1_3_7_Insurance_Customer_Segmentation.ipynb

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        "# <span style=\"color:blue;\">**Insurance Customer Segmentation Using K-Means Clustering**</span>\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Project Overview**</span>\n",
        "\n",
        "This project aims to segment insurance customers using machine learning techniques, focusing on K-Means Clustering. The process includes data loading, exploratory data analysis (EDA), preprocessing, model training, and evaluation.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 1: Import Libraries**</span>\n",
        "\n",
        "**Key Libraries:**\n",
        "- **Pandas**: Data manipulation and analysis.\n",
        "- **NumPy**: Numerical operations.\n",
        "- **Matplotlib**: Plotting library for static, animated, and interactive visualizations.\n",
        "- **Seaborn**: High-level interface for drawing attractive and informative statistical graphics.\n",
        "- **Scikit-learn**: Machine learning library providing tools for model building, evaluation, and preprocessing.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 2: Load and Display Data**</span>\n",
        "\n",
        "**Data Loading and Display:**\n",
        "- Use `pd.read_csv()` to load CSV files into DataFrame objects.\n",
        "- Use `.head()` to show the first few rows, `.shape` to get dimensions, `.columns` to list column names, and `.dtypes` to display data types.\n",
        "- Use `.describe()` to get a statistical summary, including count, mean, std, min, and percentiles.\n",
        "\n",
        "**Notes:**\n",
        "- Display samples to understand the data structure.\n",
        "- Display shapes and columns to get an overview.\n",
        "- Display data types and summary statistics for a deeper understanding.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 3: Exploratory Data Analysis (EDA)**</span>\n",
        "\n",
        "**Objective:**\n",
        "- Understand the data and its underlying patterns through visualizations.\n",
        "\n",
        "**Visualization Techniques:**\n",
        "- **Count Plot**: For categorical variables to show the count of each category.\n",
        "- **Histogram**: For continuous variables to show the distribution.\n",
        "- **Line Plot**: For relationships between two continuous variables.\n",
        "\n",
        "**Handling Missing Values:**\n",
        "- Important to identify and appropriately handle missing data.\n",
        "\n",
        "**Notes:**\n",
        "- Identify trends and patterns in the data.\n",
        "- Check for missing values and handle them accordingly.\n",
        "- Visualize distributions and relationships between variables.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 4: Data Preprocessing**</span>\n",
        "\n",
        "**Objective:**\n",
        "- Clean and transform data to make it suitable for machine learning models.\n",
        "\n",
        "**Steps:**\n",
        "1. **Separate Features and Target Variable:**\n",
        "   - Drop the target column from features.\n",
        "   - Set the target column as the target variable.\n",
        "2. **List Categorical and Numerical Columns:**\n",
        "   - Identify and list categorical and numerical columns.\n",
        "3. **Preprocessing Pipelines:**\n",
        "   - **Numerical Data:**\n",
        "     - Impute missing values with the mean.\n",
        "     - Scale data.\n",
        "   - **Categorical Data:**\n",
        "     - Impute missing values with the most frequent value.\n",
        "     - One-hot encode the data.\n",
        "\n",
        "**Notes:**\n",
        "- Properly preprocess data to improve model performance.\n",
        "- Handle missing values and encode categorical variables appropriately.\n",
        "- Standardize numerical features for consistency.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 5: Model Training and Evaluation**</span>\n",
        "\n",
        "**Objective:**\n",
        "- Train machine learning models and evaluate their performance.\n",
        "\n",
        "**Steps:**\n",
        "1. **Split Data:**\n",
        "   - Divide data into training and validation sets using `train_test_split`.\n",
        "2. **Initialize Model:**\n",
        "   - Choose an appropriate machine learning model (e.g., RandomForestClassifier).\n",
        "3. **Train the Model:**\n",
        "   - Fit the model to the training data.\n",
        "4. **Evaluate the Model:**\n",
        "   - Predict on the validation set.\n",
        "   - Calculate accuracy, classification report, and confusion matrix.\n",
        "\n",
        "**Notes:**\n",
        "- Splitting data helps in evaluating model performance on unseen data.\n",
        "- Choose the right model based on the problem.\n",
        "- Evaluate model performance using metrics like accuracy, classification report, and confusion matrix.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 6: Predictions and Submission**</span>\n",
        "\n",
        "**Objective:**\n",
        "- Predict on the test set and prepare a submission file.\n",
        "\n",
        "**Steps:**\n",
        "1. **Predict on Test Set:**\n",
        "   - Transform test data and make predictions.\n",
        "2. **Prepare Submission File:**\n",
        "   - Add predictions to the submission data.\n",
        "   - Save the submission file to a CSV.\n",
        "\n",
        "**Notes:**\n",
        "- Ensure predictions are correctly formatted for submission.\n",
        "- Save results for future reference or competition submission.\n",
        "\n",
        "## <span style=\"color:darkorange;\">**Step 7: Save and Display Plots and Data**</span>\n",
        "\n",
        "**Objective:**\n",
        "- Save plots and data for reporting and future reference.\n",
        "\n",
        "**Steps:**\n",
        "1. **Save Plots as PDF:**\n",
        "   - Use `PdfPages` to save plots in a PDF file.\n",
        "2. **Display and Save Data:**\n",
        "   - Display heads of datasets and save important results as CSV files.\n",
        "\n",
        "**Notes:**\n",
        "- Saving plots and data helps in documentation and future analysis.\n",
        "- Displaying heads of datasets ensures the correctness of transformations."
