Beauty_csv.ipynb

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      "source": [
        "**EXPLANATORY DATA ANALYSIS AND SOME POSSILE TASKS FROM THE BEAUTY.CSV DATASET**"
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        "# <font color=\"#4CAF50\"><b>Project Title: Beauty_csv EDA and Some Possible Tasks</b></font>\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Overview</b></font>\n",
        "\n",
        "The project aims to explore the Beauty dataset (`beauty.csv`) by conducting **Exploratory Data Analysis (EDA)** and performing **Predictive Modeling** and **Clustering Analysis** as potential tasks. EDA is carried out to clean the dataset, handle missing and duplicate values, check for outliers, and visualize data distributions. Following this, predictive tasks such as wage prediction using **Linear Regression** and union membership prediction using **Logistic Regression** are considered. Finally, a **K-Means Clustering** analysis explores demographic and socio-economic patterns.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>1. Data Exploration and Cleaning</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>1.1 Data Inspection</b></font>\n",
        "- The dataset is first loaded and inspected using functions like `.head()`, `.info()`, and `.describe()` to get an overview of its structure, data types, and summary statistics.\n",
        "\n",
        "### <font color=\"#FFC107\"><b>1.2 Handling Missing Values</b></font>\n",
        "- Missing values in the dataset are filled using the mean of the respective columns. This ensures completeness for further analysis without significantly altering data distributions.\n",
        "\n",
        "### <font color=\"#FFC107\"><b>1.3 Removing Duplicates</b></font>\n",
        "- Duplicate entries in the dataset are checked and removed to prevent biases in analysis and modeling.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>2. Data Visualization</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>2.1 Histogram Plots</b></font>\n",
        "- Histograms for each numerical column are generated to visualize data distributions. Kernel Density Estimation (KDE) is used to observe trends in the underlying data.\n",
        "- A summary histogram of all numerical features is also produced to further explore their distributions.\n",
        "\n",
        "### <font color=\"#FFC107\"><b>2.2 Correlation Heatmap</b></font>\n",
        "- A correlation heatmap is used to visualize pairwise relationships between numerical variables. This helps to understand which features might have strong linear relationships with each other.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>3. Data Preprocessing</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>3.1 Categorical Variables</b></font>\n",
        "- Categorical variables are converted into dummy variables using `pd.get_dummies()` to facilitate machine learning modeling.\n",
        "\n",
        "### <font color=\"#FFC107\"><b>3.2 Outlier Detection</b></font>\n",
        "- Outliers are detected using the **Interquartile Range (IQR)** method, but this process is currently commented out for demonstration purposes.\n",
        "\n",
        "### <font color=\"#FFC107\"><b>3.3 Feature Scaling</b></font>\n",
        "- **StandardScaler** is applied to standardize features, ensuring that all variables have a mean of 0 and a standard deviation of 1, which is essential for many machine learning algorithms.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>4. Predictive Modeling: Wage Prediction</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>4.1 Linear Regression</b></font>\n",
        "- **Objective:** Predict wage levels based on various independent variables.\n",
        "- The dataset is split into training and testing sets, with **Linear Regression** applied to predict wages. The model is evaluated using **Mean Squared Error (MSE)** as the primary metric.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>5. Predicting Union Membership</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>5.1 Logistic Regression</b></font>\n",
        "- **Objective:** Predict whether an individual is a member of a union (binary classification task).\n",
        "- A **Logistic Regression** model is applied to predict union membership, and the model's performance is evaluated using **accuracy**, **classification report**, and **confusion matrix**. This allows for the assessment of how well the model performs in terms of precision, recall, and overall accuracy.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#FF5722\"><b>6. Clustering Analysis: Socio-Economic Patterns</b></font>\n",
        "\n",
        "### <font color=\"#FFC107\"><b>6.1 K-Means Clustering</b></font>\n",
        "- **Objective:** Cluster individuals into groups based on socio-economic features (excluding wage).\n",
        "- The **Elbow Method** is used to determine the optimal number of clusters by plotting the inertia for a range of values for **K**. The optimal number of clusters is selected based on the elbow point in the plot.\n",
        "- The selected number of clusters is used to perform **K-Means Clustering**, and cluster labels are added to the dataset for further analysis.\n",
        "- The mean values for each cluster are calculated to analyze and interpret the cluster characteristics.\n",
        "\n",
        "---\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Conclusion</b></font>\n",
        "The project successfully explored and cleaned the Beauty dataset through **EDA**, followed by two main predictive tasks:\n",
        "- **Linear Regression** to predict wage levels\n",
        "- **Logistic Regression** to predict union membership\n",
        "\n",
        "Additionally, a **K-Means Clustering** analysis was performed to explore socio-economic patterns among individuals in the dataset.\n",
        "\n",
        "This project serves as a solid foundation for further predictive modeling and clustering tasks that could be performed with this dataset.\n",
        "\n",
        "---\n",
        "\n",
        "**<font color=\"#D32F2F\">Note:</font>**\n",
        "- Ensure that **StandardScaler** is applied only after splitting the dataset into training and testing sets to avoid data leakage.\n",
        "- While filling missing values with the mean is a common approach, alternative imputation techniques, such as **K-Nearest Neighbors** or **Regression Imputation**, could be explored for better results.\n",
        "- Model tuning, such as hyperparameter optimization, can be conducted in the future to further improve performance.\n",
        "\n",
        "---\n",
        "\n",
        "**References**:\n",
        "- Documentation from [Pandas](https://pandas.pydata.org/), [Seaborn](https://seaborn.pydata.org/), and [Scikit-learn](https://scikit-learn.org/stable/) were used for data manipulation, visualization, and machine learning tasks."
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        "# <font color=\"#4CAF50\"><b>Importing the Libraries</b></font>\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Essential Libraries</b></font>\n",
        "- **Note:** In this step, we import the necessary libraries required for data exploration, visualization, and modeling. Some of the key libraries include:\n",
        "  - **Pandas:** For data manipulation and analysis.\n",
        "  - **NumPy:** For numerical operations and handling arrays.\n",
        "  - **Matplotlib/Seaborn:** For data visualization to generate plots, graphs, and heatmaps.\n",
        "  - **Scikit-learn:** For machine learning tasks like data preprocessing, model building, and evaluation.\n",
        "  - **Other Libraries:** Depending on the specific tasks ahead, additional libraries such as `statsmodels`, `scipy`, or `xgboost` may be required later.\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Version Checking (Optional)</b></font>\n",
        "- **Note:** Checking the version of each library ensures compatibility and allows you to maintain consistency across different development environments. It's especially useful if you encounter any unexpected behavior or need to debug issues related to library updates."
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      "cell_type": "code",
      "source": [
        "# Import Necessary Libraries"
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      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing pandas library for data manipulation"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "import numpy as np  # Importing numpy library for numerical operations"
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      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Importing matplotlib library for data visualization"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Importing seaborn library for enhanced data visualization"
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        "# <font color=\"#4CAF50\"><b>Step 1: Data Exploration and Cleaning</b></font>\n",
        "\n",
        "## <font color=\"#2196F3\"><b>1.1 Data Inspection</b></font>\n",
        "- **Note:** The dataset should first be inspected to understand its structure, data types, and potential issues. This helps to identify missing values, incorrect data types, and provides a general overview of how the dataset looks before diving into any specific analysis.\n",
        "\n",
        "## <font color=\"#2196F3\"><b>1.2 Handling Missing Values</b></font>\n",
        "- **Note:** Missing values can distort the results of analyses and machine learning models. In this step, mean imputation is used to handle missing numerical values, ensuring no data points are dropped unnecessarily. Consider using more advanced imputation techniques like KNN or regression imputation for better results in future iterations.\n",
        "\n",
        "## <font color=\"#2196F3\"><b>1.3 Removing Duplicates</b></font>\n",
        "- **Note:** Duplicates can skew analysis results by over-representing some data points. Therefore, removing duplicates is critical in preparing the dataset for accurate analysis. Check the dataset for identical rows and remove them to maintain the integrity of the dataset."
      ],
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      "cell_type": "code",
      "source": [
        "# Load the Data"
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      "source": [
        "df = pd.read_csv('/content/beauty.csv')  # Loading the dataset from the provided file path"
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      "source": [
        "# Inspect the Data"
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      "source": [
        "df.head()  # Displaying the first few rows of the dataset to understand its structure and contents"
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              "    wage  exper  union  goodhlth  black  female  married  service  educ  looks\n",
              "0   5.73     30      0         1      0       1        1        1    14      4\n",
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              "3  11.57     38      0         1      0       0        1        1    16      3\n",
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              "    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",
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              "    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-d7712b75-eb64-4ee9-9161-3e25d589d8d2 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\": 1260,\n  \"fields\": [\n    {\n      \"column\": \"wage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.660639037142856,\n        \"min\": 1.02,\n        \"max\": 77.72,\n        \"num_unique_values\": 520,\n        \"samples\": [\n          7.63,\n          3.37,\n          8.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 0,\n        \"max\": 48,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          7,\n          45,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"union\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"goodhlth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"black\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"service\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"educ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 17,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"looks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pFrPzp3Vhi1x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JvHpW79Ghi4_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.info()  # Providing an overview of the dataset including column names, data types, and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZE0phFDhkA0",
        "outputId": "a71e60c8-8c0b-46df-f4b8-9af4efc15dc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1260 entries, 0 to 1259\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   wage      1260 non-null   float64\n",
            " 1   exper     1260 non-null   int64  \n",
            " 2   union     1260 non-null   int64  \n",
            " 3   goodhlth  1260 non-null   int64  \n",
            " 4   black     1260 non-null   int64  \n",
            " 5   female    1260 non-null   int64  \n",
            " 6   married   1260 non-null   int64  \n",
            " 7   service   1260 non-null   int64  \n",
            " 8   educ      1260 non-null   int64  \n",
            " 9   looks     1260 non-null   int64  \n",
            "dtypes: float64(1), int64(9)\n",
            "memory usage: 98.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WmcKwtvYhn2-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9s4H2jNFhn6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()  # Generating summary statistics of numerical columns to gain insights into data distribution and potential outliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rss3CtJc0bN4",
        "outputId": "5ed7ae96-32d9-44fe-e57b-8c8b541b6388"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              wage        exper        union     goodhlth        black  \\\n",
              "count  1260.000000  1260.000000  1260.000000  1260.000000  1260.000000   \n",
              "mean      6.306690    18.206349     0.272222     0.933333     0.073810   \n",
              "std       4.660639    11.963485     0.445280     0.249543     0.261564   \n",
              "min       1.020000     0.000000     0.000000     0.000000     0.000000   \n",
              "25%       3.707500     8.000000     0.000000     1.000000     0.000000   \n",
              "50%       5.300000    15.000000     0.000000     1.000000     0.000000   \n",
              "75%       7.695000    27.000000     1.000000     1.000000     0.000000   \n",
              "max      77.720000    48.000000     1.000000     1.000000     1.000000   \n",
              "\n",
              "            female      married      service         educ        looks  \n",
              "count  1260.000000  1260.000000  1260.000000  1260.000000  1260.000000  \n",
              "mean      0.346032     0.691270     0.273810    12.563492     3.185714  \n",
              "std       0.475892     0.462153     0.446089     2.624489     0.684877  \n",
              "min       0.000000     0.000000     0.000000     5.000000     1.000000  \n",
              "25%       0.000000     0.000000     0.000000    12.000000     3.000000  \n",
              "50%       0.000000     1.000000     0.000000    12.000000     3.000000  \n",
              "75%       1.000000     1.000000     1.000000    13.000000     4.000000  \n",
              "max       1.000000     1.000000     1.000000    17.000000     5.000000  "
            ],
            "text/html": [
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              "  <div id=\"df-1e582313-e215-4971-8143-d3360f7df2a7\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wage</th>\n",
              "      <th>exper</th>\n",
              "      <th>union</th>\n",
              "      <th>goodhlth</th>\n",
              "      <th>black</th>\n",
              "      <th>female</th>\n",
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              "      <th>educ</th>\n",
              "      <th>looks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
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              "      <td>1260.000000</td>\n",
              "      <td>1260.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.306690</td>\n",
              "      <td>18.206349</td>\n",
              "      <td>0.272222</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.073810</td>\n",
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              "      <td>0.691270</td>\n",
              "      <td>0.273810</td>\n",
              "      <td>12.563492</td>\n",
              "      <td>3.185714</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>11.963485</td>\n",
              "      <td>0.445280</td>\n",
              "      <td>0.249543</td>\n",
              "      <td>0.261564</td>\n",
              "      <td>0.475892</td>\n",
              "      <td>0.462153</td>\n",
              "      <td>0.446089</td>\n",
              "      <td>2.624489</td>\n",
              "      <td>0.684877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.707500</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.300000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.695000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.720000</td>\n",
              "      <td>48.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>17.000000</td>\n",
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              "    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-25e409a7-8097-4927-9fb7-ffac8ac78898 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\": \"wage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 440.84650803205784,\n        \"min\": 1.02,\n        \"max\": 1260.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.306690476190475,\n          5.3,\n          1260.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 439.23954739436124,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.206349206349206,\n          15.0,\n          1260.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"union\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.3402127984242,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2722222222222222,\n          1.0,\n          0.44528041140365426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"goodhlth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.2156731939613,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.9333333333333333,\n          1.0,\n          0.24954287038910442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"black\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.40995693376004,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.07380952380952381,\n          1.0,\n          0.26156447847233044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.3349373093052,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.346031746031746,\n          1.0,\n          0.4758923383045756\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.26768945229446,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6912698412698413,\n          1.0,\n          0.4621529617032598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"service\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 445.3400917209065,\n        \"min\": 0.0,\n        \"max\": 1260.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.27380952380952384,\n          1.0,\n          0.44608945476960515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"educ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 441.75438119311934,\n        \"min\": 2.6244891533517283,\n        \"max\": 1260.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1260.0,\n          12.563492063492063,\n          13.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"looks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 444.4759396340116,\n        \"min\": 0.6848774158752056,\n        \"max\": 1260.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1260.0,\n          3.1857142857142855,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "635QosQDhtpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_kQ8oz3rhts0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for Missing Values"
      ],
      "metadata": {
        "id": "0UA9VknB0bQe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()  # Checking for missing values in each column of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSDbhrC-0-Jo",
        "outputId": "6e6fee75-b262-4f86-dcac-691494a019b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "wage        0\n",
              "exper       0\n",
              "union       0\n",
              "goodhlth    0\n",
              "black       0\n",
              "female      0\n",
              "married     0\n",
              "service     0\n",
              "educ        0\n",
              "looks       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vVnagbGJh0qI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle Missing Values"
      ],
      "metadata": {
        "id": "cFQGlZfn0-bo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(df.mean(), inplace=True)  # Filling missing values with the mean of each column to ensure completeness of the dataset"
      ],
      "metadata": {
        "id": "6n94Omhf0-ef"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w8dpL7-51FFa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for Duplicate Values"
      ],
      "metadata": {
        "id": "461ZgQS11FLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_entries = df.duplicated().sum()  # Counting duplicate rows in the dataset"
      ],
      "metadata": {
        "id": "_89CbzAM1FPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of duplicate entries:\", duplicate_entries)  # Displaying the number of duplicate entries found"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sl-68ErCiDJm",
        "outputId": "e194f9ae-c961-4887-ab75-5baa8c45d72a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate entries: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qe5MnEyMiDMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicate Values"
      ],
      "metadata": {
        "id": "MTI14q_F2TYJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)  # Removing duplicate rows from the dataset to avoid bias in analysis"
      ],
      "metadata": {
        "id": "vag35BeL2Tba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fFLI96kviMVv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pA7-amzhcscw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2iDQJC9Jcsgy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF5722\"><b>Step 2: Data Visualization</b></font>\n",
        "\n",
        "## <font color=\"#FFC107\"><b>2.1 Histogram Plots</b></font>\n",
        "- **Note:** Histograms help visualize the distribution of numerical variables, providing insight into their spread, central tendency, and any potential skewness. Pay attention to any variables that exhibit extreme skewness, as these may require transformation or normalization before further analysis.\n",
        "\n",
        "## <font color=\"#FFC107\"><b>2.2 Correlation Heatmap</b></font>\n",
        "- **Note:** The correlation heatmap reveals the pairwise relationships between numerical variables. Variables with strong correlations should be noted as potential candidates for feature engineering or selection in predictive modeling. However, be cautious about multicollinearity when applying regression-based models."
