJyLgxQLoDzQBERETkXBigXwHmgiIiInAsDlAuQeqA4iJyIiMgpMEC5AM8/78IrKrGi2GKVuTVERETEAOUC9H9ewgOAaxwHRUREJDsGKBegVSuhVd34qjiVARERkfwYoFyEbTZyDiQnIiKSHwOUi+Dz8IiIiJwHA5SL8ODz8IiIiJwGA5SLcNdyKgMiIiJnwQDlIjw4BoqIiMhpMEC5CHeOgSIiInIaDFAuwlN6Hh57oIiIiOTGAOUi3P+cTJPzQBEREcmPAcpFePzZA3WNg8iJiIhkxwDlIqQeKF7CIyIikh0DlIvgGCgiIiLnwQDlIjgPFBERkfNggHIRnAeKiIjIecgaoEJCQqBQKEq94uLiAADXr19HXFwcatWqBU9PTwwcOBA5OTl228jKykJ0dDTc3d3h6+uLiRMnoqTEPmRs3boVHTp0gE6nQ6NGjbBs2bJSbVm4cCFCQkLg5uaG8PBw7Ny5s9qOuzI4DxQREZHzkDVA7dq1C9nZ2dIrMTERAPD0008DAMaPH48ff/wRa9aswS+//ILz589jwIAB0uctFguio6NRVFSEHTt2YPny5Vi2bBmmTp0q1Zw6dQrR0dF45JFHkJ6ejnHjxuHFF1/E5s2bpZpVq1YhPj4e06ZNw549e9C2bVtERUUhNzfXQWfi7jy07IEiIiJyGsKJjB07VjRs2FBYrVaRl5cnNBqNWLNmjbT+8OHDAoBISUkRQgixYcMGoVQqhdFolGoWL14sDAaDKCwsFEIIMWnSJNGyZUu7/QwaNEhERUVJ7zt16iTi4uKk9xaLRQQEBIiEhIRyt91kMgkAwmQyVeygyyk967IInrxOdElIqpbtExERPYgq+/vtNGOgioqK8NVXX+GFF16AQqFAWloaiouLERkZKdU0a9YMQUFBSElJAQCkpKSgdevW8PPzk2qioqJgNpuRkZEh1dy8DVuNbRtFRUVIS0uzq1EqlYiMjJRqnIFtDBQn0iQiIpKfWu4G2KxduxZ5eXkYNmwYAMBoNEKr1cLb29uuzs/PD0ajUaq5OTzZ1tvW3anGbDajoKAAly9fhsViKbPmyJEjt21vYWEhCgsLpfdms7n8B1sJtjFQ1zgGioiISHZO0wP173//G3379kVAQIDcTSmXhIQEeHl5Sa/AwMBq3Z9ec6MHqshihcUqqnVfREREdGdOEaDOnDmDn3/+GS+++KK0zN/fH0VFRcjLy7OrzcnJgb+/v1Rz6115tvd3qzEYDNDr9ahduzZUKlWZNbZtlGXKlCkwmUzS6+zZsxU76ApSqxTSPxdbrNW6LyIiIrozpwhQS5cuha+vL6Kjo6VlYWFh0Gg0SEpKkpZlZmYiKysLERERAICIiAgcOHDA7m65xMREGAwGtGjRQqq5eRu2Gts2tFotwsLC7GqsViuSkpKkmrLodDoYDAa7V3XSqP76qhigiIiI5CX7GCir1YqlS5ciJiYGavVfzfHy8kJsbCzi4+NRs2ZNGAwGvPLKK4iIiEDnzp0BAL1790aLFi3w3HPPYdasWTAajXjzzTcRFxcHnU4HABg1ahQWLFiASZMm4YUXXkBycjJWr16N9evXS/uKj49HTEwMOnbsiE6dOmHevHm4evUqhg8f7tiTcQc3B6gSCy/hERERyUn2APXzzz8jKysLL7zwQql1H374IZRKJQYOHIjCwkJERUVh0aJF0nqVSoV169Zh9OjRiIiIgIeHB2JiYjBjxgypJjQ0FOvXr8f48eMxf/581K9fH//6178QFRUl1QwaNAgXLlzA1KlTYTQa0a5dO2zatKnUwHI5qZQKKBWAVbAHioiISG4KIQS7M6qA2WyGl5cXTCZTtV3Oa/LmRhSVWLH9tUdRz1tfLfsgIiJ6kFT299spxkBR+WiUNwaSl7AHioiISFYMUC5Eo77xdfESHhERkbwYoFyIWmkLULzqSkREJCcGKBei/XMuKPZAERERyYsByoWoVeyBIiIicgYMUC7ENhs5B5ETERHJiwHKhWjZA0VEROQUGKBciK0HqtjKHigiIiI5MUC5ENvjXIpLGKCIiIjkxADlQjR/TmNQYuUlPCIiIjkxQLkQNacxICIicgoMUC5Ew0HkREREToEByoVoOI0BERGRU2CAciF/9UAxQBEREcmJAcqFcCZyIiIi58AA5UI0yj8v4XEeKCIiIlkxQLkQDiInIiJyDgxQLoTTGBARETkHBigXwkHkREREzoEByoX8NY0BL+ERERHJiQHKhfAuPCIiIufAAOVCeAmPiIjIOTBAuRBOY0BEROQcGKBciEZ94+sqKuElPCIiIjkxQLkQNXugiIiInAIDlAuxjYHiXXhERETyYoByIbYAVcRB5ERERLJigHIhamkeKAYoIiIiOTFAuRAt54EiIiJyCgxQLoTPwiMiInIODFAuRK38cxC5lT1QREREcmKAciFaNXugiIiInAEDlAux9UBxDBQREZG8ZA9Q586dw7PPPotatWpBr9ejdevW2L17t7ReCIGpU6eibt260Ov1iIyMxLFjx+y2cenSJQwdOhQGgwHe3t6IjY3FlStX7Gr279+P7t27w83NDYGBgZg1a1aptqxZswbNmjWDm5sbWrdujQ0bNlTPQVcSn4VHRETkHGQNUJcvX0bXrl2h0WiwceNGHDp0CHPmzIGPj49UM2vWLHz00UdYsmQJUlNT4eHhgaioKFy/fl2qGTp0KDIyMpCYmIh169Zh27ZtGDFihLTebDajd+/eCA4ORlpaGmbPno3p06fj008/lWp27NiBIUOGIDY2Fnv37kX//v3Rv39/HDx40DEnoxw0nMaAiIjIOQgZTZ48WXTr1u22661Wq/D39xezZ8+WluXl5QmdTie++eYbIYQQhw4dEgDErl27pJqNGzcKhUIhzp07J4QQYtGiRcLHx0cUFhba7btp06bS+2eeeUZER0fb7T88PFyMHDmyXMdiMpkEAGEymcpVXxl7sy6L4MnrRJeEpGrbBxER0YOksr/fsvZA/fDDD+jYsSOefvpp+Pr6on379vjss8+k9adOnYLRaERkZKS0zMvLC+Hh4UhJSQEApKSkwNvbGx07dpRqIiMjoVQqkZqaKtX06NEDWq1WqomKikJmZiYuX74s1dy8H1uNbT+3KiwshNlstntVNw2nMSAiInIKsgaokydPYvHixWjcuDE2b96M0aNH49VXX8Xy5csBAEajEQDg5+dn9zk/Pz9pndFohK+vr916tVqNmjVr2tWUtY2b93G7Gtv6WyUkJMDLy0t6BQYGVvj4K0p6Fh6nMSAiIpKVrAHKarWiQ4cOeO+999C+fXuMGDECL730EpYsWSJns8plypQpMJlM0uvs2bPVvk9pEHkJe6CIiIjkJGuAqlu3Llq0aGG3rHnz5sjKygIA+Pv7AwBycnLsanJycqR1/v7+yM3NtVtfUlKCS5cu2dWUtY2b93G7Gtv6W+l0OhgMBrtXdVMr/7yEZ2WAIiIikpOsAapr167IzMy0W3b06FEEBwcDAEJDQ+Hv74+kpCRpvdlsRmpqKiIiIgAAERERyMvLQ1pamlSTnJwMq9WK8PBwqWbbtm0oLi6WahITE9G0aVPpjr+IiAi7/dhqbPtxBtIlPM4DRUREJK9qGtReLjt37hRqtVq8++674tixY2LFihXC3d1dfPXVV1LNzJkzhbe3t/j+++/F/v37xRNPPCFCQ0NFQUGBVNOnTx/Rvn17kZqaKn799VfRuHFjMWTIEGl9Xl6e8PPzE88995w4ePCgWLlypXB3dxeffPKJVLN9+3ahVqvFBx98IA4fPiymTZsmNBqNOHDgQLmOxRF34V3Mvy6CJ68TwZPXCavVWm37ISIielBU9vdb1gAlhBA//vijaNWqldDpdKJZs2bi008/tVtvtVrFW2+9Jfz8/IROpxO9evUSmZmZdjV//PGHGDJkiPD09BQGg0EMHz5c5Ofn29Xs27dPdOvWTeh0OlGvXj0xc+bMUm1ZvXq1aNKkidBqtaJly5Zi/fr15T4ORwSovGtFUoAqLLZU236IiIgeFJX9/VYIIXg9qAqYzWZ4eXnBZDJV23iogiILmk/dBADIeDsKHjp1teyHiIjoQVHZ32/ZH+VC5af+cx4ogOOgiIiI5MQA5UJsd+EBvBOPiIhITgxQLkShUHA2ciIiIifAAOVi1EpOZUBERCQ3BigXY+uBKmIPFBERkWwYoFwMJ9MkIiKSHwOUi5Geh8ceKCIiItkwQLkYNQeRExERyY4BysVIl/CsvIRHREQkFwYoFyNNY1DCHigiIiK5MEC5GNs0BsXsgSIiIpINA5SL0ahtd+GxB4qIiEguDFAuRqPkIHIiIiK5MUC5mL/uwuMlPCIiIrkwQLkYzgNFREQkPwYoF8OZyImIiOTHAOVipGkMrOyBIiIikgsDlItR2y7hcR4oIiIi2TBAuRjbXXiciZyIiEg+DFAuxjYGqoiDyImIiGTDAOVi1BxETkREJDsGKBej/XMQOWciJyIikg8DlItRS5fw2ANFREQkFwYoF6NmDxQREZHsGKBcjJYzkRMREcmOAcrFqJV/BihOY0BERCQbBigXo1HzEh4REZHcGKBcjMbWA8VB5ERERLJhgHIxtkHkHANFREQkHwYoF6PhIHIiIiLZMUC5GI00jQEv4REREcmFAcrF8Fl4RERE8pM1QE2fPh0KhcLu1axZM2n99evXERcXh1q1asHT0xMDBw5ETk6O3TaysrIQHR0Nd3d3+Pr6YuLEiSgpKbGr2bp1Kzp06ACdTodGjRph2bJlpdqycOFChISEwM3NDeHh4di5c2e1HPO94rPwiIiI5Cd7D1TLli2RnZ0tvX799Vdp3fjx4/Hjjz9izZo1+OWXX3D+/HkMGDBAWm+xWBAdHY2ioiLs2LEDy5cvx7JlyzB16lSp5tSpU4iOjsYjjzyC9PR0jBs3Di+++CI2b94s1axatQrx8fGYNm0a9uzZg7Zt2yIqKgq5ubmOOQkVoFH+eQnPyh4oIiIi2QgZTZs2TbRt27bMdXl5eUKj0Yg1a9ZIyw4fPiwAiJSUFCGEEBs2bBBKpVIYjUapZvHixcJgMIjCwkIhhBCTJk0SLVu2tNv2oEGDRFRUlPS+U6dOIi4uTnpvsVhEQECASEhIKPexmEwmAUCYTKZyf6YyEjOMInjyOvH4gl+rdT9EREQPgsr+fsveA3Xs2DEEBASgQYMGGDp0KLKysgAAaWlpKC4uRmRkpFTbrFkzBAUFISUlBQCQkpKC1q1bw8/PT6qJioqC2WxGRkaGVHPzNmw1tm0UFRUhLS3NrkapVCIyMlKqcSZ8Fh4REZH81HLuPDw8HMuWLUPTpk2RnZ2Nt99+G927d8fBgwdhNBqh1Wrh7e1t9xk/Pz8YjUYAgNFotAtPtvW2dXeqMZvNKCgowOXLl2GxWMqsOXLkyG3bXlhYiMLCQum92Wyu2MFXEp+FR0REJL9KBaiTJ0+iQYMG97zzvn37Sv/cpk0bhIeHIzg4GKtXr4Zer7/n7VenhIQEvP322w7fLweRExERya9Sl/AaNWqERx55BF999RWuX79eZY3x9vZGkyZNcPz4cfj7+6OoqAh5eXl2NTk5OfD39wcA+Pv7l7orz/b+bjUGgwF6vR61a9eGSqUqs8a2jbJMmTIFJpNJep09e7ZSx1xR0kzkHEROREQkm0oFqD179qBNmzaIj4+Hv78/Ro4cWSW3/V+5cgUnTpxA3bp1ERYWBo1Gg6SkJGl9ZmYmsrKyEBERAQCIiIjAgQMH7O6WS0xMhMFgQIsWLaSam7dhq7FtQ6vVIiwszK7GarUiKSlJqimLTqeDwWCwezmCdAmvhD1QREREsrmXkevFxcXiv//9r3jssceERqMRLVu2FHPmzBG5ubnl+vyECRPE1q1bxalTp8T27dtFZGSkqF27tvT5UaNGiaCgIJGcnCx2794tIiIiREREhPT5kpIS0apVK9G7d2+Rnp4uNm3aJOrUqSOmTJki1Zw8eVK4u7uLiRMnisOHD4uFCxcKlUolNm3aJNWsXLlS6HQ6sWzZMnHo0CExYsQI4e3tbXd339046i68w9kmETx5nQh756dq3Q8REdGDoLK/31UyjcH169fF3LlzhU6nEwqFQuh0OvHcc8+J8+fP3/FzgwYNEnXr1hVarVbUq1dPDBo0SBw/flxaX1BQIF5++WXh4+Mj3N3dxZNPPimys7PttnH69GnRt29fodfrRe3atcWECRNEcXGxXc2WLVtEu3bthFarFQ0aNBBLly4t1ZaPP/5YBAUFCa1WKzp16iR+++23Cp0DRwWo47n5InjyOtF62qa7FxMREdEdVfb3WyGEqPS1oN27d+Pzzz/HypUr4eHhgZiYGMTGxuL333/H22+/DbPZ7LQzelc1s9kMLy8vmEymar2cl/XHNfSYvQXuWhUOzehTbfshIiJ6EFT297tSd+HNnTsXS5cuRWZmJvr164cvvvgC/fr1g1J5Y3xOaGgoli1bhpCQkMpsnu5AzYcJExERya5SAWrx4sV44YUXMGzYMNStW7fMGl9fX/z73/++p8ZRaTc/TFgIAYVCIXOLiIiIHjyVClDHjh27a41Wq0VMTExlNk93oFH9FZgsViH1SBEREZHjVGoag6VLl2LNmjWllq9ZswbLly+/50bR7dl6oACgmJfxiIiIZFGpAJWQkIDatWuXWu7r64v33nvvnhtFt3dzjxMn0yQiIpJHpQJUVlYWQkNDSy0PDg6WHgZM1UOj/Osr40ByIiIieVQqQPn6+mL//v2llu/btw+1atW650bR7SmVCqiUfz7OhQ8UJiIikkWlAtSQIUPw6quvYsuWLbBYLLBYLEhOTsbYsWMxePDgqm4j3ULNAEVERCSrSt2F98477+D06dPo1asX1Oobm7BarXj++ec5BsoBtColCkusHEROREQkk0oFKK1Wi1WrVuGdd97Bvn37oNfr0bp1awQHB1d1+6gMf02myR4oIiIiOVQqQNk0adIETZo0qaq2UDnZpjJgDxQREZE8KhWgLBYLli1bhqSkJOTm5sJ6y+30ycnJVdI4KttfAYo9UERERHKoVIAaO3Ysli1bhujoaLRq1YqPE3Ew6RIe54EiIiKSRaUC1MqVK7F69Wr069evqttD5SA9D6+El/CIiIjkUKlpDLRaLRo1alTVbaFy4jQGRERE8qpUgJowYQLmz58PIdgDIget+sbXxkt4RERE8qjUJbxff/0VW7ZswcaNG9GyZUtoNBq79d9++22VNI7KxrvwiIiI5FWpAOXt7Y0nn3yyqttC5aRR8RIeERGRnCoVoJYuXVrV7aAK4DQGRERE8qrUGCgAKCkpwc8//4xPPvkE+fn5AIDz58/jypUrVdY4KpsUoHgXHhERkSwq1QN15swZ9OnTB1lZWSgsLMT//d//oUaNGnj//fdRWFiIJUuWVHU76Sa2S3hF7IEiIiKSRaV6oMaOHYuOHTvi8uXL0Ov10vInn3wSSUlJVdY4KputB4rPwiMiIpJHpXqg/ve//2HHjh3QarV2y0NCQnDu3LkqaRjdnpZ34REREcmqUj1QVqsVFoul1PLff/8dNWrUuOdG0Z2peQmPiIhIVpUKUL1798a8efOk9wqFAleuXMG0adP4eBcH4F14RERE8qrUJbw5c+YgKioKLVq0wPXr1/H3v/8dx44dQ+3atfHNN99UdRvpFn+NgeIlPCIiIjlUKkDVr18f+/btw8qVK7F//35cuXIFsbGxGDp0qN2gcqoetke5sAeKiIhIHpUKUACgVqvx7LPPVmVbqJw4jQEREZG8KhWgvvjiizuuf/755yvVGCoftZI9UERERHKqVIAaO3as3fvi4mJcu3YNWq0W7u7uDFDVTLqEx5nIiYiIZFGpu/AuX75s97py5QoyMzPRrVs3DiJ3AOlhwlb2QBEREcmh0s/Cu1Xjxo0xc+bMUr1TVPU0nEiTiIhIVlUWoIAbA8vPnz9flZukMvz1MGH2QBEREcmhUgHqhx9+sHt9//33WLJkCZ599ll07dq1Ug2ZOXMmFAoFxo0bJy27fv064uLiUKtWLXh6emLgwIHIycmx+1xWVhaio6Ph7u4OX19fTJw4ESUlJXY1W7duRYcOHaDT6dCoUSMsW7as1P4XLlyIkJAQuLm5ITw8HDt37qzUcTiCdAmPg8iJiIhkUalB5P3797d7r1AoUKdOHTz66KOYM2dOhbe3a9cufPLJJ2jTpo3d8vHjx2P9+vVYs2YNvLy8MGbMGAwYMADbt28HAFgsFkRHR8Pf3x87duxAdnY2nn/+eWg0Grz33nsAgFOnTiE6OhqjRo3CihUrkJSUhBdffBF169ZFVFQUAGDVqlWIj4/HkiVLEB4ejnnz5iEqKgqZmZnw9fWtxBmqXrYeKE5jQEREJBMhs/z8fNG4cWORmJgoevbsKcaOHSuEECIvL09oNBqxZs0aqfbw4cMCgEhJSRFCCLFhwwahVCqF0WiUahYvXiwMBoMoLCwUQggxadIk0bJlS7t9Dho0SERFRUnvO3XqJOLi4qT3FotFBAQEiISEhHIfh8lkEgCEyWQq/8FX0g/p50Tw5HVi8Ccp1b4vIiKi+1llf7+rdAxUZcTFxSE6OhqRkZF2y9PS0lBcXGy3vFmzZggKCkJKSgoAICUlBa1bt4afn59UExUVBbPZjIyMDKnm1m1HRUVJ2ygqKkJaWppdjVKpRGRkpFRTlsLCQpjNZruXo/BZeERERPKq1CW8+Pj4ctfOnTv3tutWrlyJPXv2YNeuXaXWGY1GaLVaeHt72y338/OD0WiUam4OT7b1tnV3qjGbzSgoKMDly5dhsVjKrDly5Mht256QkIC33377tuurE8dAERERyatSAWrv3r3Yu3cviouL0bRpUwDA0aNHoVKp0KFDB6lOoVDcdhtnz57F2LFjkZiYCDc3t8o0Q1ZTpkyxC5JmsxmBgYEO2fdfY6A4jQEREZEcKhWgHnvsMdSoUQPLly+Hj48PgBuTaw4fPhzdu3fHhAkT7rqNtLQ05Obm2gUui8WCbdu2YcGCBdi8eTOKioqQl5dn1wuVk5MDf39/AIC/v3+pu+Vsd+ndXHPrnXs5OTkwGAzQ6/VQqVRQqVRl1ti2URadTgedTnfX46wOvIRHREQkr0qNgZozZw4SEhKk8AQAPj4++Oc//1nuu/B69eqFAwcOID09XXp17NgRQ4cOlf5Zo9EgKSlJ+kxmZiaysrIQEREBAIiIiMCBAweQm5sr1SQmJsJgMKBFixZSzc3bsNXYtqHVahEWFmZXY7VakZSUJNU4G636Rs9eCQMUERGRLCrVA2U2m3HhwoVSyy9cuID8/PxybaNGjRpo1aqV3TIPDw/UqlVLWh4bG4v4+HjUrFkTBoMBr7zyCiIiItC5c2cAQO/evdGiRQs899xzmDVrFoxGI958803ExcVJvUOjRo3CggULMGnSJLzwwgtITk7G6tWrsX79emm/8fHxiImJQceOHdGpUyfMmzcPV69exfDhwytzeqodZyInIiKSV6UC1JNPPonhw4djzpw56NSpEwAgNTUVEydOxIABA6qscR9++CGUSiUGDhyIwsJCREVFYdGiRdJ6lUqFdevWYfTo0YiIiICHhwdiYmIwY8YMqSY0NBTr16/H+PHjMX/+fNSvXx//+te/pDmgAGDQoEG4cOECpk6dCqPRiHbt2mHTpk2lBpY7C7WS80ARERHJSSGEqHA3xrVr1/CPf/wDn3/+OYqLiwHceIxLbGwsZs+eDQ8PjypvqLMzm83w8vKCyWSCwWCo1n0dz81H5Nxt8HbXIH1q72rdFxER0f2ssr/fleqBcnd3x6JFizB79mycOHECANCwYcMHMjjJgc/CIyIiktc9TaSZnZ2N7OxsNG7cGB4eHqhEZxZVghSgrDzfREREcqhUgPrjjz/Qq1cvNGnSBP369UN2djaAG4O+yzOFAd2bm6cxYGglIiJyvEoFqPHjx0Oj0SArKwvu7u7S8kGDBmHTpk1V1jgqm20mciEAC3uhiIiIHK5SY6B++uknbN68GfXr17db3rhxY5w5c6ZKGka3Z+uBAm5MZaBWydgYIiKiB1CleqCuXr1q1/Nkc+nSJdlm536Q3BygOJUBERGR41UqQHXv3h1ffPGF9F6hUMBqtWLWrFl45JFHqqxxVDbbJTyAs5ETERHJoVKX8GbNmoVevXph9+7dKCoqwqRJk5CRkYFLly5h+/btVd1GuoVCoYBGpUCxRXA2ciIiIhlUqgeqVatWOHr0KLp164YnnngCV69exYABA7B37140bNiwqttIZbDNRs4HChMRETlehXugiouL0adPHyxZsgRvvPFGdbSJykGjUqCgmGOgiIiI5FDhHiiNRoP9+/dXR1uoArTqG19dCS/hEREROVylLuE9++yz+Pe//13VbaEKuHkyTSIiInKsSg0iLykpweeff46ff/4ZYWFhpZ6BN3fu3CppHN2eLUDxEh4REZHjVShAnTx5EiEhITh48CA6dOgAADh69KhdjUKhKOujVMXUf05lwAcKExEROV6FAlTjxo2RnZ2NLVu2ALjx6JaPPvoIfn5+1dI4uj2tdAmPY6CIiIgcrUJjoG59cO3GjRtx9erVKm0QlY80BsrKHigiIiJHq9QgcptbAxU5joaX8IiIiGRToQClUChKjXHimCd5qHkJj4iISDYVGgMlhMCwYcOkBwZfv34do0aNKnUX3rffflt1LaQyaTmNARERkWwqFKBiYmLs3j/77LNV2hgqP9slPE5jQERE5HgVClBLly6trnZQBdkGkXMmciIiIse7p0HkJB+NmpfwiIiI5MIA5aI0yj/vwmOAIiIicjgGKBfFR7kQERHJhwHKRUmX8Eo4BoqIiMjRGKBclG0agxLORE5ERORwDFAuitMYEBERyYcBykVJM5HzEh4REZHDMUC5KA1nIiciIpINA5SL0qo4jQEREZFcGKBclIYPEyYiIpJNhR7lQvLJysrCxYsXpfc52VcBALkXL2LPnj1Vvr/atWsjKCioyrdLRER0P2CAcgFZWVlo1rw5Cq5dk5Z5tu+HWr1fxqbEJHwVl1Dl+9S7u+PI4cMMUURERGWQNUAtXrwYixcvxunTpwEALVu2xNSpU9G3b18AwPXr1zFhwgSsXLkShYWFiIqKwqJFi+Dn5ydtIysrC6NHj8aWLVvg6emJmJgYJCQkQK3+69C2bt2K+Ph4ZGRkIDAwEG+++SaGDRtm15aFCxdi9uzZMBqNaNu2LT7++GN06tSp2s9BeVy8eBEF165h6OTZ8AtqCAA4dUWJPZeARu0i8Pz/fVul+8vJOoEV70/ExYsXGaCIiIjKIGuAql+/PmbOnInGjRtDCIHly5fjiSeewN69e9GyZUuMHz8e69evx5o1a+Dl5YUxY8ZgwIAB2L59OwDAYrEgOjoa/v7+2LFjB7Kzs/H8889Do9HgvffeAwCcOnUK0dHRGDVqFFasWIGkpCS8+OKLqFu3LqKiogAAq1atQnx8PJYsWYLw8HDMmzcPUVFRyMzMhK+vr2zn51Z+QQ1Rv3FLAEB+thm4lAOtuyfqN64nc8uIiIgeLAohhFONQq5ZsyZmz56Np556CnXq1MHXX3+Np556CgBw5MgRNG/eHCkpKejcuTM2btyIv/3tbzh//rzUK7VkyRJMnjwZFy5cgFarxeTJk7F+/XocPHhQ2sfgwYORl5eHTZs2AQDCw8Px0EMPYcGCBQAAq9WKwMBAvPLKK3jttdfK1W6z2QwvLy+YTCYYDIaqPCXYs2cPwsLCEL/wWylAZRrzsSnDiPo+egzsUL9K9/f7sQzMjRuAtLQ0dOjQoUq3TURE5Ewq+/vtNHfhWSwWrFy5ElevXkVERATS0tJQXFyMyMhIqaZZs2YICgpCSkoKACAlJQWtW7e2u6QXFRUFs9mMjIwMqebmbdhqbNsoKipCWlqaXY1SqURkZKRUU5bCwkKYzWa7lyOplDemMbBanSr/EhERPRBkD1AHDhyAp6cndDodRo0ahe+++w4tWrSA0WiEVquFt7e3Xb2fnx+MRiMAwGg02oUn23rbujvVmM1mFBQU4OLFi7BYLGXW2LZRloSEBHh5eUmvwMDASh1/Zf2Zn2Bxrg5EIiKiB4LsAapp06ZIT09HamoqRo8ejZiYGBw6dEjuZt3VlClTYDKZpNfZs2cduv+/eqAculsiIiKCE0xjoNVq0ahRIwBAWFgYdu3ahfnz52PQoEEoKipCXl6eXS9UTk4O/P39AQD+/v7YuXOn3fZycnKkdbb/tS27ucZgMECv10OlUkGlUpVZY9tGWXQ6HXQ6XeUOugooFX8GKPZAEREROZzsPVC3slqtKCwsRFhYGDQaDZKSkqR1mZmZyMrKQkREBAAgIiICBw4cQG5urlSTmJgIg8GAFi1aSDU3b8NWY9uGVqtFWFiYXY3VakVSUpJU44xsPVAWjoEiIiJyOFl7oKZMmYK+ffsiKCgI+fn5+Prrr7F161Zs3rwZXl5eiI2NRXx8PGrWrAmDwYBXXnkFERER6Ny5MwCgd+/eaNGiBZ577jnMmjULRqMRb775JuLi4qTeoVGjRmHBggWYNGkSXnjhBSQnJ2P16tVYv3691I74+HjExMSgY8eO6NSpE+bNm4erV69i+PDhspyX8rD1QHEMFBERkePJGqByc3Px/PPPIzs7G15eXmjTpg02b96M//u//wMAfPjhh1AqlRg4cKDdRJo2KpUK69atw+jRoxEREQEPDw/ExMRgxowZUk1oaCjWr1+P8ePHY/78+ahfvz7+9a9/SXNAAcCgQYNw4cIFTJ06FUajEe3atcOmTZtKDSx3JrwLj4iISD5ONw+Uq3L0PFB/XCnEV6lZcNMoMbJHwyrdH+eBIiKiB4XLzwNFFcO78IiIiOTDAOWilEqOgSIiIpILA5SLUik4BoqIiEguDFAuytYDJcC5oIiIiByNAcpF2XqgAPZCERERORoDlItS3vTNcRwUERGRYzFAuSj7HigZG0JERPQAYoByUQqFArYMxR4oIiIix2KAcmG8E4+IiEgeDFAujHNBERERyYMByoWxB4qIiEgeDFAuTMUeKCIiIlkwQLmwP/MT78IjIiJyMAYoFyaNgeIlPCIiIodigHJh0hgoXsIjIiJyKAYoF8YxUERERPJggHJhSt6FR0REJAsGKBdmex4ex0ARERE5FgOUC7ONgeIlPCIiIsdigHJhtrvw2AFFRETkWAxQLowzkRMREcmDAcqFcR4oIiIieTBAuTCOgSIiIpIHA5QLs92Fx0t4REREjsUA5cL+molc5oYQERE9YBigXJiKY6CIiIhkwQDlwpR8lAsREZEsGKBcmNoWoCwMUERERI7EAOXC1KobX1+x1SpzS4iIiB4sDFAuTPNnD1QJe6CIiIgcigHKhdl6oErYA0VERORQDFAuzNYDVcweKCIiIoeSNUAlJCTgoYceQo0aNeDr64v+/fsjMzPTrub69euIi4tDrVq14OnpiYEDByInJ8euJisrC9HR0XB3d4evry8mTpyIkpISu5qtW7eiQ4cO0Ol0aNSoEZYtW1aqPQsXLkRISAjc3NwQHh6OnTt3VvkxVyWpB8rCHigiIiJHkjVA/fLLL4iLi8Nvv/2GxMREFBcXo3fv3rh69apUM378ePz4449Ys2YNfvnlF5w/fx4DBgyQ1lssFkRHR6OoqAg7duzA8uXLsWzZMkydOlWqOXXqFKKjo/HII48gPT0d48aNw4svvojNmzdLNatWrUJ8fDymTZuGPXv2oG3btoiKikJubq5jTkYl2O7CK+E8UERERA6lEMJ5JhG6cOECfH198csvv6BHjx4wmUyoU6cOvv76azz11FMAgCNHjqB58+ZISUlB586dsXHjRvztb3/D+fPn4efnBwBYsmQJJk+ejAsXLkCr1WLy5MlYv349Dh48KO1r8ODByMvLw6ZNmwAA4eHheOihh7BgwQIAgNVqRWBgIF555RW89tprd2272WyGl5cXTCYTDAZDlZ6XPXv2ICwsDPELv0X9xi2l5b9fvob/7jkHH3cNno8IqbL9/X4sA3PjBiAtLQ0dOnSosu0SERE5m8r+fjvVGCiTyQQAqFmzJgAgLS0NxcXFiIyMlGqaNWuGoKAgpKSkAABSUlLQunVrKTwBQFRUFMxmMzIyMqSam7dhq7Fto6ioCGlpaXY1SqUSkZGRUo0z+msQudNkYCIiogeCWu4G2FitVowbNw5du3ZFq1atAABGoxFarRbe3t52tX5+fjAajVLNzeHJtt627k41ZrMZBQUFuHz5MiwWS5k1R44cKbO9hYWFKCwslN6bzeYKHvG94zQGRERE8nCaHqi4uDgcPHgQK1eulLsp5ZKQkAAvLy/pFRgY6PA2SBNpchA5ERGRQzlFgBozZgzWrVuHLVu2oH79+tJyf39/FBUVIS8vz64+JycH/v7+Us2td+XZ3t+txmAwQK/Xo3bt2lCpVGXW2LZxqylTpsBkMkmvs2fPVvzA79HNg8idaCgbERHRfU/WACWEwJgxY/Ddd98hOTkZoaGhduvDwsKg0WiQlJQkLcvMzERWVhYiIiIAABEREThw4IDd3XKJiYkwGAxo0aKFVHPzNmw1tm1otVqEhYXZ1VitViQlJUk1t9LpdDAYDHYvR9Oo/vr6LBwHRURE5DCyjoGKi4vD119/je+//x41atSQxix5eXlBr9fDy8sLsbGxiI+PR82aNWEwGPDKK68gIiICnTt3BgD07t0bLVq0wHPPPYdZs2bBaDTizTffRFxcHHQ6HQBg1KhRWLBgASZNmoQXXngBycnJWL16NdavXy+1JT4+HjExMejYsSM6deqEefPm4erVqxg+fLjjT0w52XqgAKDYKqBWydgYIiKiB4isAWrx4sUAgIcffthu+dKlSzFs2DAAwIcffgilUomBAweisLAQUVFRWLRokVSrUqmwbt06jB49GhEREfDw8EBMTAxmzJgh1YSGhmL9+vUYP3485s+fj/r16+Nf//oXoqKipJpBgwbhwoULmDp1KoxGI9q1a4dNmzaVGljuTJRKBVQKBSxC3JhMU8MERURE5AiyBqjyjNtxc3PDwoULsXDhwtvWBAcHY8OGDXfczsMPP4y9e/fesWbMmDEYM2bMXdvkTNQqBSwlgnfiEREROZBTDCKnylOr/nweHh8oTERE5DAMUC5OrbQ9D489UERERI7CAOXiNLYeKM4FRURE5DAMUC5O6oHiNAZEREQOwwDl4mxjoHgJj4iIyHEYoFycbTJNDiInIiJyHAYoF2ebTNPCHigiIiKHYYBycZzGgIiIyPEYoFychtMYEBERORwDlIvjIHIiIiLHY4BycWoOIiciInI4BigXp1GyB4qIiMjRGKBcnK0HqoQzkRMRETkMA5SL++suPPZAEREROQoDlIv76y489kARERE5CgOUi5PuwmMPFBERkcMwQLk420zkxeyBIiIichgGKBf31yBy9kARERE5CgOUi5OmMeAlPCIiIodhgHJx0kSavIRHRETkMAxQLo6DyImIiByPAcrF2aYxsFgFrIIhioiIyBEYoFycrQcK4EByIiIiR2GAcnG2aQwAoIQPFCYiInIIBigXp1AopBDFHigiIiLHYIC6D0jPw+OdeERERA7BAHUfUNueh8c78YiIiByCAeo+oFHxEh4REZEjMUDdB6TJNDmInIiIyCEYoO4DHERORETkWAxQ9wGN7YHC7IEiIiJyCAao+wB7oIiIiByLAeo+wGkMiIiIHEvWALVt2zY89thjCAgIgEKhwNq1a+3WCyEwdepU1K1bF3q9HpGRkTh27JhdzaVLlzB06FAYDAZ4e3sjNjYWV65csavZv38/unfvDjc3NwQGBmLWrFml2rJmzRo0a9YMbm5uaN26NTZs2FDlx1td/rqExx4oIiIiR5A1QF29ehVt27bFwoULy1w/a9YsfPTRR1iyZAlSU1Ph4eGBqKgoXL9+XaoZOnQoMjIykJiYiHXr1mHbtm0YMWKEtN5sNqN3794IDg5GWloaZs+ejenTp+PTTz+Vanbs2IEhQ4YgNjYWe/fuRf/+/dG/f38cPHiw+g6+CvESHhERkWOp5dx537590bdv3zLXCSEwb948vPnmm3jiiScAAF988QX8/Pywdu1aDB48GIcPH8amTZuwa9cudOzYEQDw8ccfo1+/fvjggw8QEBCAFStWoKioCJ9//jm0Wi1atmyJ9PR0zJ07Vwpa8+fPR58+fTBx4kQAwDvvvIPExEQsWLAAS5YsccCZuDecxoCIiMixnHYM1KlTp2A0GhEZGSkt8/LyQnh4OFJSUgAAKSkp8Pb2lsITAERGRkKpVCI1NVWq6dGjB7RarVQTFRWFzMxMXL58Waq5eT+2Gtt+ylJYWAiz2Wz3kouGPVBEREQO5bQBymg0AgD8/Pzslvv5+UnrjEYjfH197dar1WrUrFnTrqasbdy8j9vV2NaXJSEhAV5eXtIrMDCwoodYZWw9UCUcRE5EROQQThugnN2UKVNgMpmk19mzZ2Vri3QXHgeRExEROYTTBih/f38AQE5Ojt3ynJwcaZ2/vz9yc3Pt1peUlODSpUt2NWVt4+Z93K7Gtr4sOp0OBoPB7iUXjZI9UERERI7ktAEqNDQU/v7+SEpKkpaZzWakpqYiIiICABAREYG8vDykpaVJNcnJybBarQgPD5dqtm3bhuLiYqkmMTERTZs2hY+Pj1Rz835sNbb9ODs1HyZMRETkULIGqCtXriA9PR3p6ekAbgwcT09PR1ZWFhQKBcaNG4d//vOf+OGHH3DgwAE8//zzCAgIQP/+/QEAzZs3R58+ffDSSy9h586d2L59O8aMGYPBgwcjICAAAPD3v/8dWq0WsbGxyMjIwKpVqzB//nzEx8dL7Rg7diw2bdqEOXPm4MiRI5g+fTp2796NMWPGOPqUVMpfl/DYA0VEROQIsk5jsHv3bjzyyCPSe1uoiYmJwbJlyzBp0iRcvXoVI0aMQF5eHrp164ZNmzbBzc1N+syKFSswZswY9OrVC0qlEgMHDsRHH30krffy8sJPP/2EuLg4hIWFoXbt2pg6dardXFFdunTB119/jTfffBOvv/46GjdujLVr16JVq1YOOAv3zk2tAgBcL2aAIiIicgRZA9TDDz8MIW5/2UmhUGDGjBmYMWPGbWtq1qyJr7/++o77adOmDf73v//dsebpp5/G008/fecGOykP3Y2v8UphCYQQUCgUMreIiIjo/ua0Y6Co/Dy0N3qgLFaBohL2QhEREVU3Bqj7gFqlhE5946u8WmSRuTVERET3Pwao+4SH9q/LeERERFS9GKDuEx66G5fxrjFAERERVTsGqPuEbSA5L+ERERFVPwao+8TNd+IRERFR9WKAuk/Y7sTjJTwiIqLqxwB1n+AlPCIiIsdhgLpP8C48IiIix2GAuk9Id+EVldxxdnciIiK6dwxQ9wnbJbxii0CRhbORExERVScGqPuERqWEVnXj67xWyHFQRERE1YkB6j5iu4x3tYjjoIiIiKoTA9R9hAPJiYiIHIMB6j5iGwfFS3hERETViwHqPsJLeERERI7BAHUf4eNciIiIHIMB6j5iGwPFS3hERETViwHqPsJLeERERI7BAHUfkZ6Hxx4oIiKiasUAdR+xXcIrslhxvZghioiIqLowQN1HtGolanloAQDHc6/I3BoiIqL7FwPUfaZ5XQMA4HC2WeaWEBER3b8YoO4zTf1rQAHgvOk6TAXFcjeHiIjovsQAdZ/x1KkRWNMdAHCEvVBERETVggHqPtS8bg0AwGFjPoQQMreGiIjo/sMAdR9qWMcTGpUCpoJi/O/YRRSVWOVuEhER0X2FAeo+pFEp0T7QBwCw92welqecxvbjF3HucgEsVvZIERER3Su13A2g6hHRsBb8vdzwy9ELMBUUY/eZy9h95jL0GhWa+HmiRV0DfA1u5drW+bwC7M3Kw9GcfLQIMKBXM1+oVczeRET04GKAuo+F1vZAYE09judewek/ruHMH1dRUGzBvt9N2Pe7CfW89egQ5I3Q2h5QKBSlPp9lKsZHS3dia+YFu+V1vdzwUvcGGNYlBEpl6c8RERHd7xig7nNqpRLN/A1o5m+A1Spw5tI1HMk24/iFKziXV4BzeQXwcdegfaAP6vvo4a5TwVigQO3H/oH4ny7CKgCVUoHmdWugUR1PbDt2Edmm65ix7hB2nPgDc55pCy+9xmHHY7UKXCu2wEOrKjP0EREROQID1ANEqVQgtLYHQmt7IP96Mfb9bsKBcyZcvlaM5Mzcmyo18GjxMKwC6NvKH5P7NENIbQ8AwPViC1btOot3NxzGz4dz8MSCX7HkuTA08zdUW7t/v3wNS345gU0Hjbh0tQhWATTzr4FnOwfjyfb1pGcAEhEROYpC8D53OwsXLsTs2bNhNBrRtm1bfPzxx+jUqdNdP2c2m+Hl5QWTyQSDoWrDxJ49exAWFob4hd+ifuOWVbrtohIrMs6bcCjbDHNBCYosVmiVAhd3rcNrgx5GZIcmZX7u+KUizNqRh4vXLNCqgNEdvdEzWF/pdtSuXRtBQUF2yy5dLcIHP2Vi9a6zKLnN4Pc6NXR454lW6NPKv9L7JiKiB1dlf7/5f91vsmrVKsTHx2PJkiUIDw/HvHnzEBUVhczMTPj6+srdvGqhVSvRPsgH7YNu3LVXVGLF0d3b8PnPn2Dyz5/c8bNKvQG1H/sHENoB81PzMHNlMsy//QfXs/ZXuB16d3ccOXwYQUFBuF5swTc7s/Bh4lGYr5cAALo1qo1RPRuiib8nVAoF1qafx/Idp5F16RpGfZWG6NZ18Xp0c9TzrnyIIyIi5yOEQN61Ypw3FUCvUaFBHU+5mwSAAcrO3Llz8dJLL2H48OEAgCVLlmD9+vX4/PPP8dprr8ncOsfQqpUovHpjBvPokW+gaZuwO9YLARwyWXDErIQ+tAP0oR3gphLw0Qp4aQT0agG9SsBdBehVAholcOvQpZysE1gxazJW/rQDF7WnsOnENZgLb8xdFeKtRmx7A1rW0QD5Wfg9/8Zn2rsDLR8xYHWGAmszr2L9gWz8lJGN6MYe6N3QHf6ed/7TLqvHi4iI5HetqAR7zuQh9dQfSD15CQfOmVBQbAEAPNOxPmY91VbmFt7AAPWnoqIipKWlYcqUKdIypVKJyMhIpKSkyNgy+dQKCC7XJcNAAJ0LirE36zIyzptx3QJkFyiQXVC6Vq1UwEOnhl6jgkalgACQp2yEwFe/xqLjngCuAABK8owwpf4XZ/Ztxi/izhOBanxDUfPRF4HgtlibeRVrM6+i6OIZFBlPwGLOhbXwKgAloAAABaBUQqv3REzsS9C4ueNakQXXiiwoKLKgqMQKjVoBjUoJrUoJjVoJnUoJjUoJjVoBrUoFjVohLdOqbetu1CmVClisVpRYBUosAiVWIb23WASKb3lfYhUosVph+bPeYr2pxiJgFQI6tQpuGhX0WiX0GhX02hvnT69RQq+9sU75Zyq9+UJnWVfnb3fB/uZQaxucryhj3e2UZyCAM40VcNTtB466z0HhoCNy3PHIrzx/r3f7uxd32crdP3+3/d/bv1X32v4/Lv4Bc37+XbZxZxYrUFBsRX6RQM7VEpzPt+B0XjEsZXzQoFOi6Pq1u2zRcRig/nTx4kVYLBb4+fnZLffz88ORI0dK1RcWFqKwsFB6bzKZANy4llrVrly5ESp+P5aBwoLq/+PJyToBADCePooTHu7l/lwggLo+gKlYgctFClwtUaDAosB1C1BgUaDYqkARgKIC4PKtH1YoIa7lwaAoQC1rHnxUJii6hAFd7twDZiNQANOVDOSoaiNf4Ql1jTpQ16hzx8+s3nW23MdGRESOU5J/AdfPHkLRucO4fv4ILKZcwFqCY3o9Xmq/C4GBgVW2L9vvdkUDKQNUJSUkJODtt98utbwqv9RbrZ73VrVtuyw/Lf0APzl0j0RERLd3vaAArVq1qpZt5+fnw8vLq9z1DFB/ql27NlQqFXJycuyW5+TkwN+/9B1eU6ZMQXx8vPTearXi0qVLqFWrVpXPT2Q2mxEYGIizZ89W+R1+9BeeZ8fgeXYMnmfH4Hl2nOo610II5OfnIyAgoEKfY4D6k1arRVhYGJKSktC/f38AN0JRUlISxowZU6pep9NBp9PZLfP29q7WNhoMBv4L6gA8z47B8+wYPM+OwfPsONVxrivS82TDAHWT+Ph4xMTEoGPHjujUqRPmzZuHq1evSnflEREREQEMUHYGDRqECxcuYOrUqTAajWjXrh02bdpUamA5ERERPdgYoG4xZsyYMi/ZyUmn02HatGmlLhlS1eJ5dgyeZ8fgeXYMnmfHcbZzzUe5EBEREVWQUu4GEBEREbkaBigiIiKiCmKAIiIiIqogBigiIiKiCmKAcnILFy5ESEgI3NzcEB4ejp07d8rdJKeRkJCAhx56CDVq1ICvry/69++PzMxMu5rr168jLi4OtWrVgqenJwYOHFhqtvmsrCxER0fD3d0dvr6+mDhxIkpKSuxqtm7dig4dOkCn06FRo0ZYtmxZqfY8KN/VzJkzoVAoMG7cOGkZz3PVOHfuHJ599lnUqlULer0erVu3xu7du6X1QghMnToVdevWhV6vR2RkJI4dO2a3jUuXLmHo0KEwGAzw9vZGbGys9DxNm/3796N79+5wc3NDYGAgZs2aVaota9asQbNmzeDm5obWrVtjw4YN1XPQDmaxWPDWW28hNDQUer0eDRs2xDvvvGP3HDSe58rZtm0bHnvsMQQEBEChUGDt2rV2653pvJanLXclyGmtXLlSaLVa8fnnn4uMjAzx0ksvCW9vb5GTkyN305xCVFSUWLp0qTh48KBIT08X/fr1E0FBQeLKlStSzahRo0RgYKBISkoSu3fvFp07dxZdunSR1peUlIhWrVqJyMhIsXfvXrFhwwZRu3ZtMWXKFKnm5MmTwt3dXcTHx4tDhw6Jjz/+WKhUKrFp0yap5kH5rnbu3ClCQkJEmzZtxNixY6XlPM/37tKlSyI4OFgMGzZMpKamipMnT4rNmzeL48ePSzUzZ84UXl5eYu3atWLfvn3i8ccfF6GhoaKgoECq6dOnj2jbtq347bffxP/+9z/RqFEjMWTIEGm9yWQSfn5+YujQoeLgwYPim2++EXq9XnzyySdSzfbt24VKpRKzZs0Shw4dEm+++abQaDTiwIEDjjkZ1ejdd98VtWrVEuvWrROnTp0Sa9asEZ6enmL+/PlSDc9z5WzYsEG88cYb4ttvvxUAxHfffWe33pnOa3nacjcMUE6sU6dOIi4uTnpvsVhEQECASEhIkLFVzis3N1cAEL/88osQQoi8vDyh0WjEmjVrpJrDhw8LACIlJUUIceNfeKVSKYxGo1SzePFiYTAYRGFhoRBCiEmTJomWLVva7WvQoEEiKipKev8gfFf5+fmicePGIjExUfTs2VMKUDzPVWPy5MmiW7dut11vtVqFv7+/mD17trQsLy9P6HQ68c033wghhDh06JAAIHbt2iXVbNy4USgUCnHu3DkhhBCLFi0SPj4+0nm37btp06bS+2eeeUZER0fb7T88PFyMHDny3g7SCURHR4sXXnjBbtmAAQPE0KFDhRA8z1Xl1gDlTOe1PG0pD17Cc1JFRUVIS0tDZGSktEypVCIyMhIpKSkytsx5mUwmAEDNmjUBAGlpaSguLrY7h82aNUNQUJB0DlNSUtC6dWu72eajoqJgNpuRkZEh1dy8DVuNbRsPyncVFxeH6OjoUueC57lq/PDDD+jYsSOefvpp+Pr6on379vjss8+k9adOnYLRaLQ7fi8vL4SHh9udZ29vb3Ts2FGqiYyMhFKpRGpqqlTTo0cPaLVaqSYqKgqZmZm4fPmyVHOn78KVdenSBUlJSTh69CgAYN++ffj111/Rt29fADzP1cWZzmt52lIeDFBO6uLFi7BYLKUeI+Pn5wej0ShTq5yX1WrFuHHj0LVrV7Rq1QoAYDQaodVqSz3k+eZzaDQayzzHtnV3qjGbzSgoKHggvquVK1diz549SEhIKLWO57lqnDx5EosXL0bjxo2xefNmjB49Gq+++iqWL18O4K/zdKfjNxqN8PX1tVuvVqtRs2bNKvku7ofz/Nprr2Hw4MFo1qwZNBoN2rdvj3HjxmHo0KEAeJ6rizOd1/K0pTz4KBe6L8TFxeHgwYP49ddf5W7Kfefs2bMYO3YsEhMT4ebmJndz7ltWqxUdO3bEe++9BwBo3749Dh48iCVLliAmJkbm1t0/Vq9ejRUrVuDrr79Gy5YtkZ6ejnHjxiEgIIDnmSqEPVBOqnbt2lCpVKXuZMrJyYG/v79MrXJOY8aMwbp167BlyxbUr19fWu7v74+ioiLk5eXZ1d98Dv39/cs8x7Z1d6oxGAzQ6/X3/XeVlpaG3NxcdOjQAWq1Gmq1Gr/88gs++ugjqNVq+Pn58TxXgbp166JFixZ2y5o3b46srCwAf52nOx2/v78/cnNz7daXlJTg0qVLVfJd3A/neeLEiVIvVOvWrfHcc89h/PjxUu8qz3P1cKbzWp62lAcDlJPSarUICwtDUlKStMxqtSIpKQkREREytsx5CCEwZswYfPfdd0hOTkZoaKjd+rCwMGg0GrtzmJmZiaysLOkcRkRE4MCBA3b/0iYmJsJgMEg/ZhEREXbbsNXYtnG/f1e9evXCgQMHkJ6eLr06duyIoUOHSv/M83zvunbtWmoajqNHjyI4OBgAEBoaCn9/f7vjN5vNSE1NtTvPeXl5SEtLk2qSk5NhtVoRHh4u1Wzbtg3FxcVSTWJiIpo2bQofHx+p5k7fhSu7du0alEr7nz6VSgWr1QqA57m6ONN5LU9byqXcw83J4VauXCl0Op1YtmyZOHTokBgxYoTw9va2u5PpQTZ69Gjh5eUltm7dKrKzs6XXtWvXpJpRo0aJoKAgkZycLHbv3i0iIiJERESEtN52e33v3r1Fenq62LRpk6hTp06Zt9dPnDhRHD58WCxcuLDM2+sfpO/q5rvwhOB5rgo7d+4UarVavPvuu+LYsWNixYoVwt3dXXz11VdSzcyZM4W3t7f4/vvvxf79+8UTTzxR5m3g7du3F6mpqeLXX38VjRs3trsNPC8vT/j5+YnnnntOHDx4UKxcuVK4u7uXug1crVaLDz74QBw+fFhMmzbNpW+vv1lMTIyoV6+eNI3Bt99+K2rXri0mTZok1fA8V05+fr7Yu3ev2Lt3rwAg5s6dK/bu3SvOnDkjhHCu81qettwNA5ST+/jjj0VQUJDQarWiU6dO4rfffpO7SU4DQJmvpUuXSjUFBQXi5ZdfFj4+PsLd3V08+eSTIjs72247p0+fFn379hV6vV7Url1bTJgwQRQXF9vVbNmyRbRr105otVrRoEEDu33YPEjf1a0Biue5avz444+iVatWQqfTiWbNmolPP/3Ubr3VahVvvfWW8PPzEzqdTvTq1UtkZmba1fzxxx9iyJAhwtPTUxgMBjF8+HCRn59vV7Nv3z7RrVs3odPpRL169cTMmTNLtWX16tWiSZMmQqvVipYtW4r169dX/QHLwGw2i7Fjx4qgoCDh5uYmGjRoIN544w272+J5nitny5YtZf43OSYmRgjhXOe1PG25G4UQN02/SkRERER3xTFQRERERBXEAEVERERUQQxQRERERBXEAEVERERUQQxQRERERBXEAEVERERUQQxQRERERBXEAEVED4xhw4ahf//+TrOdu1m2bBm8vb2rfT9EVHGcSJOIHhgmkwlCCCmUPPzww2jXrh3mzZtXoe0MGzYMeXl5WLt2bZW38WYFBQXIz8+Hr69vte6HiCpOLXcDiIgcxcvLS+4mVIher4