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        "## <span style=\"color:darkorange;\">**Step 1: Import Libraries**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "In this step, we will import all the necessary libraries required for data manipulation, visualization, and machine learning tasks. Importing libraries is crucial as they provide the functions and tools needed for the various steps in the machine learning pipeline.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Libraries**</span>\n",
        "\n",
        "1. **Pandas**:\n",
        "   - **Purpose**: Used for data manipulation and analysis.\n",
        "   - **Functions**: DataFrame creation, reading/writing CSV files, data cleaning, merging, reshaping, and statistical analysis.\n",
        "   - **Example Usage**: `import pandas as pd`\n",
        "\n",
        "2. **NumPy**:\n",
        "   - **Purpose**: Fundamental package for numerical computations in Python.\n",
        "   - **Functions**: Provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.\n",
        "   - **Example Usage**: `import numpy as np`\n",
        "\n",
        "3. **Matplotlib**:\n",
        "   - **Purpose**: Plotting library for creating static, animated, and interactive visualizations in Python.\n",
        "   - **Functions**: Plotting graphs and charts such as line plots, scatter plots, bar plots, histograms, and more.\n",
        "   - **Example Usage**: `import matplotlib.pyplot as plt`\n",
        "\n",
        "4. **Seaborn**:\n",
        "   - **Purpose**: High-level interface for drawing attractive and informative statistical graphics.\n",
        "   - **Functions**: Simplifies the process of creating complex visualizations such as heatmaps, violin plots, and pair plots.\n",
        "   - **Example Usage**: `import seaborn as sns`\n",
        "\n",
        "5. **Scikit-learn**:\n",
        "   - **Purpose**: Machine learning library providing simple and efficient tools for data mining and data analysis.\n",
        "   - **Functions**: Includes algorithms for classification, regression, clustering, and dimensionality reduction. Also offers preprocessing, model selection, and evaluation functions.\n",
        "   - **Example Usage**: `from sklearn.model_selection import train_test_split`\n",
        "\n",
        "6. **Matplotlib.backends.backend_pdf**:\n",
        "   - **Purpose**: Part of the Matplotlib library used for saving plots as PDF files.\n",
        "   - **Functions**: Provides the `PdfPages` class to facilitate the saving of multiple plots into a single PDF file.\n",
        "   - **Example Usage**: `from matplotlib.backends.backend_pdf import PdfPages`\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Pandas** and **NumPy** are foundational libraries for data manipulation and numerical operations, forming the backbone of most data science projects.\n",
        "- **Matplotlib** and **Seaborn** are essential for data visualization, helping to understand data patterns, trends, and insights through various graphical representations.\n",
        "- **Scikit-learn** is indispensable for building and evaluating machine learning models, offering a wide range of algorithms and tools to streamline the machine learning workflow.\n",
        "- Importing all necessary libraries at the beginning of the project ensures a smooth workflow and avoids import errors later in the code.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "import pandas as pd  # Import pandas library for data manipulation\n",
        "import numpy as np  # Import NumPy library for numerical computations\n",
        "import matplotlib.pyplot as plt  # Import Matplotlib for plotting\n",
        "import seaborn as sns  # Import Seaborn for enhanced visualizations\n",
        "from sklearn.model_selection import train_test_split, cross_val_score  # Import functions for model training and evaluation\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder  # Import preprocessing tools\n",
        "from sklearn.compose import ColumnTransformer  # Import ColumnTransformer for combining preprocessing steps\n",
        "from sklearn.pipeline import Pipeline  # Import Pipeline to streamline preprocessing and modeling steps\n",
        "from sklearn.impute import SimpleImputer  # Import SimpleImputer to handle missing values\n",
        "from sklearn.ensemble import RandomForestClassifier  # Import RandomForestClassifier for classification tasks\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Import metrics for model evaluation\n",
        "from matplotlib.backends.backend_pdf import PdfPages  # Import PdfPages for saving plots as PDF"
      ],
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      "cell_type": "code",
      "source": [
        "import pandas as pd  # Import pandas library for data manipulation"
      ],
      "metadata": {
        "id": "sODBRKnpHTqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Import numpy library for numerical operations"
      ],
      "metadata": {
        "id": "Rv0K4ReLI2vN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting"
      ],
      "metadata": {
        "id": "kEo1xK9EI2yV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Import seaborn for enhanced visualizations"
      ],
      "metadata": {
        "id": "rus00KNKI21N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score  # Import functions for splitting data"
      ],
      "metadata": {