      ],
      "metadata": {
        "id": "7C5Cnbzics7Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the Data"
      ],
      "metadata": {
        "id": "0mgr551X2Tei"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    sns.histplot(df[column], kde=True)  # Generating histogram plots for each numerical variable with kernel density estimation\n",
        "    plt.title(f'Histogram of {column}')  # Adding title to each plot for clarity\n",
        "    plt.xlabel(column)  # Adding x-axis label\n",
        "    plt.ylabel('Frequency')  # Adding y-axis label\n",
        "    plt.show()  # Displaying the plot for each variable"
      ],
      "metadata": {
        "id": "QubpddfD2Tho",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4b8da74a-8087-4cd7-c1ab-024f5ffd2dd7"
      },
      "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": 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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": 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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": "pum_i7Mj2Tl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nIKKtdPYiWQY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Data Distribution"
      ],
      "metadata": {
        "id": "rqwuJTBgiWg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=30, figsize=(20,15), density=True)  # Generating histograms for all numerical variables to visualize their distributions\n",
        "plt.show()  # Displaying the histograms"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "yhmNILFH2TrZ",
        "outputId": "26630959-5090-43d5-a23d-e02832205ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 12 Axes>"
            ],
            "image/png": 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v7w+K+vIAtLEvtsEx165dG7169WqDI9Pe6F8AKAb9C9tC/5IN7/d9IktZbykHGbTvDFq7Nv8e2kcGrf06W9O7tItBSd++faOsrCzWrFnTaPuaNWuisrKyyfopU6ZEVVVVw+O6urp48803Y4cddoiSkq3/DcotqampiUGDBsU//vGPqKio2ObjZYXc0pNdOnJLR27ptHZuSZLE2rVrY+DAga1QHcWgf+kc5JaO3NKTXTpyS0f/0nm1tA+JiKisrGzR+oi271+KzfcWGdSTgwwiZBDR+TJoSe/SLgYlXbt2jWHDhsWCBQtiwoQJEbH5h++CBQti8uTJTdbncrnI5XKNtvXu3bvV66qoqOgUXxCFJrf0ZJeO3NKRWzqtmZvfxOzY9C+di9zSkVt6sktHbunoXzqflvYhERGjRo2KBQsWxNlnn92wrbq6OkaNGrXF1ylU/1JsvrfIoJ4cZBAhg4jOlcHW9i7tYlASEVFVVRUTJ06M4cOHx4gRI+Lqq6+O9evXx2mnnVbs0gAAmqV/AQCK5YP6kFNOOSV22mmnmDVrVkREfOtb34rDDjssfvCDH8TRRx8dt912WzzxxBNxww03FPNtAEC70G4GJSeeeGK89tprceGFF8bq1atj//33j3nz5jX5oDEAgPZC/wIAFMsH9SGrVq2K0tLShvUHHXRQ3HrrrfG9730vLrjggvjIRz4S99xzT+yzzz7FegsA0G60m0FJRMTkyZO3eIloIeVyuZg2bVqTy0t5f3JLT3bpyC0duaUjN7ZE/9KxyS0duaUnu3Tklo7cOr/360MWLlzYZNvnPve5+NznPtfGVXUc/o3IoJ4cZBAhg4hsZ1CSJElS7CIAAAAAAACKofSDlwAAAAAAAHROBiUAAAAAAEBmGZQAAAAAAACZZVACAAAAAABkVmYHJbNnz45dd901unXrFiNHjozHH3/8fdffeeedsddee0W3bt1i3333jblz5xao0valJbndeOONceihh8b2228f22+/fYwePfoDc+7MWvo1V++2226LkpKSmDBhQtsW2E61NLe33norJk2aFAMGDIhcLhd77LFHJv+9tjS3q6++Ovbcc8/o3r17DBo0KM4555x45513ClRt+/DQQw/FMcccEwMHDoySkpK45557PnCfhQsXxsc//vHI5XKx++67x5w5c9q8TrJN/5KO/iUdvUs6epf09C8tp3+B9P75z3/Gl7/85dhhhx2ie/fuse+++8YTTzxR7LIKZtOmTTF16tQYPHhwdO/ePYYMGRIzZ86MJEmKXVqb+aDvmUmSxIUXXhgDBgyI7t27x+jRo+O5554rTrFt6P1yyOfz8Z3vfCf23Xff2G677WLgwIFxyimnxMsvv1y8gttAS35+fv3rX4+SkpK4+uqrC1ZfIWxNBs8++2wce+yx0atXr9huu+3iwAMPjFWrVhW+2ALJ5KDk9ttvj6qqqpg2bVo8+eSTMXTo0Bg3bly8+uqrza5/9NFH46STTorTTz89nnrqqZgwYUJMmDAhnnnmmQJXXlwtzW3hwoVx0kknxYMPPhiLFi2KQYMGxdixY+Of//xngSsvvpZmV2/lypXx7W9/Ow499NACVdq+tDS3jRs3xpgxY2LlypXx61//OpYtWxY33nhj7LTTTgWuvLhamtutt94a559/fkybNi2effbZuOmmm+L222+PCy64oMCVF9f69etj6NChMXv27K1av2LFijj66KPjiCOOiKVLl8bZZ58dX/3qV+P+++9v40rJKv1LOvqXdPQu6ehd0tO/pKN/gXT+9a9/xcEHHxzl5eXxhz/8If7617/GD37wg9h+++2LXVrBXH755XHdddfFT37yk3j22Wfj8ssvjyuuuCKuueaaYpfWZj7oe+YVV1wRP/7xj+P666+Pxx57LLbbbrsYN25cpxvCv18OGzZsiCeffDKmTp0aTz75ZNx1112xbNmyOPbYY4tQadvZ2p+fd999dyxevDgGDhxYoMoK54MyeP755+OQQw6JvfbaKxYuXBh/+ctfYurUqdGtW7cCV1pASQaNGDEimTRpUsPjTZs2JQMHDkxmzZrV7PrPf/7zydFHH91o28iRI5Ovfe1rbVpne9PS3N7r3XffTXr27Jn87Gc/a6sS26002b377rvJQQcdlPz0pz9NJk6cmBx33HEFqLR9aWlu1113XbLbbrslGzduLFSJ7VJLc5s0aVJy5JFHNtpWVVWVHHzwwW1aZ3sWEcndd9/9vmvOO++8ZO+992607cQTT0zGjRvXhpWRZfqXdPQv6ehd0tG7pKd/2Xb6F9h63/nOd5JDDjmk2GUU1dFHH5185StfabTt+OOPT770pS8VqaLCeu/3zLq6uqSysjK58sorG7a99dZbSS6XS371q18VocLC2JqfHY8//ngSEcmLL75YmKIKbEsZvPTSS8lOO+2UPPPMM8kuu+yS/PCHPyx4bYXSXAYnnnhi8uUvf7k4BRVJ5q4o2bhxYyxZsiRGjx7dsK20tDRGjx4dixYtanafRYsWNVofETFu3Lgtru+M0uT2Xhs2bIh8Ph99+vRpqzLbpbTZXXTRRdGvX784/fTTC1Fmu5Mmt9/97ncxatSomDRpUvTv3z/22WefuPTSS2PTpk2FKrvo0uR20EEHxZIlSxpub/HCCy/E3Llz49Of/nRBau6o/GygkPQv6ehf0tG7pKN3SU//Ujh+NsBmv/vd72L48OHxuc99Lvr16xcHHHBA3HjjjcUuq6AOOuigWLBgQfz973+PiIj/7//7/+KRRx6J8ePHF7my4lixYkWsXr260ffIXr16xciRIzP/PfLtt9+OkpKS6N27d7FLKZi6uro4+eST49xzz42999672OUUXF1dXdx3332xxx57xLhx46Jfv34xcuTIrbrFZ0fWpdgFFNrrr78emzZtiv79+zfa3r9///jb3/7W7D6rV69udv3q1avbrM72Jk1u7/Wd73wnBg4c2KQx7+zSZPfII4/ETTfdFEuXLi1Ahe1TmtxeeOGF+OMf/xhf+tKXYu7cubF8+fI466yzIp/Px7Rp0wpRdtGlye2LX/xivP7663HIIYdEkiTx7rvvxte//vXM3bqipbb0s6Gmpib+/e9/R/fu3YtUGZ2R/iUd/Us6epd09C7p6V8KR/8Cm73wwgtx3XXXRVVVVVxwwQXxP//zP/Hf//3f0bVr15g4cWKxyyuI888/P2pqamKvvfaKsrKy2LRpU1xyySXxpS99qdilFUV9j5z1/vm93nnnnfjOd74TJ510UlRUVBS7nIK5/PLLo0uXLvHf//3fxS6lKF599dVYt25dXHbZZXHxxRfH5ZdfHvPmzYvjjz8+HnzwwTjssMOKXWKbyNyghOK47LLL4rbbbouFCxd27nvZtYK1a9fGySefHDfeeGP07du32OV0KHV1ddGvX7+44YYboqysLIYNGxb//Oc/48orr8zUyYaWWrhwYVx66aVx7bXXxsiRI2P58uXxrW99K2bOnBlTp04tdnkARaN/2Tp6l/T0LunpX4BtUVdXF8OHD49LL700IiIOOOCAeOaZZ+L666/PzKDkjjvuiF/+8pdx6623xt57793wuUUDBw7MTAa8v3w+H5///OcjSZK47rrril1OwSxZsiR+9KMfxZNPPhklJSXFLqco6urqIiLiuOOOi3POOSciIvbff/949NFH4/rrrzco6Sz69u0bZWVlsWbNmkbb16xZE5WVlc3uU1lZ2aL1nVGa3Op9//vfj8suuyweeOCB2G+//dqyzHappdk9//zzsXLlyjjmmGMattV/g+rSpUssW7YshgwZ0rZFtwNpvuYGDBgQ5eXlUVZW1rDtox/9aKxevTo2btwYXbt2bdOa24M0uU2dOjVOPvnk+OpXvxoREfvuu2+sX78+zjzzzPjud78bpaWZu0vjVtnSz4aKigq/jUmr07+ko39JR++Sjt4lPf1L4ehfYLMBAwbExz72sUbbPvrRj8ZvfvObIlVUeOeee26cf/758YUvfCEiNn8fffHFF2PWrFmZHJTU/7xZs2ZNDBgwoGH7mjVrYv/99y9SVcVTPyR58cUX449//GOmriZ5+OGH49VXX42dd965YdumTZvi//yf/xNXX311rFy5snjFFUjfvn2jS5cuzX6ffOSRR4pUVdvLXPfYtWvXGDZsWCxYsKBhW11dXSxYsCBGjRrV7D6jRo1qtD4iorq6eovrO6M0uUVEXHHFFTFz5syYN29eDB8+vBCltjstzW6vvfaKp59+OpYuXdrw59hjj40jjjgili5dGoMGDSpk+UWT5mvu4IMPjuXLlzecnImI+Pvf/x4DBgzIzImGNLlt2LChycmE+hM2SZK0XbEdnJ8NFJL+JR39Szp6l3T0LunpXwrHzwbY7OCDD45ly5Y12vb3v/89dtlllyJVVHhb+j76nz+TsmTw4MFRWVnZ6HtkTU1NPPbYY5n7Hlk/JHnuuefigQceiB122KHYJRXUySefHH/5y18a9bYDBw6Mc889N+6///5il1cQXbt2jQMPPDB73yeL+lHyRXLbbbcluVwumTNnTvLXv/41OfPMM5PevXsnq1evTpIkSU4++eTk/PPPb1j/5z//OenSpUvy/e9/P3n22WeTadOmJeXl5cnTTz9drLdQFC3N7bLLLku6du2a/PrXv05eeeWVhj9r164t1lsompZm914TJ05MjjvuuAJV2360NLdVq1YlPXv2TCZPnpwsW7Ysuffee5N+/folF198cbHeQlG0NLdp06YlPXv2TH71q18lL7zwQjJ//vxkyJAhyec///livYWiWLt2bfLUU08lTz31VBIRyVVXXZU89dRTyYsvvpgkSZKcf/75ycknn9yw/oUXXkh69OiRnHvuucmzzz6bzJ49OykrK0vmzZtXrLdAJ6d/SUf/ko7eJR29S3r6l3T0L5DO448/nnTp0iW55JJLkueeey755S9/mfTo0SP5xS9+UezSCmbixInJTjvtlNx7773JihUrkrv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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xy4UrtrGidG_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jy--GbgyidKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Correlations"
      ],
      "metadata": {
        "id": "9CpX8wXr2Tt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True)  # Generating a heatmap to visualize pairwise correlations between numerical variables\n",
        "plt.title('Correlation Heatmap')  # Adding title to the heatmap\n",
        "plt.show()  # Displaying the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "eJl-_muL2TwE",
        "outputId": "81809e81-966d-4b5c-cd78-7382816a2ce7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wqjOGryEik5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "apHMOHnTilQ6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hfUiwp74c0X3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF5722\"><b>Step 3: Data Preprocessing</b></font>\n",
        "\n",
        "## <font color=\"#FFC107\"><b>3.1 Categorical Variables</b></font>\n",
        "- **Note:** Categorical variables should be converted into numerical form using encoding techniques like one-hot encoding to ensure compatibility with machine learning models. Ensure that the encoding process does not introduce unnecessary dimensionality, which may lead to overfitting in models.\n",
        "\n",
        "## <font color=\"#FFC107\"><b>3.2 Outlier Detection</b></font>\n",
        "- **Note:** Outliers can have a disproportionate effect on statistical analyses and machine learning models. Use techniques like IQR or Z-score methods to detect and potentially remove or transform outliers, depending on the model requirements. Outlier removal should be justified and not performed arbitrarily.\n",
        "\n",
        "## <font color=\"#FFC107\"><b>3.3 Feature Scaling</b></font>\n",
        "- **Note:** Standardizing numerical features is critical for models that rely on distance calculations (e.g., K-Means, SVM, Logistic Regression). StandardScaler ensures that all variables have the same scale, preventing features with larger ranges from dominating the analysis."
      ],
      "metadata": {
        "id": "SqSjeghcc2B_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle Categorical Variables"
      ],
      "metadata": {
        "id": "2FX-yla-22Z2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, drop_first=True)  # Converting categorical variables into dummy/indicator variables for analysis"
      ],
      "metadata": {
        "id": "NSc7ia0U22cU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1q1k2PL122fA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier Detection"
      ],
      "metadata": {
        "id": "feTxp2DT22h2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q1 = df.quantile(0.25)"
      ],
      "metadata": {
        "id": "uohTcHce22kX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q3 = df.quantile(0.75)"
      ],
      "metadata": {
        "id": "yfHFBuddiywA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "hOvi-Z5_iyzq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "metadata": {
        "id": "WQJDw3yWiy4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9559JNYCi3Mn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling"
      ],
      "metadata": {
        "id": "spKz05hk3D9p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # Importing StandardScaler for feature scaling"
      ],
      "metadata": {
        "id": "et3Llo703EBv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initializing StandardScaler object"
      ],
      "metadata": {
        "id": "8zQ1i5Yd3EEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)  # Standardizing features to have mean of 0 and standard deviation of 1"
      ],
      "metadata": {
        "id": "GxHHaOD-3tNG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZJ1JFjy6jjyV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Processed Data"
      ],
      "metadata": {
        "id": "s5iGd5Rt3tPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('processed_data.csv', index=False)  # Saving the processed data to a CSV file for future use"
      ],
      "metadata": {
        "id": "PPzne62J3tR9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F5mSB05t4Fex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cJtJeqUC4Fh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FEvWRiLAc4u4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POSSIBLE TASKS FROM THE DATASET"
      ],
      "metadata": {
        "id": "elOa6oOV4LSw"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e2oM7khVdgaP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j7s1eufndhIK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF5722\"><b>Step 4: Predictive Modeling - Wage Prediction</b></font>\n",
        "\n",
        "## <font color=\"#FFC107\"><b>4.1 Linear Regression</b></font>\n",
        "- **Note:** Linear regression is used to model the relationship between the dependent variable (wage) and one or more independent variables. After splitting the dataset into training and test sets, the model's performance is evaluated using the Mean Squared Error (MSE), which quantifies the difference between predicted and actual wage values. Consider adding regularization techniques (Lasso or Ridge) if overfitting is observed."
      ],
      "metadata": {
        "id": "waFKwbIudhWK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PREDICTIVE MODELING (LINEAR REGRESSION TO PREDICT WAGES)"
      ],
      "metadata": {
        "id": "FRyF8GCK4P4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Necessary Libraries"
      ],
      "metadata": {
        "id": "focLTQM8j0SB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Re-importing pandas for data manipulation"
      ],
      "metadata": {
        "id": "KkEZq8xY4Fj5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Importing train_test_split for data splitting"
      ],
      "metadata": {
        "id": "fv7JXKyXj5K-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  # Importing LinearRegression for modeling"
      ],
      "metadata": {
        "id": "o_sDdD7Tj8Py"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error  # Importing mean_squared_error for model evaluation"
      ],
      "metadata": {
        "id": "VoBuxC8Zj8TE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BhiEYxFIj5PZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data"
      ],
      "metadata": {
        "id": "kIz6qwIFkLw-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('processed_data.csv')  #loading the processed dataset"
      ],
      "metadata": {
        "id": "_XJALDKY4Fmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M9L3xBA7kM4K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the Data"
      ],
      "metadata": {
        "id": "811qBmnMkM7S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['wage'])  # Extracting features by removing the target variable 'wage'"
      ],
      "metadata": {
        "id": "84zBU7Mx4FpF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['wage']  # Extracting the target variable 'wage'"
      ],
      "metadata": {
        "id": "snHR5QdK4FrI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sDOBQyzNkUrO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Data into Training and Testing Sets"
      ],
      "metadata": {
        "id": "7SvZ7AknkUuz"
      },
      "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)  # Splitting the data into training and testing sets\n"
      ],
      "metadata": {
        "id": "JFlKU6IM4FtQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aK9XO0WjkblN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Model"
      ],
      "metadata": {
        "id": "aWFJJJHmkboj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()  # Initializing Linear Regression model"
      ],
      "metadata": {
        "id": "hCGRiMiB4FwF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)  # Fitting the model on the training data"
      ],
      "metadata": {
        "id": "9kezkTih4y7i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "b78000db-c8cf-455d-f251-d58cf7f0fb2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qRVdMcyXkjyp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Predictions"
      ],
      "metadata": {
        "id": "RsxiQjyckj2K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)  # Making predictions on the test data"
      ],
      "metadata": {
        "id": "PBFQRrLe4y-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vo7-XFShkpCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Model"
      ],
      "metadata": {
        "id": "cFae9E4EkpFX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)  # Calculating mean squared error"
      ],
      "metadata": {
        "id": "7L20H0mt4zBV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Mean Squared Error:\", mse)  # Displaying the mean squared error as a measure of model performance"
      ],
      "metadata": {
        "id": "1l8JN1hl4zDy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9560e5c9-f7e6-4751-ca54-664dd90ee4b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 1.4749727922570892\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yYxtBvSqkwlF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dj9tKPPs5Hs5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DESCRIPTIVE ANALYSIS"
      ],
      "metadata": {
        "id": "VtXg8oxq5KmI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data"
      ],
      "metadata": {
        "id": "cniO4zKQk8PZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('processed_data.csv')  # Re-loading the processed dataset"
      ],
      "metadata": {
        "id": "UJvsHz9B5HvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fXtbIPS25Hyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display Summary Statistics"
      ],
      "metadata": {
        "id": "53xaDlM5lA4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.describe())  # Printing summary statistics of the dataset to understand central tendency and dispersion of numerical variables"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeIFquWj5H2Z",
        "outputId": "92ac70b4-e0a8-41ea-ea5b-c13f6567db39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               wage         exper         union      goodhlth         black  \\\n",
            "count  1.257000e+03  1.257000e+03  1.257000e+03  1.257000e+03  1.257000e+03   \n",
            "mean   1.582752e-16 -1.257723e-16 -2.261075e-17  2.063231e-16  7.913762e-17   \n",
            "std    1.000398e+00  1.000398e+00  1.000398e+00  1.000398e+00  1.000398e+00   \n",
            "min   -1.133464e+00 -1.523590e+00 -6.101399e-01 -3.736882e+00 -2.826604e-01   \n",
            "25%   -5.586881e-01 -8.549204e-01 -6.101399e-01  2.676028e-01 -2.826604e-01   \n",
            "50%   -2.176830e-01 -2.698349e-01 -6.101399e-01  2.676028e-01 -2.826604e-01   \n",
            "75%    2.970415e-01  7.331689e-01  1.638968e+00  2.676028e-01 -2.826604e-01   \n",