der5e7GURUBl7CIyKnYbVaMWvWLDRq1Ag6nQ5BQUF49913AQCTJ09GkyZN4O7ujgYNGuCtt96yeyL79OnT0a5dO3zyyScIDAyEu7s7nnnmGZhMJqnm5ktvw4YNwy+//IL58+dDoVBAoVDg9OnTsFgsiI2NRWhoKPR6PZo2bYr58+dX+Fiys7MRHR0NvV6P0NBQfP311wgJCbHr7Zo7dy5at24NDw8PBAYG4uWXX8aVK1ek9bdewrMd45dffomQkBB4eXlh8ODByM/Pr3D7iOjesAeKiJzGlClT8Nlnn+HDDz9Et27dkJ2djSNHjgAAatSogWXLliEgIAAHDhzASy+9hBo1amDSpEnS548fP47Vq1fjxx9/hNlsRmxsLF5++WWsWLGi1L7mz5+Po0ePolWrVpgxYwYAoE6dOrBarahfvz7WrFmDWrVqYceOHRgxYgTq1q2LZ555ptzH8vzzz+PixYvYunUrNBoN4uPjkZuba1ejVCrx0UcfITQ0FCdPnsTLL7+MSZMmYdGiRbfd7okTJ7B27VqsW7cOly9fxjPPPIOZM2dKQZOIHKRCjx4mIqomZrNZ6HQ68dlnn5Wrfvbs2SIsLEx6P23aNKFSqcTvv/8uLdu4caNQKpUiOztbCCFETEyMeOKJJ6T1PXv2FGPHjr3rvuLi4sTAgQOl97du51aHDx8WAMSuXbukZceOHRMAxIcffnjbz61Zs0bUqlVLer906VLh5eVld4zu7u7CbDZLyyZOnCjCw8PvegxEVLXYA0VETuHw4cMoLCxEr169yly/atUqfPTRRzhx4gSuXLmCkpISGAwGu5qgoCDUq1dPeh8REQGr1YrMzEz4+/uXuy0LFy7E559/jqysLBQUFKCoqAjt2rUrs3bFihUYOXKk9H7jxo24dOkS1Go1OnToIC1v1KgRfHx87D77888/IyEhAUeOHIHZbEZJSQmuX7+Oa9euwd3dvcz9hYSEoEaNGtL7unXrlurZIqLqxzFQROQU7jRYOiUlBUOHDkW/fv2wbt067N27F2+88QaKioqqvB0rV67EP/7xD8TGxuKnn35Ceno6hg8fftt9Pf7440hPT5deHTt2LNd+Tp8+jb/97W9o06YN/vvf/yItLQ0LFy4EgDsel0ajsXuvUChgtVrLeXREVFXYA0VETqFx48bQ6/VISkrCiy++aLdux44dCA4OxhtvvCEtO3PmTKltZGVl4fz58wgICAAA/Pbbb1AqlWjatGmZ+9RqtbBYLHbLtm/fji5duuDll1+Wlp04ceK27a5Ro4ZdjxAANG3aFCUlJdi7dy/CwsIA3BifdfnyZakmLS0NVqsVc+bMgVJ54//Lrl69+rb7ISLnwgBFRE7Bzc0NkydPxqRJk6DVatG1a1dcuHABGRkZaNy4MbKysrBy5Uo89NBDWL9+Pb777rsytxETE4MPPvgAZrMZr776Kp555pnbXr4LCQlBamoqTp8+DU9PT9SsWRONGzfGF198gc2bNyM0NBRffvkldu3ahdDQ0HIfS7NmzRAZGYkRI0Zg8eLF0Gg0mDBhAvR6PRQKBYAbl/SKi4vx8ccf47HHHsP27duxZMmSyp08InI4XsIjIqfx1ltvYcKECZg6dSqaN2+OQYMGITc3F48//jjGjx+PMWPGoF27dtixYwfeeuutUp9v1KgRBgwYgH79+qF3795o06bNHe9o+8c//gGVSoUWLVqgTp06yMrKwsiRIzFgwAAMGjQI4eHh+OOPP+x6o8rriy++gJ+fH3r06IEnn3xSumvQzc0NANC2bVvMnTsX77//Plq1aoUVK1YgISGhwvshInlwJnIiui9Mnz4da9euRXp6utxNKdPvv/+OwMBA/Pzzz7cdKE9EroOX8IiIqkFycjKuXLmC1q1bIzs7G5MmTUJISAh69Oghd9OIqAowQBERVYPi4mK8/vrrOHnyJGrUqIEuXbpgxYoVpe6iIyLXxEt4RERERBXEQeREREREFcQARURERFRBDFBEREREFcQARURERFRBDFBEREREFcQARURERFRBDFBEREREFcQARURERFRBDFBEREREFfT/CbrRsg+9vqEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K4s6a5jj3Jqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3HUQ5O8u3JyY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Visualize Categorical Variables\n",
        "# Similarly, we visualize the levels of categorical variables using bar charts."
      ],
      "metadata": {
        "id": "cpjBM7fY3J1X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.select_dtypes(include='object').columns:\n",
        "    sns.countplot(x=column, data=df)\n",
        "    plt.title(f'Counts of {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Count')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wjIZUnzj3VVG",
        "outputId": "2b414d71-96aa-46cb-d35c-6af3c422ccdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAIGCAYAAABJZbv3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABs0klEQVR4nO3dd1gU1/s28HsRKaIUFUEUERsWiCI2NCooEcXYTTTW2E3AiB0Tu0k0Go2xxJLEGv3G2HvB3rChWLD3Cqg0sVCf9w/fnR8rllGRXeD+XNdeujNnZ58ZltmbMzNnNCIiICIiIqI3MtJ3AURERETZAUMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExHlCpGRkWjbti0KFSoEjUaDadOmfbT32rNnDzQaDfbs2ZOpy9VoNBgzZkymLvNd3bhxAxqNBr/++utb244ZMwYajSYLqiLKGgxNRDnE1atX0adPH5QqVQpmZmawtLREnTp18Pvvv+PZs2f6Lg8A8Mcff2DhwoV6ee8BAwZg27ZtGD58OJYsWYLGjRvrpY632bx5s96DERG9mrG+CyCiD7dp0yZ88cUXMDU1RZcuXeDq6oqkpCQcOHAAQ4YMQXh4OObNm6fvMvHHH3+gcOHC+Prrr7P8vXft2oUWLVpg8ODBWf7e72Lz5s2YNWvWK4PTs2fPYGycfXbbI0aMQFBQkL7LIMo02ee3j4he6fr162jfvj2cnJywa9cuFC1aVJnn7++PK1euYNOmTXqs0DBERUXB2tr6vV6bkpKCtLQ0mJiYZG5R78jMzEyv7/+ujI2Ns1XII3obHp4jyuYmTZqEhIQE/P333zqBSatMmTLo37+/8jwlJQXjx49H6dKlYWpqipIlS+L7779HYmKizuted/5MyZIldXqKFi5cCI1Gg4MHD2LgwIGwtbWFhYUFWrVqhQcPHui8Ljw8HHv37oVGo4FGo4GXlxcAIDk5GWPHjkXZsmVhZmaGQoUK4dNPP0VwcPBb1//atWv44osvULBgQeTLlw+1atXSCYna+kQEs2bNUt77ddKfszNt2jRlO507dw4AcOHCBbRt2xYFCxaEmZkZqlWrhvXr17+1zv379+OLL75AiRIlYGpqCkdHRwwYMEDn0OnXX3+NWbNmAYBSZ/paX/UzOXnyJJo0aQJLS0vkz58fDRs2xOHDh3XaqP0ZAcDx48fh6+uLwoULw9zcHM7Ozujevfsr12nevHnK9qlevTqOHTumM/9V5zRpNBoEBARg6dKlcHFxgZmZGTw8PLBv3763bkMifeOfAETZ3IYNG1CqVCnUrl1bVfuePXti0aJFaNu2LQYNGoQjR45gwoQJOH/+PNasWfPedfTr1w82NjYYPXo0bty4gWnTpiEgIADLly8HAEybNg39+vVD/vz58cMPPwAA7OzsALz4cp0wYQJ69uyJGjVqID4+HsePH8eJEyfw2WefvfY9IyMjUbt2bTx9+hTfffcdChUqhEWLFqF58+ZYuXIlWrVqhXr16mHJkiXo3LkzPvvsM3Tp0kXV+ixYsADPnz9H7969YWpqioIFCyI8PBx16tRBsWLFEBQUBAsLC/z3339o2bIlVq1ahVatWr12eStWrMDTp0/xzTffoFChQjh69ChmzJiBO3fuYMWKFQCAPn364N69ewgODsaSJUveWmN4eDjq1q0LS0tLDB06FHnz5sXcuXPh5eWFvXv3ombNmjrt3/YzioqKQqNGjWBra4ugoCBYW1vjxo0bWL16dYb3XrZsGR4/fow+ffpAo9Fg0qRJaN26Na5du4a8efO+se69e/di+fLl+O6772Bqaoo//vgDjRs3xtGjR+Hq6vrW9SbSGyGibCsuLk4ASIsWLVS1DwsLEwDSs2dPnemDBw8WALJr1y5lGgAZPXp0hmU4OTlJ165dlecLFiwQAOLj4yNpaWnK9AEDBkiePHkkNjZWmVapUiWpX79+hmVWrlxZmjZtqmod0gsMDBQAsn//fmXa48ePxdnZWUqWLCmpqak66+Pv7//WZV6/fl0AiKWlpURFRenMa9iwobi5ucnz58+VaWlpaVK7dm0pW7asMm337t0CQHbv3q1Me/r0aYb3mjBhgmg0Grl586Yyzd/fX163a375Z9KyZUsxMTGRq1evKtPu3bsnBQoUkHr16inT1P6M1qxZIwDk2LFjr9s8yvYpVKiQREdHK9PXrVsnAGTDhg3KtNGjR2dYFwACQI4fP65Mu3nzppiZmUmrVq1e+75EhoCH54iysfj4eABAgQIFVLXfvHkzAGDgwIE60wcNGgQAH3TuU+/evXUOxdStWxepqam4efPmW19rbW2N8PBwXL58+Z3ec/PmzahRowY+/fRTZVr+/PnRu3dv3LhxQzmk9j7atGkDW1tb5Xl0dDR27dqFL7/8Eo8fP8bDhw/x8OFDPHr0CL6+vrh8+TLu3r372uWZm5sr/3/y5AkePnyI2rVrQ0Rw8uTJd64vNTUV27dvR8uWLVGqVClletGiRdGhQwccOHBA+Xxove1npD3na+PGjUhOTn7j+7dr1w42NjY6ywJeHC59G09PT3h4eCjPS5QogRYtWmDbtm1ITU196+uJ9IWhiSgbs7S0BAA8fvxYVfubN2/CyMgIZcqU0Zlub28Pa2trVQHndUqUKKHzXPuFGhMT89bXjhs3DrGxsShXrhzc3NwwZMgQnD59+q2vu3nzJlxcXDJMr1ChgjL/fTk7O+s8v3LlCkQEI0eOhK2trc5j9OjRAF4c3nqdW7du4euvv0bBggWRP39+2Nraon79+gCAuLi4d67vwYMHePr06WvXPy0tDbdv39aZ/rafUf369dGmTRuMHTsWhQsXRosWLbBgwYIM57upWdablC1bNsO0cuXK4enTpxnOsSIyJDyniSgbs7S0hIODA86ePftOr/uQAQdf1xOQJ0+eV04Xkbcus169erh69SrWrVuH7du346+//sJvv/2GOXPmoGfPnu9d64dI3zMEAGlpaQCAwYMHw9fX95WveTmMaqWmpuKzzz5DdHQ0hg0bhvLly8PCwgJ3797F119/rSz7Y3vbz0ij0WDlypU4fPgwNmzYgG3btqF79+6YMmUKDh8+jPz586teFlFOxNBElM19/vnnmDdvHkJCQuDp6fnGtk5OTkhLS8Ply5eV3hjgxQnVsbGxcHJyUqbZ2NggNjZW5/VJSUm4f//+e9f6prBWsGBBdOvWDd26dUNCQgLq1auHMWPGvDE0OTk54eLFixmmX7hwQZmfWbSHwPLmzQsfH593eu2ZM2dw6dIlLFq0SOdE9FddHag20Nra2iJfvnyvXX8jIyM4Ojq+U51atWrVQq1atfDTTz9h2bJl6NixI/79999MC7CvOgx76dIl5MuXT+eQKJGh4eE5omxu6NChsLCwQM+ePREZGZlh/tWrV/H7778DAPz8/AAgwy1Epk6dCgBo2rSpMq106dIZLgOfN2/eB51zYmFhkSGIAcCjR490nufPnx9lypR55WGh9Pz8/HD06FGEhIQo0548eYJ58+ahZMmSqFix4nvX+rIiRYrAy8sLc+fOfWVwfNNhJW2vTPpeGBFRfi7pWVhYAMArt9PLy2zUqBHWrVuHGzduKNMjIyOxbNkyfPrpp8rhW7ViYmIy9BRVqVIFAN76s3gXISEhOHHihPL89u3bWLduHRo1avTaHiwiQ8CeJqJsrnTp0li2bBnatWuHChUq6IwIfujQIaxYsUIZV6ly5cro2rUr5s2bh9jYWNSvXx9Hjx7FokWL0LJlS3h7eyvL7dmzJ/r27Ys2bdrgs88+w6lTp7Bt2zYULlz4vWv18PDA7Nmz8eOPP6JMmTIoUqQIGjRogIoVK8LLywseHh4oWLAgjh8/jpUrVyIgIOCNywsKCsL//vc/NGnSBN999x0KFiyIRYsW4fr161i1ahWMjDL378JZs2bh008/hZubG3r16oVSpUohMjISISEhuHPnDk6dOvXK15UvXx6lS5fG4MGDcffuXVhaWmLVqlWvPP9He4L0d999B19fX+TJkwft27d/5XJ//PFHBAcH49NPP8W3334LY2NjzJ07F4mJiZg0adI7r9+iRYvwxx9/oFWrVihdujQeP36MP//8E5aWlkrgzgyurq7w9fXVGXIAAMaOHZtp70H0Uejvwj0iykyXLl2SXr16ScmSJcXExEQKFCggderUkRkzZuhcIp+cnCxjx44VZ2dnyZs3rzg6Osrw4cN12oiIpKamyrBhw6Rw4cKSL18+8fX1lStXrrx2yIGXL1N/1WX3ERER0rRpUylQoIAAUIYf+PHHH6VGjRpibW0t5ubmUr58efnpp58kKSnpret99epVadu2rVhbW4uZmZnUqFFDNm7cmKEd3nHIgcmTJ7/2/bp06SL29vaSN29eKVasmHz++eeycuXKN677uXPnxMfHR/Lnzy+FCxeWXr16yalTpwSALFiwQGmXkpIi/fr1E1tbW9FoNDqX7OMVw0CcOHFCfH19JX/+/JIvXz7x9vaWQ4cO6bRR+zM6ceKEfPXVV1KiRAkxNTWVIkWKyOeff64zPMCbts/L9b1uyAF/f3/5559/pGzZsmJqairu7u4624rIUGlEeNYeERFlDY1GA39/f8ycOVPfpRC9M57TRERERKQCQxMRERGRCgxNRERERCrw6jkiIsoyPI2WsjP2NBERERGpwJ6mTJKWloZ79+6hQIECH3SLCiIiIso6IoLHjx/DwcHhrWO7MTRlknv37r33LQuIiIhIv27fvo3ixYu/sQ1DUyYpUKAAgBcb/V1vXUBERET6ER8fD0dHR+V7/E0YmjKJ9pCcpaUlQxMREVE2o+bUGp4ITkRERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCoY67sAovfhMWSxvkswCKGTu+i7BCKiXIM9TUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKSCXkPTvn370KxZMzg4OECj0WDt2rXKvOTkZAwbNgxubm6wsLCAg4MDunTpgnv37uksIzo6Gh07doSlpSWsra3Ro0cPJCQk6LQ5ffo06tatCzMzMzg6OmLSpEkZalmxYgXKly8PMzMzuLm5YfPmzR9lnYmIiCh70mtoevLkCSpXroxZs2ZlmPf06VOcOHECI0eOxIkTJ7B69WpcvHgRzZs312nXsWNHhIeHIzg4GBs3bsS+ffvQu3dvZX58fDwaNWoEJycnhIaGYvLkyRgzZgzmzZuntDl06BC++uor9OjRAydPnkTLli3RsmVLnD179uOtPBEREWUrGhERfRcBABqNBmvWrEHLli1f2+bYsWOoUaMGbt68iRIlSuD8+fOoWLEijh07hmrVqgEAtm7dCj8/P9y5cwcODg6YPXs2fvjhB0RERMDExAQAEBQUhLVr1+LChQsAgHbt2uHJkyfYuHGj8l61atVClSpVMGfOHFX1x8fHw8rKCnFxcbC0tHzPrUBqeQxZrO8SDELo5C76LoGIKFt7l+/vbHVOU1xcHDQaDaytrQEAISEhsLa2VgITAPj4+MDIyAhHjhxR2tSrV08JTADg6+uLixcvIiYmRmnj4+Oj816+vr4ICQl5bS2JiYmIj4/XeRAREVHOlW1C0/PnzzFs2DB89dVXShKMiIhAkSJFdNoZGxujYMGCiIiIUNrY2dnptNE+f1sb7fxXmTBhAqysrJSHo6Pjh60gERERGbRsEZqSk5Px5ZdfQkQwe/ZsfZcDABg+fDji4uKUx+3bt/VdEhEREX1Exvou4G20genmzZvYtWuXzvFGe3t7REVF6bRPSUlBdHQ07O3tlTaRkZE6bbTP39ZGO/9VTE1NYWpq+v4rRkRERNmKQfc0aQPT5cuXsWPHDhQqVEhnvqenJ2JjYxEaGqpM27VrF9LS0lCzZk2lzb59+5CcnKy0CQ4OhouLC2xsbJQ2O3fu1Fl2cHAwPD09P9aqERERUTaj19CUkJCAsLAwhIWFAQCuX7+OsLAw3Lp1C8nJyWjbti2OHz+OpUuXIjU1FREREYiIiEBSUhIAoEKFCmjcuDF69eqFo0eP4uDBgwgICED79u3h4OAAAOjQoQNMTEzQo0cPhIeHY/ny5fj9998xcOBApY7+/ftj69atmDJlCi5cuIAxY8bg+PHjCAgIyPJtQkRERIZJr0MO7NmzB97e3hmmd+3aFWPGjIGzs/MrX7d79254eXkBeDG4ZUBAADZs2AAjIyO0adMG06dPR/78+ZX2p0+fhr+/P44dO4bChQujX79+GDZsmM4yV6xYgREjRuDGjRsoW7YsJk2aBD8/P9XrwiEHshaHHHiBQw4QEX2Yd/n+NphxmrI7hqasxdD0AkMTEdGHybHjNBERERHpC0MTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCroNTTt27cPzZo1g4ODAzQaDdauXaszX0QwatQoFC1aFObm5vDx8cHly5d12kRHR6Njx46wtLSEtbU1evTogYSEBJ02p0+fRt26dWFmZgZHR0dMmjQpQy0rVqxA+fLlYWZmBjc3N2zevDnT15eIiIiyL72GpidPnqBy5cqYNWvWK+dPmjQJ06dPx5w5c3DkyBFYWFjA19cXz58/V9p07NgR4eHhCA4OxsaNG7Fv3z707t1bmR8fH49GjRrByckJoaGhmDx5MsaMGYN58+YpbQ4dOoSvvvoKPXr0wMmTJ9GyZUu0bNkSZ8+e/XgrT0RERNmKRkRE30UAgEajwZo1a9CyZUsAL3qZHBwcMGjQIAwePBgAEBcXBzs7OyxcuBDt27fH+fPnUbFiRRw7dgzVqlUDAGzduhV+fn64c+cOHBwcMHv2bPzwww+IiIiAiYkJACAoKAhr167FhQsXAADt2rXDkydPsHHjRqWeWrVqoUqVKpgzZ84r601MTERiYqLyPD4+Ho6OjoiLi4OlpWWmbx/S5TFksb5LMAihk7vouwQiomwtPj4eVlZWqr6/DfacpuvXryMiIgI+Pj7KNCsrK9SsWRMhISEAgJCQEFhbWyuBCQB8fHxgZGSEI0eOKG3q1aunBCYA8PX1xcWLFxETE6O0Sf8+2jba93mVCRMmwMrKSnk4Ojp++EoTERGRwTLY0BQREQEAsLOz05luZ2enzIuIiECRIkV05hsbG6NgwYI6bV61jPTv8bo22vmvMnz4cMTFxSmP27dvv+sqEhERUTZirO8CsitTU1OYmprquwwiIiLKIgbb02Rvbw8AiIyM1JkeGRmpzLO3t0dUVJTO/JSUFERHR+u0edUy0r/H69po5xMREREZbGhydnaGvb09du7cqUyLj4/HkSNH4OnpCQDw9PREbGwsQkNDlTa7du1CWloaatasqbTZt28fkpOTlTbBwcFwcXGBjY2N0ib9+2jbaN+HiIiISK+hKSEhAWFhYQgLCwPw4uTvsLAw3Lp1CxqNBoGBgfjxxx+xfv16nDlzBl26dIGDg4NyhV2FChXQuHFj9OrVC0ePHsXBgwcREBCA9u3bw8HBAQDQoUMHmJiYoEePHggPD8fy5cvx+++/Y+DAgUod/fv3x9atWzFlyhRcuHABY8aMwfHjxxEQEJDVm4SIiIgMlF7PaTp+/Di8vb2V59og07VrVyxcuBBDhw7FkydP0Lt3b8TGxuLTTz/F1q1bYWZmprxm6dKlCAgIQMOGDWFkZIQ2bdpg+vTpynwrKyts374d/v7+8PDwQOHChTFq1CidsZxq166NZcuWYcSIEfj+++9RtmxZrF27Fq6urlmwFYiIiCg7MJhxmrK7dxnngT4cx2l6geM0ERF9mBwxThMRERGRIWFoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVGBoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVGBoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVDDWdwG5iceQxfouwWCETu6i7xKIiIjeCXuaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVDDo0paamYuTIkXB2doa5uTlKly6N8ePHQ0SUNiKCUaNGoWjRojA3N4ePjw8uX76ss5zo6Gh07NgRlpaWsLa2Ro8ePZCQkKDT5vTp06hbty7MzMzg6OiISZMmZck6EhERUfZg0KHpl19+wezZszFz5kycP38ev/zyCyZNmoQZM2YobSZNmoTp06djzpw5OHLkCCwsLODr64vnz58rbTp27Ijw8HAEBwdj48aN2LdvH3r37q3Mj4+PR6NGjeDk5ITQ0FBMnjwZY8aMwbx587J0fYmIiMhwGfSQA4cOHUKLFi3QtGlTAEDJkiXxv//9D0ePHgXwopdp2rRpGDFiBFq0aAEAWLx4Mezs7LB27Vq0b98e58+fx9atW3Hs2DFUq1YNADBjxgz4+fnh119/hYODA5YuXYqkpCTMnz8fJiYmqFSpEsLCwjB16lSdcJVeYmIiEhMTlefx8fEfc1MQERGRnhl0T1Pt2rWxc+dOXLp0CQBw6tQpHDhwAE2aNAEAXL9+HREREfDx8VFeY2VlhZo1ayIkJAQAEBISAmtrayUwAYCPjw+MjIxw5MgRpU29evVgYmKitPH19cXFixcRExPzytomTJgAKysr5eHo6Ji5K09EREQGxaB7moKCghAfH4/y5csjT548SE1NxU8//YSOHTsCACIiIgAAdnZ2Oq+zs7NT5kVERKBIkSI6842NjVGwYEGdNs7OzhmWoZ1nY2OTobbhw4dj4MCByvP4+HgGJyIiohzMoEPTf//9h6VLl2LZsmXKIbPAwEA4ODiga9eueq3N1NQUpqameq2BiIiIso5Bh6YhQ4YgKCgI7du3BwC4ubnh5s2bmDBhArp27Qp7e3sAQGRkJIoWLaq8LjIyElWqVAEA2NvbIyoqSme5KSkpiI6OVl5vb2+PyMhInTba59o2RERElLsZ9DlNT58+hZGRbol58uRBWloaAMDZ2Rn29vbYuXOnMj8+Ph5HjhyBp6cnAMDT0xOxsbEIDQ1V2uzatQtpaWmoWbOm0mbfvn1ITk5W2gQHB8PFxeWVh+aIiIgo9zHo0NSsWTP89NNP2LRpE27cuIE1a9Zg6tSpaNWqFQBAo9EgMDAQP/74I9avX48zZ86gS5cucHBwQMuWLQEAFSpUQOPGjdGrVy8cPXoUBw8eREBAANq3bw8HBwcAQIcOHWBiYoIePXogPDwcy5cvx++//65zzhIRERHlbgZ9eG7GjBkYOXIkvv32W0RFRcHBwQF9+vTBqFGjlDZDhw7FkydP0Lt3b8TGxuLTTz/F1q1bYWZmprRZunQpAgIC0LBhQxgZGaFNmzaYPn26Mt/Kygrbt2+Hv78/PDw8ULhwYYwaNeq1ww0QERFR7qOR9MNr03uLj4+HlZUV4uLiYGlp+co2HkMWZ3FVhit0cpcPej235Qsfuh2JiHI7Nd/fWgZ9eI6IiIjIUDA0EREREanA0ERERESkAkMTERERkQrvFZpKlSqFR48eZZgeGxuLUqVKfXBRRERERIbmvULTjRs3kJqammF6YmIi7t69+8FFERERERmadxqnaf369cr/t23bBisrK+V5amoqdu7ciZIlS2ZacURERESG4p1Ck3aUbY1Gk+GGuXnz5kXJkiUxZcqUTCuOiIiIyFC8U2hKf8+3Y8eOoXDhwh+lKCIiIiJD8163Ubl+/Xpm10FERERk0N773nM7d+7Ezp07ERUVpfRAac2fP/+DCyMiIiIyJO8VmsaOHYtx48ahWrVqKFq0KDQaTWbXRURERGRQ3is0zZkzBwsXLkTnzp0zux4iIiIig/Re4zQlJSWhdu3amV0LERERkcF6r9DUs2dPLFu2LLNrISIiIjJY73V47vnz55g3bx527NiBTz75BHnz5tWZP3Xq1EwpjoiIiMhQvFdoOn36NKpUqQIAOHv2rM48nhROREREOdF7habdu3dndh1EREREBu29zmkiIiIiym3eq6fJ29v7jYfhdu3a9d4FERERERmi9wpN2vOZtJKTkxEWFoazZ89muJEvERERUU7wXqHpt99+e+X0MWPGICEh4YMKIiIiIjJEmXpOU6dOnXjfOSIiIsqRMjU0hYSEwMzMLDMXSURERGQQ3uvwXOvWrXWeiwju37+P48ePY+TIkZlSGBEREZEhea/QZGVlpfPcyMgILi4uGDduHBo1apQphREREREZkvcKTQsWLMjsOoiIiIgM2nuFJq3Q0FCcP38eAFCpUiW4u7tnSlFEREREhua9QlNUVBTat2+PPXv2wNraGgAQGxsLb29v/Pvvv7C1tc3MGomIiIj07r2unuvXrx8eP36M8PBwREdHIzo6GmfPnkV8fDy+++67zK6RiIiISO/eq6dp69at2LFjBypUqKBMq1ixImbNmsUTwYmIiChHeq+eprS0NOTNmzfD9Lx58yItLe2DiyIiIiIyNO8Vmho0aID+/fvj3r17yrS7d+9iwIABaNiwYaYVR0RERGQo3is0zZw5E/Hx8ShZsiRKly6N0qVLw9nZGfHx8ZgxY0Zm10hERESkd+91TpOjoyNOnDiBHTt24MKFCwCAChUqwMfHJ1OLIyIiIjIU79TTtGvXLlSsWBHx8fHQaDT47LPP0K9fP/Tr1w/Vq1dHpUqVsH///o9VKxEREZHevFNomjZtGnr16gVLS8sM86ysrNCnTx9MnTo104ojIiIiMhTvFJpOnTqFxo0bv3Z+o0aNEBoa+sFFERERERmadwpNkZGRrxxqQMvY2BgPHjz44KKIiIiIDM07haZixYrh7Nmzr51/+vRpFC1a9IOLIiIiIjI07xSa/Pz8MHLkSDx//jzDvGfPnmH06NH4/PPPM604IiIiIkPxTkMOjBgxAqtXr0a5cuUQEBAAFxcXAMCFCxcwa9YspKam4ocffvgohRIRERHp0zv1NNnZ2eHQoUNwdXXF8OHD0apVK7Rq1Qrff/89XF1dceDAAdjZ2WVqgXfv3kWnTp1QqFAhmJubw83NDcePH1fmiwhGjRqFokWLwtzcHD4+Prh8+bLOMqKjo9GxY0dYWlrC2toaPXr0QEJCgk6b06dPo27dujAzM4OjoyMmTZqUqetBRERE2ds7D27p5OSEzZs3IyYmBleuXIGIoGzZsrCxscn04mJiYlCnTh14e3tjy5YtsLW1xeXLl3Xea9KkSZg+fToWLVoEZ2dnjBw5Er6+vjh37hzMzMwAAB07dsT9+/cRHByM5ORkdOvWDb1798ayZcsAAPHx8WjUqBF8fHwwZ84cnDlzBt27d4e1tTV69+6d6etFRERE2c97jQgOADY2NqhevXpm1pLBL7/8AkdHRyxYsECZ5uzsrPxfRDBt2jSMGDECLVq0AAAsXrwYdnZ2WLt2Ldq3b4/z589j69atOHbsGKpVqwYAmDFjBvz8/PDrr7/CwcEBS5cuRVJSEubPnw8TExNUqlQJYWFhmDp1KkMTERERAXjPe89llfXr16NatWr44osvUKRIEbi7u+PPP/9U5l+/fh0RERE6t2+xsrJCzZo1ERISAgAICQmBtbW1EpgAwMfHB0ZGRjhy5IjSpl69ejAxMVHa+Pr64uLFi4iJiXllbYmJiYiPj9d5EBERUc5l0KHp2rVrmD17NsqWLYtt27bhm2++wXfffYdFixYBACIiIgAgw3lUdnZ2yryIiAgUKVJEZ76xsTEKFiyo0+ZVy0j/Hi+bMGECrKyslIejo+MHri0REREZMoMOTWlpaahatSp+/vlnuLu7o3fv3ujVqxfmzJmj79IwfPhwxMXFKY/bt2/ruyQiIiL6iAw6NBUtWhQVK1bUmVahQgXcunULAGBvbw/gxUjl6UVGRirz7O3tERUVpTM/JSUF0dHROm1etYz07/EyU1NTWFpa6jyIiIgo5zLo0FSnTh1cvHhRZ9qlS5fg5OQE4MVJ4fb29ti5c6cyPz4+HkeOHIGnpycAwNPTE7GxsTr3xNu1axfS0tJQs2ZNpc2+ffuQnJystAkODoaLi8tHuSqQiIiIsh+DDk0DBgzA4cOH8fPPP+PKlStYtmwZ5s2bB39/fwCARqNBYGAgfvzxR6xfvx5nzpxBly5d4ODggJYtWwJ40TPVuHFj9OrVC0ePHsXBgwcREBCA9u3bw8HBAQDQoUMHmJiYoEePHggPD8fy5cvx+++/Y+DAgfpadSIiIjIw7z3kQFaoXr061qxZg+HDh2PcuHFwdnbGtGnT0LFjR6XN0KFD8eTJE/Tu3RuxsbH49NNPsXXrVmWMJgBYunQpAgIC0LBhQxgZGaFNmzaYPn26Mt/Kygrbt2+Hv78/PDw8ULhwYYwaNYrDDRAREZFCIyKi7yJygvj4eFhZWSEuLu615zd5DFmcxVUZrtDJXT7o9dyWL3zodiQiyu3UfH9rGfThOSIiIiJDwdBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkgrG+CyAiygk8hizWdwkGI3RyF32XQPRRsKeJiIiISAWGJiIiIiIVGJqIiIiIVGBoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVGBoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSIVuFpokTJ0Kj0SAwMFCZ9vz5c/j7+6NQoULInz8/2rRpg8jISJ3X3bp1C02bNkW+fPlQpEgRDBkyBCkpKTpt9uzZg6pVq8LU1BRlypTBwoULs2CNiIiIKLvINqHp2LFjmDt3Lj755BOd6QMGDMCGDRuwYsUK7N27F/fu3UPr1q2V+ampqWjatCmSkpJw6NAhLFq0CAsXLsSoUaOUNtevX0fTpk3h7e2NsLAwBAYGomfPnti2bVuWrR8REREZtmwRmhISEtCxY0f8+eefsLGxUabHxcXh77//xtSpU9GgQQN4eHhgwYIFOHToEA4fPgwA2L59O86dO4d//vkHVapUQZMmTTB+/HjMmjULSUlJAIA5c+bA2dkZU6ZMQYUKFRAQEIC2bdvit99+e21NiYmJiI+P13kQERFRzpUtQpO/vz+aNm0KHx8fnemhoaFITk7WmV6+fHmUKFECISEhAICQkBC4ubnBzs5OaePr64v4+HiEh4crbV5etq+vr7KMV5kwYQKsrKyUh6Oj4wevJxERERkugw9N//77L06cOIEJEyZkmBcREQETExNYW1vrTLezs0NERITSJn1g0s7XzntTm/j4eDx79uyVdQ0fPhxxcXHK4/bt2++1fkRERJQ9GOu7gDe5ffs2+vfvj+DgYJiZmem7HB2mpqYwNTXVdxlERESURQy6pyk0NBRRUVGoWrUqjI2NYWxsjL1792L69OkwNjaGnZ0dkpKSEBsbq/O6yMhI2NvbAwDs7e0zXE2nff62NpaWljA3N/9Ia0dERETZiUGHpoYNG+LMmTMICwtTHtWqVUPHjh2V/+fNmxc7d+5UXnPx4kXcunULnp6eAABPT0+cOXMGUVFRSpvg4GBYWlqiYsWKSpv0y9C20S6DiIiIyKAPzxUoUACurq460ywsLFCoUCFleo8ePTBw4EAULFgQlpaW6NevHzw9PVGrVi0AQKNGjVCxYkV07twZkyZNQkREBEaMGAF/f3/l8Frfvn0xc+ZMDB06FN27d8euXbvw33//YdOmTVm7wkRERGSwDDo0qfHbb7/ByMgIbdq0QWJiInx9ffHHH38o8/PkyYONGzfim2++gaenJywsLNC1a1eMGzdOaePs7IxNmzZhwIAB+P3331G8eHH89ddf8PX11ccqERERkQHKdqFpz549Os/NzMwwa9YszJo167WvcXJywubNm9+4XC8vL5w8eTIzSiQiIqIcyKDPaSIiIiIyFAxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREamQ7cZpIiKinM1jyGJ9l2AQQid30XcJ9BL2NBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKSCQYemCRMmoHr16ihQoACKFCmCli1b4uLFizptnj9/Dn9/fxQqVAj58+dHmzZtEBkZqdPm1q1baNq0KfLly4ciRYpgyJAhSElJ0WmzZ88eVK1aFaampihTpgwWLlz4sVePiIiIshGDDk179+6Fv78/Dh8+jODgYCQnJ6NRo0Z48uSJ0mbAgAHYsGEDVqxYgb179+LevXto3bq1Mj81NRVNmzZFUlISDh06hEWLFmHhwoUYNWqU0ub69eto2rQpvL29ERYWhsDAQPTs2RPbtm3L0vUlIiIiw2Ws7wLeZOvWrTrPFy5ciCJFiiA0NBT16tVDXFwc/v77byxbtgwNGjQAACxYsAAVKlTA4cOHUatWLWzfvh3nzp3Djh07YGdnhypVqmD8+PEYNmwYxowZAxMTE8yZMwfOzs6YMmUKAKBChQo4cOAAfvvtN/j6+mb5ehMREZHhMeieppfFxcUBAAoWLAgACA0NRXJyMnx8fJQ25cuXR4kSJRASEgIACAkJgZubG+zs7JQ2vr6+iI+PR3h4uNIm/TK0bbTLeJXExETEx8frPIiIiCjnyjahKS0tDYGBgahTpw5cXV0BABERETAxMYG1tbVOWzs7O0RERCht0gcm7XztvDe1iY+Px7Nnz15Zz4QJE2BlZaU8HB0dP3gdiYiIyHBlm9Dk7++Ps2fP4t9//9V3KQCA4cOHIy4uTnncvn1b3yURERHRR2TQ5zRpBQQEYOPGjdi3bx+KFy+uTLe3t0dSUhJiY2N1epsiIyNhb2+vtDl69KjO8rRX16Vv8/IVd5GRkbC0tIS5ufkrazI1NYWpqekHrxsRERFlDwbd0yQiCAgIwJo1a7Br1y44OzvrzPfw8EDevHmxc+dOZdrFixdx69YteHp6AgA8PT1x5swZREVFKW2Cg4NhaWmJihUrKm3SL0PbRrsMIiIiIoPuafL398eyZcuwbt06FChQQDkHycrKCubm5rCyskKPHj0wcOBAFCxYEJaWlujXrx88PT1Rq1YtAECjRo1QsWJFdO7cGZMmTUJERARGjBgBf39/paeob9++mDlzJoYOHYru3btj165d+O+//7Bp0ya9rTsREREZFoPuaZo9ezbi4uLg5eWFokWLKo/ly5crbX777Td8/vnnaNOmDerVqwd7e3usXr1amZ8nTx5s3LgRefLkgaenJzp16oQuXbpg3LhxShtnZ2ds2rQJwcHBqFy5MqZMmYK//vqLww0QERGRwqB7mkTkrW3MzMwwa9YszJo167VtnJycsHnz5jcux8vLCydPnnznGomIiCh3MOieJiIiIiJDwdBEREREpAJDExEREZEKDE1EREREKhj0ieBE9PF5DFms7xIMQujkLvougYgMHHuaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVGBoIiIiIlKBoYmIiIhIBYYmIiIiIhUYmoiIiIhUYGgiIiIiUoGhiYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAVjfRdAREREH4fHkMX6LsEghE7ukinLYU8TERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCowNBERERGpwNBEREREpAJDExEREZEKDE0vmTVrFkqWLAkzMzPUrFkTR48e1XdJREREZAAYmtJZvnw5Bg4ciNGjR+PEiROoXLkyfH19ERUVpe/SiIiISM8YmtKZOnUqevXqhW7duqFixYqYM2cO8uXLh/nz5+u7NCIiItIzY30XYCiSkpIQGhqK4cOHK9OMjIzg4+ODkJCQDO0TExORmJioPI+LiwMAxMfHv/Y9UhOfZWLF2dubtpMa3JYvfOh2BLgttfiZzDzclpmDv9+Z503bUjtPRN6+ICEREbl7964AkEOHDulMHzJkiNSoUSND+9GjRwsAPvjggw8++OAjBzxu37791qzAnqb3NHz4cAwcOFB5npaWhujoaBQqVAgajUaPlb1efHw8HB0dcfv2bVhaWuq7nGyN2zLzcFtmDm7HzMNtmXmyw7YUETx+/BgODg5vbcvQ9P8VLlwYefLkQWRkpM70yMhI2NvbZ2hvamoKU1NTnWnW1tYfs8RMY2lpabAf3uyG2zLzcFtmDm7HzMNtmXkMfVtaWVmpascTwf8/ExMTeHh4YOfOncq0tLQ07Ny5E56ennqsjIiIiAwBe5rSGThwILp27Ypq1aqhRo0amDZtGp48eYJu3brpuzQiIiLSM4amdNq1a4cHDx5g1KhRiIiIQJUqVbB161bY2dnpu7RMYWpqitGjR2c4rEjvjtsy83BbZg5ux8zDbZl5ctq21IioucaOiIiIKHfjOU1EREREKjA0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQREZGO7du349KlS/ouI8dIf3N3yt4Ymuij4EgWb7Zp0yZ9l2Dw+BnSj+PHj6NPnz6YNm0arl27pu9ysr1p06bBz88PsbGx+i4l1/iY+w6GJvootDct1t7LLy0tTZ/lGJTLly+jWbNm+Prrr/VdikHS7vAM9cbXOV21atXQv39/HD16FNOmTcOVK1f0XVK2VqtWLYSGhqJXr14MTh9ZVuw7OLglfTRz5szBokWLEBISou9SDEpqaio2bNiAbt26oVWrVpg/f76+SzIYIgKNRoP9+/dj06ZNePr0KcqUKYPvvvtO36XlCikpKTA2fnGjiKlTp2LVqlWoWrUqBg8eDCcnJz1Xl/2kpaXByMgIJ0+eROPGjVG3bl3MnTsXhQoV0ndpOY5237F7926sWbMGjx49wieffILvvvsO5ubmmfY+7Gmij6Z27dq4fv061q9fr+9SDIaIIE+ePGjZsiWWLl2KlStXYsCAAfouy2BoNBqsXr0azZs3x927d2FiYoIBAwagc+fOePr0qb7Ly/Hy5MkDANizZw+ePXuG+/fvY/78+Zg8eTKuX7+u5+qyF+2XOAAULlwYU6ZMwerVqzFs2DDExMToubqcR6PRYM2aNWjZsiUSEhLg6uqKkSNHonPnzoiKisq092Fookzx8uG31NRUFCtWDNWqVcPBgwdf2SY30nbsbt++HXv37oWDgwN+//13fPPNN3quTD9mz56NnTt3Ks9v3ryJoKAgjB07FkuWLMHAgQNhbW0Na2tr5MuXT4+V5mza302NRoNt27ahQYMGyJcvH8aNG4eePXti48aNmDp1KoPTO9BoNMofAdWqVcPhw4fh7u6OpUuX8lDdR3Dnzh2MGDEC48ePx/z58xEQEAArKysUL14cRYoUybw3EqIPkJKSovM8IiJC5/miRYvE1NRUTp06lZVlGbRNmzaJmZmZTJ06VRYtWiRBQUFibm4u3bt313dpWWrEiBFibW0tt27dUqadOXNG3N3dRUTk5s2bUqxYMenTp48y/9ChQ1leZ0527tw55f/JycmSkpIi7du3l06dOum0mzx5shQrVkz8/f3lxo0bWV1mtnXjxg2xs7OTadOmiYhIQkKCbN++XQoWLCht2rSR6OhoPVeY/aWlpYmIyOXLl6Vq1aqSlpYmN27cEAcHB+ndu7fSLiQkJFPej6GJ3lunTp1k2bJlkpiYKCIic+bMkdq1a8uECRMkJiZGUlNTRUTk888/l+HDh0tycrIyLbdKTU2VHj16SJcuXZRpz549k//++0/MzMzkm2++0WN1WScmJkZ8fHxk6tSpIiJy/vx5iY+Pl8uXL4urq6usW7dOSpYsKb1795bk5GQReRGoWrRoIWFhYfosPcdYs2aNuLq6yrJly3Smd+3aVb788ksR0f2jqE+fPmJjYyPdunWTq1evZmmt2dWFCxfEyclJTp48qTN9y5YtYmJiIn379pUHDx7op7gcIj4+XkRErl27JqVKlZJ///1XSpUqpbPvOHv2rHh7e8vx48c/+P14eI7eW1RUFPz9/bFlyxYAQOnSpdGoUSNMnToVzZs3x3fffYcHDx7Azc0NwcHBEBEYGRnl+kvJb9++rdM1b2ZmhmbNmqFPnz6YM2cOunfvrr/isoixsTGio6MREhKCP//8E5UqVcKNGzdgY2MDJycndOrUCTVq1MDcuXOVE5OXLFmC6OhoFC1aVM/V5wxFihSBi4sL5s2bh+XLlyvTHR0dceDAAdy9exd58uRBamoqAKBSpUqwsrLC/fv3YWFhoa+yDV76/VvBggXx6NEjHD58WKdNjRo1ULp0acydOxcDBw7M9fvE93X69GnUqFED165dg5OTE+rWrYtevXrBzc1NZ9+xdOlSPHv2DMWKFfvwN/3g2EW5zqVLl5T/d+zYUSwtLWXNmjXKtIcPH8qvv/4q9evXl9KlS0tAQIBoNBr55Zdf9FCt4Zk/f76UL19eduzYoTN91qxZUqVKFSlZsqTcvXtXT9V9XOl7GiMjI8XU1FTMzc1lypQpyvT169eLo6OjdOzYUdatWycHDhyQ/v37i5WVFQ/zZoIFCxbI8+fPRUQkLCxM2rVrJ59++qksXbpUaePh4SFVqlSRmzdvSlJSkoiIDBs2TKZNm8aekdfQHibS9rxrt/HAgQOlSpUqOvvI1NRU6du3r2zYsEFnf0rvZv/+/eLq6irbtm0TkRenPlSvXl18fX3lv//+ky1btmT6voOhid5Jy5YtZfTo0Uq3p4hIu3btlOD05MkTEfm/Hcjs2bPF399f8uXLJ15eXvLs2TO91K0P2m1w8+ZNOX36tLITPXv2rHz22Wfy5Zdf6gSnwYMHy5gxY5RtmNOMGzdOPvvsM+X5uXPnRKPRiImJiXz99ddy//59Zd6yZcvE19dXChQoIJUrVxZPT08elssEISEhUq9ePbl27Zoy7ejRo0pw+ueff0RE5Pr161KtWjWxt7eXzz77TBo3biwmJiY650DR/9H+rm/dulV69OghDRo0kKFDh8rZs2fl/v370r59e/nkk09k6tSpsnv3bhk4cKAUK1ZMoqKi9Fx59teyZUupVq2a8nz16tXSoUMHZd9Rv379TP1ji6GJ3sm+ffuUv6TS94a0a9dOrKysZM2aNUo4SO/w4cNiYmIi//77b5bVaghWrlwp9vb24ujoKCVKlJANGzaIiMiePXvE19dXSpcuLd7e3tK4cWOxtLSUs2fP6rnizKftXTp06JBcuXJFRF58yURFRcnx48flzJkzYmFhIR07dtQJTjExMXLt2jW5f/++xMXF6aX2nOb58+fy6NEjEREJDQ1Vfle1walOnTo6v6MTJkyQwMBA+fbbbyU8PFwvNWcXa9asEXNzcxk5cqT89NNP0qRJE7G2tpa4uDg5efKkcuFDuXLlpEyZMnLixAl9l5ytaIPpyy5duiQVK1aUv/76S5mWnJwsd+7ckdjYWOWcp8zC0ETv5ffff5e2bdtKaGioMi19cNIGKxFReqW6dOki/fr1y/Jas5r2l/vChQtSrlw5+e233+TIkSPSvn17sbW1lYULF4rIi6s9li9fLu3atZOBAwfmyMCkde/ePalUqZI8evRITp06JbVq1dL5jBw8eFAJTi9fgUmZI/2Xzu3bt8Xd3V2aNm36yuC0ZMmS176WMnr48KF8+umnMn36dBF5cRVx0aJFM1zYER0dLXfu3FGCK73Z0KFDZe7cucrzXbt2iaenp6xcuVI5TBwXFydt2rSRjh07Ku0+5gVHDE30XlavXi1FixaVnj176vzF1K5dO7GxsZG1a9fqfCmKiPj4+EinTp1yxQ74wIEDMn/+fBkwYIDO9B49ekiRIkVk0aJF8vTpUxF58Que07fJrVu3xNnZWTp37iwHDx6U4sWLS7169ZTzZURe9ERZWFhI165dGZw+smfPnsmsWbOkVq1a8sUXX2QITl5eXjJ//nylfU7/fL6Ln376SRYtWqQz7e7du+Ls7CyXL1+WO3fuSPHixaVXr17K/DVr1ugMrUFvFx8fLz/99JPOobUrV66Il5eX1K5dW1xcXGT58uXy5MkTOXfunJiYmMj69es/el0MTfRWr0vtGzZsEEdHR+nevbtOcPrqq69Eo9HIvn37lGnaS2/T90zlZD4+PqLRaKR+/fpKONLq0aOHFC9eXObOnSuPHz/WU4Uf18tfsikpKfLLL7+Iu7u7bNy4UUJDQ8XZ2Vnq1KmjE5xCQkJEo9FI7969c/3wFJklLS1N5+ehHUbgyZMnMm/ePPHw8NAJTseOHRM/Pz9p0qRJph/ayO4iIyNl8ODBGc7tioyMlMaNG8u///4rTk5O0rt3b2U7X79+XXr06CHbt2/XR8nZmnYbbtu2TenFE3lxusfgwYOlWLFiUr9+ffn555+lQ4cO0qlTp49+KJ+hid4o/c5206ZN8u+//8qpU6eUXqR169a9MjiNGjVK52TxxMREiYmJybK69SX99urQoYOYm5vLhg0bdIKBiMiXX34pLi4uEhsbm9UlfnTasPPywH2xsbHi5uYmn3/+uYiInDhxQkqWLJkhOB09elTOnz+fdQXnUNrzx7RfPFu3bpWePXtKr169lC/wZ8+eyZ9//pkhOIWGhsqdO3f0U7iB026jvXv3yrx585Tpbdq0EY1GI+3bt9dpP2zYMPnkk0+4PVXQ7j9fDvrDhw8XjUajE5xEXhzW/+WXX6RYsWKi0WikXLlyHz3oMzSRKkOGDBFbW1spVKiQVK1aVUaMGKFc5bVu3TpxcnKSnj17ypEjR3Relz445VTpf7lf7h3x9fWVokWLyrZt2zJsi3v37mVJffpw5coVKVy4sLRo0UIiIyOVz8qRI0fE1NRUGdQyNDRUSpUqJV5eXhkO59L727Ztm2g0GuVwxcaNG8Xc3FyaN28udevWFY1GI3PmzBGR/wtOtWrVEl9f31deyEG6h9EfPXokAQEBUqxYMfnzzz+VNvXr15dSpUrJzJkzZd68efLNN99IgQIFeOWnCtpt+/DhQ+Uio5UrV0pYWJjExsbKuHHjpECBAjJjxowMr3348KH8/PPPcuHChY9eJ0MTvVL6xH/jxg3x8vKSsLAwuXv3rgwbNkxq1aol/fv3V74M169fLyYmJvLzzz/rs+wsp91OO3fulEGDBkmrVq1k9erVOleBNWrUSIoWLSrBwcG5IkSKvLiixdraWjQajTRq1EimTZsmZ86cERGRAQMGiIeHhxw7dkxEXgQnGxsbady4sT5LzlGio6Plm2++EXNzc9myZYvMmTNHZs+eLSIijx8/lgkTJkiePHlk5syZIvIiOM2YMUO8vb3ZI/IWK1askBkzZsjhw4clMDBQXFxclAD6/Plz+fLLL6VGjRpSqVIladOmjZw+fVrPFWcPaWlp8vDhQ3Fzc5OJEyfKvHnzRKPRKBcl3L9/X0aPHi0FChRQPrcikuV/bDE0UQbpe0tiYmLk6tWr0rp1ayUgPX36VEaPHi01a9aUwMBAZfr+/fsz3IsuN1izZo1YWVlJu3btpEePHlKgQAEJCgrSuRrOz89PTExMZNeuXXqs9OPSfm60wfD333+XAQMGyA8//CB9+/aV6tWry5YtW+To0aPi4uIiY8aMEZEXh49Onjwply9f1lvtOVFMTIz4+/uLsbGxVKpUSf777z9lXmpqqkycOFGMjIzkjz/+EJEXX/i54RD6+9D+cXTt2jXJly+fEpIuXrwoAQEB4uLionOV18OHDyU+Pj5XjUuXWSZNmiR2dnavPByXPjhpP7dZjaGJXmvUqFHi4uIilStXlipVqujMe/r0qYwZM0Zq164t3bp10+nSz03B6fjx4+Lk5KQzRoiFhYUULFhQvv32W53u4tatW8vFixf1UeZHpf1Cefmk9j179kjjxo1l8+bN8vTpU5kxY4ZYW1vL1KlTpXHjxmJlZcW/wj+C9H/0JCQkyLBhw0Sj0cisWbN05qelpcmkSZNEo9HoHGKiV9u1a5csW7ZMhgwZojP9woULSnDidnx/2s/lxYsXxdLSUmxsbGTSpEkZej7v378vY8eO1dvnlqGJFOl3tv/++68ULFhQ/vjjD+natavY29tL+/btdQ4vPXv2TAYOHCi9evXKtVc6bd26Vb7//nsReXFHcycnJwkMDJQFCxaIRqORgQMHZrhZZ050//59cXR0lO+//15u3rypTB8/frwULlxY2fHt379funfvLk2bNhWNRiPNmjWTlJQUXtKeSbTbMTQ0VIKDgyU1NVXi4uKkf//+YmxsLBs3btRpl5aWJtOmTeNI36+h3U4PHjyQr7/+WjQajXh7e2cYJuTChQsSGBgotra2Gca4oneTkJAgp0+fll9//VWKFy8u48aNy3Bbqfj4eBk/frxeLhhhaKIM/v33X5kzZ45yS4Xnz5/L7NmzxcPDQzp27KjTk5SYmKjsPHJLcEq/s7xz546cO3dOEhMTpVmzZtK9e3el161ChQpiYmIiQUFBOtspJ4qJiZGxY8eKlZWVNGjQQH777TdlXteuXaVr167KlYIRERGya9cuadq0KXuaMpH287Vy5Uqxs7OTsWPHKj2bMTExEhAQIHnz5s0QnOjNVq5cKX5+fhIaGirdu3eX/Pnzy+HDh0VEt1c9PDxchg4dqly1SOqkv2ffy1cTjx8/XooXLy4//fST8ofXy2M3ZTWGJtJx6dIlcXBwEI1GI3///bcy/cmTJzJnzhzx8PCQLl26ZDgEl9N3wElJSco6vurqogcPHkjVqlWVm57Gx8dLjx49ZMqUKbnqXJ3w8HBp27atlClTRry8vOTChQvy33//SdeuXSU4OFinbU7/zOjDvn37xNLSUubMmZNhfDBtcMqXL5+sXr1aTxVmL5cvX5YqVarI33//LampqXLnzh1p3ry5FC5cWLmtTPp94ctDi9CbafcBmzZtkjZt2kjFihVl+PDhOmP8jR8/XpydnaVTp07SrVs30Wg0er0FDUMT6Xj27JmsXr1aKlWqJHXq1NGZ9/TpU5k7d64UL15cxo4dq6cKs9bOnTt1nm/cuFF8fX2ladOm0r17d2Xk6mvXril/EZ08eVJGjRollSpVypWDAz569Eg2btwo7u7uUqpUKQkKChIPDw/p3bu3vkvLsbRfPsOGDZMvvvhCZ176L/UnT55Ily5dxNbWVhISErK0xuzmxIkTMnToUOnQoYM8efJE6Um/c+eOfP7551K4cGHlsGZuOo/zQ738x9K6deskf/78MmjQIJk3b5588skn4uvrK6tWrVLaTJ8+Xdq1ayeNGjXSe+80Q1Mu9rrDaU+ePJH169dLqVKlxNfXN8O8tWvX5oqdxKFDh8Te3l65Fcrhw4dFo9HIt99+K/7+/uLq6iolSpSQvXv3iojIL7/8Ivnz55dSpUpJ0aJFc83o528SGBgojRs3Vgaf44myH1enTp2kdevWIpLx9/vUqVOSmpoqT58+1RkSgzJKSUmRr776SgoUKCCurq4Z5t+5c0datmwpGo0mS8YGyim0VxNqz409f/68VKxYUbkSLikpSWxtbaV48eLi5eUl69atU16bkJBgEFcjMjTlUi+f9D1u3Dj58ccflcvknz59KuvWrZOyZctKkyZNXrmMnB6cIiMj5ccffxQ3NzcZNGiQzJo1SyZOnKjMT01NFT8/PylevLgy+vWhQ4ckJCREbt++ra+yDUL6vyZ3794tw4YNkwIFCnCk748sKChIihYtqgwDov05xMXFydChQ2X//v36LC9biY+Pl759+0qRIkXk559/znBY/tatW9K+ffsceUXsx7Bo0SIpVqyYcrPilJQUuXTpkowZM0bi4+Pl9u3b4uzsLAEBAXL69GmxtbUVb29vWbZsmZ4r18XQlAulDztDhw6VEiVKSMOGDaVZs2ZiY2MjBw8eFJEXwWn9+vVSvnx5qVatmr7K1Yv0V838/PPPUrVqVbGzs5Nff/1VRP7v3IXk5GSpUKGCfPvtt3qr1VC93A3/se8JlZtp/3KPjIwUNzc3qVGjhnJoOC0tTYYPHy4lS5bM9WH+dbSf1Zf/EIyLi5POnTtLrVq1ZMaMGRnOWcrpfzhmpl27dkmNGjWkSpUqSnBKSEhQPpPdu3eXzp07K0OXtGjRQgoWLCjt27c3qNMcjEC5hq+vL06ePIk8efIAAGbPno1ly5Zh1apV2LFjB9q1a4fY2Fj4+Phg+/btMDc3x2effYaxY8eidOnSSEtL0/MaZL3ChQujZ8+e+OKLL5CWloYDBw4AAPLmzYvk5GQYGxujatWqiI2N1W+hBkij0eg8t7S01FMlOVtqaiqMjY3x4MEDREdHY/r06UhOTkaZMmXQoEEDNGrUCPPmzcPq1atRvHhxfZdrcEQEGo0GwcHB6N+/Pxo1aoRFixbhwoULsLS0xIwZM1CmTBksXboUf/75J5KTk5XXavel9HZeXl6YOnUq8uXLh/r16+PRo0ewsLBAsWLFAADXr1+Ho6Mj8ufPDwCwt7fHhAkTMHnyZBQoUECfpetgaMolvvrqK9y/fx8VK1YEACQlJeHSpUsYP348qlWrho0bN+Kbb77B5MmT0apVK7Ru3Rr79++HmZkZWrZsiX///RdGRka5Ijhpd6JHjx7F0aNHYWtriz59+mDQoEE4fvw4+vfvD+BFcAKA58+fIy0tDSkpKRARfZZOuUD6z1hKSgry5MmDmzdvolSpUjhw4AC8vLywf/9+DB48GNWrV4eXlxcOHz4Md3d3PVZtuDQaDdauXYtWrVpBo9GgePHimDdvHkaNGoWwsDBYWVlh5syZcHFxwYwZM7Bw4UJ9l5ztaPeptWvXxi+//AJLS0t4eXnh0aNH0Gg0ePToEczNzXHlyhUsWrQIQUFBWLduHVq0aGF4QV+v/VyUJR49eiRVqlRRRq3+559/JCoqSsLCwuTatWty/vx5KVOmjHIjxFWrVolGoxGNRqOMR5JbaLvpV69eLba2tvLDDz8oJ80+fPhQJkyYIHZ2duLn5yfff/+9DB48WExMTJT7qhF9LOkHpEzv9u3bYmNjI3379uXhovdw4sQJKV26tLJ/jIuLEysrKyldurQ0b95cudluTEyM9O7dW65fv67HarO/1NRUOXTokNSsWVNcXV3lwYMHIiKyY8cOqV69ulSoUEFcXFz0OqzAm2hE+KdxThcXF4e+ffvi+fPnyJs3L0JCQhASEqIk+JUrV2LatGnYsGEDbGxssHv3bixfvhyurq7o27cvjI2N9bwGWWv79u1o3bo1pk+fjjZt2sDKykqZFx8fjzlz5mDGjBl4/vw5pk6dilq1aqFs2bJ6rJhyOvn/f6nv2LED//vf/2BnZ4eaNWuiRYsW2LlzJ0JCQvDDDz8oh0S17V/+P2V04MAB/PPPP5g9ezZu3ryJhg0bolGjRqhVqxYGDhyI+vXrIygoCDVq1OC2fEfa7XX69Gncu3cPcXFx8PLygp2dHY4fP45+/fohPj4ee/bsga2tLW7dugUTExMYGxujcOHC+i7/1fQY2CgLHTlyRJycnMTU1FQWLFigM2/+/PnKpbOPHj2S5s2b64ypk/7WKTldamqqfPPNN9K3b18ReTHEQlhYmAwcOFCmTJkiZ86ckefPn8vIkSPF29tbGaeJ6GPbunWr5M2bV1q1aiVVq1aVChUqyNSpU5X5uWVE/syWmJgo169fl9TUVGnTpo107dpV6bGrVauW2NnZSYcOHeTZs2cckPU9rFq1SooUKSINGjRQhhLQ3tz4wIEDUrt2balcubJERkbquVJ1GJpyOO0v+dKlS8XExESqV68uX375pc6lxw8ePJDPP/9cNBqNlCtXTlxdXZWrRHLDTiL9Oj5//lz8/PykSZMmcvr0aenWrZs0bNhQKlasKG5ubtKpUycReXGV0sOHD/VVMuUyN2/elBkzZsjs2bNF5MVgqmPHjpVixYrJhAkTlHY8PPdm6W8u/fLgno8ePRJXV1eZP3++0qZjx44yceLEDPc+I3WOHTsmtra2OiFJo9HIL7/8IiIvfh4hISFSoUIFqV27drYI/gxNuURiYqJER0fLjh07pH79+tKqVStlaAERkejoaFm1apUsX75c2fHmph6m/fv3K8fQDxw4IAULFpRChQrJF198IStWrBCRF6PSVq9eXRkDhygrnDt3TipUqCClS5eWTZs2KdPv3r0rY8eOleLFiytfQvR2a9eulZo1a4qnp6cMHjxYmR4ZGSl169aVPn36SEhIiIwcOVLc3NwkKipKj9VmT9pw+tdff0nDhg1FROTKlSvi7OyscxRDG0aPHDmSbc4Vy10nq+RSycnJMDExgYmJCRo2bIiEhAT89ttv+PXXXzFo0CDUqVMHNjY2aN26tfIa7WXMuUFcXBwmTZqE0NBQbNiwAXXq1MGpU6cQERGBatWqKVcr3bx5E0WKFMkVVxCSfsn/PxckJSUFFhYW8PT0xIoVK3DhwgX4+fkBABwcHNCzZ0/kyZMHY8aMgYmJCQIDA/VbuIELDQ1Fr1698PXXX8PY2BizZ8/GlStXsGDBAhQpUgRt2rTBwoULsX79epiYmGD16tWwtbXVd9nZhvZzGx4eDldXVzx9+hTOzs549uwZ6tevj6ZNm2L27NkAgC1btiA8PBwBAQGoUaOGnit/B3oObZQFtKl/3759ypUK69atE29vb2nbtq3s3r1bj9XpT/rDcvv375c2bdpIuXLl5Pjx4zrtjh07JkFBQWJpaanXu2tT7nLkyBEZOXKkiIhcvHhR+vTpIyVKlNC5kbbIi1t6TJo0SS5duqSPMg1aWlqazu/5iRMnZNy4cTrP7ezspGnTpsoNjs+dOyfHjx/nIbn3tHbtWtFoNHLmzBnZvn27aDQasbCwkMGDB+v8LPr06SPt2rVTBrPMLhiacpBXnX+kPUa8evVq0Wg0sn79emXe+vXrpVKlSjJ8+PAsq1Ff0h8r1x52fPk+RocOHZIWLVqIi4uLcpnx+fPnpV27dlKlShVlGlFWmDBhghQpUkQ5bHH27Fnx9/cXFxcX5bwbLZ7L9GrafeKBAwdkzpw50qpVKxk0aJBOm5MnT0qRIkWkefPmEhMTo4cqc46HDx/KlClTZNq0acq0cePGiampqaxYsUISExPl3r17EhQUJIULF5bw8HA9Vvt+GJpyiPSh4OV7JG3fvl3y5s2rnIz3cg9LbtnhXr9+XWJjY0VEJCQkRD799FO5evWqTptDhw5Jo0aNpEKFCsqNOM+ePcu/Oumj0/5epv/99fb2Fk9PT+V5eHi4crNo7U1O6c02btwoGo1GatasKebm5lKpUiU5deqUzn4wLCxM8uTJI1999VW2OBnZEJ06dUpsbGykfPnysnnzZmX61atXpV+/fsqFRlWrVpXSpUsb7DhMb8PQlMNMnTpVmjZtKn379pVVq1aJiMiKFStk+fLlOu1e3jHk9OCUlJQk3t7eYm9vLzExMbJz506pVq2aNGjQIMMJiAsXLhSNRiP29vY8HEdZauvWrdKnTx/ZunWriLwISTVq1JBJkyYpbc6fPy9du3aV6tWrK38EkC5tILp//7506tRJ/v77b3n8+LGcP39e7O3tpWHDhhluHn3mzBnefPcDnDlzRr766isxMTFRvm/SB9MDBw7IggULZPPmzXLnzh19lfnBGJqyufQfykmTJom1tbX0799fqlWrJjVr1tTpJs3tf0GdOXNGqlevLu7u7hITEyO7du2S+vXrS7169XR6nEJCQsTX11d69+4tV65c0WPFlJskJiZKjx49RKPRSKVKlWTw4MESHR0tAwYMkC+//FLnM3rp0iVlpHp6tQMHDkiTJk3k008/1Tm0fvv2bbGzs5MGDRoovcmUOc6ePStt2rQRCwsL5W4SqampOeq7h/eey8ZSU1OV0WmPHDmCyMhIZXTvNWvWoE6dOli4cCEmTpwIADAyMkJqaqo+S9YL+f9Xv1WsWBGLFy+GiYkJ/Pz84O7ujlGjRsHIyAhdunTBlStXkJqaii1btqB48eL49ddfUbp0aT1XTzmZpLshg4mJCb788kt4eXmhX79+2LVrF/r37w9LS0ts3LgRa9asUdqWLVsW9vb2+ig527C3t8f169dx6NAhhIWFKdOLFy+O48eP49KlS+jYsSMuX76svyJziKSkJABApUqVMG3aNDRu3BjNmzfHkSNHYGRklLPuyann0EbvYdiwYTpjBa1fv15cXV2lXLlyOt3Lt27dkkGDBknVqlVl4sSJ+ihVL7R/1aQ/0Vs7WKeIyKBBg0Sj0UiNGjUkJiZG9u7dKz4+PqLRaKR69epiYWEhp0+fzvK6KXfR9hIfPHhQZ2Tv3r17S5MmTURE5Oeff5YBAwaIsbGxaDQa2bNnj15qzQ5edSHMjRs3pEqVKuLl5ZXhKuGbN29KuXLl5MaNG1lUYc6g3c7Hjh2Tc+fOKc9v3LghI0eOlLS0NAkPD5d27dqJg4ODHDhwQJ/lZjqGpmxmy5Yt0qlTJ52BJ48dOybt27eXAgUK6ByOE3nRFT1kyBApVqyYLF68OKvL1Zs7d+7IF198Ibt27dKZ/ssvv0ihQoXkr7/+kqpVqyqH6hITE+Xvv/+WOXPm8JAcfRTaMP/06VN5/vy53LhxQx49eiQTJ04US0tLadmypZw6dUqSk5PF19dXfv75ZxF5MSjgwIEDxcbGRq5du6bPVTBY2i/u48ePy6JFi2Tv3r3K4ctLly7JJ598Io0aNcoQnHL6uZwfy+bNm8XU1FQ59+7GjRvi4OAg/fv3V9qcO3dOmjRpImXLls1Rt6BhaMpmUlJSlJ3v8uXLlUtkz507Jx07dpQaNWpkuLfcjRs3ZObMmblqB3H16lXx9PQUPz8/5S+dCRMmSMGCBSU4OFhEXmyzKlWqSJUqVeTRo0f6LJdyOO3v7Llz56R169bi6uoqxsbG4unpKYGBgXL16lWpU6eO1KlTR/r16yd///23fPPNNzo9nrwc/s1WrVolVlZW4uzsLE5OTtKhQwc5d+6ciPxfcPLz85Nt27Ypr8kpX+RZ6dGjR/Lzzz/L5MmTReTFMAMODg7St2/fDNvzwoUL2fqk71dhaMpG0n8gT58+LS4uLuLn5ydxcXEi8uKSz06dOknt2rUzBCet3BScLl26JI0bN5YWLVpIr169xNbWVmeHKfLiSiRnZ2epWbOmpKamcidKmU77mTp9+rRYWVmJv7+//PXXX7Jq1Spp0aKFGBsbS/fu3SUqKkrmzp0r3t7eYmxsLPb29jJ69OgMy8nt0p9UrD3sfu/ePWnXrp3Mnz9fEhIS5M8//xQfHx/x9fVVxgK6dOmSODo6SuvWrXkrpPcUHh4uJiYm4uzsrHPkYvHixTqfz5z8WWVoyqaSkpJk4cKFUqdOHWnWrJly6XFYWJh07txZ6tatK7NmzdJzlfp38eJF+eyzz8Tc3Fx+/fVXZXr6He/Fixd52IM+qqioKHF3d5egoKAM02fOnCmmpqbSrVs3EXlxH8hhw4aJRqMRa2triY+Pz9FfQu/j1q1byv9DQkLkyy+/lKZNm+qMp7Z8+XJp2LCh+Pr6Kj1OV65cyTA2G71d+s9f//79RaPRyLhx4yQxMVGPVekHQ1M2pO0tSkpKkn/++UeqV6+uE5xOnTolTZs2lT59+nBnKy92lI0aNZImTZrI/v37lek56TJYMmwnTpwQV1dXOXPmjPL7q/38xcbGyo8//ihmZmaydu1a5TVbtmxhmH+FJ0+eSO3ataVy5coi8mJcNWdnZ7Gzs5N79+7ptF2+fLn4+vpKrVq1OLzAe3jd94e/v7+YmprKmjVrsrYgA8DQZODSf7FrP8Daf7X3kfvnn3+kVq1a0rx5c+VQ3eXLl5XXMjj936E6X1/fHHc1Bxm+BQsWiJmZmfL85d/Ja9euiZWVlXKeCL1ecnKyrFu3Tj755BPlKsOVK1eKs7OztGvXLsPo/YsXL5aWLVvKzZs39VFutpX+FjQTJ06U4cOHyz///KPM79u3r5ibm8u6dev0VaJeMDQZsFf1hKS/l1zt2rXlzp07kpycLIsXL1ZOJE1ISHjjMnKrS5cuyeeffy61atWSkJAQfZdDucj+/fvFzMxMVq5c+do27u7uEhgYmIVVZT/a/VlycrJs2bJFXFxcpHHjxiLy4o/HmjVrSteuXTMM/BkfH5/lteYEq1atEktLS+ncubO0atVKypcvL23atFHmf/vtt2JpaSn//fefHqvMWhzc0kDt3bsX+/fvBwD06tULgwcPBvBigMp///0XX3/9Nbp06YJixYrB2NgYX331FTp37oxKlSrB3NxcWY6REX/EWmXLlsXkyZNRvHhxODg46LscykVKliwJS0tLLF68GDdv3lSmp6WlAQBiYmJgbm4ODw8PfZVokLTbJzk5GcCL/VlaWhqMjY3h4+OD3377DdevX4efnx86duyIb7/9FhcuXMCIESNw7949ZTkFChTQS/3Z2dWrVzFkyBBMnDgRixcvxoQJExAZGYmiRYsqbWbNmoVmzZohMDAQCQkJeqw2C+k7tZGutLQ0iY+PFxcXF/nss8+kXbt2YmVlpdwGIDExUVxdXXXGY9J2o6a/Mo49TK+XG09eJP1btWqVmJiYSOfOneXs2bM680aMGCElS5bkQIuvcOPGDQkMDNS5D+TLPU4VKlSQzp07i4jIvHnzpGLFivLtt9/mqquFM9v+/fvFzc1NRF78DBwdHaVPnz7K/PSnOeSmW/oY6zu0kS6NRoMCBQogJCQErq6uiIyMxLx581C5cmUAL261cPToUZ3eJI1GAxFBnjx5lGnsYXo9ExMTfZdAuVCLFi0wffp0BAQE4OjRo6hTpw6KFi2K69evY8uWLdi5cyecnJz0XabBiY+Px7p16/D8+XMEBASgUqVKOj1OXl5eCAoKwi+//ILDhw+jV69eSEtLg6+vr84+kd5Nvnz5YG9vj6NHj6Jt27Zo0qQJZs2aBQAICwvD//73PxQqVAjly5fPVbf04TerAUpKSsKDBw9gb2+P0qVLY9WqVdi1a5cyP/2XvvZectp70BGRYcqTJw/69OmDAwcOoFKlSjhy5Aj27NkDa2trHDp0CO7u7vou0SC5ublh9erVOHr0KKZNm4bw8HAA/3cvTTMzM7Ro0QIPHz7E4cOHAQB9+vRByZIl9Vi14ZMX5zQr/3+ZjY0Nzp8/j1q1aqFJkyaYO3euEkIXLVqEc+fOwdbWNktrNgQaedXWoiyXlpb2yt6hu3fvokmTJihatCiCgoLg7e2th+qIKDOlpqbCyMgIGo3mtb/7pOvkyZPo2bMnqlatigEDBqBixYoAgJSUFDx//hxt27ZF37590bJlS4gI/5B8i2fPnsHc3BxJSUkwMTHBgQMHcOTIEVhYWKBZs2YoVqwYduzYAT8/P/To0QOdOnWCubk5li5dir///hv79++Hm5ubvlcjyzE0GYD0O80dO3bg1q1bcHBwQNmyZVG6dGlcunQJbdu2haOjI/r37w8fHx94e3vD29sbY8aM0W/xRPTO0n+p8wtePW1wqly5Mvr16wd3d3ckJyfjp59+wuLFi7Fnzx6UKFFC32UavCVLlmDo0KEICwuDnZ0dVqxYge7du6NMmTJ48uQJUlNTsWnTJpQvXx4rV67EwIEDkZqaCisrK1hYWODPP/9ElSpV9L0aesHQZECGDh2K//3vfzA1NYWxsTGePn2KJUuWoH79+rh06RI6duyIxMREJCUlIW/evAgNDeX5OUSUq5w6dQr9+vXDo0eP4OLigjx58uDw4cPYsGFDrv0if1f79u1DUFAQEhISsHnzZkyfPh2VKlVCp06dEBYWhtGjR+PgwYM4fPgwXFxccPfuXcTExMDExAS2trawsbHR9yroDUOTnqWkpMDY2Bj//PMPAgMDsXHjRlSqVAmXL1/G9OnTsXz5cuzYsQN16tTB7du3ERwcjKdPn6Jv374wNjZWXk9ElFvcunULa9aswf79++Hu7o4vvvgC5cqV03dZ2cqhQ4cwdOhQREZGokSJEpgyZYoSOq9cuYL+/fvj4MGDOHLkCFxcXPRbrAFhaNKT4OBg+Pj4QKPRICUlBWPGjMHFixexYsUKpU1ERAT69++PqKgorF69OkO6T01N5dUhRET0RtpTQF4+FHz69GkEBQUhODgYp06dQsWKFZW2V65cwaBBg7BhwwZcuXIFpUqV0uMaGA6efagH0dHR6N27NypWrAgRgbGxMVJTUxEWFoZnz54p7ezt7dG4cWNcu3YNiYmJGZbDwERERG9jZGSEW7duITg4GMCLc5o6duyITz75BD/88AM8PDzQvHlzPHjwQAlXZcqUwaRJk/DFF18og4sSQ5Ne2NjYYOnSpTA2NkbVqlUhImjdujWMjY3x999/Iy4uTmlbtmxZFChQAE+ePNFjxURElF2lpqaib9++GD58OH744Qd069YN9erVAwDUqVMHv/32G4oWLQovLy9ERUUpY/+5uLjgn3/+4eG5dHh4Tk/S0tJw5MgRdOvWDdbW1jh8+DCGDx+OTZs24YsvvsCXX34JExMTfPPNN0hJSUFwcDCvsCEiovfm5uaG8PBwDBw4EL/++qvOvEOHDiEoKAhxcXHYtm1brhqw8l0wNGWRo0eP4tGjR2jSpIly8nZKSgpOnDiBdu3aoUSJEti7dy/Gjx+PNWvWICwsDG5ubjAxMcGhQ4eQN29ejudCRERvpf2uePbsGYyMjHDz5k0ULlwYTZs2xbNnz5A/f34EBQXBz89P5zslJCQEPXv2hKWlJQ4ePAiNRsM/1l/C0JQFdu/ejYYNGwIAatasifLly6NFixaoWrUqSpQogWPHjqF3796wsLDAgQMHkJiYiB07dqBgwYKoUaMG8uTJw6vkiIjorbSB6fz58xgxYgQuXbqECxcuoH79+nBxccHMmTPRsGFDPH/+HN9//32G4HT+/HmYmZnB2dlZj2thuBiassDVq1fRuXNnJCcno3DhwihXrhwWL16MQoUKwdXVFd7e3rC2tsaIESNQvnx5bN++XSfd8yo5IiJ6G+3VcWfOnEHdunXRqVMnuLu7w9raGkuWLMH69evRtWtX/Pjjj+jatSuePn2K4cOHo1mzZggKCsL9+/exaNEifa+GQWNoyiKXL1/GsGHDkJSUhJ9++gklSpTAiRMnMGPGDMTExODo0aOwtbXFnTt38N1332HatGn6LpmIiLKZBw8ewNfXF76+vpgwYYLO9P/++w8DBw5Er169MG3aNPj5+eHu3buwtrbG+fPnsWnTJnh6euqxesPH0JSFLl26hO+++w5paWkYO3as8uFMTU3F5s2bce3aNYSEhGDJkiXImzevnqslIqLs5uTJk+jSpQv+97//oUKFCsiTJ49yyC4uLg4zZ87EuHHjsHPnTri5uWHBggVISEhA27ZtUb58eX2Xb/AYmrLY5cuX0a9fPwDA999/r1z2+bLk5GQGJyIieicLFy7EN998o4z59/KAltevX4e7uzuCgoIQFBSkrzKzLV6KlcXKli2LGTNmQKPRYMKECTh48OAr2zEwERHRuypTpgwAYNWqVQCQ4eo3Z2dnlCpVCpGRkVleW07A0KQHZcuWxfTp05EnTx4EBgbi9OnT+i6JiIhygJIlS8LS0hKLFy/GzZs3lelpaWkAgJiYGJibm8PDw0NfJWZrDE16UrZsWUyePBn16tWDq6urvsshIqIcoHjx4pg9eza2bt2KkSNHIjw8HACUYQWmTp2Ke/fuoW7duvosM9viOU0GggNXEhFRZkhNTcVff/2FgIAAlC5dGnXq1EHRokVx/fp1bNmyBTt37oS7u7u+y8yWGJqIiIhyoCNHjmDSpEm4ePEirK2tUblyZfTr149XyX0AhiYiIqIcKjU1FUZGRtBoNDyikQm49YiIiHIobWACMl5JR++OPU1EREREKrCniYiIiEgFhiYiIiIiFRiaiIiIiFRgaCIiIiJSgaGJiIiISAWGJiIiIiIVGJqIiIiIVGBoIqJszcvLC4GBgQazHLXGjBmDKlWqvLFNVtdERG9mrO8CiIiy0p49e+Dt7Y2YmBhYW1sr01evXo28efPqr7BXMMSaiHIzhiYiMlhJSUkwMTHJkvcqWLBglrzPuzDEmohyMx6eIyKD4eXlhYCAAAQGBqJw4cLw9fXF2bNn0aRJE+TPnx92dnbo3LkzHj58+NplLFmyBNWqVUOBAgVgb2+PDh06ICoqCgBw48YNeHt7AwBsbGyg0Wjw9ddfK++d/lBYTEwMunTpAhsbG+TLlw9NmjTB5cuXlfkLFy6EtbU1tm3bhgoVKiB//vxo3Lgx7t+/r7TZs2cPatSoAQsLC1hbW6NOnTq4efNmhnpLliwJKysrtG/fHo8fP9bZHulrKlmyJMaPH4+vvvoKFhYWKFasGGbNmvXO25mI3g9DExEZlEWLFsHExAQHDx7ExIkT0aBBA7i7u+P48ePYunUrIiMj8eWXX7729cnJyRg/fjxOnTqFtWvX4saNG0owcnR0xKpVqwAAFy9exP379/H777+/cjlff/01jh8/jvXr1yMkJAQiAj8/PyQnJyttnj59il9//RVLlizBvn37cOvWLQwePBgAkJKSgpYtW6J+/fo4ffo0QkJC0Lt3b52bpl69ehVr167Fxo0bsXHjRuzduxcTJ0584/aZPHkyKleujJMnTyIoKAj9+/dHcHCwqm1LRB9IiIgMRP369cXd3V15Pn78eGnUqJFOm9u3bwsAuXjxovKa/v37v3aZx44dEwDy+PFjERHZvXu3AJCYmJgM761dzqVLlwSAHDx4UJn/8OFDMTc3l//++09ERBYsWCAA5MqVK0qbWbNmiZ2dnYiIPHr0SADInj17XlnX6NGjJV++fBIfH69MGzJkiNSsWfOVNYmIODk5SePGjXWW065dO2nSpMlr15+IMg97mojIoHh4eCj/P3XqFHbv3o38+fMrj/LlywN40UvzKqGhoWjWrBlKlCiBAgUKoH79+gCAW7duqa7h/PnzMDY2Rs2aNZVphQoVgouLC86fP69My5cvH0qXLq08L1q0qHIosGDBgvj666/h6+uLZs2a4ffff9c5dAe8ONxWoECBV77+dTw9PTM8T18TEX08DE1EZFAsLCyU/yckJKBZs2YICwvTeVy+fBn16tXL8NonT57A19cXlpaWWLp0KY4dO4Y1a9YAeHFSeWZ7+co2jUYDEVGeL1iwACEhIahduzaWL1+OcuXK4fDhw298fVpaWqbXSUSZg1fPEZHBqlq1KlatWoWSJUvC2Pjtu6sLFy7g0aNHmDhxIhwdHQEAx48f12mjvRovNTX1tcupUKECUlJScOTIEdSuXRsA8OjRI1y8eBEVK1Z8p3Vwd3eHu7s7hg8fDk9PTyxbtgy1atV6p2Wklz50aZ9XqFDhvZdHROqxp4mIDJa/vz+io6Px1Vdf4dixY7h69Sq2bduGbt26vTL0lChRAiYmJpgxYwauXbuG9evXY/z48TptnJycoNFosHHjRjx48AAJCQkZllO2bFm0aNECvXr1woEDB3Dq1Cl06tQJxYoVQ4sWLVTVfv36dQwfPhwhISG4efMmtm/fjsuXL39wwDl48CAmTZqES5cuYdasWVixYgX69+//QcskInUYmojIYDk4OODgwYNITU1Fo0aN4ObmhsDAQFhbW8PIKOPuy9bWFgsXLsSKFStQsWJFTJw4Eb/++qtOm2LFimHs2LEICgqCnZ0dAgICXvneCxYsgIeHBz7//HN4enpCRLB582bVg03my5cPFy5cQJs2bVCuXDn07t0b/v7+6NOnz7tviHQGDRqE48ePw93dHT/++COmTp0KX1/fD1omEamjkfQH4ImIyGCVLFkSgYGBvLUKkZ6wp4mIiIhIBYYmIiIiIhV4eI6IiIhIBfY0EREREanA0ERERESkAkMTERERkQoMTUREREQqMDQRERERqcDQRERERKQCQxMRERGRCgxNRERERCr8P931teHxlu+5AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pIVFpStp3VX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RIczyHuu3VaQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8: Check Correlations\n",
        "# Now, we want to see if there are any relationships between our numerical variables by creating a correlation matrix."
      ],
      "metadata": {
        "id": "gls-MLnv3Vc1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "TG9rUAoe3VfK",
        "outputId": "dfc7351b-8d5f-459e-e81a-77a93cc1724a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-6175d103b930>: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 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "NE-y7UyVSZdr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ish3mCsJ3Vhp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tQfB_otj3VmT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WDe6Sk7PSbiT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "9LxhV16DSgis"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧹 **3. Data Preprocessing**\n",
        "\n",
        "### **Objective:**\n",
        "Data preprocessing ensures that the dataset is in a suitable format for machine learning models. This step involves scaling numerical features, encoding categorical variables, and transforming data into formats that models can work with.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Feature Scaling**: Standardize numerical features using `StandardScaler` to normalize the data. This ensures that features are on the same scale, preventing certain features from dominating the model.\n",
        "\n",
        "- **One-Hot Encoding**: Transform categorical variables into numerical format using one-hot encoding. This step is necessary for machine learning models, as most models require numerical input."
      ],
      "metadata": {
        "id": "W-z8tpiAShgc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Handle Missing Values\n",
        "# We need to address any missing values in our dataset. Here, we choose to fill missing numerical values with the mean of each column."
      ],
      "metadata": {
        "id": "_Ttt35dJ3VpX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(df.mean(numeric_only=True), inplace=True)"
      ],
      "metadata": {
        "id": "_S4kwKrN4wQi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: Missing values can cause issues in our analysis, so we fill them with the mean of each numerical column. This ensures that our analysis isn't affected by missing data."
      ],
      "metadata": {
        "id": "IM1n-NCL4wat"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EHTsvVZu4wdn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 10: Handle Duplicate Values\n",
        "# We check for and remove any duplicate rows in our dataset to ensure data integrity and avoid bias in our analysis.\n"
      ],
      "metadata": {
        "id": "BmGWUA1q4wft"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "fX_np1VH4wii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explanation: Duplicate rows can skew our analysis and lead to biased results. By removing duplicates, we ensure that each observation in our dataset is unique and representative.\n"
      ],
      "metadata": {
        "id": "V0CRUtPS4wlC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NpShI02Q4wnr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 11: Handle Categorical Variables\n",
        "# We convert categorical variables into numerical form using one-hot encoding to make them suitable for analysis by machine learning algorithms.\n"
      ],
      "metadata": {
        "id": "LiDdntJt4wqG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "aWVTBEwO5CMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UHZcX3KZ5CPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 12: Handle Outliers\n",
        "# Outliers can skew our analysis, so we detect and handle them using the Interquartile Range (IQR) method."
      ],
      "metadata": {
        "id": "ipxJC8pG5CS5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import zscore"
      ],
      "metadata": {
        "id": "wL1jhbQt5CgW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Z-scores for numerical features"
      ],
      "metadata": {
        "id": "onb10-wn-t3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z_scores = X_train.apply(zscore)"
      ],
      "metadata": {
        "id": "BWm9qzaH-t6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set threshold for Z-score (e.g., 3 or -3)"
      ],
      "metadata": {
        "id": "LHdxNyVE-0m_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 3"
      ],
      "metadata": {
        "id": "n0Ez1C3E-0ql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify outliers based on Z-score"
      ],
      "metadata": {
        "id": "wib-n9Xg-0tp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers = (z_scores > threshold) | (z_scores < -threshold)"
      ],
      "metadata": {
        "id": "Xr0BSi24-t9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Flag outliers in the dataset"
      ],
      "metadata": {
        "id": "82FEwyqO_AWq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_outliers = X_train.copy()"
      ],
      "metadata": {
        "id": "J2aUUPkI_AZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_outliers[outliers] = np.nan"
      ],
      "metadata": {
        "id": "mPOSwNqB_AdF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset with flagged outliers"
      ],
      "metadata": {
        "id": "aHy7h21b_AgF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Data with flagged outliers:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5shqKMC3_Lo9",
        "outputId": "c1eb8144-3be6-4df4-ba29-0e9247c4647b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data with flagged outliers:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train_outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKShKnpx_LsY",
        "outputId": "404f73bf-b107-44a4-893d-3465a9ab5a47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            age    fnlwgt  education-num  capital-gain  capital-loss  \\\n",
            "25099 -1.687182 -1.387830       0.370810           0.0           0.0   \n",
            "5652   0.520999  0.046638       0.370810           0.0           0.0   \n",
            "25813  0.520999 -1.712215       0.370810           0.0           0.0   \n",
            "29737  0.910678 -0.452216      -2.696799           0.0           0.0   \n",
            "12334  0.910678 -0.446927       0.370810           0.0           0.0   \n",
            "...         ...       ...            ...           ...           ...   \n",
            "8636   1.105518 -0.816872       0.370810           0.0           0.0   \n",
            "24318 -0.648038 -0.712469       0.370810           0.0           0.0   \n",
            "27038 -1.687182  1.659335       0.370810           0.0           0.0   \n",
            "5529   0.910678  0.582920       0.370810           0.0           0.0   \n",
            "21039 -0.907824 -1.806788       0.370810           0.0           0.0   \n",
            "\n",
            "       hours-per-week  workclass_Federal-gov  workclass_Local-gov  \\\n",
            "25099       -0.027540                    0.0                  0.0   \n",
            "5652        -0.027540                    0.0                  0.0   \n",
            "25813       -0.027540                    0.0                  0.0   \n",
            "29737       -0.027540                    0.0                  0.0   \n",
            "12334       -0.027540                    0.0                  0.0   \n",
            "...               ...                    ...                  ...   \n",
            "8636        -0.027540                    0.0                  0.0   \n",
            "24318       -0.027540                    0.0                  0.0   \n",
            "27038        2.060891                    0.0                  0.0   \n",
            "5529              NaN                    0.0                  0.0   \n",
            "21039       -0.027540                    0.0                  0.0   \n",
            "\n",
            "       workclass_Never-worked  workclass_Private  ...  \\\n",
            "25099                     0.0                0.0  ...   \n",
            "5652                      0.0                0.0  ...   \n",
            "25813                     0.0                0.0  ...   \n",
            "29737                     0.0                0.0  ...   \n",
            "12334                     0.0                0.0  ...   \n",
            "...                       ...                ...  ...   \n",
            "8636                      0.0                0.0  ...   \n",
            "24318                     0.0                0.0  ...   \n",