        "id": "5MdlZ05vI24i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder  # Import preprocessing modules"
      ],
      "metadata": {
        "id": "u-NZLz_KI258"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer  # Import ColumnTransformer for preprocessing pipelines"
      ],
      "metadata": {
        "id": "2f0GL-JBI3IF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline  # Import Pipeline for chaining preprocessing and modeling"
      ],
      "metadata": {
        "id": "0-8AtG5rI3J2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer  # Import SimpleImputer for handling missing values"
      ],
      "metadata": {
        "id": "OTHaRECaI3LL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier  # Import RandomForestClassifier for modeling"
      ],
      "metadata": {
        "id": "Fr-jwZ2cI3Op"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Import metrics for model evaluation\n"
      ],
      "metadata": {
        "id": "VYqmYON8I3Ru"
      },
      "execution_count": null,
      "outputs": []
    },
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        "id": "pr3c_TCWI3T4"
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      "source": [
        "## <span style=\"color:darkorange;\">**Step 2: Load and Inspect Datasets**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "In this step, we will load the datasets required for our project. Inspecting the datasets involves looking at the first few rows, checking the shape, and understanding the structure of the data. This is crucial for getting an initial sense of the data and identifying any potential issues early on.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Loading Datasets**:\n",
        "   - **Purpose**: Importing the data from CSV files into Pandas DataFrames.\n",
        "   - **Function**: `pd.read_csv()`\n",
        "   - **Example Usage**: `train_data = pd.read_csv(\"/content/train.csv\")`\n",
        "\n",
        "2. **Inspecting Data**:\n",
        "   - **Head**: Displays the first few rows of the DataFrame to give a quick glance at the data.\n",
        "     - **Function**: `DataFrame.head()`\n",
        "     - **Example Usage**: `train_data.head()`\n",
        "   - **Shape**: Provides the number of rows and columns in the DataFrame.\n",
        "     - **Function**: `DataFrame.shape`\n",
        "     - **Example Usage**: `train_data.shape`\n",
        "   - **Columns**: Lists all column names in the DataFrame.\n",
        "     - **Function**: `DataFrame.columns`\n",
        "     - **Example Usage**: `train_data.columns`\n",
        "   - **Data Types**: Shows the data type of each column.\n",
        "     - **Function**: `DataFrame.dtypes`\n",
        "     - **Example Usage**: `train_data.dtypes`\n",
        "   - **Summary Statistics**: Gives a statistical summary of the DataFrame.\n",
        "     - **Function**: `DataFrame.describe()`\n",
        "     - **Example Usage**: `train_data.describe()`\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Loading Data**: Use `pd.read_csv()` to load the CSV files into DataFrames. Make sure the file paths are correct.\n",
        "- **Inspecting Data**:\n",
        "  - **Head**: Use `head()` to see a sample of the dataset, which helps in verifying that the data has been loaded correctly.\n",
        "  - **Shape**: Check the shape to understand the dimensions of the dataset.\n",
        "  - **Columns**: Listing columns helps in identifying what features are available in the dataset.\n",
        "  - **Data Types**: Knowing the data types is important for preprocessing steps such as encoding categorical variables and scaling numerical variables.\n",
        "  - **Summary Statistics**: Provides insights into the distribution and spread of the data, highlighting potential outliers and anomalies.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "import pandas as pd  # Import pandas library for data manipulation\n",
        "\n",
        "# Load datasets\n",
        "train_data = pd.read_csv(\"/content/train.csv\")  # Load train data from CSV file\n",
        "test_data = pd.read_csv(\"/content/test.csv\")  # Load test data from CSV file\n",
        "submission_data = pd.read_csv(\"/content/sample_submission.csv\")  # Load submission data from CSV file\n",
        "\n",
        "# Display samples of train, test, and submission data\n",
        "print(\"Train Data Sample:\")  # Print header\n",
        "print(train_data.head())  # Display first few rows of train data\n",
        "\n",
        "print(\"\\nTest Data Sample:\")  # Print header\n",
        "print(test_data.head())  # Display first few rows of test data\n",
        "\n",
        "print(\"\\nSubmission Data Sample:\")  # Print header\n",
        "print(submission_data.head())  # Display first few rows of submission data\n",
        "\n",
        "# Display shape of datasets\n",
        "print(\"\\nTrain Data Shape:\", train_data.shape)  # Display shape of train data\n",
        "print(\"Test Data Shape:\", test_data.shape)  # Display shape of test data\n",
        "print(\"Submission Data Shape:\", submission_data.shape)  # Display shape of submission data\n",
        "\n",
        "# Display columns of datasets\n",
        "print(\"\\nTrain Data Columns:\", train_data.columns.tolist())  # Display columns of train data\n",
        "print(\"Test Data Columns:\", test_data.columns.tolist())  # Display columns of test data\n",
        "print(\"Submission Data Columns:\", submission_data.columns.tolist())  # Display columns of submission data\n",