            "max    1.531628e+01  2.488425e+00  1.638968e+00  2.676028e-01  3.537814e+00   \n",
            "\n",
            "             female       married       service          educ         looks  \n",
            "count  1.257000e+03  1.257000e+03  1.257000e+03  1.257000e+03  1.257000e+03  \n",
            "mean  -6.783224e-17 -1.130537e-16 -2.261075e-17  2.713290e-16  2.487182e-16  \n",
            "std    1.000398e+00  1.000398e+00  1.000398e+00  1.000398e+00  1.000398e+00  \n",
            "min   -7.274593e-01 -1.493775e+00 -6.150524e-01 -2.879947e+00 -3.190867e+00  \n",
            "25%   -7.274593e-01 -1.493775e+00 -6.150524e-01 -2.147542e-01 -2.695853e-01  \n",
            "50%   -7.274593e-01  6.694451e-01 -6.150524e-01 -2.147542e-01 -2.695853e-01  \n",
            "75%    1.374647e+00  6.694451e-01  1.625878e+00  1.659877e-01  1.191056e+00  \n",
            "max    1.374647e+00  6.694451e-01  1.625878e+00  1.688955e+00  2.651696e+00  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XYoYGxJv5H48"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1d7LwHvq5H68"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Nyw0k2I5H8l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DATA VISUALIZATION(HISTOGRAM OF WAGES)"
      ],
      "metadata": {
        "id": "M4ohMNp15TDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data"
      ],
      "metadata": {
        "id": "EZYa7vsB5bAy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('processed_data.csv')  # Re-loading the processed dataset"
      ],
      "metadata": {
        "id": "QUoQlIql5bD5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmmpcG845bGk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Histogram of Wages"
      ],
      "metadata": {
        "id": "s7u6aDsdlT1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size\n",
        "plt.hist(df['wage'], bins=30, color='blue', alpha=0.7)  # Generating histogram of 'wage' variable with specified bins and color\n",
        "plt.title('Histogram of Wages')  # Adding title to the plot\n",
        "plt.xlabel('Wage')  # Adding x-axis label\n",
        "plt.ylabel('Frequency')  # Adding y-axis label\n",
        "plt.grid(True)  # Adding grid lines for better visualization\n",
        "plt.show()  # Displaying the histogram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "fcqvAUst5bJa",
        "outputId": "21717f74-c5e4-4450-d70f-b0d85af6e76d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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OICXp6NGj3C0KAAAAgNMvv/yiWrVqFbidICUpKChI0qU3Kzg42MPV+DaHw6Hly5erW7dustvtni4HV0G/fAe98h30ynfQK99Br3xHaehVamqqIiIinBmhIAQpyXk5X3BwMEHqGjkcDlWsWFHBwcE+e/KUJfTLd9Ar30GvfAe98h30yneUpl5d7SM/3GwCAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAwRpAAAAADAEEEKAAAAAAyV93QBKFm9ehXv8RcsKN7jAwAAAN6AFSkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMOQ1QWrq1Kmy2WwaOXKkcywjI0NxcXGqWrWqrrvuOvXr10/Hjx932e/IkSPq2bOnKlasqNDQUD3zzDO6ePFiCVcPAAAAoCzxiiC1ZcsWzZw5U82aNXMZf/rpp/X1119r/vz5Wrt2rY4ePap7773XuT07O1s9e/ZUVlaWNmzYoNmzZysxMVHjxo0r6ZcAAAAAoAzxeJBKS0vTgw8+qH/+85+6/vrrneNnz57VrFmzNH36dHXq1EktW7ZUQkKCNmzYoI0bN0qSli9frj179mjOnDlq0aKFunfvrkmTJik+Pl5ZWVmeekkAAAAASrnyni4gLi5OPXv2VJcuXTR58mTn+LZt2+RwONSlSxfn2E033aTIyEglJyerbdu2Sk5OVtOmTRUWFuacExMToyFDhmj37t269dZb833OzMxMZWZmOh+npqZKkhwOhxwOR1G/RK9itxfv8XPfv9L+PpYW9Mt30CvfQa98B73yHfTKd5SGXhW2do8GqU8//VTbt2/Xli1b8mxLSUmRv7+/QkJCXMbDwsKUkpLinPO/ISp3e+62gkyZMkUTJ07MM758+XJVrFjR9GX4lNjY4j1+UlLuf5OK94lQpOiX76BXvoNe+Q565Tvole/w5V6dP3++UPM8FqR++eUXjRgxQklJSQoMDCzR5x47dqxGjRrlfJyamqqIiAh169ZNwcHBJVpLSevfv3iPP2eOQ0lJSeratavsxb38hWvmcNAvX0GvfAe98h30ynfQK99RGnqVe7Xa1XgsSG3btk0nTpzQbbfd5hzLzs7Wt99+q7///e9atmyZsrKydObMGZdVqePHjys8PFySFB4ers2bN7scN/eufrlz8hMQEKCAgIA843a73WcbXljFvcqa+/aVhfeyNKFfvoNe+Q565Tvole+gV77Dl3tV2Lo9drOJzp0764cfftDOnTudX61atdKDDz7o/LfdbtfKlSud++zdu1dHjhxRVFSUJCkqKko//PCDTpw44ZyTlJSk4OBgNW7cuMRfEwAAAICywWMrUkFBQWrSpInLWKVKlVS1alXn+ODBgzVq1ChVqVJFwcHBGjZsmKKiotS2bVtJUrdu3dS4cWM9/PDDmjZtmlJSUvTiiy8qLi4u3xUnAAAAACgKHr9r35W89dZb8vPzU79+/ZSZmamYmBi99957zu3lypXTN998oyFDhigqKkqVKlVSbGysXn75ZQ9WDQAAAKC086ogtWbNGpfHgYGBio+PV3x8fIH71K5dW4sXLy7mygAAAADg/3j8D/ICAAAAgK8hSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgq7+kCkFevXp6uAAAAAMCVeHRF6v3331ezZs0UHBys4OBgRUVFacmSJc7tGRkZiouLU9WqVXXdddepX79+On78uMsxjhw5op49e6pixYoKDQ3VM888o4sXL5b0SwEAAABQhng0SNWqVUtTp07Vtm3btHXrVnXq1Em9e/fW7t27JUlPP/20vv76a82fP19r167V0aNHde+99zr3z87OVs+ePZWVlaUNGzZo9uzZSkxM1Lhx4zz1kgAAAACUAR69tK/XZdewvfLKK3r//fe1ceNG1apVS7NmzdLcuXPVqVMnSVJCQoJuvvlmbdy4UW3bttXy5cu1Z88erVixQmFhYWrRooUmTZqk5557ThMmTJC/v3++z5uZmanMzEzn49TUVEmSw+GQw+EopldbeHa7pytwX+775w3vI66OfvkOeuU76JXvoFe+g175jtLQq8LWbrMsyyrmWgolOztb8+fPV2xsrHbs2KGUlBR17txZp0+fVkhIiHNe7dq1NXLkSD399NMaN26cFi5cqJ07dzq3Hzp0SPXq1dP27dt166235vtcEyZM0MSJE/OMz507VxUrVizqlwYAAADAR5w/f14DBw7U2bNnFRwcXOA8j99s4ocfflBUVJQyMjJ03XXX6YsvvlDjxo21c+dO+fv7u4QoSQoLC1NKSookKSUlRWFhYXm2524ryNixYzVq1Cjn49TUVEVERKhbt25XfLNKSv/+nq7AfXPmOJSUlKSuXbvK7stLa2WEw0G/fAW98h30ynfQK99Br3xHaehV7tVqV+PxINWoUSPt3LlTZ8+e1eeff67Y2FitXbu2WJ8zICBAAQEBecbtdrtXNNyHV0KdlyV6y3uJwqFfvoNe+Q565Tvole+gV77Dl3tV2Lo9HqT8/f3VoEEDSVLLli21ZcsW/e1vf1P//v2VlZWlM2fOuKxKHT9+XOHh4ZKk8PBwbd682eV4uXf1y50DAAAAAEXN6/4gb05OjjIzM9WyZUvZ7XatXLnSuW3v3r06cuSIoqKiJElRUVH64YcfdOLECeecpKQkBQcHq3HjxiVeOwAAAICywaMrUmPHjlX37t0VGRmpc+fOae7cuVqzZo2WLVumypUra/DgwRo1apSqVKmi4OBgDRs2TFFRUWrbtq0kqVu3bmrcuLEefvhhTZs2TSkpKXrxxRcVFxeX76V7AAAAAFAUPBqkTpw4oUceeUTHjh1T5cqV1axZMy1btkxdu3aVJL311lvy8/NTv379lJmZqZiYGL333nvO/cuVK6dvvvlGQ4YMUVRUlCpVqqTY2Fi9/PLLnnpJAAAAAMoAjwapWbNmXXF7YGCg4uPjFR8fX+Cc2rVra/HixUVdGgAAAAAUyOs+IwUAAAAA3o4gBQAAAACGCFIAAAAAYIggBQAAAACGCFIAAAAAYIggBQAAAACGCFIAAAAAYIggBQAAAACGCFIAAAAAYKi8pwtA6dK/vxQbe+m/DkfRHvvrr4v2eAAAAIC7WJECAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAw5FaQ+vnnn4u6DgAAAADwGW4FqQYNGig6Olpz5sxRRkZGUdcEAAAAAF7NrSC1fft2NWvWTKNGjVJ4eLieeuopbd68uahrAwAAAACv5FaQatGihf72t7/p6NGj+vDDD3Xs2DG1b99eTZo00fTp0/X7778XdZ0AAAAA4DWu6WYT5cuX17333qv58+frtdde04EDBzRmzBhFRETokUce0bFjx4qqTgAAAADwGtcUpLZu3aq//OUvqlGjhqZPn64xY8bo4MGDSkpK0tGjR9W7d++iqhMAAAAAvEZ5d3aaPn26EhIStHfvXvXo0UMfffSRevToIT+/S7msbt26SkxMVJ06dYqyVgAAAADwCm4Fqffff1+PPfaYBg0apBo1auQ7JzQ0VLNmzbqm4gAAAADAG7kVpPbv33/VOf7+/oqNjXXn8AAAAADg1dz6jFRCQoLmz5+fZ3z+/PmaPXv2NRcFAAAAAN7MrSA1ZcoUVatWLc94aGioXn311WsuCgAAAAC8mVtB6siRI6pbt26e8dq1a+vIkSPXXBQAAAAAeDO3glRoaKi+//77POO7du1S1apVr7koAAAAAPBmbgWpBx54QMOHD9fq1auVnZ2t7OxsrVq1SiNGjNCAAQOKukYAAAAA8Cpu3bVv0qRJ+u9//6vOnTurfPlLh8jJydEjjzzCZ6QAAAAAlHpuBSl/f3/NmzdPkyZN0q5du1ShQgU1bdpUtWvXLur6AAAAAMDruBWkct1444268cYbi6oWAAAAAPAJbgWp7OxsJSYmauXKlTpx4oRycnJctq9atapIigMAAAAAb+RWkBoxYoQSExPVs2dPNWnSRDabrajrAgAAAACv5VaQ+vTTT/XZZ5+pR48eRV0PAAAAAHg9t25/7u/vrwYNGhR1LQAAAADgE9wKUqNHj9bf/vY3WZZV1PUAAAAAgNdz69K+devWafXq1VqyZIluueUW2e12l+0LFiwokuIAAAAAwBu5FaRCQkLUt2/foq4FAAAAAHyCW0EqISGhqOsAAAAAAJ/h1mekJOnixYtasWKFZs6cqXPnzkmSjh49qrS0tCIrDgAAAAC8kVsrUocPH9bdd9+tI0eOKDMzU127dlVQUJBee+01ZWZmasaMGUVdJwAAAAB4DbdWpEaMGKFWrVrp9OnTqlChgnO8b9++WrlyZZEVBwAAAADeyK0Vqe+++04bNmyQv7+/y3idOnX022+/FUlhAAAAAOCt3FqRysnJUXZ2dp7xX3/9VUFBQddcFAAAAAB4M7eCVLdu3fT22287H9tsNqWlpWn8+PHq0aNHUdUGAAAAAF7JrUv73nzzTcXExKhx48bKyMjQwIEDtX//flWrVk2ffPJJUdcIAAAAAF7FrSBVq1Yt7dq1S59++qm+//57paWlafDgwXrwwQddbj4BAAAAAKWRW0FKksqXL6+HHnqoKGsBAAAAAJ/gVpD66KOPrrj9kUcecasYAAAAAPAFbgWpESNGuDx2OBw6f/68/P39VbFiRYIUAAAAgFLNrbv2nT592uUrLS1Ne/fuVfv27bnZBAAAAIBSz60glZ+GDRtq6tSpeVarAAAAAKC0KbIgJV26AcXRo0eL8pAAAAAA4HXc+ozUwoULXR5blqVjx47p73//u9q1a1ckhQEAAACAt3IrSPXp08flsc1mU/Xq1dWpUye9+eabRVEXAAAAAHgtt4JUTk5OUdcBAAAAAD6jSD8jBQAAAABlgVsrUqNGjSr03OnTp7vzFAAAAADgtdwKUjt27NCOHTvkcDjUqFEjSdK+fftUrlw53Xbbbc55NputaKoEAAAAAC/iVpDq1auXgoKCNHv2bF1//fWSLv2R3kcffVR33nmnRo8eXaRFAgAAAIA3ceszUm+++aamTJniDFGSdP3112vy5MnctQ8AAABAqedWkEpNTdXvv/+eZ/z333/XuXPnrrkoAAAAAPBmbgWpvn376tFHH9WCBQv066+/6tdff9W///1vDR48WPfee29R1wgAAAAAXsWtz0jNmDFDY8aM0cCBA+VwOC4dqHx5DR48WK+//nqRFggAAAAA3satIFWxYkW99957ev3113Xw4EFJUv369VWpUqUiLQ4AAAAAvNE1/UHeY8eO6dixY2rYsKEqVaoky7KKqi4AAAAA8FpuBamTJ0+qc+fOuvHGG9WjRw8dO3ZMkjR48GBufQ4AAACg1HMrSD399NOy2+06cuSIKlas6Bzv37+/li5dWmTFAQAAAIA3cuszUsuXL9eyZctUq1Ytl/GGDRvq8OHDRVIYAAAAAHgrt1ak0tPTXVaicp06dUoBAQHXXBQAAAAAeDO3gtSdd96pjz76yPnYZrMpJydH06ZNU3R0dJEVBwAAAADeyK1L+6ZNm6bOnTtr69atysrK0rPPPqvdu3fr1KlTWr9+fVHXCAAAAABexa0VqSZNmmjfvn1q3769evfurfT0dN17773asWOH6tevX9Q1AgAAAIBXMV6RcjgcuvvuuzVjxgy98MILxVETAAAAAHg14xUpu92u77//vjhqAQAAAACf4NalfQ899JBmzZpV1LUAAAAAgE9w62YTFy9e1IcffqgVK1aoZcuWqlSpksv26dOnF0lxAAAAAOCNjFakfv75Z+Xk5OjHH3/UbbfdpqCgIO3bt087duxwfu3cubPQx5syZYpuv/12BQUFKTQ0VH369NHevXtd5mRkZCguLk5Vq1bVddddp379+un48eMuc44cOaKePXuqYsWKCg0N1TPPPKOLFy+avDQAAAAAKDSjFamGDRvq2LFjWr16tSSpf//+eueddxQWFubWk69du1ZxcXG6/fbbdfHiRf31r39Vt27dtGfPHucq19NPP61FixZp/vz5qly5soYOHap7773XeZv17Oxs9ezZU+Hh4dqwYYOOHTumRx55RHa7Xa+++qpbdQEAAADAlRgFKcuyXB4vWbJE6enpbj/50qVLXR4nJiYqNDRU27Zt01133aWzZ89q1qxZmjt3rjp16iRJSkhI0M0336yNGzeqbdu2Wr58ufbs2aMVK1YoLCxMLVq00KRJk/Tcc89pwoQJ8vf3d7s+AAAAAMiPW5+RynV5sLpWZ8+elSRVqVJFkrRt2zY5HA516dLFOeemm25SZGSkkpOT1bZtWyUnJ6tp06Yuq2IxMTEaMmSIdu/erVtvvTXP82RmZiozM9P5ODU1VdKlW7s7HI4ifU3usNs9XYH77HaHy3+Lkhe0ptTJ/X73hu97XBm98h30ynfQK99Br3xHaehVYWs3ClI2m002my3PWFHIycnRyJEj1a5dOzVp0kSSlJKSIn9/f4WEhLjMDQsLU0pKinPO5ZcW5j7OnXO5KVOmaOLEiXnGly9frooVK17rS7lmsbGeruDaDRyYVOTHXLy4yA+J/19SUtH3C8WDXvkOeuU76JXvoFe+w5d7df78+ULNM760b9CgQQoICJB06UYQf/7zn/PctW/BggUmh5UkxcXF6ccff9S6deuM9zU1duxYjRo1yvk4NTVVERER6tatm4KDg4v9+a+mf39PV+A+u92hgQOTNHduVzkcRbu0Nm9ekR4OuvT/uCQlJalr166y+/JSaBlAr3wHvfId9Mp30CvfURp6lXu12tUYBanYy5ZKHnroIZPdCzR06FB98803+vbbb1WrVi3neHh4uLKysnTmzBmXVanjx48rPDzcOWfz5s0ux8u9q1/unMsFBAQ4w+D/stvtXtFwH14JdXI47EUepLygNaWWt3zv4+role+gV76DXvkOeuU7fLlXha3bKEglJCS4VUxBLMvSsGHD9MUXX2jNmjWqW7euy/aWLVvKbrdr5cqV6tevnyRp7969OnLkiKKioiRJUVFReuWVV3TixAmFhoZKurSUGBwcrMaNGxdpvQAAAAAgXePNJq5VXFyc5s6dq6+++kpBQUHOzzRVrlxZFSpUUOXKlTV48GCNGjVKVapUUXBwsIYNG6aoqCi1bdtWktStWzc1btxYDz/8sKZNm6aUlBS9+OKLiouLy3fVCQAAAACulUeD1Pvvvy9J6tixo8t4QkKCBg0aJEl666235Ofnp379+ikzM1MxMTF67733nHPLlSunb775RkOGDFFUVJQqVaqk2NhYvfzyyyX1MgAAAACUMR4NUoW5fXpgYKDi4+MVHx9f4JzatWtrMbd0AwAAAFBC/DxdAAAAAAD4GoIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAofKeLgAorF69ivf4X39dvMcHAABA6cGKFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAYIkgBAAAAgCGCFAAAAAAY8miQ+vbbb9WrVy/VrFlTNptNX375pct2y7I0btw41ahRQxUqVFCXLl20f/9+lzmnTp3Sgw8+qODgYIWEhGjw4MFKS0srwVcBAAAAoKzxaJBKT09X8+bNFR8fn+/2adOm6Z133tGMGTO0adMmVapUSTExMcrIyHDOefDBB7V7924lJSXpm2++0bfffqsnn3yypF4CAAAAgDKovCefvHv37urevXu+2yzL0ttvv60XX3xRvXv3liR99NFHCgsL05dffqkBAwboP//5j5YuXaotW7aoVatWkqR3331XPXr00BtvvKGaNWuW2GsBAAAAUHZ4NEhdyaFDh5SSkqIuXbo4xypXrqw2bdooOTlZAwYMUHJyskJCQpwhSpK6dOkiPz8/bdq0SX379s332JmZmcrMzHQ+Tk1NlSQ5HA45HI5iekWFZ7d7ugL32e0Ol//6Ei9ofYnL/X73hu97XBm98h30ynfQK99Br3xHaehVYWv32iCVkpIiSQoLC3MZDwsLc25LSUlRaGioy/by5curSpUqzjn5mTJliiZOnJhnfPny5apYseK1ln7NYmM9XcG1GzgwydMlGFu82NMVeE5Sku/1q6yiV76DXvkOeuU76JXv8OVenT9/vlDzvDZIFaexY8dq1KhRzsepqamKiIhQt27dFBwc7MHKLunf39MVuM9ud2jgwCTNndtVDodvLa3Nm+fpCkqew+FQUlKSunbtKrsvL4WWAfTKd9Ar30GvfAe98h2loVe5V6tdjdcGqfDwcEnS8ePHVaNGDef48ePH1aJFC+ecEydOuOx38eJFnTp1yrl/fgICAhQQEJBn3G63e0XDfXgl1MnhsPtckPKC1nuMt3zv4+role+gV76DXvkOeuU7fLlXha3ba/+OVN26dRUeHq6VK1c6x1JTU7Vp0yZFRUVJkqKionTmzBlt27bNOWfVqlXKyclRmzZtSrxmAAAAAGWDR1ek0tLSdODAAefjQ4cOaefOnapSpYoiIyM1cuRITZ48WQ0bNlTdunX10ksvqWbNmurTp48k6eabb9bdd9+tJ554QjNmzJDD4dDQoUM1YMAA7tgHAAAAoNh4NEht3bpV0dHRzse5n1uKjY1VYmKinn32WaWnp+vJJ5/UmTNn1L59ey1dulSBgYHOfT7++GMNHTpUnTt3lp+fn/r166d33nmnxF8LAAAAgLLDo0GqY8eOsiyrwO02m00vv/yyXn755QLnVKlSRXPnzi2O8gAAAAAgX177GSkAAAAA8FYEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwRJACAAAAAEMEKQAAAAAwVN7TBQDeolev4jv2118X37EBAABQ8liRAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABDBCkAAAAAMESQAgAAAABD5T1dAFAW9OpVvMf/+uviPT4AAABcsSIFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIbKe7oAAN6tV6/iPf7XXxfv8QEAAIoDK1IAAAAAYIgVKaAUcHfVyG6XYmOl/v0lh6NoawIAACjNCFIASi0uSwQAAMWFS/sAAAAAwBArUgA8qrhXjQAAAIpDqVmRio+PV506dRQYGKg2bdpo8+bNni4JAAAAQClVKoLUvHnzNGrUKI0fP17bt29X8+bNFRMToxMnTni6NAAAAAClUKm4tG/69Ol64okn9Oijj0qSZsyYoUWLFunDDz/U888/7+HqAMDc5Zc8FvUdFrlRBgAA18bng1RWVpa2bdumsWPHOsf8/PzUpUsXJScn57tPZmamMjMznY/Pnj0rSTp16pQc3AP6Gjl0/vx5SScl2T1dDK6Kfl2LkydL8tmKtlfF+dm0xMTiO7YkDRpUvMe/Vna7Q3/603n1739SDodrr4r7vYEZh+PSeXXy5EnZ7fwM9Gb0yjsU5ufvlX4GXok3/Xw8d+6cJMmyrCvOs1lXm+Hljh49qhtuuEEbNmxQVFSUc/zZZ5/V2rVrtWnTpjz7TJgwQRMnTizJMgEAAAD4kF9++UW1atUqcLvPr0i5Y+zYsRo1apTzcU5Ojk6dOqWqVavKZrN5sDLfl5qaqoiICP3yyy8KDg72dDm4CvrlO+iV76BXvoNe+Q565TtKQ68sy9K5c+dUs2bNK87z+SBVrVo1lStXTsePH3cZP378uMLDw/PdJyAgQAEBAS5jISEhxVVimRQcHOyzJ09ZRL98B73yHfTKd9Ar30GvfIev96py5cpXnePzd+3z9/dXy5YttXLlSudYTk6OVq5c6XKpHwAAAAAUFZ9fkZKkUaNGKTY2Vq1atVLr1q319ttvKz093XkXPwAAAAAoSqUiSPXv31+///67xo0bp5SUFLVo0UJLly5VWFiYp0srcwICAjR+/Pg8l07CO9Ev30GvfAe98h30ynfQK99Rlnrl83ftAwAAAICS5vOfkQIAAACAkkaQAgAAAABDBCkAAAAAMESQAgAAAABDBCkYi4+PV506dRQYGKg2bdpo8+bNV5w/f/583XTTTQoMDFTTpk21ePHiEqq0bJsyZYpuv/12BQUFKTQ0VH369NHevXuvuE9iYqJsNpvLV2BgYAlVXHZNmDAhz/t+0003XXEfzivPqFOnTp5e2Ww2xcXF5Tufc6rkfPvtt+rVq5dq1qwpm82mL7/80mW7ZVkaN26catSooQoVKqhLly7av3//VY9r+jsPV3elXjkcDj333HNq2rSpKlWqpJo1a+qRRx7R0aNHr3hMd36O4uqudl4NGjQoz/t+9913X/W4peW8IkjByLx58zRq1CiNHz9e27dvV/PmzRUTE6MTJ07kO3/Dhg164IEHNHjwYO3YsUN9+vRRnz599OOPP5Zw5WXP2rVrFRcXp40bNyopKUkOh0PdunVTenr6FfcLDg7WsWPHnF+HDx8uoYrLtltuucXlfV+3bl2BczmvPGfLli0ufUpKSpIk/elPfypwH86pkpGenq7mzZsrPj4+3+3Tpk3TO++8oxkzZmjTpk2qVKmSYmJilJGRUeAxTX/noXCu1Kvz589r+/bteumll7R9+3YtWLBAe/fu1T333HPV45r8HEXhXO28kqS7777b5X3/5JNPrnjMUnVeWYCB1q1bW3Fxcc7H2dnZVs2aNa0pU6bkO//++++3evbs6TLWpk0b66mnnirWOpHXiRMnLEnW2rVrC5yTkJBgVa5cueSKgmVZljV+/HirefPmhZ7PeeU9RowYYdWvX9/KycnJdzvnlGdIsr744gvn45ycHCs8PNx6/fXXnWNnzpyxAgICrE8++aTA45j+zoO5y3uVn82bN1uSrMOHDxc4x/TnKMzl16vY2Fird+/eRscpTecVK1IotKysLG3btk1dunRxjvn5+alLly5KTk7Od5/k5GSX+ZIUExNT4HwUn7Nnz0qSqlSpcsV5aWlpql27tiIiItS7d2/t3r27JMor8/bv36+aNWuqXr16evDBB3XkyJEC53JeeYesrCzNmTNHjz32mGw2W4HzOKc879ChQ0pJSXE5bypXrqw2bdoUeN648zsPxePs2bOy2WwKCQm54jyTn6MoOmvWrFFoaKgaNWqkIUOG6OTJkwXOLW3nFUEKhfbHH38oOztbYWFhLuNhYWFKSUnJd5+UlBSj+SgeOTk5GjlypNq1a6cmTZoUOK9Ro0b68MMP9dVXX2nOnDnKycnRHXfcoV9//bUEqy172rRpo8TERC1dulTvv/++Dh06pDvvvFPnzp3Ldz7nlXf48ssvdebMGQ0aNKjAOZxT3iH33DA5b9z5nYeil5GRoeeee04PPPCAgoODC5xn+nMURePuu+/WRx99pJUrV+q1117T2rVr1b17d2VnZ+c7v7SdV+U9XQCA4hcXF6cff/zxqteLR0VFKSoqyvn4jjvu0M0336yZM2dq0qRJxV1mmdW9e3fnv5s1a6Y2bdqodu3a+uyzzzR48GAPVoYrmTVrlrp3766aNWsWOIdzCnCfw+HQ/fffL8uy9P77719xLj9HPWPAgAHOfzdt2lTNmjVT/fr1tWbNGnXu3NmDlZUMVqRQaNWqVVO5cuV0/Phxl/Hjx48rPDw8333Cw8ON5qPoDR06VN98841Wr16tWrVqGe1rt9t166236sCBA8VUHfITEhKiG2+8scD3nfPK8w4fPqwVK1bo8ccfN9qPc8ozcs8Nk/PGnd95KDq5Ierw4cNKSkq64mpUfq72cxTFo169eqpWrVqB73tpO68IUig0f39/tWzZUitXrnSO5eTkaOXKlS7/j+v/ioqKcpkvSUlJSQXOR9GxLEtDhw7VF198oVWrVqlu3brGx8jOztYPP/ygGjVqFEOFKEhaWpoOHjxY4PvOeeV5CQkJCg0NVc+ePY3245zyjLp16yo8PNzlvElNTdWmTZsKPG/c+Z2HopEbovbv368VK1aoatWqxse42s9RFI9ff/1VJ0+eLPB9L3XnlafvdgHf8umnn1oBAQFWYmKitWfPHuvJJ5+0QkJCrJSUFMuyLOvhhx+2nn/+eef89evXW+XLl7feeOMN6z//+Y81fvx4y263Wz/88IOnXkKZMWTIEKty5crWmjVrrGPHjjm/zp8/75xzeb8mTpxoLVu2zDp48KC1bds2a8CAAVZgYKC1e/duT7yEMmP06NHWmjVrrEOHDlnr16+3unTpYlWrVs06ceKEZVmcV94mOzvbioyMtJ577rk82zinPOfcuXPWjh07rB07dliSrOnTp1s7duxw3ult6tSpVkhIiPXVV19Z33//vdW7d2+rbt261oULF5zH6NSpk/Xuu+86H1/tdx7cc6VeZWVlWffcc49Vq1Yta+fOnS6/vzIzM53HuLxXV/s5CvdcqVfnzp2zxowZYyUnJ1uHDh2yVqxYYd12221Ww4YNrYyMDOcxSvN5RZCCsXfffdeKjIy0/P39rdatW1sbN250buvQoYMVGxvrMv+zzz6zbrzxRsvf39+65ZZbrEWLFpVwxWWTpHy/EhISnHMu79fIkSOdvQ0LC7N69Ohhbd++veSLL2P69+9v1ahRw/L397duuOEGq3///taBAwec2zmvvMuyZcssSdbevXvzbOOc8pzVq1fn+zMvtx85OTnWSy+9ZIWFhVkBAQFW586d8/Swdu3a1vjx413GrvQ7D+65Uq8OHTpU4O+v1atXO49xea+u9nMU7rlSr86fP29169bNql69umW3263atWtbTzzxRJ5AVJrPK5tlWVYJLHwBAAAAQKnBZ6QAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgDg02bMmKGgoCBdvHjROZaWlia73a6OHTu6zF2zZo1sNpsOHjxYwlUCAEobghQAwKdFR0crLS1NW7dudY599913Cg8P16ZNm5SRkeEcX716tSIjI1W/fn1PlAoAKEUIUgAAn9aoUSPVqFFDa9ascY6tWbNGvXv3Vt26dbVx40aX8ejoaP3rX/9Sq1atFBQUpPDwcA0cOFAnTpxwOe7ChQvVsGFDBQYGKjo6WrNnz5bNZtOZM2ecc9atW6c777xTFSpUUEREhIYPH6709PTifskAAC9AkAIA+Lzo6GitXr3a+Xj16tXq2LGjOnTo4By/cOGCNm3apOjoaDkcDk2aNEm7du3Sl19+qf/+978aNGiQc/9Dhw7pvvvuU58+fbRr1y499dRTeuGFF1ye8+DBg7r77rvVr18/ff/995o3b57WrVunoUOHlshrBgB4ls2yLMvTRQAAcC0++OADjRw5UmfOnNGFCxdUpUoVHT16VCtWrNCMGTO0du1arVq1Sp07d9bhw4cVGRnpsv/WrVt1++2369y5c7ruuuv0/PPPa9GiRfrhhx+cc1588UW98sorOn36tEJCQvT444+rXLlymjlzpnPOunXr1KFDB6WnpyswMLDEXj8AoOSxIgUA8HkdO3ZUenq6tmzZou+++0433nijqlevrg4dOjg/J7VmzRrVq1dPkZGR2rZtm3r16qXIyEgFBQWpQ4cOkqQjR45Ikvbu3avbb7/d5Tlat27t8njXrl1KTEzUdddd5/yKiYlRTk6ODh06VDIvHADgMeU9XQAAANeqQYMGqlWrllavXq3Tp087g1HNmjUVERGhDRs2aPXq1erUqZPS09MVExOjmJgYffzxx6pevbqOHDmimJgYZWVlFfo509LS9NRTT2n48OF5tl2+4gUAKH0IUgCAUiE6Olpr1qzR6dOn9cwzzzjH77rrLi1ZskSbN2/WkCFD9NNPP+nkyZOaOnWqIiIiJMnljn/SpRtYLF682GVsy5YtLo9vu+027dmzRw0aNCimVwQA8GZc2gcAKBWio6O1bt067dy507kiJUkdOnTQzJkzlZWVpejoaEVGRsrf31/vvvuufv75Zy1cuFCTJk1yOdZTTz2ln376Sc8995z27dunzz77TImJiZIkm80mSXruuee0YcMGDR06VDt37tT+/fv11VdfcbMJACgjCFIAgFIhOjpaFy5cUIMGDRQWFuYc79Chg86dO+e8TXr16tWVmJio+fPnq3Hjxpo6dareeOMNl2PVrVtXn3/+uRYsWKBmzZrp/fffd961LyAgQJLUrFkzrV27Vvv27dOdd96pW2+9VePGjVPNmjVL7kUDADyGu/YBAFAIr7zyimbMmKFffvnF06UAALwAn5ECACAf7733nm6//XZVrVpV69ev1+uvv85lewAAJ4IUAAD52L9/vyZPnqxTp04pMjJSo0eP1tixYz1dFgDAS3BpHwAAAAAY4mYTAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhv4/5HB3wUqHzkEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hEO690pw5bMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_72nIJQo5bPk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vr9uwbzh5bTE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF5722\"><b>Step 5: Predicting Union Membership</b></font>\n",
        "\n",
        "## <font color=\"#FFC107\"><b>5.1 Logistic Regression</b></font>\n",
        "- **Note:** Logistic regression is a classification algorithm used to predict binary outcomes, such as union membership. The dataset is split into training and test sets, and the model's performance is measured using accuracy, confusion matrix, and classification report metrics. These metrics provide insights into how well the model distinguishes between union members and non-members. For further model tuning, try using techniques like cross-validation or hyperparameter optimization."