            "27038                     0.0                0.0  ...   \n",
            "5529                      0.0                0.0  ...   \n",
            "21039                     0.0                0.0  ...   \n",
            "\n",
            "       native-country_Portugal  native-country_Puerto-Rico  \\\n",
            "25099                      0.0                         0.0   \n",
            "5652                       0.0                         0.0   \n",
            "25813                      0.0                         0.0   \n",
            "29737                      0.0                         0.0   \n",
            "12334                      0.0                         0.0   \n",
            "...                        ...                         ...   \n",
            "8636                       0.0                         0.0   \n",
            "24318                      0.0                         0.0   \n",
            "27038                      0.0                         0.0   \n",
            "5529                       0.0                         0.0   \n",
            "21039                      0.0                         0.0   \n",
            "\n",
            "       native-country_Scotland  native-country_South  native-country_Taiwan  \\\n",
            "25099                      0.0                   0.0                    0.0   \n",
            "5652                       0.0                   0.0                    0.0   \n",
            "25813                      0.0                   0.0                    0.0   \n",
            "29737                      0.0                   0.0                    0.0   \n",
            "12334                      0.0                   0.0                    0.0   \n",
            "...                        ...                   ...                    ...   \n",
            "8636                       0.0                   0.0                    0.0   \n",
            "24318                      0.0                   0.0                    0.0   \n",
            "27038                      0.0                   0.0                    0.0   \n",
            "5529                       0.0                   0.0                    0.0   \n",
            "21039                      0.0                   0.0                    0.0   \n",
            "\n",
            "       native-country_Thailand  native-country_Trinadad&Tobago  \\\n",
            "25099                      0.0                             0.0   \n",
            "5652                       0.0                             0.0   \n",
            "25813                      0.0                             0.0   \n",
            "29737                      0.0                             0.0   \n",
            "12334                      0.0                             0.0   \n",
            "...                        ...                             ...   \n",
            "8636                       0.0                             0.0   \n",
            "24318                      0.0                             0.0   \n",
            "27038                      0.0                             0.0   \n",
            "5529                       0.0                             0.0   \n",
            "21039                      0.0                             0.0   \n",
            "\n",
            "       native-country_United-States  native-country_Vietnam  \\\n",
            "25099                           0.0                     0.0   \n",
            "5652                            0.0                     0.0   \n",
            "25813                           0.0                     0.0   \n",
            "29737                           0.0                     0.0   \n",
            "12334                           0.0                     0.0   \n",
            "...                             ...                     ...   \n",
            "8636                            0.0                     0.0   \n",
            "24318                           0.0                     0.0   \n",
            "27038                           0.0                     0.0   \n",
            "5529                            0.0                     0.0   \n",
            "21039                           0.0                     0.0   \n",
            "\n",
            "       native-country_Yugoslavia  \n",
            "25099                        0.0  \n",
            "5652                         0.0  \n",
            "25813                        0.0  \n",
            "29737                        0.0  \n",
            "12334                        0.0  \n",
            "...                          ...  \n",
            "8636                         0.0  \n",
            "24318                        0.0  \n",
            "27038                        0.0  \n",
            "5529                         0.0  \n",
            "21039                        0.0  \n",
            "\n",
            "[72 rows x 100 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZMihlJnT-uAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 13: Feature Scaling\n",
        "# Finally, we scale our numerical features to a standard scale to ensure that they have a similar influence on our analysis."
      ],
      "metadata": {
        "id": "VKJZnCmI5Vg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "1S0FTpyW5VkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "dMQEO4Ha5VnB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df = df.copy()  # Create a copy of the DataFrame to store the scaled data"
      ],
      "metadata": {
        "id": "XV6I8xCU5Vt8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df[df.select_dtypes(include=np.number).columns] = scaler.fit_transform(df.select_dtypes(include=np.number))"
      ],
      "metadata": {
        "id": "w6yXqrhS50p4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2l2zPW4K50sx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the scaled data to a CSV file"
      ],
      "metadata": {
        "id": "TibsYOJG5Vv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df.to_csv('/content/scaled_data.csv', index=False)"
      ],
      "metadata": {
        "id": "WfIxs0iN5oZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "McQFasj_5oeK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary:\n",
        "# This notebook guides us through the process of exploring our dataset, from loading and inspecting the data to handling missing values, duplicates, outliers, and scaling features.\n",
        "# Each step is explained in simple terms to make it accessible to beginners."
      ],
      "metadata": {
        "id": "w8UDhOv35ohO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "v17NC3IrSs3M"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a_jlbbKs5ojt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cgepysfi6Rw7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G5vXp4N76RzZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "dSbwemVsSxTZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *2. POSSIBLE TASKS*"
      ],
      "metadata": {
        "id": "6TKgOrmJ6S9J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "6GKxBWI6SygG"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GvF5MkWz6R1l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "lZ37ihIyS0jd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧠 **4. Predictive Modeling**\n",
        "\n",
        "### **Objective:**\n",
        "Predictive modeling is used to build a model that can predict the target variable (e.g., income level) based on input features. This step will involve training and evaluating a Logistic Regression model.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Logistic Regression**: Implement Logistic Regression to predict binary income levels (e.g., income >50K or <=50K). This model is suitable for classification tasks where the output is categorical.\n",
        "\n",
        "- **Model Evaluation**: Evaluate the performance of the Logistic Regression model using:\n",
        "  - **Accuracy**: The percentage of correctly classified instances.\n",
        "  - **Classification Report**: This provides detailed performance metrics like precision, recall, and F1-score.\n",
        "  - **Confusion Matrix**: A visual tool to show the number of true positives, false positives, true negatives, and false negatives."
      ],
      "metadata": {
        "id": "jje76PEVS3b1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# * PREDICTING INCOME LEVEL*\n",
        "\n",
        "> Indented block\n",
        "\n"
      ],
      "metadata": {
        "id": "4EP_o72F70BC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Import Necessary Libraries\n",
        "# We start by importing the required libraries for data manipulation, visualization, and machine learning."
      ],
      "metadata": {
        "id": "ZnDyHAHAAlbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8gLb8kZRAlon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # This library is essential for data manipulation and analysis."
      ],
      "metadata": {
        "id": "3qPvFsVZAlsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # This function is used to split the dataset into training and testing sets."
      ],
      "metadata": {
        "id": "qtfp5H-fAlvQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # StandardScaler is used for feature scaling."
      ],
      "metadata": {
        "id": "jn-HJWsMAlyV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression  # LogisticRegression is a classification algorithm suitable for binary classification tasks."
      ],
      "metadata": {
        "id": "DcMIfyaWAl2S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # These functions are used to evaluate the performance of the model."
      ],
      "metadata": {
        "id": "FErqcMW0Al5S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FPJpWpaMAl9G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Load the Data\n",
        "# Loading the dataset into a pandas DataFrame is crucial to begin our analysis."
      ],
      "metadata": {
        "id": "dwkPmbZoB5W_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/adult.data.csv')  # We read the data from a CSV file and store it in a pandas DataFrame."
      ],
      "metadata": {
        "id": "Kj4N2S0DB5Z0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OOiahWdxB5dA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# step 3: Split the dataset into features (X) and the target variable (y)."
      ],
      "metadata": {
        "id": "BTHkci09B5hR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('salary', axis=1)  # Features (independent variables) are all columns except 'salary'."
      ],
      "metadata": {
        "id": "KvAmWOsZB5mf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['salary']  # Target variable (dependent variable) is 'salary'."
      ],
      "metadata": {
        "id": "HB8Td3TMB5pI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aOnqRA2iB5rK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Split the Data\n",
        "# Splitting the dataset into training and testing sets allows us to train the model on one set and evaluate its performance on another, unseen set.\n"
      ],
      "metadata": {
        "id": "4T4PMnXpB5vb"
      },
      "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)  # We split the data into 80% training and 20% testing sets."
      ],
      "metadata": {
        "id": "YkpF2Td-B5xo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m2kzGOQfB5za"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Feature Scaling\n",
        "# Scaling numerical features ensures they have a similar scale, preventing features with larger magnitudes from dominating the model's learning process."