      ],
      "metadata": {
        "id": "pTDkFCUodVu5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# PREDICTING UNION MEMBERSHIP: A LOGISTIC REGRESSION APPROACH"
      ],
      "metadata": {
        "id": "wWjbhd2n5kGG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Necessary Libraries (Note: Already imported earlier)"
      ],
      "metadata": {
        "id": "Hxfz9L5Jleru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Re-importing train_test_split for data splitting"
      ],
      "metadata": {
        "id": "VxaQEIIg59YV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression  # Importing LogisticRegression for modeling"
      ],
      "metadata": {
        "id": "JEZhYAXuli9W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Importing evaluation metrics"
      ],
      "metadata": {
        "id": "cZGdiLlaljAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IAwM9X1t59jc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data"
      ],
      "metadata": {
        "id": "KdZwh8FjnFvR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('processed_data.csv')"
      ],
      "metadata": {
        "id": "5lusgkND59me"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ox6fQZL059o-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'union' column contains continuous values and we want to binarize it based on a threshold"
      ],
      "metadata": {
        "id": "44rAg-KunOqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 0.5  # Define the threshold for binarization"
      ],
      "metadata": {
        "id": "CfFdAB2FnQDw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['union'] = (df['union'] > threshold).astype(int)  # Binarize 'union' column based on the threshold"
      ],
      "metadata": {
        "id": "xU27SUP2nOuE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gzJhbpA1niga"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the Data"
      ],
      "metadata": {
        "id": "zXqXic0PnijJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['union'])  # Features"
      ],
      "metadata": {
        "id": "pj5fv0Lfl7jB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['union']  # Target variable"
      ],
      "metadata": {
        "id": "mbkEbWy16cau"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gVs6TrEuntSZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the Data into Training and Testing Sets"
      ],
      "metadata": {
        "id": "xGIl6WobntVl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Hr-aHnfsmAHI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8K431V3vnzHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Model"
      ],
      "metadata": {
        "id": "fgX69nUOnzKo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "1Hkeezn6mAK1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "jzd_o_Qd6cpY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "ba2d7ef8-99b3-44dc-8641-55df82ffe974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CJegPItAn8PB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Predictions"
      ],
      "metadata": {
        "id": "OqheQlZBn8SO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test) # Making predictions on the test data"
      ],
      "metadata": {
        "id": "cVn1me2S59tL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sNknARPuoBqJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Model"
      ],
      "metadata": {
        "id": "w6Y8ngE759xM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)  # Calculating accuracy of the model"
      ],
      "metadata": {
        "id": "AESAerTe59zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\", accuracy)  # Displaying the accuracy of the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ3euOvxolZx",
        "outputId": "f158e808-79a8-4257-b3d4-24854019ae5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7142857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m3h-gG1QoldB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report"
      ],
      "metadata": {
        "id": "carBoa2mmPAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report:\")  # Displaying the classification report for detailed evaluation"
      ],
      "metadata": {
        "id": "DazIDLfF591Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b242961-163d-4437-c6c7-1be7dd711045"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))  # Printing the classification report"
      ],
      "metadata": {
        "id": "fXXuZ9Zw593W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "510deeba-af98-4c61-8bbf-4c4b3531010f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.99      0.83       181\n",
            "           1       0.33      0.01      0.03        71\n",
            "\n",
            "    accuracy                           0.71       252\n",
            "   macro avg       0.53      0.50      0.43       252\n",
            "weighted avg       0.61      0.71      0.61       252\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TDtw3Lajoxe_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix"
      ],
      "metadata": {
        "id": "s9fzctENoxiV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Confusion Matrix:\")  # Displaying the confusion matrix for evaluating prediction performance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SxYg78VmTvQ",
        "outputId": "6bb49b75-6fa1-47fa-cf06-fd174f1069fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))  # Printing the confusion matrix"
      ],
      "metadata": {
        "id": "J390a5sL594-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca5c28b7-e549-4504-f02e-e9085817186e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[179   2]\n",
            " [ 70   1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8wG48qNO9Kz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rOcxF70g9K9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q2o9j_xO9LBy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#FF5722\"><b>Step 6: Clustering Analysis - Socio-Economic Patterns</b></font>\n",
        "\n",
        "## <font color=\"#FFC107\"><b>6.1 K-Means Clustering</b></font>\n",
        "- **Note:** K-Means is an unsupervised learning algorithm used to cluster individuals based on socio-economic features. The Elbow Method helps determine the optimal number of clusters by plotting the inertia across different values of K. Once the optimal K is chosen, cluster centroids are analyzed to identify the distinct characteristics of each group. This clustering technique can uncover hidden patterns within the data, but be sure to standardize the data before applying K-Means to ensure accurate clustering."
      ],
      "metadata": {
        "id": "ktA86J1PdN5Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EXPLORING DEMOGRAPHIC AND SOCIAL ECONOMIC PATTERNS: CLUSTERING ANALYSIS OF INDIVIDUALS"
      ],
      "metadata": {
        "id": "1krUsJju9MPX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Necessary Libraries"
      ],
      "metadata": {
        "id": "UWSGVeo2pHHE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing pandas for data manipulation"
      ],
      "metadata": {
        "id": "AagXdSfm9LFm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans  # Importing KMeans for clustering analysis"
      ],
      "metadata": {
        "id": "KLp3rOhgpKeO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # Importing StandardScaler for feature scaling"
      ],
      "metadata": {
        "id": "hF55hPmXpKhf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Importing matplotlib for data visualization"
      ],
      "metadata": {
        "id": "yHjduYfbpKlb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gy416-nkpR8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Data"
      ],
      "metadata": {
        "id": "rcvZVeC8pR_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('processed_data.csv')  # Loading the processed dataset"
      ],
      "metadata": {
        "id": "BK32nV7CpSDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tRQNkSKYpWrJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select Features for Clustering"
      ],
      "metadata": {
        "id": "tdBqsNoLpWuK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['wage'])  # Selecting features for clustering by excluding the target variable 'wage'"
      ],
      "metadata": {
        "id": "rXnpjWbe9LKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Boq7y_jMpdCf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize Features"
      ],
      "metadata": {
        "id": "27Pha9CzpdGG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initializing StandardScaler object"
      ],
      "metadata": {
        "id": "7ZlaAr6f9LNy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaled = scaler.fit_transform(X)  # Standardizing features to have mean of 0 and standard deviation of 1"
      ],
      "metadata": {
        "id": "O63BFa3spmpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9rXNJVUZ9LQC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine Optimal Number of Clusters (K) using the Elbow Method"
      ],
      "metadata": {
        "id": "rnfKHP3e9LSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inertia = []  # Initializing list to store inertia values"
      ],
      "metadata": {
        "id": "07ZNO6A89LUe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for k in range(1, 11):  # Iterating through different values of K\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)  # Initializing KMeans object with current K value\n",
        "    kmeans.fit(X_scaled)  # Fitting KMeans model to the scaled data\n",
        "    inertia.append(kmeans.inertia_)  # Appending inertia value to the list"
      ],
      "metadata": {
        "id": "UErVPLu49tg_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eeb7ab98-f2a1-42d7-af17-ba239084c278"
      },
      "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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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",
            "/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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hov6tKdVpyxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JYot_tsfp2DS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the Elbow Method"
      ],
      "metadata": {
        "id": "JTFYpeWppy0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size\n",
        "plt.plot(range(1, 11), inertia, marker='o', linestyle='--')  # Plotting inertia values against number of clusters\n",
        "plt.xlabel('Number of Clusters (K)')  # Adding label to x-axis\n",
        "plt.ylabel('Inertia')  # Adding label to y-axis\n",
        "plt.title('Elbow Method')  # Adding title to the plot\n",
        "plt.xticks(range(1, 11))  # Setting x-axis ticks\n",
        "plt.grid(True)  # Adding grid lines\n",
        "plt.show()  # Displaying the plot"
      ],
      "metadata": {
        "id": "PjZFD0p19tkj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "81fb3cf4-ed6d-4744-a1c8-237eb7262871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TbG3B9NDp6Tr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "syNMjKdpp6Ww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Based on the Elbow Method, choose the optimal number of clusters (K)\n",
        "# Let's assume K=3 for demonstration purposes"
      ],
      "metadata": {
        "id": "qdqUdn_d9tn3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform K-means Clustering with K=3"
      ],
      "metadata": {
        "id": "jwU-_W8C9trS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)  # Initializing KMeans object with chosen number of clusters"
      ],
      "metadata": {
        "id": "LrGBGUgv917c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clusters = kmeans.fit_predict(X_scaled)  # Performing K-means clustering and assigning cluster labels"
      ],
      "metadata": {
        "id": "QVIRye2r922F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TlwloILQ96w5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add Cluster Labels to the DataFrame"
      ],
      "metadata": {
        "id": "BUdCfMW9960U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['cluster'] = clusters  # Adding cluster labels as a new column in the dataset"
      ],
      "metadata": {
        "id": "KnA7kApm963Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "07Du2jkkqMjV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze Cluster Characteristics"
      ],
      "metadata": {
        "id": "ACNrEwhtqMm-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_means = df.groupby('cluster').mean()  # Calculating mean values of each feature for each cluster"
      ],
      "metadata": {
        "id": "bCEx7bW8-6bL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cluster_means)  # Displaying the mean values for each cluster"
      ],
      "metadata": {
        "id": "i8sVrYHy96-a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3866ef66-c869-4475-de88-e078c4d549c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             wage     exper     union  goodhlth     black    female   married  \\\n",
            "cluster                                                                         \n",
            "0       -0.336848 -0.533894 -0.129748  0.267603  0.162443  0.835855 -0.746099   \n",
            "1        0.266820  0.355264  0.090570  0.267603 -0.143312 -0.673150  0.577393   \n",
            "2       -0.024891  0.490378  0.086013 -3.736882  0.126676  0.148418  0.051382   \n",
            "\n",
            "          service      educ     looks  \n",
            "cluster                                \n",
            "0        0.290022  0.121629  0.079267  \n",
            "1       -0.202966 -0.044057 -0.034284  \n",
            "2       -0.188209 -0.400592 -0.217420  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5IM2NbM2qW6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sIXOPEsVqW9g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Elbow Method Graph for Printing"
      ],
      "metadata": {
        "id": "-Xe82PT1qXdD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the Elbow Method"
      ],
      "metadata": {
        "id": "a4-mslz2-Fjp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Setting the figure size\n",
        "plt.plot(range(1, 11), inertia, marker='o', linestyle='--')  # Plotting inertia values against number of clusters\n",
        "plt.xlabel('Number of Clusters (K)')  # Adding label to x-axis\n",
        "plt.ylabel('Inertia')  # Adding label to y-axis\n",
        "plt.title('Elbow Method')  # Adding title to the plot\n",
        "plt.xticks(range(1, 11))  # Setting x-axis ticks\n",
        "plt.grid(True)  # Adding grid lines"
      ],
      "metadata": {
        "id": "4AE6KM-t-Fmc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "611eec5d-7752-4ac4-9808-4287e8ecc51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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7dho9evRpX+Oxxx7TDz/8oK+++kq//fab9u7dqxtvvNFxvri4WD169FBBQYEWL16sjz76SFOmTNHQoUMdY1JTU9WjRw916NBBq1at0uDBg3XPPfdo5syZZfdhXZxpmpqyKFVtR8/V2j+5nwwAAAAoD5Zesti9e3d17979tOfvuOMOSdKOHTtOeT47O1sffPCBpk2bpquvvlqSNHnyZNWvX19LlixR69atNWvWLG3YsEGzZ89WWFiYmjZtqhdffFFPP/20hg8fLi8vL02cOFExMTF67bXXJEn169fXwoUL9cYbb6hr165l+6Fd2OJtB3Q4v0iDpq3Uj4+0UyCXMQIAAAAXlVvfQ7ZixQoVFhaqU6dOjmNxcXGqWbOmkpOT1bp1ayUnJ6tRo0YKCwtzjOnataseeOABrV+/Xs2aNVNycnKp1zg+ZvDgwad97/z8fOXn5zse5+TkSJIKCwtVWGj9ZX/HazifWl7pFa8Ne7O1KzNXT365SuP6NpFRwVb7cKZvoG/Oom/Oo3fOoW/OoW/OoW/Oo3fOcaW+nU8Nbh3I0tPT5eXlpeDg4FLHw8LClJ6e7hhzYhg7fv74uTONycnJ0dGjR+Xr63vSe48aNUojRow46fisWbPk5+fn9Gcqa0lJSec1/uYa0thsu2Zu2KdnP5yhKyIq5qbR59s3HEPfnEPfnEfvnEPfnEPfnEPfnEfvnOMKfcvNzT3nsW4dyKz07LPPasiQIY7HOTk5ioqKUpcuXRQYGGhhZccUFhYqKSlJnTt3lqfn+V166Ju8Uy//vFnf7/ZQv26t1Kh60EWq0vVcSN8qMvrmHPrmPHrnHPrmHPrmHPrmPHrnHFfq2/Gr586FWwey8PBwFRQUKCsrq9QsWUZGhsLDwx1jli5dWup5x1dhPHHMP1dmzMjIUGBg4ClnxyTJ29tb3t7eJx339PS0/BvgRM7Uc0/7Olq+M0sz12fo0S/X6OdH2le4ZfFd7f9Hd0HfnEPfnEfvnEPfnEPfnEPfnEfvnOMKfTuf97d0lcUL1aJFC3l6emrOnDmOY5s3b9auXbuUmJgoSUpMTNTatWu1b98+x5ikpCQFBgYqPj7eMebE1zg+5vhrVDSGYWjMTU0UU9Vf/RKi5e/l1rkdAAAAcFmW/qR9+PBhbd261fE4NTVVq1atUkhIiGrWrKnMzEzt2rVLe/fulXQsbEnHZrTCw8MVFBSkgQMHasiQIQoJCVFgYKAefvhhJSYmqnXr1pKkLl26KD4+XnfccYfGjBmj9PR0Pf/88xo0aJBjhuv+++/X22+/raeeekoDBgzQ3Llz9eWXX+qnn34q5464jiBfT/3yaHv5eNqtLgUAAAC4ZFk6Q7Z8+XI1a9ZMzZo1kyQNGTJEzZo1c+wR9v3336tZs2bq0aOHJKlv375q1qyZJk6c6HiNN954Q9dee6169+6tK664QuHh4fr2228d5+12u3788UfZ7XYlJibq9ttv15133qmRI0c6xsTExOinn35SUlKSmjRpotdee03vv/9+hVry/lRODGO5BUXa9tdhC6sBAAAALj2WzpBdddVVMs3Tr+J311136a677jrja/j4+Gj8+PGn3VxakqKjo/Xzzz+ftZbff//9jGMqql0HcjXwo2XKLSjWz4+0V5Af1zIDAAAAZcGt7yFD+Qj291R+UYn2ZB3VE1+vPmOIBgAAAHDuCGQ4q0AfT42/rbm87DYlbcjQh4t2WF0SAAAAcEkgkOGcNKoRpOevrS9J+s8vG7Vqd5a1BQEAAACXAAIZztkdraPVo1GECotNDZq6Ulm5BVaXBAAAALg1AhnOmWEYGtW7kaKr+GlP1lG99NNGq0sCAAAA3BqBDOfl+P1k7etW1ZNd61ldDgAAAODWLF32Hu6pYfUgfTIwweoyAAAAALfHDBkuWNKGDO4nAwAAAJxAIMMFmfDrNt378XI9/iX7kwEAAADni0CGC3LFZVXl5WHTnE379N6C7VaXAwAAALgVAhkuSIPIIA3rGS9JGj1js1bszLS4IgAAAMB9EMhwwW5rVVM9m0SquMTUw9N+18Ej3E8GAAAAnAsCGS6YYRh65YaGiqnqr73ZeXr8q9UqKeF+MgAAAOBsCGQoEwE+nnr7tmby8rBp7qZ9WrztgNUlAQAAAC6PfchQZhpEBunlXg1VydtD7epWtbocAAAAwOURyFCm+rSMsroEAAAAwG1wySIumvTsPL06cxP3kwEAAACnwQwZLor8omL1nrBYe7KOqpK3px64qo7VJQEAAAAuhxkyXBTeHnY9fHWsJOm/szZr2Q72JwMAAAD+iUCGi+aWy6PUq+nf+5MdOJxvdUkAAACASyGQ4aIxDEMv39BItav5Kz0nT0O+ZH8yAAAA4EQEMlxU/t4eeqdfc/l42vTbH39p4vxtVpcEAAAAuAwCGS66uPBAjbyuoSTp+1V7VVBUYnFFAAAAgGtglUWUiz4ta8iUqZ5NIuXlwe8BAAAAAIlAhnJiGIZuubym1WUAAAAALoVAhnJXUmLq3fnbJYn9yQAAAFChEchQ7uZv+UujZ2ySzZCa1QxW69pVrC4JAAAAsAQ386DcXXlZNd3YvLpKTOmRz37XfvYnAwAAQAVFIEO5MwxDL/VqqNjQStp3KF+PfbGK/ckAAABQIRHIYAk/r7/3J1uwZb/Gz9tqdUkAAABAuSOQwTKXhQXoxeuP7U/2xuw/lLztgMUVAQAAAOWLQAZL9WkZpZta1JCHzaY/D+ZaXQ4AAABQrlhlEZYbeX0D3dWmlhpWD7K6FAAAAKBcMUMGy/l5eZQKY0XFJRZWAwAAAJQfAhlcyro92ery5nwt3rbf6lIAAACAi45ABpcyNWWXtv91RI98tkr7DuVZXQ4AAABwURHI4FKGXhuvemEB2n84X4M/X6Vi9icDAADAJYxABpfi62XX+H7N5edl1+JtBzRu7harSwIAAAAuGgIZXE5saCW9fMOx/cnGztmiRVu5nwwAAACXJgIZXNINzWrolpZRMk3p0c+5nwwAAACXJvYhg8sacX0Drf4zS6GBPrIbhtXlAAAAAGWOQAaX5eNp16f3JCjEz0s2G4EMAAAAlx4uWYRLq1rJu1QY++tQvoXVAAAAAGWLQAa3kFdYrKe+Xq3uY+drXw73kwEAAODSQCCD21jzZ7b2Hy7QI5//rqLiEqvLAQAAAC4YgQxuwcfz2P5k/l52LdmeqbfmsD8ZAAAA3B+BDG6jTrVKeuXGRpKkcfO2asGWvyyuCAAAALgwBDK4leubVtdtCTVlmtLgz1cpg/vJAAAA4MYIZHA7Q6+NV/2IQB04UqBHP/9dpmlaXRIAAADgFAIZ3I6Pp13v9GuuqBBf/evKOjLYNBoAAABuio2h4ZZiqvpr3uNXycPO7xQAAADgvvhpFm7rxDC2OzNX6dncTwYAAAD3QiCD21u4Zb96vLVAj3zG/mQAAABwL5YGsvnz56tnz56KjIyUYRiaPn16qfOmaWro0KGKiIiQr6+vOnXqpC1bSu8/lZmZqX79+ikwMFDBwcEaOHCgDh8+XGrMmjVr1L59e/n4+CgqKkpjxow5qZavvvpKcXFx8vHxUaNGjfTzzz+X+efFxVG9sq9KTGnpjky9nvSH1eUAAAAA58zSQHbkyBE1adJE48ePP+X5MWPG6K233tLEiROVkpIif39/de3aVXl5f1+a1q9fP61fv15JSUn68ccfNX/+fN13332O8zk5OerSpYuio6O1YsUKvfrqqxo+fLgmTZrkGLN48WLdeuutGjhwoH7//Xf16tVLvXr10rp16y7eh0eZianqr//0PrY/2Tu/btOvm/dZXBEAAABwbiwNZN27d9dLL72kG2644aRzpmnqzTff1PPPP6/rr79ejRs31scff6y9e/c6ZtI2btyoGTNm6P3331dCQoLatWuncePG6fPPP9fevXslSVOnTlVBQYE+/PBDNWjQQH379tUjjzyi119/3fFeY8eOVbdu3fTkk0+qfv36evHFF9W8eXO9/fbb5dIHXLhrG0fqjtbRkqQhX65WWvZRiysCAAAAzs5lV1lMTU1Venq6OnXq5DgWFBSkhIQEJScnq2/fvkpOTlZwcLBatmzpGNOpUyfZbDalpKTohhtuUHJysq644gp5eXk5xnTt2lWjR4/WwYMHVblyZSUnJ2vIkCGl3r9r164nXUJ5ovz8fOXn5zse5+TkSJIKCwtVWFh4oR//gh2vwRVqKS9Pd4nVip2Z2pB2SA9PW6lP7m553qswVsS+lQX65hz65jx65xz65hz65hz65jx65xxX6tv51OCygSw9PV2SFBYWVup4WFiY41x6erpCQ0NLnffw8FBISEipMTExMSe9xvFzlStXVnp6+hnf51RGjRqlESNGnHR81qxZ8vPzO5ePWC6SkpKsLqFc3Rgmbdtn1/KdWXpuykxdFeHcptEVrW9lhb45h745j945h745h745h745j945xxX6lpube85jXTaQubpnn3221KxaTk6OoqKi1KVLFwUGBlpY2TGFhYVKSkpS586d5enpaXU55Sq0XrpSUg/q2W6XydvTfl7Prch9uxD0zTn0zXn0zjn0zTn0zTn0zXn0zjmu1LfjV8+dC5cNZOHh4ZKkjIwMRUREOI5nZGSoadOmjjH79pVewKGoqEiZmZmO54eHhysjI6PUmOOPzzbm+PlT8fb2lre390nHPT09Lf8GOJGr1VMermsWpeuaRV3Qa1TEvpUF+uYc+uY8eucc+uYc+uYc+uY8euccV+jb+by/y+5DFhMTo/DwcM2ZM8dxLCcnRykpKUpMTJQkJSYmKisrSytWrHCMmTt3rkpKSpSQkOAYM3/+/FLXcSYlJalevXqqXLmyY8yJ73N8zPH3gfsqLjE1NWWnCtmfDAAAAC7I0kB2+PBhrVq1SqtWrZJ0bCGPVatWadeuXTIMQ4MHD9ZLL72k77//XmvXrtWdd96pyMhI9erVS5JUv359devWTffee6+WLl2qRYsW6aGHHlLfvn0VGRkpSbrtttvk5eWlgQMHav369friiy80duzYUpcbPvroo5oxY4Zee+01bdq0ScOHD9fy5cv10EMPlXdLUMYenLpC//5unf47a7PVpQAAAAAnsTSQLV++XM2aNVOzZs0kSUOGDFGzZs00dOhQSdJTTz2lhx9+WPfdd58uv/xyHT58WDNmzJCPj4/jNaZOnaq4uDh17NhR11xzjdq1a1dqj7GgoCDNmjVLqampatGihR5//HENHTq01F5lbdq00bRp0zRp0iQ1adJEX3/9taZPn66GDRuWUydwsfRqWl2S9O5v2zVnY8ZZRgMAAADly9J7yK666iqZ5ulXwTMMQyNHjtTIkSNPOyYkJETTpk074/s0btxYCxYsOOOYPn36qE+fPmcuGG6ne6MI3dWmlqYs3qHHv1qtnx5pr+rBvlaXBQAAAEhy4XvIgLLy7DVxalwjSFm5hXp42kruJwMAAIDLIJDhkuftYdfbtzZXgI+HVu7K0qszuZ8MAAAAroFAhgqhZhU/vXpTE0nSJ8k7lZGTZ3FFAAAAgAvvQwaUtW4Nw/XcNXG68rJQhQX6nP0JAAAAwEVGIEOFct8VdawuAQAAAHDgkkVUWCt2HtSEX7dZXQYAAAAqMGbIUCHtOpCrW95NVlGJqdjQSuocH2Z1SQAAAKiAmCFDhVSzip/6t6klSXriq9X682CutQUBAACgQiKQocJ6ulucmkQFK/tooQZN+11HC4qVkpqpFfsNpaRmqrjk9JuWAwAAAGWBSxZRYXl52DT+tma6ZuwCrd6dpZYvJelIQbEkuz7eslwRQT4a1jNe3RpGWF0qAAAALlHMkKFCq1HZT/0SoiXpf2Hsb+nZeXrg05WasS7NitIAAABQARDIUKEVl5iavmrPKc8dv2BxxA8buHwRAAAAFwWBDBXa0tRMpWXnnfa8KSktO09LUzPLrygAAABUGAQyVGj7Dp0+jDkzDgAAADgfBDJUaKEBPmU6DgAAADgfBDJUaK1iQhQR5CPjDGNCA7zVKiak3GoCAABAxUEgQ4Vmtxka1jNekk4byoqKTe3NOlp+RQEAAKDCIJChwuvWMEITbm+u8KDSlyWGBngrNMBbmbkFuvndZKXuP2JRhQAAALhUsTE0oGOhrHN8uJK37tOsBSnq0j5BibGhOnA4X7e+t0Tb/jqiW95N1rR7Wys2tJLV5QIAAOASwQwZ8D92m6GEmBC1qGoqISZEdpuh0EAffX5fouqFBehwfpGyjxZYXSYAAAAuIcyQAWdRLcBbn93XWqn7j6hFdGWrywEAAMAlhEAGnIMQfy+F+Hs5Hq/bk60S01TjGsHWFQUAAAC3xyWLwHnauu+w7vggRf3eS9GKnQetLgcAAABujEAGnKfwIB/VDQvQofwi3flBipamZlpdEgAAANwUgQw4T5W8PTTl7svVpk4VHSkoVv8Pl2rxtv1WlwUAAAA3RCADnODn5aEP77pcV1xWTUcLi3X35GWa/8dfVpcFAAAAN0MgA5zk42nXpDtaqGNcqPKLSnTPR8u1bAeXLwIAAODcEciAC+DjadeE21uoa4MwNY0KVoPIQKtLAgAAgBth2XvgAnl52PT2bc1VUFQiPy/+kwIAAMC5Y4YMKAOedpv8vf8OY2Nnb9H03/dYWBEAAADcAb/OB8rY3E0ZemP2HzIMqbC4RH1aRlldEgAAAFwUM2RAGbvqslD1S6gp05Se/HqNpqXssrokAAAAuCgCGVDGbDZDL/VqqLva1JIkPffdWn2cvMPSmgAAAOCaCGTARWAYhob1jNd9V9SWJA39v/V6f8F2i6sCAACAqyGQAReJYRh6tnucBnWoI0l66aeNWr832+KqAAAA4EpY1AO4iAzD0BNd6snLblewn6caRAZZXRIAAABcCIEMuMgMw9CjneqWOnYkv0h+XnYZhmFRVQAAAHAFXLIIlLOs3AL1mZisV37eKNM0rS4HAAAAFmKGDChnC7bs14a0HG1Iy1FhsalhPeOZKQMAAKigmCEDylnPJpF65YZGMgxpyuId+vf0dSopYaYMAACgIiKQARa4LaGmxvRuLMOQpqXs0tPfrFExoQwAAKDCIZABFunTMkpv3NxUNkP6asWfevzLVSoqLrG6LAAAAJQjAhlgoV7Nqmvcrc1ltxlKSc3UgSMFVpcEAACAcsSiHoDFejSOkLeHTXVCKyks0MfqcgAAAFCOmCEDXECn+DDFVPV3PF6xM1N5hcUWVgQAAIDyQCADXMy8zft066QU3fvxckIZAADAJY5ABrgYbw+b7DZDC7bs192Tlym3oMjqkgAAAHCREMgAF9OmTlV9NKCV/L3sSt5+QHd9uEyH8wllAAAAlyICGeCCWsWE6JN7EhTg46GlOzJ15wcpyskrtLosAAAAlDECGeCimtesrKn3JCjI11Mrd2Xp9vdTdIhQBgAAcEkhkAEurHGNYE27N0Eh/l6KrVZJ/l7sVAEAAHAp4ac7wMU1iAzS/w1qq4ggH9lshtXlAAAAoAwxQwa4gagQP3nYj/3nWlxiavSMTcrIybO4KgAAAFwolw9khw4d0uDBgxUdHS1fX1+1adNGy5Ytc5w3TVNDhw5VRESEfH191alTJ23ZsqXUa2RmZqpfv34KDAxUcHCwBg4cqMOHD5cas2bNGrVv314+Pj6KiorSmDFjyuXzAedrzIxNmvDrNt3ybrL2Zh21uhwAAABcAJcPZPfcc4+SkpL0ySefaO3aterSpYs6deqkPXv2SJLGjBmjt956SxMnTlRKSor8/f3VtWtX5eX9PXvQr18/rV+/XklJSfrxxx81f/583XfffY7zOTk56tKli6Kjo7VixQq9+uqrGj58uCZNmlTunxc4m9tbR6tGZV/tOJCrWyYla3dmrtUlAQAAwEkuHciOHj2qb775RmPGjNEVV1yh2NhYDR8+XLGxsZowYYJM09Sbb76p559/Xtdff70aN26sjz/+WHv37tX06dMlSRs3btSMGTP0/vvvKyEhQe3atdO4ceP0+eefa+/evZKkqVOnqqCgQB9++KEaNGigvn376pFHHtHrr79u4acHTi0qxE9f/CtR0VX8tDvzqPpOWqKdB45YXRYAAACc4NKLehQVFam4uFg+Pj6ljvv6+mrhwoVKTU1Venq6OnXq5DgXFBSkhIQEJScnq2/fvkpOTlZwcLBatmzpGNOpUyfZbDalpKTohhtuUHJysq644gp5eXk5xnTt2lWjR4/WwYMHVbly5ZNqy8/PV35+vuNxTk6OJKmwsFCFhdYvTX68BleoxZ24S99C/T306YCW6j95ubbvz9XN7ybr47taqnY1f0vqcZe+uRr65jx65xz65hz65hz65jx65xxX6tv51ODSgSwgIECJiYl68cUXVb9+fYWFhemzzz5TcnKyYmNjlZ6eLkkKCwsr9bywsDDHufT0dIWGhpY67+HhoZCQkFJjYmJiTnqN4+dOFchGjRqlESNGnHR81qxZ8vPzc/ITl72kpCSrS3BL7tK3u6Ol8UfsSs/J163vLtS/mxbLw8J5b3fpm6uhb86jd86hb86hb86hb86jd85xhb7l5p77LSUuHcgk6ZNPPtGAAQNUvXp12e12NW/eXLfeeqtWrFhhaV3PPvushgwZ4nick5OjqKgodenSRYGBgRZWdkxhYaGSkpLUuXNneXp6Wl2O23DHvnXuXKD7Plmph6+uo6suq2ZJDe7YN1dA35xH75xD35xD35xD35xH75zjSn07fvXcuXD5QFanTh399ttvOnLkiHJychQREaFbbrlFtWvXVnh4uCQpIyNDERERjudkZGSoadOmkqTw8HDt27ev1GsWFRUpMzPT8fzw8HBlZGSUGnP88fEx/+Tt7S1vb++Tjnt6elr+DXAiV6vHXbhT38KDPTV9ULtSe5QVl5iyW7BnmTv1zZXQN+fRO+fQN+fQN+fQN+fRO+e4Qt/O5/1delGPE/n7+ysiIkIHDx7UzJkzdf311ysmJkbh4eGaM2eOY1xOTo5SUlKUmJgoSUpMTFRWVlapGbW5c+eqpKRECQkJjjHz588vda1nUlKS6tWrd8rLFQFXc2IYS91/RN3enK9Vu7OsKwgAAADnxOUD2cyZMzVjxgylpqYqKSlJHTp0UFxcnO6++24ZhqHBgwfrpZde0vfff6+1a9fqzjvvVGRkpHr16iVJql+/vrp166Z7771XS5cu1aJFi/TQQw+pb9++ioyMlCTddttt8vLy0sCBA7V+/Xp98cUXGjt2bKlLEgF38dqszdqy77Bufz9Fy3dkWl0OAAAAzsDlA1l2drYGDRqkuLg43XnnnWrXrp1mzpzpmAZ86qmn9PDDD+u+++7T5ZdfrsOHD2vGjBmlVmacOnWq4uLi1LFjR11zzTVq165dqT3GgoKCNGvWLKWmpqpFixZ6/PHHNXTo0FJ7lQHuYnTvxmpdO0SH84t054dLtWT7AatLAgAAwGm4/D1kN998s26++ebTnjcMQyNHjtTIkSNPOyYkJETTpk074/s0btxYCxYscLpOwFX4e3to8l2tdO/Hy7Vw637dNXmpPuh/udrGVrW6NAAAAPyDy8+QATh/vl52vd+/pa6qV015hSUaMGWZft287+xPBAAAQLkikAGXKB9Pu969o4U61Q9TflGJxs3dKtM0rS4LAAAAJ3D5SxYBOM/bw653+jXXG7P/0L+uqC3DKP+l8AEAAHB6zJABlzgvD5ue7hanYD8vx7Htfx22sCIAAAAcRyADKpiPFu9Qp9d/07cr/7S6FAAAgAqPQAZUIKZpalP6IZWY0uNfrdaXy3ZbXRIAAECFRiADKhDDMPRyr4a6o3W0TFN66ps1+nTJTqvLAgAAqLAIZEAFY7MZGnl9Aw1oGyNJen76Ok1elGpxVQAAABUTgQyogAzD0AvX1tf9V9aRJI34YYPe/W2bxVUBAABUPAQyoIIyDENPd6unR66OlSQVlbBHGQAAQHljHzKgAjMMQ0O61FOb2KpqXbuK1eUAAABUOMyQASgVxg7nF+mTJTtlmsyYAQAAXGzMkAFwKC4xNWDKMi1NzVTqX0f0wrX1ZRiG1WUBAABcspghA+Bgtxnq2SRSkvTholQN/b/1KuHeMgAAgIuGQAaglDtaR2t070YyDOmTJTv13HdrCWUAAAAXCYEMwEluubymXuvTRDZD+nzZbj359RoVE8oAAADKHIEMwCnd2LyG3uzbTHaboW9W/qkRP6y3uiQAAIBLDot6ADit65pEytNmaOj363VbQk2rywEAALjkEMgAnFH3RhG6sl41+Xnx1wUAAEBZ45JFAGd1YhhL2X5AD3y6QnmFxRZWBAAAcGkgkAE4Z0cLijVo2kr9si5d93y0XEcLilVcYiolNVMr9htKSc1k8Q8AAIDzwDVIAM6Zr5ddb9/WXAOmLNPCrft1/fiFyj5aqIycfEl2fbxluSKCfDSsZ7y6NYywulwAAACXxwwZgPPSunYVfTyglXw8bPoj4/D/wtjf0rPz9MCnKzVjXZpFFQIAALgPAhmA89asZmX5e596gv34BYsjftjA5YsAAABnQSADcN6WpmbqwJGC0543JaVl52lpamb5FQUAAOCGCGQAztu+Q3llOg4AAKCiIpABOG+hAT5lOg4AAKCiIpABOG+tYkIUEeQj4wxjPGyGAn1ZyBUAAOBMCGQAzpvdZmhYz3hJOm0oKyoxdcM7i/XJkp0yTRb3AAAAOBUCGQCndGsYoQm3N1d4UOnLEiOCfPTqTY3VoV41FRSV6IXp63T/pyuUnVtoUaUAAACui+uJADitW8MIdY4PV/LWfZq1IEVd2icoMTZUdpuhm1rU0AcLUzV6xibNXJ+hdXsWaMLtzdW4RrDVZQMAALgMZsgAXBC7zVBCTIhaVDWVEBMiu+3YRYyGYeie9rX17QNtVauKnw7mFqjSafYuAwAAqKj46QjARdWoRpB+fKS9NuzNUe1qlRzHjxYUy9fLbmFlAAAA1mOGDMBFV8nbQ61iQhyPF2/br/Zj5mnupgwLqwIAALAegQxAuXt/Qar2H87XgCnL9eKPG5RfVGx1SQAAAJYgkAEodxNub6672tSSJH2wMFW9JyxW6v4j1hYFAABgAQIZgHLn7WHX8Osa6L07WyrYz1Pr9uTo2rcW6Lvf/7S6NAAAgHLl9KIey5cv15dffqldu3apoKCg1Llvv/32ggsDcOnrHB+mXx5tr0c/X6WlqZl67IvVigjyVevaVawuDQAAoFw4NUP2+eefq02bNtq4caO+++47FRYWav369Zo7d66CgoLKukYAl7CIIF99dm9rDe5UVzc0q66EExb/AAAAuNQ5FcheeeUVvfHGG/rhhx/k5eWlsWPHatOmTbr55ptVs2bNsq4RwCXObjM0uNNlev3mJjKMY/uYZeUWaGrKTpmmaXF1AAAAF49TgWzbtm3q0aOHJMnLy0tHjhyRYRh67LHHNGnSpDItEEDFcTyMmaapp79Zo39/t073frxcmUcKzvJMAAAA9+RUIKtcubIOHTokSapevbrWrVsnScrKylJubm7ZVQegwmpTp6q87DbN3rhP3cfO15LtB6wuCQAAoMw5FciuuOIKJSUlSZL69OmjRx99VPfee69uvfVWdezYsUwLBFDxGIah/m1q6btBbVS7mr8ycvJ123tL9HrSHyoqLrG6PAAAgDLj1CqLb7/9tvLy8iRJ//73v+Xp6anFixerd+/eev7558u0QAAVV4PIIP34cDsN+7/1+mrFn3przhYt2XZAb9/WTKGBPlaXBwAAcMGcCmQhIX+vgmaz2fTMM8+UWUEAcCI/Lw+92qeJ2tWtqn9/t057so7K28NudVkAAABl4pwDWU5OjgIDAx1/PpPj4wCgrFzftLqaRgUr+2ihgvw8JR1b/KOw2JSXB3vcAwAA93TOgaxy5cpKS0tTaGiogoODHauhncg0TRmGoeLi4jItEgAkKbqKf6nHXyzbrY+Sd2rcrc0UG1rJoqoAAACcd86BbO7cuY5LFefNm3fRCgKAc1FQVKK3523VnwePque4hRpxfQP1aVHjlL8sAgAAcFXnHMiuvPJKx59jYmIUFRV10g8+pmlq9+7dZVcdAJyGl4dN3z7QRo99uUqLth7QU1+v0cIt+/XyDQ0V4ONpdXkAAADnxKkbL2JiYvTXX3+ddDwzM1MxMTEXXBQAnIvQQB99MiBBT3atJ7vN0Per96rHWwu1eneW1aUBAACcE6cC2fF7xf7p8OHD8vFhKWoA5cdmMzSoQ6y+/FdrVQ/21a7MXN38brL25eRZXRoAAMBZndey90OGDJF0bNPWF154QX5+fo5zxcXFSklJUdOmTcu0QAA4Fy2iQ/Tzo+31zDdrVDcsgH3KAACAWzivQPb7779LOjZDtnbtWnl5eTnOeXl5qUmTJnriiSfKtkIAOEdBvp56p19zmebfx7b/dVh7s/LUrm5V6woDAAA4jfO6ZHHevHmaN2+e+vfvrxkzZjgez5s3TzNnztS7776runXrlllxxcXFeuGFFxQTEyNfX1/VqVNHL774oswTftoyTVNDhw5VRESEfH191alTJ23ZsqXU62RmZqpfv34KDAxUcHCwBg4cqMOHD5cas2bNGrVv314+Pj6KiorSmDFjyuxzACg/hmHIZjt2SXV+UbEe/ux33fFhisbM2KTC4hKLqwMAACjtvO8hKyws1CeffKKdO3dejHpKGT16tCZMmKC3335bGzdu1OjRozVmzBiNGzfOMWbMmDF66623NHHiRKWkpMjf319du3ZVXt7f94/069dP69evV1JSkn788UfNnz9f9913n+N8Tk6OunTpoujoaK1YsUKvvvqqhg8frkmTJl30zwjg4jFNqUlUsExTeufXbbr53WTtzsy1uiwAAACH8w5knp6eqlmzZrls/rx48WJdf/316tGjh2rVqqWbbrpJXbp00dKlSyUdmx1788039fzzz+v6669X48aN9fHHH2vv3r2aPn26JGnjxo2aMWOG3n//fSUkJKhdu3YaN26cPv/8c+3du1eSNHXqVBUUFOjDDz9UgwYN1LdvXz3yyCN6/fXXL/pnBHDx+Hja9coNjTT+tuYK8PHQ77uydM1bC/TTmjSrSwMAAJB0nveQHffvf/9bzz33nD755BPHZtEXQ5s2bTRp0iT98ccfuuyyy7R69WotXLjQEZRSU1OVnp6uTp06OZ4TFBSkhIQEJScnq2/fvkpOTlZwcLBatmzpGNOpUyfZbDalpKTohhtuUHJysq644opS98R17dpVo0eP1sGDB1W5cuWTasvPz1d+fr7jcU5OjqRjM4iFhYVl3ovzdbwGV6jFndA357h637rUr6r4BxP12FdrtGp3tgZNW6n5f9TQC9fUk7en3bK6XL1vrozeOYe+OYe+OYe+OY/eOceV+nY+NTgVyN5++21t3bpVkZGRio6Olr+/f6nzK1eudOZlT/LMM88oJydHcXFxstvtKi4u1ssvv6x+/fpJktLT0yVJYWFhpZ4XFhbmOJeenq7Q0NBS5z08PBQSElJqzD/3Tzv+munp6acMZKNGjdKIESNOOj5r1qxSq09aLSkpyeoS3BJ9c46r9+3OSKlqiU1z9hhK2bRbs+w7ZHdq84+y5ep9c2X0zjn0zTn0zTn0zXn0zjmu0Lfc3HO/RcKpQNarVy9nnnbevvzyS02dOlXTpk1TgwYNtGrVKg0ePFiRkZHq379/udRwOs8++6xjGwDp2AxZVFSUunTposDAQAsrO6awsFBJSUnq3LmzPD09rS7HbdA357hT33pKWrTtgGqG+Cqq8rFfnhSXmLIZOuX+iheTO/XN1dA759A359A359A359E757hS345fPXcunApkw4YNc+Zp5+3JJ5/UM888o759+0qSGjVqpJ07d2rUqFHq37+/wsPDJUkZGRmKiIhwPC8jI8OxH1p4eLj27dtX6nWLioqUmZnpeH54eLgyMjJKjTn++PiYf/L29pa3t/dJxz09PS3/BjiRq9XjLuibc9ylb1fFlf7v+r8/b9Tug7kadWNjBfmWf/3u0jdXRO+cQ9+cQ9+cQ9+cR++c4wp9O5/3d/pinaysLL3//vt69tlnlZmZKenYpYp79uxx9iVPkpubK5utdIl2u10lJceWro6JiVF4eLjmzJnjOJ+Tk6OUlBQlJiZKkhITE5WVlaUVK1Y4xsydO1clJSVKSEhwjJk/f36paz2TkpJUr169U16uCODSsTfrqCYv2qGf16brmrELtGLnQatLAgAAFYhTgWzNmjW67LLLNHr0aP33v/9VVlaWJOnbb7/Vs88+W2bF9ezZUy+//LJ++ukn7dixQ999951ef/113XDDDZKOXV40ePBgvfTSS/r++++1du1a3XnnnYqMjHRcVlm/fn1169ZN9957r5YuXapFixbpoYceUt++fRUZGSlJuu222+Tl5aWBAwdq/fr1+uKLLzR27NhSlyQCuDRFBvvqq/sTVTPET3uyjurmd5M1ft5WFZeYZ38yAADABXIqkA0ZMkR33XWXtmzZIh8fH8fxa665RvPnzy+z4saNG6ebbrpJDz74oOrXr68nnnhC//rXv/Tiiy86xjz11FN6+OGHdd999+nyyy/X4cOHNWPGjFJ1TZ06VXFxcerYsaOuueYatWvXrtQeY0FBQZo1a5ZSU1PVokULPf744xo6dGipvcoAXLqaRAXrp0fa6bomkSouMfXqzM2688MU7cvJO/uTAQAALoBT95AtW7ZM77777knHq1ev7li5sCwEBATozTff1JtvvnnaMYZhaOTIkRo5cuRpx4SEhGjatGlnfK/GjRtrwYIFzpYKwM0F+HhqbN+male3qob933ot2npAfd5N1uwhV8rTFZZiBAAAlySnfsrw9vY+5cohf/zxh6pVq3bBRQGAFQzD0M0to/TDw+0UFx6gxzpdRhgDAAAXlVM/aVx33XUaOXKkYxEMwzC0a9cuPf300+rdu3eZFggA5S02tJJ+eLidejWr7ji2ctdB7dh/xMKqAADApcipQPbaa6/p8OHDCg0N1dGjR3XllVcqNjZWAQEBevnll8u6RgAodyfOjB04nK/7P1mha8ct1P+tKruVZAEAAJy6hywoKEhJSUlauHCh1qxZo8OHD6t58+bq1KlTWdcHAJYrKjFVq4q/lu7I1KOfr9LCLfs14voG8vNy6q9QAAAAhwv6aaJdu3Zq165dWdUCAC4pLNBH0+5N0Ftzt+rtuVv01Yo/tWLXQY27tZkaRAZZXR4AAHBjTgeyOXPmaM6cOdq3b59jo+bjPvzwwwsuDABciYfdpiGdL1Ni7Soa/MXv2v7XEd0wfrH+3aO+7kyMlmEYVpcIAADckFP3kI0YMUJdunTRnDlztH//fh08eLDUFwBcqhLrVNEvj16hjnGhKigu0Yqd/J0HAACc59QM2cSJEzVlyhTdcccdZV0PALi8EH8vvd+/pb5Ytls9Gkc4ZsdM02SmDAAAnBenZsgKCgrUpk2bsq4FANyGYRjq26qmAnw8JR0LYw9OXak3Z/+h4hLT4uoAAIC7cCqQ3XPPPZo2bVpZ1wIAbmvR1gP6ZV263py9Rbe9t0Rp2UetLgkAALgBpy5ZzMvL06RJkzR79mw1btxYnp6epc6//vrrZVIcALiLdnWr6o1bmuj579YpJTVT3ccu0H9vaqJO8WFWlwYAAFyYU4FszZo1atq0qSRp3bp1ZVkPALitG5rVUNOoynr4s5VatydH93y8XHe1qaVnr4mTt4fd6vIAAIALciqQzZs3r6zrAIBLQkxVf33zQBuNmbFZHyxM1ZTFO7Qn66jeu7Ol1aUBAAAXdF6B7MYbbzzrGMMw9M033zhdEAC4O28Pu164Nl5tY6vo6W/W6v4r65w0prjEVEpqplbsN1QlNVOJsaGy21ihEQCAiua8AllQUNDFqgMALjlXx4VpwVNV5eP59+WKi7fuV8ahfI2ZsUlp2XmS7Pp4y3JFBPloWM94dWsYYV3BAACg3J1XIJs8efLFqgMALkknhrEtGYfUf/JSFRafvCx+enaeHvh0pSbc3pxQBgBABeLUsvcAgPN3KK/otHuUHT864ocN7GMGAEAFQiADgHKSX1SiM2UtU1Jadp6WpmaWW00AAMBaBDIAKCf7DuWV6TgAAOD+CGQAUE5CA3zKdBwAAHB/BDIAKCetYkIUEeSj0y1ub0iKCPJRq5iQ8iwLAABYiEAGAOXEbjM0rGe8JJ0Uyo4/fu6aOL02a7MO5RWWa20AAMAaBDIAKEfdGkZowu3NFR5U+rLE8CAfTbi9uZZsz9Q7v27TDe8sVur+IxZVCQAAyst57UMGALhw3RpGqHN8uJK37tOsBSnq0j5BibGhstsMhQf5avbGDG3dd1jXv71Qb93aTFfVC7W6ZAAAcJEwQwYAFrDbDCXEhKhFVVMJMSGy245dtNg0Klg/PNROzWsGKyevSAOmLNO7v22TabI3GQAAlyICGQC4mNBAH312X2vd0jJKJaY06pdNevTzVTpaUGx1aQAAoIwRyADABXl72PWf3o008voG8rAZ+nXzPv11KN/qsgAAQBnjHjIAcFGGYejOxFq6LCxARcWmalbxs7okAABQxghkAODiWteuUurxb3/8pV2Zubo9oaYM43S7mgEAAHdAIAMAN5KRk6eHp61UTl6RNuzN1ojrGsrLg6vPAQBwV/wrDgBuJDTAW/dfVUeGIX22dLdufW+J9h3Ks7osAADgJAIZALgRwzD04FWx+rD/5Qrw8dCKnQd13bhFWvNnltWlAQAAJxDIAMANdYgL1f8Naqs61fyVnpOnmyYm69uVf1pdFgAAOE8EMgBwU7WrVdJ3g9qqY1yoCopKtGT7AatLAgAA54lFPQDAjQX6eOq9O1tq2tJd6tOyhtXlAACA88QMGQC4OZvN0O2to+XtYZckFZeYeu67tdqcfsjiygAAwNkQyADgEvPu/G2alrJLN7yzSDPWpVtdDgAAOAMCGQBcYvpeXlOJtasot6BY93+6Qm8k/aGSEtPqsgAAwCkQyADgEhPi76WPB7bS3W1rSZLGztmi+z9docP5RdYWBgAATkIgA4BLkKfdpmE9G+jVmxrLy27TrA0ZumH8Iu3Yf8Tq0gAAwAkIZABwCevTMkpf/Ku1QgO8tftgLrNkAAC4GJa9B4BLXLOalfXjw+20Mf2QGlYPsrocAABwAmbIAKACCA300ZWXVXM8/n3XQT351WrlFRZbWBUAAGCGDAAqmPyiYj007XftyTqqTemH9O4dLRQZ7Gt1WQAAVEjMkAFABePtYderfRqrsp+n1u7J1nVvL9SyHZlWlwUAQIVEIAOACqhNnar6/qF2igsP0P7DBbrtvSWamrLT6rIAAKhwCGQAUEFFhfjp2wfbqEejCBUWm/r3d+v03HdrVVhcYnVpAABUGAQyAKjA/Lw89PZtzfRk13oyDGn/oXzZDcPqsgAAqDBY1AMAKjjDMDSoQ6ya1AhW05rBstkIZAAAlBdmyAAAkqR2dauqkvex39OZpqlnvlmj6b/vsbgqAAAubcyQAQBO8vPadH2+bLc+X7ZbG9Jy9HS3ONmZOQMAoMwxQwYAOEm3huF68Ko6kqRJ87frrslLlZ1baHFVAABceghkAICT2G2GnuoWp7dvayZfT7sWbNmv68Yv1B8Zh6wuDQCAS4rLB7JatWrJMIyTvgYNGiRJysvL06BBg1SlShVVqlRJvXv3VkZGRqnX2LVrl3r06CE/Pz+FhobqySefVFFRUakxv/76q5o3by5vb2/FxsZqypQp5fURAcBlXds4Ul8/kKjqwb7aeSBXN4xfpDkbM87+RAAAcE5cPpAtW7ZMaWlpjq+kpCRJUp8+fSRJjz32mH744Qd99dVX+u2337R3717deOONjucXFxerR48eKigo0OLFi/XRRx9pypQpGjp0qGNMamqqevTooQ4dOmjVqlUaPHiw7rnnHs2cObN8PywAuKAGkUH6/qG2al07RHlFJfL35vZjAADKisv/q1qtWrVSj//zn/+oTp06uvLKK5Wdna0PPvhA06ZN09VXXy1Jmjx5surXr68lS5aodevWmjVrljZs2KDZs2crLCxMTZs21Ysvvqinn35aw4cPl5eXlyZOnKiYmBi99tprkqT69etr4cKFeuONN9S1a9dy/8wA4GqqVPLWJwMTtHLnQSXUruI4bpqmDPYtAwDAaS4fyE5UUFCgTz/9VEOGDJFhGFqxYoUKCwvVqVMnx5i4uDjVrFlTycnJat26tZKTk9WoUSOFhYU5xnTt2lUPPPCA1q9fr2bNmik5ObnUaxwfM3jw4NPWkp+fr/z8fMfjnJwcSVJhYaEKC62/8f14Da5Qizuhb86hb85xx741jwp01Lt132E9/38bNKZ3Q9UM8SvXOtyxd66AvjmHvjmHvjmP3jnHlfp2PjW4VSCbPn26srKydNddd0mS0tPT5eXlpeDg4FLjwsLClJ6e7hhzYhg7fv74uTONycnJ0dGjR+Xr63tSLaNGjdKIESNOOj5r1iz5+ZXvDyZncvwST5wf+uYc+uYcd+3buPV2bc0x1HPcAt1Vt0T1gs1yr8Fde2c1+uYc+uYc+uY8euccV+hbbm7uOY91q0D2wQcfqHv37oqMjLS6FD377LMaMmSI43FOTo6ioqLUpUsXBQYGWljZMYWFhUpKSlLnzp3l6elpdTlug745h745x9371rxdngZ9tkpr/szRxE12PdOtnu5KrFkulzC6e++sQt+cQ9+cQ9+cR++c40p9O3713Llwm0C2c+dOzZ49W99++63jWHh4uAoKCpSVlVVqliwjI0Ph4eGOMUuXLi31WsdXYTxxzD9XZszIyFBgYOApZ8ckydvbW97e3icd9/T0tPwb4ESuVo+7oG/OoW/Ocde+RVXx1Jf/aqN/f7dO36z8U6/8slmbMg7rlRsaycfTXi41uGvvrEbfnEPfnEPfnEfvnOMKfTuf93f5VRaPmzx5skJDQ9WjRw/HsRYtWsjT01Nz5sxxHNu8ebN27dqlxMRESVJiYqLWrl2rffv2OcYkJSUpMDBQ8fHxjjEnvsbxMcdfAwBwaj6edv23T2MNvTZedpuhb1fu0S3vJivzSIHVpQEA4BbcIpCVlJRo8uTJ6t+/vzw8/p7UCwoK0sCBAzVkyBDNmzdPK1as0N13363ExES1bt1aktSlSxfFx8frjjvu0OrVqzVz5kw9//zzGjRokGOG6/7779f27dv11FNPadOmTXrnnXf05Zdf6rHHHrPk8wKAOzEMQwPaxejjAa0U7OcpH0+7Anzc5gIMAAAs5Rb/Ys6ePVu7du3SgAEDTjr3xhtvyGazqXfv3srPz1fXrl31zjvvOM7b7Xb9+OOPeuCBB5SYmCh/f3/1799fI0eOdIyJiYnRTz/9pMcee0xjx45VjRo19P7777PkPQCch7axVfX9oHby97bL037s930lJaZsNpbFBwDgdNwikHXp0kWmeerVu3x8fDR+/HiNHz/+tM+Pjo7Wzz//fMb3uOqqq/T7779fUJ0AUNHVrFJ6ldkXf9qg4hJTL1wb7whpAADgb24RyAAA7mdTeo6mLN4h05Q2px/SO/2aq0qlkxdDAgCgIuPXlQCAiyIuPFDv3dFSlbw9lJKaqeveXqT1e7OtLgsAAJdCIAMAXDSd4sM0fVAbxVT1156so+o9YbF+WL3X6rIAAHAZBDIAwEUVGxqg6YPa6srLqimvsEQPf/a7xs7eYnVZAAC4BAIZAOCiC/L11Id3Xa77r6wjw5DiIgKsLgkAAJfAoh4AgHJhtxl6pnucbmxeXZeF/R3IiopL5MEKjACACop/AQEA5erEMLYn66g6vf6bZm/IsLAiAACsQyADAFjm3d+2aceBXN37yXK9PXfLafecBADgUkUgAwBY5oVr43VH62iZpvTfWX9o0LSVOpJfZHVZAACUGwIZAMAynnabXuzVUKNubCRPu6Gf16ar94TF2p2Za3VpAACUCwIZAMByt7aqqc/uba2qlby1Kf2Qrnt7IZtIAwAqBAIZAMAltKwVoh8ebqvGNYIUEeSr2lUrWV0SAAAXHcveAwBcRkSQr778V6JyjhbK18suSTJNUwXFJfL2sFtcHQAAZY8ZMgCAS/HxtCs00Mfx+O25W3Xzu0uUkZMnSSouMZWSmqkV+w2lpGaquISVGQEA7osZMgCAy8rOLdSHi1J1MLdQ145bqLva1NKnS3YqLTtPkl0fb1muiCAfDesZr24NI6wuFwCA88YMGQDAZQX5eWr6oLaqFxagvw7l69WZm/8Xxv6Wnp2nBz5dqRnr0iyqEgAA5xHIAAAuLbqKv766P1HeHqf+J+v4BYsjftjA5YsAALdDIAMAuLz1e3OUX1Ry2vOmpLTsPC1NzSy/ogAAKAMEMgCAy9t3KO/sg85jHAAAroJABgBweaEBPmcfJGlv1lGZJpctAgDcB4EMAODyWsWEKCLIR8ZZxo2esVl9JiYreduBcqkLAIALRSADALg8u83QsJ7xknRSKDv+uGNcqLw8bFq+86BufW+J+r2/RCt2HizXOgEAOF8EMgCAW+jWMEITbm+u8KDSly+GB/lo4u3N9cFdl2v+kx10R+toedoNLdp6QL0nLNboGZssqhgAgLNjY2gAgNvo1jBCnePDlbx1n2YtSFGX9glKjA2V3XZsniw8yEcv9mqo+66orXFzt+iblXvUPraq4/mmacowznbhIwAA5YdABgBwK3aboYSYEB3YaCohJsQRxk4UFeKnMTc10aOdLlP1YF/H8Tdmb9HOA0f0aMe6ql2tUnmWDQDAKRHIAACXrBPD2JH8In24MFWH84v045o03disuh7pWFdRIX4WVggAqOi4hwwAUCH4e3vo8/taq2NcqIpLTH214k9d/dqven76WqVns38ZAMAaBDIAQIXRsHqQPrjrcn33YBu1r1tVhcWmPl2yS1e8Ok/fr95rdXkAgAqIQAYAqHCa1aysTwYm6PP7WqtVrRCZpqlmUcFWlwUAqIC4hwwAUGG1rl1FX/yrtbb9daTUvWRPfb1aEUG+Gtg+RoE+nhZWCAC41BHIAAAVmmEYig39e8XFzemH9OXyPyVJUxbv0L+urK3+ibXk780/mQCAsscliwAAnKBuaCW906+5YkMrKftoocbM2KwrxszT+wu2K6+w2OryAACXGAIZAAAnsNkMXdMoQjMHX6E3bmmi6Cp+OnCkQC/9tFFXvjpP6/ZkW10iAOASwvUXAACcgt1m6IZmNXRt40h9u/JPvTVnq/KLSlS7mr/VpQEALiEEMgAAzsDTbtMtl9dUr2bVtf2vI/LzOvZPZ0mJqQenrlS3huHq2SRSdpthcaUAAHfEJYsAAJwDbw+76kcEOh7/si5dM9ana/AXq9Ttzfn6eW2aSkpMCysEALgjAhkAAE64ql41Pdm1noJ8PbVl32E9OHWlrh23UHM2Zsg0CWYAgHNDIAMAwAn+3h4a1CFWC57uoEc71lUlbw9tSMvRwI+W64Z3FivzSIHVJQIA3ACBDACACxDo46nHOl+mBU910P1X1pGvp10lpqnKfmwoDQA4Oxb1AACgDFT299Iz3eM0sF2MDuYWyDCOLfJxKK9QT329RvddUVvNala2uEoAgKshkAEAUIaqBXirWoC34/GHC3fol3Xp+mVdujrVD9VjnS9Tg8ggCysEALgSLlkEAOAiuqFZdd3UooZshjR74z71eGuhHpy6QlsyDlldGgDABRDIAAC4iGpW8dN/+zRR0pArdV2TSBmG9PPadHV5c76GfLmKpfIBoIIjkAEAUA7qVKukt25tpl8eba+uDcJkmpJpSjY2lAaACo17yAAAKEdx4YF6946WWvtntoJPWIlx+1+HNWXxDg3qEKuwQB8LKwQAlCcCGQAAFmhUo/TCHmPnbNH/rdqrL5bt1h2to3X/VXVUtZL3aZ4NALhUcMkiAAAuoO/lNdUyurLyi0r0/sJUXTFmnl6duUlZuWwwDQCXMgIZAAAuILFOFX11f6I+GtBKjWsEKbegWOPnbVP70fP0/oLtVpcHALhIuGQRAAAXYRiGrrysmq6oW1VJGzL0etIf2pR+SEcLiq0uDQBwkRDIAABwMYZhqEuDcHWqH6Zf1qXrynrVHOcWbd2vzemHdFtCTfl42i2sEgBQFghkAAC4KJvNUI/GEY7Hpmlq1C8btW5PjibN366Hro7VzS2j5OXBHQgA4K74GxwAADdRYkr9EqIVEeSj9Jw8PT99na5+7Vd9tXy3iopLrC4PAOAEAhkAAG7CbjN0a6uamvfEVRreM17VArz158GjevLrNeryxnzN27zP6hIBAOfJ5QPZnj17dPvtt6tKlSry9fVVo0aNtHz5csd50zQ1dOhQRUREyNfXV506ddKWLVtKvUZmZqb69eunwMBABQcHa+DAgTp8+HCpMWvWrFH79u3l4+OjqKgojRkzplw+HwAA58vH06672sZo/pMd9Nw1cars56nt+48o7wyLfxSXmEpJzdSK/YZSUjNVXGKWY8UAgNNx6XvIDh48qLZt26pDhw765ZdfVK1aNW3ZskWVK1d2jBkzZozeeustffTRR4qJidELL7ygrl27asOGDfLx8ZEk9evXT2lpaUpKSlJhYaHuvvtu3XfffZo2bZokKScnR126dFGnTp00ceJErV27VgMGDFBwcLDuu+8+Sz47AABn4+tl131X1NFtCdH67vc96tog3HHuh9V7VcnbQ1fVq6aZ69M14ocNSsvOk2TXx1uWKyLIR8N6xqtbw4jTvwEA4KJz6UA2evRoRUVFafLkyY5jMTExjj+bpqk333xTzz//vK6//npJ0scff6ywsDBNnz5dffv21caNGzVjxgwtW7ZMLVu2lCSNGzdO11xzjf773/8qMjJSU6dOVUFBgT788EN5eXmpQYMGWrVqlV5//XUCGQDA5VXy9tAdraMdj4/kF2n49+t14EiBYqr6KXV/7knPSc/O0wOfrtSE25sTygDAQi4dyL7//nt17dpVffr00W+//abq1avrwQcf1L333itJSk1NVXp6ujp16uR4TlBQkBISEpScnKy+ffsqOTlZwcHBjjAmSZ06dZLNZlNKSopuuOEGJScn64orrpCXl5djTNeuXTV69GgdPHiw1Izccfn5+crPz3c8zsnJkSQVFhaqsLCwzHtxvo7X4Aq1uBP65hz65hz65jx6d2b5BYXq1TRCn6bsOmUYkyRTkiFpxA/rdVXdKrLbjHKt0Z3w/eYc+uY8euccV+rb+dTg0oFs+/btmjBhgoYMGaLnnntOy5Yt0yOPPCIvLy/1799f6enpkqSwsLBSzwsLC3OcS09PV2hoaKnzHh4eCgkJKTXmxJm3E18zPT39lIFs1KhRGjFixEnHZ82aJT8/Pyc/cdlLSkqyugS3RN+cQ9+cQ9+cR+9Or7Gk22obmvzH6fcqMyWlZefr7S9mqG4Q95SdDd9vzqFvzqN3znGFvuXmnvqXYafi0oGspKRELVu21CuvvCJJatasmdatW6eJEyeqf//+ltb27LPPasiQIY7HOTk5ioqKUpcuXRQYGGhhZccUFhYqKSlJnTt3lqenp9XluA365hz65hz65jx6d26K16RJf6w967ig6Hhd0yb6rOMqKr7fnEPfnEfvnONKfTt+9dy5cOlAFhERofj4+FLH6tevr2+++UaSFB5+7ObljIwMRUT8ff17RkaGmjZt6hizb1/pZYCLioqUmZnpeH54eLgyMjJKjTn++PiYf/L29pa3t/dJxz09PS3/BjiRq9XjLuibc+ibc+ib8+jdmUUE+5/TuJd/2azvVqWpe8NwdWsYrrphARe5MvfE95tz6Jvz6J1zXKFv5/P+Lr3sfdu2bbV58+ZSx/744w9FRx/7LV5MTIzCw8M1Z84cx/mcnBylpKQoMTFRkpSYmKisrCytWLHCMWbu3LkqKSlRQkKCY8z8+fNLXeuZlJSkevXqnfJyRQAA3EGrmBBFBPnodHeHGZK8PWyyGdKGtBy9lvSHOr8xXx1f+1X/nblZ6/Zkl2e5AFAhuXQge+yxx7RkyRK98sor2rp1q6ZNm6ZJkyZp0KBBkiTDMDR48GC99NJL+v7777V27VrdeeedioyMVK9evSQdm1Hr1q2b7r33Xi1dulSLFi3SQw89pL59+yoyMlKSdNttt8nLy0sDBw7U+vXr9cUXX2js2LGlLkkEAMDd2G2GhvU8dqXJP0PZ8cdj+zbV8uc7a0zvxupQr5o87Ya2/XVEb8/bqhf+b12p55gm95kBQFlz6UsWL7/8cn333Xd69tlnNXLkSMXExOjNN99Uv379HGOeeuopHTlyRPfdd5+ysrLUrl07zZgxw7EHmSRNnTpVDz30kDp27CibzabevXvrrbfecpwPCgrSrFmzNGjQILVo0UJVq1bV0KFDWfIeAOD2ujWM0ITbm5+wD9kx4f/Yh+zmy6N08+VRyskr1NyN+/TLujS1qVPVMT47t1A9xi1Qx7hQdWsYoVYxIazMCABlwKUDmSRde+21uvbaa0973jAMjRw5UiNHjjztmJCQEMcm0KfTuHFjLViwwOk6AQBwVd0aRqhzfLiSt+7TrAUp6tI+QYmxoacMVIE+nurVrLp6Nate6vjsjRn68+BRfZS8Ux8l71QVfy91jg9Tt4bhalOnqrw8XPqiGwBwWS4fyAAAwIWz2wwlxITowEZTCU7Mbl3bJEKV/T31y9p0JW3M0IEjBfp82W59vmy3Anw8NKFfC7WrW/XsLwQAKIVABgAAzsrbw66r48J0dVyYCotLlLI9U7+sS9PM9Rk6cCRfl4VVcoyd/8dfyj5aqA5xoarkzY8aAHAm/C0JAADOi6fdpnZ1q6pd3aoaeX1DbUzLUWjg3/duvzt/mxZtPSAvD5uuqFtN3RuGq1P9MAX5sXw3APwTgQwAADjNbjPUsHqQ47FpmmoWVVl7Dh7VjgO5mr0xQ7M3ZsjDZqhNbFX1bByhPi2jLKwYAFwLgQwAAJQZwzD0RNd6erzLZdqUfki/rEvXzHXp2pxxSPP/+EumaZYKZAcO56tKJW8LKwYAaxHIAABAmTMMQ/UjAlU/IlBDOl+m7X8d1i/r0hUb+ve9ZunZeWrznzlqEhWs7g3D1b1hhKJC/CysGgDKH4EMAABcdLWrVdKgDrGljq3YeVCmpN93Zen3XVl65edNahAZqG4NwtW9UbhiQwOsKRYAyhGbhgAAAEv0aByhlGc76sXrG6hNnSqyGdL6vTl6LekPdXp9vmasS7e6RAC46JghAwAAlgkN9NEdibV0R2ItZR4pUNKGdP2yLl3LUjOVWKeKY9yXy3Zr61+H1a1huJrWCJbtPPdRAwBXRSADAAAuIcTfS7dcXlO3XF5TeYXF8vG0O85NTdmp1X9ma9L87QoP9FG3huHq2iBcrZzY5BoAXAmBDAAAuJwTw5gk3X9lHf28Ll1zN2YoPSdPUxbv0JTFO1TF30vXN62uoT3jLaoUAC4MgQwAALi87o0i1L1RhPIKi7Vo6379si5dSRsydOBIgdJzjpYau2DLX7q8VshJoQ4AXBGBDAAAuA0fT7s61g9Tx/phKiwu0ZLtBxTg4+k4v3XfYd3xwVL5e9l1VVyoujcMV4d6ofL35kceAK6Jv50AAIBb8rTb1L5utVLH9mYdVUSQj9Ky8/TTmjT9tCZN3h42XXFZNXVvGK5O8WEKPCHAAYDVWPYeAABcMq64rJoWP3O1pg9qq39dWVvRVfyUX1SipA0ZGvLlaqVsz3SMNU3zrK9XXGIqJTVTK/YbSknNVHHJ2Z8DAOeDGTIAAHBJMQxDTaOC1TQqWM90i9Om9EP6ZV26fvvjL7WvW9Ux7o3ZW7Q09YC6N4xQ1wbhCg/yKfU6M9alacQPG5SWnSfJro+3LFdEkI+G9YxXt4YR5fypAFyqCGQAAOCSZRiG6kcEqn5EoIZ0vqzUuZ/W7NW2v45oyfZMDft+vZrXDFb3hhHq1jBc6/dm64FPV+qf82Hp2Xl64NOVmnB7c0IZgDJBIAMAABXSlLtbaeb6YxtRr9h5UCt3ZWnlriy9/PNGeXnYTgpjkmRKMiSN+GGDOseHswcagAtGIAMAABVSVIif7mlfW/e0r62MnLxj4WxtupZsP6CCopLTPs+UlJadp6WpmUqsU6X8CgZwSWJRDwAAUOGFBfrozsRa+uy+1nqpV8Nzes7KXQdVwiIfAC4QgQwAAOAEtatVOqdxr87crJYvz9b3q/de5IoAXMoIZAAAACdoFROiiCAfnenuMG8Pm/w8bco8UqDQAG/H8UVb9+vpr9fo+9V7tf9w/sUvFoDb4x4yAACAE9hthob1jNcDn66UIZVa3ON4SBvbt6k61g/T6t1ZalQjyHF+1vp0fbF8t75YvluSFBceoHaxVdW2blW1qhUif29+9AJQGn8rAAAA/EO3hhGacHvzE/YhOyb8H/uQtawVUup5PRpHytNu08Kt+7Up/ZDj6/2FqfKwGVr49NUn7XcGoGIjkAEAAJxCt4YR6hwfruSt+zRrQYq6tE9QYmzoGZe6bxUTolYxx0La/sP5WrztgBZt2a+FW/fLMKSwwL8vb3z8y9U6mFugtrFV1S62qi4LqyTDYBl9oKIhkAEAAJyG3WYoISZEBzaaSogJOa99x6pW8tZ1TSJ1XZNImaaprNxCR+AqLjGVtCFdOXlFmrtpn2N829gqahtbVW1jq6p6sO9F+UwAXAuBDAAA4CIzDEOV/b0cj22GNO3e1lq0db8WbTugpakHtP9wvv5v1V7936q9ahldWV8/0MYx/lBeoQJ8PK0oHcBFRiADAAAoZ4ZhqGH1IDWsHqR/XVlH+UXF+n1X1rGAtnW/rrismmNsdm6hWryUpLiIAMfljS2jQ+TrZbfwEwAoKwQyAAAAi3l72NW6dhW1rl1Fj3epJ9P8e23HVX9mqajE1Lo9OVq3J0fv/rZdXnabWkRXVtvYKureKEJ1znHvNACuh0AGAADgYk5c3OPKy6pp6XMdtWjbfi3aekCLtu5XWnaekrcfUPL2A6pSydsRyDKPFCjzSIHqVPNngRDATRDIAAAAXFxooI9uaFZDNzSrIdM0tX3/ES3eemz1xnaxVR3jfli9V8O+X6+wQG/H5Y1tY6sqLJCl9gFXRSADAABwI4ZhqE61SqpTrZLuSKxV6tyBw/ny8rApIydf367co29X7pEkxYZWUts6VfRwx7qqWsn7FK8KwCoEMgAAgEvEkC719GCHWC3fcVCLtu3X4q37tWZPtrbuO6wd+4/oqW5xjrG//fGXPO2GmtesLB9PFggBrEIgAwAAuIT4eNrVrm5Vtat77FLG7NxCJW8/oN2ZufL3/vtHv//O3Ky1e7Ll7WFTq5gQtalz7BLH+MjA89pvDcCFIZABAABcwoL8PNWtYXipYyUlpuqGVVJ6Tp7+OpSvBVv2a8GW/RotKdjPUz0aRejlGxpZUzBQwRDIAAAAKhibzdDrNzeVaZrauu+wFv5v/7Ml2zOVlVuonLwix1jTNDXihw1qVjNYbepUVbWAs9+DVlxiKiU1Uyv2G6qSmqnE2FBm3YDTIJABAABUUIZhqG5YgOqGBejutjEqKi7R6j+PXcZ43JZ9hzVl8Q5NWXzscVx4wLHLG+tWUauYKqrkXfrHyRnr0jTihw1Ky86TZNfHW5YrIshHw3rGq1vDiHL8dIB7sJ19CAAAACoCj/9tON2wepDjmK+nXfddUVvxEYGSpE3ph/TholQNmLJcTUfM0keLdzjGzliXpgc+Xfm/MPa39Ow8PfDpSs1Yl1YunwNwJ8yQAQAA4LSiQvz03DX1JR1bVj95+wHHBtW7MnNVq6q/pGOXKT733TqZp3gNU5IhacQPG9Q5PpzLF4ETEMgAAABwTqpU8ta1jSN1beNISdLuzFzHPWVLUzOVeaTgtM81JaVl52lpaqYS61Qpj3IBt0AgAwAAgFOiQvwcf953KO8MI/92fFzq/iPy9rApMtj3otQGuAsCGQAAAC5YaIDPeY3776zN+mlNmiKCfNS8ZmU1qxmsFtGV1SAySF4eLHOAioNABgAAgAvWKiZEEUE+Ss/OO+V9ZIak8CAftYoJkSTlFxbLbjOUlp2nn9am6ae1xxb88PKwqXnNYE27p7Vs3GuGCoBABgAAgAtmtxka1jNeD3y6UoZUKpQdj1XDesY7FvR4v//lyi0o0po/s7Vi50H9vuugVuw8qIO5hcotKC4Vxv71yXL5eNrVIrqymtesrLjwAHnYmUXDpYFABgAAgDLRrWGEJtze/IR9yI4JP80+ZH5eHmpdu4pa1z62yIdpmtpxIFfZRwsdY3ILijR74z4Vl5j6v1V7JR1bir9JVJCa16ysdrFV1Sa2ajl8OuDiIJABAACgzHRrGKHO8eFK3rpPsxakqEv7BCXGhp7TUveGYSjmf8voH+dhs+mju1tpxc6DWrnr2NehvCIt2Z6pJdsztTMz1xHITNPUl8t3q0lUsOqGBrC8PtwCgQwAAABlym4zlBATogMbTSXEhFxQMPLysKld3apqV/dY6CopMbXtr8OOgNamzt+zY9v3H9HT36yVJAV4e6hpzWA1q1lZLaIrq2lUsIJ8PS/sgwEXAYEMAAAAbsNmM1Q3LEB1wwLUt1XNUueOFhSrbWwVrdqVpUP5RVqwZb8WbNkvSTIM6bnu9XXvFbUlHdvI2mYcm5UDrEQgAwAAwCWhYfUgTb2ntYpLTG1OP6QVuw7q950HtWLXQe08kKtaJ1wOOX/LX3rsi1VqFhXsWCykSVSw/L358Rjli+84AAAAXFLsNkPxkYGKjwzUHa2jJUn7D+er0glh6/ddWcrKLdS8zX9p3ua/JEk2Q4oLD1Tz6GANbFf7pPvZgIuBQAYAAIBLXtVK3qUeP9QhVh3jQv+3UEiWVu48qD1ZR7UhLUcb0nJ0R+tajrHzNu/T5vRDal6zshrXCJKPp72cq8eljEAGAACACsfLw6YmUcFqEhWsu9seO5aenaeVuw5q9Z9ZqhtayTH2/37fo+n/W3Lfw2aoQWSgY7GQ5tGVFRnkw71ocBqBDAAAANCx/dKuaRShaxqV3i+tTWxV5RWWaOWug9p3KF+r/8zW6j+zNWXxDtkMac3wro7LIfdmHVWVSl7y9mAWDefG5bc4Hz58uAzDKPUVFxfnOJ+Xl6dBgwapSpUqqlSpknr37q2MjIxSr7Fr1y716NFDfn5+Cg0N1ZNPPqmioqJSY3799Vc1b95c3t7eio2N1ZQpU8rj4wEAAMDF3dwyShPvaKGU5zpq4dMdNLZvU93VppYaVQ9Sg8igUvemPTRtpRoNn6XeExbrlZ83asa6NO3LyTvDq5dWXGIqJTVTK/YbSknNVHGJeTE+ElyIW8yQNWjQQLNnz3Y89vD4u+zHHntMP/30k7766isFBQXpoYce0o033qhFixZJkoqLi9WjRw+Fh4dr8eLFSktL05133ilPT0+98sorkqTU1FT16NFD999/v6ZOnao5c+bonnvuUUREhLp27Vq+HxYAAAAuyTAM1ajspxqV/XR90+qSVCowFZeYSsvOU0FRiVbsPKgVOw86ztWo7KtO9cM0/LoGp339GevSNOKHDUrLzpNk18dblisiyEfDesarW8OI0z4P7s0tApmHh4fCw8NPOp6dna0PPvhA06ZN09VXXy1Jmjx5surXr68lS5aodevWmjVrljZs2KDZs2crLCxMTZs21Ysvvqinn35aw4cPl5eXlyZOnKiYmBi99tprkqT69etr4cKFeuONNwhkAAAAOK0TN7222wwtfuZq7TyQ69i4esXOg/oj45D+PHhUadlHHWNN09S/Plmh2NBKal6zsg7mFuipr9fon/Nh6dl5euDTlZpwe3NC2SXKLQLZli1bFBkZKR8fHyUmJmrUqFGqWbOmVqxYocLCQnXq1MkxNi4uTjVr1lRycrJat26t5ORkNWrUSGFhYY4xXbt21QMPPKD169erWbNmSk5OLvUax8cMHjz4tDXl5+crPz/f8TgnJ0eSVFhYqMLCwjL65M47XoMr1OJO6Jtz6Jtz6Jvz6J1z6Jtz6JtzKnLfqgd5qXrjMF3X+NjPn4fyirRmT7b8PO2Ofuw+mKtZGzI0a0PGmV5KpiRD0ogf1uuqulVKBUCU5krfc+dTg8sHsoSEBE2ZMkX16tVTWlqaRowYofbt22vdunVKT0+Xl5eXgoODSz0nLCxM6enpkqT09PRSYez4+ePnzjQmJydHR48ela+v70l1jRo1SiNGjDjp+KxZs+Tn5+f05y1rSUlJVpfgluibc+ibc+ib8+idc+ibc+ibc+jb37Ilpa079ufcIqlvbUM7DhvanGXoYMHpg5YpKS07X89NnqGEaqZ8XP4neGu5wvdcbm7uOY91+f87u3fv7vhz48aNlZCQoOjoaH355ZenDErl5dlnn9WQIUMcj3NychQVFaUuXbooMDDQsrqOKywsVFJSkjp37ixPT0+ry3Eb9M059M059M159M459M059M059O3sbvrf//6wJk1Dvlp71vHf7rDr2x1SdIif4iMCjn1FBqpx9SAF+9FjV/qeO3713Llw+UD2T8HBwbrsssu0detWde7cWQUFBcrKyio1S5aRkeG45yw8PFxLly4t9RrHV2E8ccw/V2bMyMhQYGDgaUOft7e3vL29Tzru6elp+TfAiVytHndB35xD35xD35xH75xD35xD35xD384uItj/nMaF+Hsq80ihdmbmamdmrn5Zf+zn11E3NtKtrWpKkvZkHdWa3VlqEBmkqBDfCrk/mit8z53P+7tdIDt8+LC2bdumO+64Qy1atJCnp6fmzJmj3r17S5I2b96sXbt2KTExUZKUmJiol19+Wfv27VNoaKikY9OYgYGBio+Pd4z5+eefS71PUlKS4zUAAACAi6VVTIgignyUnp130qIe0rF7yMKDfLTw6auVfbRQ6/dma/3enP99ZathZJBj7LxN+/T89GPXRQb4eCg+IlANIoPUIDJQDaoHKrZaJXnYXX7nqwrF5QPZE088oZ49eyo6Olp79+7VsGHDZLfbdeuttyooKEgDBw7UkCFDFBISosDAQD388MNKTExU69atJUldunRRfHy87rjjDo0ZM0bp6el6/vnnNWjQIMcM1/3336+3335bTz31lAYMGKC5c+fqyy+/1E8//WTlRwcAAEAFYLcZGtYzXg98ulKGVCqUHZ/fGtYzXnaboRB/L7WvW03t61Y75Wv5ednVsHqg/kg/rEN5RUpJzVRKaqbj/LR7E9SmTlVJUur+I8rKLVBceKB8vdjI2iouH8j+/PNP3XrrrTpw4ICqVaumdu3aacmSJapW7dg34RtvvCGbzabevXsrPz9fXbt21TvvvON4vt1u148//qgHHnhAiYmJ8vf3V//+/TVy5EjHmJiYGP3000967LHHNHbsWNWoUUPvv/8+S94DAACgXHRrGKEJtzc/YR+yY8LPcx+yG5vX0I3Na6igqERb9x0+YTYtWxvTDqlBxN+zadNSduq9BamyGVKdapWOzaIdn02LDFIQ96WVC5cPZJ9//vkZz/v4+Gj8+PEaP378acdER0efdEniP1111VX6/fffnaoRAAAAuFDdGkaoc3y4krfu06wFKerSPkGJsaFOLXXv5WFTfGSg4iMD1ed/x0pKTNlOeC1Pu01VK3lr/+F8bdl3WFv2Hdb0VXsd55c821HhQT6Sjs2m+XjaFB7oUyHvS7uYXD6QAQAAABWF3WYoISZEBzaaSogJKdN9x2z/eK2nusXpqW5x2peTp/V7c7Ruz/9m09KydSS/WGGBfy9g98rPG5W0IUMh/l5q8L+gd3w2LaaK/0mvjXNHIAMAAAAqsNBAH4UG+qhDXKjj2NGC4lIzYQVFJbLbDGUeKdCCLfu1YMt+x7mqlby09LlOjlC2J+uoqlbykrcH96WdCwIZAAAAgFL+ucjHRwNaKa+wWJvTDznuSVu/N0eb0nNUo7JfqRmyuycvVer+I4oNDfjf/WjHZtPqRwQowIf70v6JQAYAAADgrHw87WoSFawmUcGOY8UlpjKPFDgeFxaX6MDhAhUWm9qYlqONaTn6esXfr9GhXjVNvruV43H20UIF+VbskEYgAwAAAOAUu81QtYC/7zXztNu0/PlO2pN11LFX2ob/zaalZeeVCl/FJaZavzJHgb4eJ6zueGw2rUbl89vUurjEVEpqplbsN1QlNdPpxVCsQCADAAAAUGYMw1CNyn6qUdlPXRuEO44fOJyvvKISx+PdmbnKKyrW0ZxiZeTs09xN+xznAn08dGdiLT3RtZ4kyTRNFZeYp9zUesa6tBO2C7Dr4y3LFXGe2wVYiUAGAAAA4KKrUsm71ONaVf21dnhXbUrLKXVf2h8Zh5STV1Rqhis9J09Xvfqr4sIDFH/CbNruzFw9+vmqUptpS1J6dp4e+HSlJtze3OVDGYEMAAAAgCUqeXuoZa0QtawV4jhWUFSiLfsOlbq8ccPeHOUXlWj1n9la/Wf2WV/XlGRIGvHDBnWOD3fpyxcJZAAAAABchpeHTQ0ig0od61AvVHMfv9JxX9r6vdlavTtLOXlFp30dU1Jadp6WpmYqsU6Vi1y18whkAAAAAFyazWaodrVKql2tkno2iZQk/d