      ],
      "metadata": {
        "id": "ZK_Ft_jpCU5i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify numerical columns\n",
        "numerical_columns = X_train.select_dtypes(include=['int64', 'float64']).columns"
      ],
      "metadata": {
        "id": "_YUBxhUKCVDo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the StandardScaler.\n",
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "QvJvB2_vCVG8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Uw0luYt8CVLa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and transform the training features, scaling only the numerical columns."
      ],
      "metadata": {
        "id": "F-OnroXoCVNZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled = X_train.copy()"
      ],
      "metadata": {
        "id": "gR0wLcFQC5sE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled[numerical_columns] = scaler.fit_transform(X_train_scaled[numerical_columns])"
      ],
      "metadata": {
        "id": "vo7HqTD0C5vI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XlV65LLrC5y7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform the testing features using the same scaler, scaling only the numerical columns."
      ],
      "metadata": {
        "id": "OvL3R6pDC52Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled = X_test.copy()"
      ],
      "metadata": {
        "id": "RTdJj0RaC55q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_scaled[numerical_columns] = scaler.transform(X_test_scaled[numerical_columns])"
      ],
      "metadata": {
        "id": "Hz97xsoNC5-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0A93d6TuC6C9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Encode Categorical Variables\n",
        "# Since logistic regression cannot handle categorical variables directly, we encode them using one-hot encoding."
      ],
      "metadata": {
        "id": "Gow8ERrLC6FC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify categorical columns\n",
        "categorical_columns = X_train.select_dtypes(include=['object']).columns"
      ],
      "metadata": {
        "id": "vcFyj9tRDOpr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fUyjwfovDOtY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply one-hot encoding to categorical columns"
      ],
      "metadata": {
        "id": "5Nkx0eGUDOxQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_encoded = pd.get_dummies(X_train_scaled, columns=categorical_columns, drop_first=True)"
      ],
      "metadata": {
        "id": "dQgPlZ3VDyd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_encoded = pd.get_dummies(X_test_scaled, columns=categorical_columns, drop_first=True)"
      ],
      "metadata": {
        "id": "9mmOl4rmDyhJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vFy3AOhsDykK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Model Selection and Training\n",
        "# We choose logistic regression as our classification algorithm and train it on the encoded training data."
      ],
      "metadata": {
        "id": "Vlhoo-UMD76c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()  # Initialize the logistic regression model."
      ],
      "metadata": {
        "id": "z6NMCEmOD79z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train_encoded, y_train)  # Train the model on the encoded training data."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "LmuPYv6WD8A-",
        "outputId": "c3fdfc8e-0863-44f5-9175-4ebefd2fbb61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "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>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ebT_WyGeEYgE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8: Predictions and Evaluation\n",
        "# We make predictions on the test set and evaluate the model's performance using various metrics."
      ],
      "metadata": {
        "id": "TZweBi6wEY3J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply one-hot encoding to the entire dataset (training + testing) to ensure consistency in feature names\n",
        "combined_data = pd.concat([X_train_scaled, X_test_scaled], axis=0)"
      ],
      "metadata": {
        "id": "zT_VsFr3D8KO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_encoded = pd.get_dummies(combined_data, columns=categorical_columns, drop_first=True)"
      ],
      "metadata": {
        "id": "XV4HDPv9E8S_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IlmK0ORtE8WM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the combined dataset back into training and testing sets\n",
        "X_train_encoded = combined_encoded.iloc[:len(X_train_scaled)]"
      ],
      "metadata": {
        "id": "iKsUnHocE8Zm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_encoded = combined_encoded.iloc[len(X_train_scaled):]"
      ],
      "metadata": {
        "id": "668eYybVE8cm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "msFBIi7bFH2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set using the trained model\n",
        "y_pred = model.predict(X_test_encoded)"
      ],
      "metadata": {
        "id": "BGyj6raQFH5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "inygS9YZE8gf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate accuracy to measure the overall performance of the model."
      ],
      "metadata": {
        "id": "4Ak8QeuLFS5G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "q0IRqJJXFS8M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3vd2H8xFS_H",
        "outputId": "813449da-dddb-4d95-d693-ed4587539ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8581298940580377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EB0nlK-2FTCg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate classification report to get a detailed summary of the model's performance."
      ],
      "metadata": {
        "id": "JVOIIFp2FTFG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUO11XBpFTIc",
        "outputId": "591c3ce8-d7f0-44d0-97d0-f62ebc776cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       <=50K       0.88      0.94      0.91      4942\n",
            "        >50K       0.75      0.61      0.68      1571\n",
            "\n",
            "    accuracy                           0.86      6513\n",
            "   macro avg       0.82      0.77      0.79      6513\n",
            "weighted avg       0.85      0.86      0.85      6513\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YLwF3RmQFfUo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate confusion matrix to visualize the model's performance in predicting income levels."
      ],
      "metadata": {
        "id": "9KxD3aUjFgfV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fl1zDQqGFgin",
        "outputId": "fa2dca5a-eadb-4f55-f1fe-e4286585e714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[4624  318]\n",
            " [ 606  965]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1VW8-hvXFnM6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Interpretation\n",
        "# Interpreting the results of model evaluation allows us to understand the model's strengths and weaknesses."
      ],
      "metadata": {
        "id": "JunyRlYeFnlI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# By following these steps and incorporating the suggested visualizations, we ensure a comprehensive and informative analysis suitable for learning purposes."
      ],
      "metadata": {
        "id": "rThXxF8LFvvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "daSoyg13S8tE"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s82KKXSjGgpR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IYk8rmYoGgsb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4J9_oFH7GgvI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "XqzH_LzCS-LL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔢 **5. Clustering**\n",
        "\n",
        "### **Objective:**\n",
        "Clustering is an unsupervised learning technique used to group individuals into clusters based on similarities in their features. K-Means is a popular clustering algorithm that will be used in this project.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **K-Means Clustering**: Apply the K-Means clustering algorithm to group individuals based on their demographic and employment features. This will help identify similarities in the dataset and potentially uncover patterns.\n",
        "\n",
        "- **Cluster Analysis**: Analyze and interpret the formed clusters. Understanding the cluster composition can provide insights into the characteristics of individuals who share similar attributes."
      ],
      "metadata": {
        "id": "V_XtIBLrS--K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *CLUSTERING *"
      ],
      "metadata": {
        "id": "H2zm2vmDGiSJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Clustering is like grouping similar things together. Here, we're trying to group individuals based on similarities in their demographic or employment characteristics.\n",
        "# We use a method called KMeans, which tries to find groups (clusters) in the data by iteratively assigning data points to the nearest cluster center."
      ],
      "metadata": {
        "id": "2CAq3DSEGg1R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "D5QPkPbOGg5Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lKepOX_MGg9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# First, we initialize the KMeans model and specify the number of clusters we want to find."
      ],
      "metadata": {
        "id": "NstzuSKiGhBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=3, random_state=42)"
      ],
      "metadata": {
        "id": "MfgGMyiVGhEX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_karCevHGwlU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Then, we fit the model to our data, which means we let it learn from the patterns in the data."
      ],
      "metadata": {
        "id": "8hgWctxjGwoy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.fit(df.select_dtypes(include=np.number))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "BcJQbfhPGwsM",
        "outputId": "3a4a81c5-d93f-4a83-c4a0-f14404cca437"
      },
      "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, random_state=42)"
            ],
            "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, random_state=42)</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, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BMIN0YpIGwv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Finally, we get the labels assigned to each data point, indicating which cluster they belong to."
      ],
      "metadata": {
        "id": "3fO4_KKeGwy-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_labels = kmeans.labels_"
      ],
      "metadata": {
        "id": "rWLLC-hfGw1D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w9lOkaysGw3j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View cluster labels"
      ],
      "metadata": {
        "id": "DRvkNdGKGw5r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cluster_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWmDp4W_Gw8F",
        "outputId": "fab95081-ef67-4c62-9f01-be4fa68a3138"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 2 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "wBRW-Ks6TCNk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cN2ZqBVeHAqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CDR7iu_WHA7W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oOxcmMG7HA-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "eLDyoJ1ITD10"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔍 **6. Anomaly Detection**\n",
        "\n",
        "### **Objective:**\n",
        "Anomaly detection identifies data points that significantly differ from the majority of the data. These anomalies can be outliers or errors that need to be handled to improve model performance.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Outlier Detection**: Use statistical methods like Z-score or Interquartile Range (IQR) to detect outliers. Outliers may distort the results and negatively affect the model’s accuracy.\n",
        "\n",
        "- **Isolation Forest / DBSCAN**: Apply anomaly detection algorithms like Isolation Forest or DBSCAN for identifying outliers in high-dimensional datasets. These algorithms are effective at detecting unusual data points without labeled data.\n",
        "\n",
        "- **Visualizing Anomalies**: Visualize detected anomalies using scatter plots or other relevant charts. This helps in understanding the impact of outliers on the dataset."
      ],
      "metadata": {
        "id": "wNv6FIl8TEqk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *ANOMALY DETECTION *"
      ],
      "metadata": {
        "id": "mmV_ame8HCM7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Anomaly detection is about finding unusual patterns or outliers in the data that don't conform to normal behavior.\n",
        "# Here, we're using a method called Isolation Forest, which isolates anomalies in the data by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of that feature.\n"
      ],
      "metadata": {
        "id": "oxZLuMCxHBCF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest"
      ],
      "metadata": {
        "id": "8UNNrOQ1HBFo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sm2DUuVOHBI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We initialize the Isolation Forest model and fit it to our data, allowing it to learn what's normal and what's not."
      ],
      "metadata": {
        "id": "_hmqw1-CHBLl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isolation_forest = IsolationForest(contamination=0.05, random_state=42)"
      ],
      "metadata": {
        "id": "wmhYLcAtHQ3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isolation_forest.fit(df.select_dtypes(include=np.number))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "OpGHljtVHQ7C",
        "outputId": "654c2775-746e-4daa-b865-1ae2f188e7dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(contamination=0.05, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.05, random_state=42)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IsolationForest</label><div class=\"sk-toggleable__content\"><pre>IsolationForest(contamination=0.05, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9NvfRTwqHQ-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Then, we use the model to predict outliers in the data. Data points with a prediction of -1 are considered outliers."
      ],
      "metadata": {
        "id": "0cfkTD3VHRCz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_preds = isolation_forest.predict(df.select_dtypes(include=np.number))"
      ],
      "metadata": {
        "id": "fODDAKGoHRFt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JNkSqR2xHRH7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View outlier predictions"
      ],
      "metadata": {
        "id": "h3QL-K57HRKd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(outlier_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-zoVoFWHf_i",
        "outputId": "5bfd54e3-bbef-49ff-9d3d-e9272f10542a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 1 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "3qUCdfCoTJ5L"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xYsh22MpHgC1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "krL6ykTvHgGz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PfWcezGYHgKx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "KXLnXscHTLr1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📊 **7. Feature Importance**\n",
        "\n",
        "### **Objective:**\n",
        "Feature importance identifies which features contribute the most to model predictions. Understanding feature importance helps improve model interpretability and informs feature selection.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Model-Based Feature Importance**: Use models like Random Forest or XGBoost to calculate feature importance scores. These models provide a ranking of features based on their contribution to the prediction task.\n",
        "\n",
        "- **SHAP Values**: Visualize the contribution of each feature using SHAP (Shapley Additive Explanations). SHAP values provide a deeper understanding of how individual features affect model predictions.\n",
        "\n",
        "- **Visual Representation**: Create bar plots to show the relative importance of different features. This allows stakeholders to interpret which features are most influential in predicting the target variable."
      ],
      "metadata": {
        "id": "G1zz9RAHTMiN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *FEATURE IMPORTANCE ANALYSIS*"
      ],
      "metadata": {
        "id": "sSP7tdizHkQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature importance analysis helps us understand which features (or characteristics) of the data are most important in predicting the outcome.\n",
        "# Here, we're using a method called RandomForestClassifier, which is a type of machine learning model that can tell us which features are most important in predicting a target variable.\n"
      ],
      "metadata": {
        "id": "9xNjEazdHgNp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "nunbdx75HgSX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5tunskHQHgVd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We fit the RandomForestClassifier to our data, allowing it to learn the relationships between features and the target variable."
      ],
      "metadata": {
        "id": "7HU89fI8HgXy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "RokS36kAHvt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_classifier.fit(X_train_encoded, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "AVoNENIlHvxZ",
        "outputId": "e452cafa-40d3-44bc-90a6-eea61268e4aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4fFtnzZsHv1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Then, we can extract the feature importances, which tell us how much each feature contributes to predicting the outcome."
      ],
      "metadata": {
        "id": "KhZUQ0iEHv5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances = rf_classifier.feature_importances_"
      ],
      "metadata": {
        "id": "v_ty_9WkHv8r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W6iHRp_RIAe0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View feature importances"
      ],
      "metadata": {
        "id": "u3iozzllHwBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(feature_importances)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wkt0_kbGHwEl",
        "outputId": "09e335f1-f303-4f5b-9c90-21029c4a9cff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.51931600e-01 1.60328648e-01 6.41861615e-02 1.00275506e-01\n",
            " 3.04413876e-02 8.27767229e-02 5.59393594e-03 6.10120110e-03\n",
            " 1.66017701e-06 1.15331183e-02 6.79170831e-03 8.93528221e-03\n",
            " 4.49733941e-03 7.68862998e-05 1.77076579e-03 8.63830710e-04\n",
            " 2.49908801e-04 6.65832444e-04 1.98944528e-03 1.10946939e-03\n",
            " 2.47715690e-03 3.06263876e-03 1.25808493e-02 4.06903321e-03\n",
            " 7.42209934e-03 8.87379818e-03 5.67173670e-05 5.34797553e-03\n",
            " 5.45887123e-03 3.21522670e-04 7.68919053e-02 9.26037469e-04\n",
            " 3.11460690e-02 2.05557999e-03 1.94002585e-03 5.84751891e-03\n",
            " 3.77867422e-06 7.19837393e-03 1.92951853e-02 4.34657364e-03\n",
            " 2.82141232e-03 4.04649599e-03 6.48350342e-03 1.09390827e-04\n",
            " 1.46925187e-02 3.06937237e-03 7.46004854e-03 5.13319935e-03\n",
            " 4.78111690e-03 1.88157272e-02 2.30244193e-03 1.32303842e-02\n",
            " 9.26190484e-03 1.01134603e-02 3.00293478e-03 4.77419899e-03\n",
            " 8.19330298e-04 6.93622916e-03 1.98438070e-02 3.04728239e-04\n",
            " 1.05537421e-03 4.58299230e-04 2.74167268e-04 7.70359516e-04\n",
            " 2.07789987e-04 1.33869098e-04 2.14556825e-04 9.45392772e-04\n",
            " 3.89971509e-04 1.07699756e-03 3.24706959e-04 1.51110187e-04\n",
            " 1.98595676e-04 8.34004258e-06 3.54780386e-06 1.21818103e-04\n",
            " 7.29661616e-05 7.97854220e-04 3.03060592e-04 2.51360607e-04\n",
            " 7.33504602e-04 4.48602669e-04 5.26461448e-04 6.96120900e-05\n",
            " 1.79113674e-03 1.68617241e-04 3.53933566e-05 6.89811513e-05\n",
            " 9.26822575e-04 4.32479155e-04 2.12158027e-04 4.48158320e-04\n",
            " 4.91141369e-05 6.02493604e-04 3.71849690e-04 6.21419251e-05\n",
            " 1.00564319e-04 7.00086176e-03 3.99920923e-04 3.46661940e-04]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rGLBn1FxHwJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the feature importances to a file"
      ],
      "metadata": {
        "id": "BiHufBGyIMRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt('/content/feature_importances.txt', feature_importances)"
      ],
      "metadata": {
        "id": "Sui2lMwfIMVA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "pCXZ5C63TPYk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mnRLooIXIMYZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pRRmD_yCIMdu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kpx6rHmzIMi2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "6JUJHnL0TSdE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🧠 **8. Natural Language Processing (NLP)**\n",
        "\n",
        "### **Objective:**\n",
        "Natural Language Processing (NLP) techniques will be applied to any textual data available in the dataset, extracting meaningful insights and potentially improving the model's performance.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Text Preprocessing**: Tokenize, stem, and lemmatize the text data to prepare it for analysis. Preprocessing transforms raw text into a format suitable for further analysis.\n",
        "\n",
        "- **Sentiment Analysis**: Apply sentiment analysis to extract sentiment information from textual features. This can provide additional features that might help in predicting the target variable.\n",
        "\n",
        "- **Text Classification**: Classify text data into predefined categories based on the content. Techniques such as bag-of-words, TF-IDF, or more advanced NLP models like BERT can be used for this task."
      ],
      "metadata": {
        "id": "Jp8hCvo4TTNO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *NATURAL LANGUAGE PROCESSING *"
      ],
      "metadata": {
        "id": "w2oFNxcPIYMZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Natural Language Processing (NLP) is a field of study focused on making computers understand and process human language.\n",
        "# Here, we're using a technique called TF-IDF (Term Frequency-Inverse Document Frequency) to analyze and extract insights from textual data.\n"
      ],
      "metadata": {
        "id": "PPiKN8_OIkcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "metadata": {
        "id": "7qpk5RBNIkfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fPlFFHYyIkig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We initialize a TF-IDF vectorizer, which converts text data into numerical representations that machine learning models can understand.\n",
        "# Then, we fit the vectorizer to our text data, allowing it to learn the vocabulary and calculate the TF-IDF scores for each word.\n",
        "# Finally, we can view the feature names, which represent the unique words in our text data and their corresponding TF-IDF scores.\n"
      ],
      "metadata": {
        "id": "nayL2adYIkla"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer()"
      ],
      "metadata": {
        "id": "-5MY5ouSJC-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ftCqVCH3JDOQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and transform the 'occupation' column to TF-IDF vectors\n",
        "# This step allows the vectorizer to learn the vocabulary and calculate the TF-IDF scores for each word."
      ],
      "metadata": {
        "id": "Zb9H_oVcJDQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_data_tfidf = tfidf_vectorizer.fit_transform(df['occupation'])"
      ],
      "metadata": {
        "id": "jBQNjxnTJDTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WTf7kCOVJq8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the feature names\n",
        "# Feature names represent the unique words in our text data and their corresponding TF-IDF scores.\n",
        "# We use get_feature_names_out() method to obtain the feature names."
      ],
      "metadata": {
        "id": "k7klIrEYJrEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = tfidf_vectorizer.get_feature_names_out()"
      ],
      "metadata": {
        "id": "ixFCg4kvJrId"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ir9g1rkkJrLU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the feature names"
      ],
      "metadata": {
        "id": "Q7caOCIdJrOI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Feature Names (Words in 'occupation' Column and Their TF-IDF Scores):\")\n",
        "print(feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhlNXYgpKqAr",
        "outputId": "ea25b53f-7de0-4faf-c4ba-1c1a26712e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Names (Words in 'occupation' Column and Their TF-IDF Scores):\n",
            "['adm' 'armed' 'cleaners' 'clerical' 'craft' 'exec' 'farming' 'fishing'\n",
            " 'forces' 'handlers' 'house' 'inspct' 'machine' 'managerial' 'moving' 'op'\n",
            " 'other' 'priv' 'prof' 'protective' 'repair' 'sales' 'serv' 'service'\n",
            " 'specialty' 'support' 'tech' 'transport']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "NDmj54aWTWNM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qwPAOWGxTXeM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fMvmiou8TXij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 💡 **Insights and Conclusions**\n",
        "\n",
        "### **Objective:**\n",
        "This step involves interpreting the results of the EDA, predictive modeling, clustering, anomaly detection, and feature importance analysis. The goal is to summarize the key findings and insights.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Data Insights**: Summarize key patterns, trends, and potential issues identified during the exploratory data analysis.\n",
        "\n",
        "- **Model Interpretation**: Discuss the results of the Logistic Regression model and interpret its performance. Highlight areas where the model is performing well and where it might need improvement.\n",
        "\n",
        "- **Clustering Insights**: Analyze the formed clusters and draw conclusions about the similarities between individuals within each cluster.\n",
        "\n",
        "- **Anomaly Insights**: Interpret the impact of anomalies on the dataset and the overall analysis.\n",
        "\n",
        "- **Feature Importance Insights**: Identify which features are most important for predicting the target variable and discuss their implications for the analysis."
      ],
      "metadata": {
        "id": "OOpZRXI3TXzc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----"
      ],
      "metadata": {
        "id": "8XBseNc3TeF-"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E_KWWJJvTe8G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x58MJMT6TfA5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "_zjjgAYLTfWM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚀 **Future Enhancements**\n",
        "\n",
        "### **Objective:**\n",
        "Future enhancements focus on improving model performance, exploring new techniques, and refining the analysis process. These improvements can help in achieving better predictive accuracy and providing deeper insights.\n",
        "\n",
        "### **Key Activities:**\n",
        "\n",
        "- **Model Improvement**: Experiment with other classification algorithms such as Random Forest, SVM, or XGBoost to improve predictive accuracy.\n",
        "\n",
        "- **Advanced Clustering**: Try hierarchical clustering or DBSCAN for a different approach to clustering, especially when the data is not well-suited for K-Means.\n",
        "\n",
        "- **Hyperparameter Tuning**: Fine-tune model parameters using techniques like GridSearchCV to optimize model performance.\n",
        "\n",
        "- **Deep Learning Models**: Implement deep learning models such as neural networks for more complex tasks that might improve predictions.\n",
        "\n",
        "- **Advanced NLP**: Explore advanced NLP techniques like GPT or BERT for better handling of textual data and improving sentiment analysis."
      ],
      "metadata": {
        "id": "wNqiBjAPTgTc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "----"
      ],
      "metadata": {
        "id": "78BfiUexTkFl"
      }
    }
  ]
}