/ve/ToF6vO+tx9h/IucnUX5uS74gAAAADAxYUG+pzbuIBzG2cVAhkAAAAAt9MqJkQRQT463d1hhqSIIB+1igk5zQjXQCADAAAA4HbsNkPDesZL0kmh7PjjYT3jXXpBD4lABgAAAMBNdWsYoQm3N1d4UOnLEsODfNxiyXuJRT0AAAAAuLFuDSPUOT5cyVv3adaCFHVpn6DE2FCXnxk7jkAGAAAAwK3ZbYYSYkJ0YKOphJgQtwljEpcsAgAAAIBlCGQAAAAAYBECGQAAAABYhEAGAAAAABYhkAEAAACARQhkAAAAAGARAhkAAAAAWIRABgAAAAAWIZABAAAAgEUIZAAAAABgEQIZAAAAAFiEQAYAAAAAFiGQAQAAAIBFPKwu4FJhmqYkKScnx+JKjiksLFRubq5ycnLk6elpdTlug745h745h745j945h745h745h745j945x5X6djwTHM8IZ0IgKyOHDh2SJEVFRVlcCQAAAABXcOjQIQUFBZ1xjGGeS2zDWZWUlGjv3r0KCAiQYRhWl6OcnBxFRUVp9+7dCgwMtLoct0HfnEPfnEPfnEfvnEPfnEPfnEPfnEfvnONKfTNNU4cOHVJkZKRstjPfJcYMWRmx2WyqUaOG1WWcJDAw0PJvSHdE35xD35xD35xH75xD35xD35xD35xH75zjKn0728zYcSzqAQAAAAAWIZABAAAAgEUIZJcob29vDRs2TN7e3laX4lbom3Pom3Pom/PonXPom3Pom3Pom/PonXPctW8s6gEAAAAAFmGGDAAAAAAsQiADAAAAAIsQyAAAAADAIgQyAAAAALAIgewSM3/+fPXs2VORkZEyDEPTp0+3uiSXN2rUKF1++eUKCAhQaGioevXqpc2bN1tdlluYMGGCGjdu7NiAMTExUb/88ovVZbmd//znPzIMQ4MHD7a6FJc2fPhwGYZR6isuLs7qstzCnj17dPvtt6tKlSry9fVVo0aNtHz5cqvLcnm1atU66XvOMAwNGjTI6tJcWnFxsV544QXFxMTI19dXderU0YsvvijWkTu7Q4cOafDgwYqOjpavr6/atGmjZcuWWV2Wyznbz7umaWro0KGKiIiQr6+vOnXqpC1btlhT7DkgkF1ijhw5oiZNmmj8+PFWl+I2fvvtNw0aNEhLlixRUlKSCgsL1aVLFx05csTq0lxejRo19J///EcrVqzQ8uXLdfXVV+v666/X+vXrrS7NbSxbtkzvvvuuGjdubHUpbqFBgwZKS0tzfC1cuNDqklzewYMH1bZtW3l6euqXX37Rhg0b9Nprr6ly5cpWl+byli1bVur7LSkpSZLUp08fiytzbaNHj9aECRP09ttva+PGjRo9erTGjBmjcePGWV2ay7vnnnuUlJSkTz75RGvXrlWXLl3UqVMn7dmzx+rSXMrZft4dM2aM3nrrLU2cOFEpKSny9/dX165dlZeXV86VniMTlyxJ5nfffWd1GW5n3759piTzt99+s7oUt1S5cmXz/ffft7oMt3Do0CGzbt26ZlJSknnllVeajz76qNUlubRhw4aZTZo0sboMt/P000+b7dq1s7qMS8Kjjz5q1qlTxywpKbG6FJfWo0cPc8CAAaWO3XjjjWa/fv0sqsg95Obmmna73fzxxx9LHW/evLn573//26KqXN8/f94tKSkxw8PDzVdffdVxLCsry/T29jY/++wzCyo8O2bIgH/Izs6WJIWEhFhciXspLi7W559/riNHjigxMdHqctzCoEGD1KNHD3Xq1MnqUtzGli1bFBkZqdq1a6tfv37atWuX1SW5vO+//14tW7ZUnz59FBoaqmbNmum9996zuiy3U1BQoE8//VQDBgyQYRhWl+PS2rRpozlz5uiPP/6QJK1evVoLFy5U9+7dLa7MtRUVFam4uFg+Pj6ljvv6+nI1wHlITU1Venp6qX9bg4KClJCQoOTkZAsrOz0PqwsAXElJSYkGDx6stm3bqmHDhlaX4xbWrl2rxMRE5eXlqVKlSvruu+8UHx9vdVku7/PPP9fKlSu5N+A8JCQkaMqUKapXr57S0tI0YsQItW/fXuvWrVNAQIDV5bms7du3a8KECRoyZIiee+45LVu2TI888oi8vLzUv39/q8tzG9OnT1dWVpbuuusuq0txec8884xycnIUFxcnu92u4uJivfzyy+rXr5/Vpbm0gIAAJSYm6sUXX1T9+vUVFhamzz77TMnJyYqNjbW6PLeRnp4uSQoLCyt1PCwszHHO1RDIgBMMGjRI69at4zdR56FevXpatWqVsrOz9fXXX6t///767bffCGVnsHv3bj366KNKSko66TehOL0Tf7veuHFjJSQkKDo6Wl9++aUGDhxoYWWuraSkRC1bttQrr7wiSWrWrJnWrVuniRMnEsjOwwcffKDu3bsrMjLS6lJc3pdffqmpU6dq2rRpatCggVatWqXBgwcrMjKS77mz+OSTTzRgwABVr15ddrtdzZs316233qoVK1ZYXRouIi5ZBP7noYce0o8//qh58+apRo0aVpfjNry8vBQbG6sWLVpo1KhRatKkicaOHWt1WS5txYoV2rdvn5o3by4PDw95eHjot99+01tvvSUPDw8VFxdbXaJbCA4O1mWXXaatW7daXYpLi4iIOOkXJPXr1+dyz/Owc+dOzZ49W/fcc4/VpbiFJ598Us8884z69u2rRo0a6Y477tBjjz2mUaNGWV2ay6tTp45+++03HT58WLt379bSpUtVWFio2rVrW12a2wgPD5ckZWRklDqekZHhOOdqCGSo8EzT1EMPPaTvvvtOc+fOVUxMjNUlubWSkhLl5+dbXYZL69ixo9auXatVq1Y5vlq2bKl+/fpp1apVstvtVpfoFg4fPqxt27YpIiLC6lJcWtu2bU/ayuOPP/5QdHS0RRW5n8mTJys0NFQ9evSwuhS3kJubK5ut9I+YdrtdJSUlFlXkfvz9/RUREaGDBw9q5syZuv76660uyW3ExMQoPDxcc+bMcRzLyclRSkqKy97jziWLl5jDhw+X+m1xamqqVq1apZCQENWsWdPCylzXoEGDNG3aNP3f//2fAgICHNcXBwUFydfX1+LqXNuzzz6r7t27q2bNmjp06JCmTZumX3/9VTNnzrS6NJcWEBBw0j2K/v7+qlKlCvcunsETTzyhnj17Kjo6Wnv37tWwYcNkt9t16623Wl2aS3vsscfUpk0bvfLKK7r55pu1dOlSTZo0SZMmTbK6NLdQUlKiyZMnq3///vLw4Memc9GzZ0+9/PLLqlmzpho0aKDff/9dr7/+ugYMGGB1aS5v5syZMk1T9erV09atW/Xkk08qLi5Od999t9WluZSz/bw7ePBgvfTSS6pbt65iYmL0wgsvKDIyUr169bKu6DOxeplHlK158+aZkk766t+/v9WluaxT9UuSOXnyZKtLc3kDBgwwo6OjTS8vL7NatWpmx44dzVmzZlldllti2fuzu+WWW8yIiAjTy8vLrF69unnLLbeYW7dutbost/DDDz+YDRs2NL29vc24uDhz0qRJVpfkNmbOnGlKMjdv3mx1KW4jJyfHfPTRR82aNWuaPj4+Zu3atc1///vfZn5+vtWlubwvvvjCrF27tunl5WWGh4ebgwYNMrOysqwuy+Wc7efdkpIS84UXXjDDwsJMb29vs2PHji7937BhmmybDgAAAABW4B4yAAAAALAIgQwAAAAALEIgAwAAAACLEMgAAAAAwCIEMgAAAACwCIEMAAAAACxCIAMAAAAAixDIAAAAAMAiBDIAgEvasWOHDMPQqlWrrC7FYdOmTWrdurV8fHzUtGnTC3otwzA0ffr0MqnLFcyZM0f169dXcXHxOT9n4sSJ6tmz50WsCgBcH4EMAHBKd911lwzD0H/+859Sx6dPny7DMCyqylrDhg2Tv7+/Nm/erDlz5px2XHp6uh5++GHVrl1b3t7eioqKUs+ePc/4nAvx66+/yjAMZWVlXZTXPxdPPfWUnn/+edntdknSlClTFBwcXGrMxo0bFRUVpT59+qigoEADBgzQypUrtWDBAgsqBgDXQCADAJyWj4+PRo8erYMHD1pdSpkpKChw+rnbtm1Tu3btFB0drSpVqpxyzI4dO9SiRQvNnTtXr776qtauXasZM2aoQ4cOGjRokNPvXR5M01RRUdF5P2/hwoXatm2bevfufdoxy5YtU/v27dWtWzd98cUX8vLykpeXl2677Ta99dZbF1I2ALg1AhkA4LQ6deqk8PBwjRo16rRjhg8fftLle2+++aZq1arleHzXXXepV69eeuWVVxQWFqbg4GCNHDlSRUVFevLJJxUSEqIaNWpo8uTJJ73+pk2b1KZNG/n4+Khhw4b67bffSp1ft26dunfvrkqVKiksLEx33HGH9u/f7zh/1VVX6aGHHtLgwYNVtWpVde3a9ZSfo6SkRCNHjlSNGjXk7e2tpk2basaMGY7zhmFoxYoVGjlypAzD0PDhw0/5Og8++KAMw9DSpUvVu3dvXXbZZWrQoIGGDBmiJUuWnPI5p5rhWrVqlQzD0I4dOyRJO3fuVM+ePVW5cmX5+/urQYMG+vnnn7Vjxw516NBBklS5cmUZhqG77rrL8ZlGjRqlmJgY+fr6qkmTJvr6669Pet9ffvlFLVq0kLe3txYuXKjVq1erQ4cOCggIUGBgoFq0aKHly5efsnZJ+vzzz9W5c2f5+Pic8vzcuXN19dVXa+DAgXrvvfdks/3940fPnj31/fff6+jRo6d9fQC4lBHIAACnZbfb9corr2jcuHH6888/L+i15s6dq71792r+/Pl6/fXXNWzYMF177bWqXLmyUlJSdP/99+tf//rXSe/z5JNP6vHHH9fvv/+uxMRE9ezZUwcOHJAkZWVl6eqrr1azZs20fPlyzZgxQxkZGbr55ptLvcZHH30kLy8vLVq0SBMnTjxlfWPHjtVrr72m//73v1qzZo26du2q6667Tlu2bJEkpaWlqUGDBnr88ceVlpamJ5544qTXyMzM1IwZMzRo0CD5+/ufdP6fl/Cdj0GDBik/P1/z58/X2rVrNXr0aFWqVElRUVH65ptvJEmbN29WWlqaxo4dK0kaNWqUPv74Y02cOFHr16/XY489pttvv/2kUPvMM8/oP//5jzZu3KjGjRurX79+qlGjhpYtW6YVK1bomWeekaen52lrW7BggVq2bHnKc99995169Oih559/XqNHjz7pfMuWLVVUVKSUlBRnWwMAbs3D6gIAAK7thhtuUNOmTTVs2DB98MEHTr9OSEiI3nrrLdlsNtWrV09jxoxRbm6unnvuOUnSs88+q//85z9auHCh+vbt63jeQw895LgUbsKECZoxY4Y++OADPfXUU3r77bfVrFkzvfLKK47xH374oaKiovTHH3/osssukyTVrVtXY8aMOWN9//3vf/X000873nv06NGaN2+e3nzzTY0fP17h4eHy8PBQpUqVFB4efsrX2Lp1q0zTVFxcnNN9Op1du3apd+/eatSokSSpdu3ajnMhISGSpNDQUEfoy8/P1yuvvKLZs2crMTHR8ZyFCxfq3Xff1ZVXXul4/siRI9W5c+dS7/X/7d1dSJNfAMfx77IUozJKEQyxLAVXJpoVNJAW2YroJigIKSgMAsV8SbIi0hKaQTfJjPBKbyRIDUvKoAxyBEnDXuZLa4VdZCQZyNKgVv+LcLS2+dK/2B//vw88N+flec5z7n6c85ynvLzc9x4pKSmTjm1wcJCEhISAco/Hw549ezh58iTHjx8P2nf+/PnExMQwODg41RSIiMxKWiETEZEp1dTU0NDQQF9f32/fY/Xq1X5b1eLj433hAn6sxi1dupT379/79ZsIEwBz584lOzvbN44nT57Q2dnJggULfNdEiHC73b5+69atm3Rso6OjvH37FpPJ5FduMplm9M7fv3+fdtuZKioqorq6GpPJxJkzZ3j69Omk7V++fMnY2Bi5ubl+89PY2Og3N0DA6lZpaSn5+fls3boVq9Ua0P5X4+PjQbcrRkdHk5ubS319/aTzGB0dzdjY2KTPEBGZrRTIRERkSjk5OVgsFk6cOBFQN2fOnIAg8uXLl4B2v255MxgMQcu+ffs27XF5PB527dpFT0+P3+VyucjJyfG1C7Z98G9ISUnBYDDQ398/o34TQfXnefx1DvPz83n16hX79+/n2bNnZGdnU1tbG/KeHo8HgPb2dr+56e3t9fuODALnp7KyEqfTyc6dO7l37x5Go5HW1taQz4qNjQ168EtERATXr18nKysLs9kcMpSNjIwQFxcX8v4iIrOZApmIiEyL1Wrlxo0bPHz40K88Li6Od+/e+YWJP/nvsJ8Pwvj69SuPHz8mLS0NgKysLJxOJ8uXL2fVqlV+10xC2KJFi0hISMBut/uV2+12jEbjtO+zZMkSLBYLNpuNT58+BdSHOpZ+IowMDQ35yoLNYWJiIkeOHKGlpYWysjLq6+sBiIyMBPD7B5jRaCQqKoo3b94EzE1iYuKU75KamkpJSQl37txh9+7dQQ9cmZCZmUlvb2/QuqioKFpaWli/fj1mszmgndvt5vPnz2RmZk45JhGR2UiBTEREpiU9PZ28vLyAI8o3b97M8PAwFy5cwO12Y7PZuHXr1h97rs1mo7W1lf7+fgoKCvj48SOHDh0Cfhx0MTIywr59++ju7sbtdtPR0cHBgwdn9INi+HF4SE1NDVevXmVgYICKigp6eno4evTojMfr9XrZsGEDzc3NuFwu+vr6uHTpkt/2y59NhKTKykpcLhft7e1cvHjRr01xcTEdHR28fv0ah8NBZ2enL5gmJSVhMBi4efMmw8PDeDweFi5cyLFjxygpKaGhoQG3243D4aC2tpaGhoaQ4x8fH6ewsJD79+8zODiI3W6nu7vb96xgLBYLXV1dIeujoqJobm5m48aNmM1mnE6nr+7BgwckJyezcuXKkP1FRGYzBTIREZm2s2fPBmwpTEtLo66uDpvNRkZGBo8ePQp6AuHvslqtWK1WMjIy6Orqoq2tjdjYWADfqpbX62Xbtm2kp6dTXFzM4sWL/b5Xm46ioiJKS0spKysjPT2d27dv09bWNuWBFr9KTk7G4XBgNpspKytjzZo15ObmcvfuXS5fvhy0z7x582hqaqK/v5+1a9dSU1NDdXW1Xxuv10tBQQFpaWls376d1NRU6urqAFi2bBlVVVVUVFQQHx9PYWEhAOfOneP06dOcP3/e16+9vZ0VK1aEHH9ERAQfPnzgwIEDpKamsnfvXnbs2EFVVVXIPnl5eTidTgYGBkK2iYyM5Nq1a2zatAmz2czz588BaGpq4vDhwyH7iYjMdobvf/MLZBEREflfKC8vZ3R0lCtXrky7j9PpZMuWLbx48YKYmJi/ODoRkf8urZCJiIjIv3bq1CmSkpJmdCjL0NAQjY2NCmMi8r+mFTIREREREZEw0QqZiIiIiIhImCiQiYiIiIiIhIkCmYiIiIiISJgokImIiIiIiISJApmIiIiIiEiYKJCJiIiIiIiEiQKZiIiIiIhImCiQiYiIiIiIhIkCmYiIiIiISJj8Azt/HXVPpJ7HAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Px1qhrXWqeWH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XTZ050ScqeZp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Save the graph as an image file (e.g., PNG or PDF)"
      ],
      "metadata": {
        "id": "EJipKjL1qedF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig('elbow_method_graph.png')  # Saving the plot as PNG image"
      ],
      "metadata": {
        "id": "wtR5CXhO-Fpv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf6b7a6b-8816-408b-937b-1a94540921cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig('elbow_method_graph.pdf', format='pdf')  # Saving the plot as PDF file"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3TPyfQ9U-L43",
        "outputId": "ab4bdce6-676a-40c1-fbd5-d7c1dd3c53a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rhuS9zQeqnSC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the graph"
      ],
      "metadata": {
        "id": "U2OntPipqnVk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.show()  # Displaying the plot"
      ],
      "metadata": {
        "id": "We55FKvQ_ssR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yeojncyfqstd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the graph as a PNG image file"
      ],
      "metadata": {
        "id": "0zGbkIfUqswm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.savefig('elbow_method_graph.png')  # Saving the plot as PNG image"
      ],
      "metadata": {
        "id": "00vhOuW1_svX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b5005f8b-be81-4dab-9e52-75ccfe158ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4XB_vRB1q9m4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the saved image"
      ],
      "metadata": {
        "id": "zsZt26oEq9qR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image  # Importing Image module for displaying images"
      ],
      "metadata": {
        "id": "jnutUSFG_sxx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Image('elbow_method_graph.png')  # Displaying the saved PNG image"
      ],
      "metadata": {
        "id": "9zrahK0M_x1E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "723b4dde-d138-4cbd-f3a7-31e2faa5b54d",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tMQAYSkIdpGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jqG2d2EcdpKH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "psbya3Nmdpgh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color=\"#4CAF50\"><b>Conclusion</b></font>\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Summary of Insights</b></font>\n",
        "- **Note:** In this project, we conducted a comprehensive **Exploratory Data Analysis (EDA)** on the `Beauty_csv` dataset, identifying key patterns and trends.\n",
        "  - We explored the **distribution of variables** and detected missing values, providing a foundation for further data cleaning and preprocessing.\n",
        "  - **Visualizations** helped in identifying relationships between different variables, giving insights into the structure of the data.\n",
        "  - Potential tasks were outlined, such as **predictive modeling** and **customer segmentation**, that can be further pursued for business insights.\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Recommendations for Future Work</b></font>\n",
        "- **Note:** Based on our EDA, the following tasks could be considered for deeper analysis:\n",
        "  - **Predictive Modeling:** Use machine learning algorithms to predict beauty product sales, customer preferences, or product success rates.\n",
        "  - **Customer Segmentation:** Leverage clustering techniques to identify distinct customer segments and tailor marketing strategies accordingly.\n",
        "  - **Sentiment Analysis:** Apply NLP techniques to analyze customer reviews, identifying key themes and trends in customer feedback.\n",
        "\n",
        "## <font color=\"#2196F3\"><b>Final Thoughts</b></font>\n",
        "- **Note:** This EDA has laid a strong foundation for more advanced analysis and machine learning tasks. Future efforts can build on this groundwork to extract actionable insights, optimize business strategies, and enhance decision-making processes within the beauty industry."
      ],
      "metadata": {
        "id": "s2xei8Pcdp2J"
      }
    }
  ]
}