        "\n",
        "# Display data types of datasets\n",
        "print(\"\\nTrain Data Types:\")  # Print header\n",
        "print(train_data.dtypes)  # Display data types of train data\n",
        "\n",
        "print(\"\\nTest Data Types:\")  # Print header\n",
        "print(test_data.dtypes)  # Display data types of test data\n",
        "\n",
        "print(\"\\nSubmission Data Types:\")  # Print header\n",
        "print(submission_data.dtypes)  # Display data types of submission data\n",
        "\n",
        "# Summary statistics of train data\n",
        "print(\"\\nTrain Data Summary Statistics:\")  # Print header\n",
        "print(train_data.describe())  # Display summary statistics of train data"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Load datasets"
      ],
      "metadata": {
        "id": "KTACXG0hI3a9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.read_csv(\"/content/train.csv\")  # Load train data from CSV file"
      ],
      "metadata": {
        "id": "bVIDSrtlI3fV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = pd.read_csv(\"/content/test.csv\")  # Load test data from CSV file"
      ],
      "metadata": {
        "id": "YPTbtGh9I3mV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_data = pd.read_csv(\"/content/sample_submission.csv\")  # Load submission data from CSV file"
      ],
      "metadata": {
        "id": "99arltfUI3pt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MUyg1iXXI3ri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KYq-IVJEI3u4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display samples of train, test, and submission data"
      ],
      "metadata": {
        "id": "r55Sp0akI30F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train Data Sample:\")  # Print header\n",
        "print(train_data.head())  # Display first few rows of train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAmCVNz7I33N",
        "outputId": "70398b32-6bc3-44c3-cc82-12bd5a8eba64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data Sample:\n",
            "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0   1    Male   44                1         28.0                   0   \n",
            "1   2    Male   76                1          3.0                   0   \n",
            "2   3    Male   47                1         28.0                   0   \n",
            "3   4    Male   21                1         11.0                   1   \n",
            "4   5  Female   29                1         41.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
            "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
            "1    1-2 Year             No         33536.0                  26.0      183   \n",
            "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
            "3    < 1 Year             No         28619.0                 152.0      203   \n",
            "4    < 1 Year             No         27496.0                 152.0       39   \n",
            "\n",
            "   Response  \n",
            "0         1  \n",
            "1         0  \n",
            "2         1  \n",
            "3         0  \n",
            "4         0  \n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest Data Sample:\") # Print header\n",
        "print(test_data.head())  # Display first few rows of test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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          "text": [
            "\n",
            "Test Data Sample:\n",
            "       id Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0  381110   Male   25                1         11.0                   1   \n",
            "1  381111   Male   40                1         28.0                   0   \n",
            "2  381112   Male   47                1         28.0                   0   \n",
            "3  381113   Male   24                1         27.0                   1   \n",
            "4  381114   Male   27                1         28.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \n",
            "0    < 1 Year             No         35786.0                 152.0       53  \n",
            "1    1-2 Year            Yes         33762.0                   7.0      111  \n",
            "2    1-2 Year            Yes         40050.0                 124.0      199  \n",
            "3    < 1 Year            Yes         37356.0                 152.0      187  \n",
            "4    < 1 Year             No         59097.0                 152.0      297  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mwr0ESdiKC22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t6IM6BagKC-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSubmission Data Sample:\") # Print header\n",
        "print(submission_data.head())  # Display first few rows of submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xv2_VtluKDBW",
        "outputId": "d51e2e0b-968a-4462-f4fa-b9baf8e21cd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Submission Data Sample:\n",
            "       id  Response\n",
            "0  381110         0\n",
            "1  381111         0\n",
            "2  381112         0\n",
            "3  381113         0\n",
            "4  381114         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bdkhzt4GKK2-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p6coIHgcKK6e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display shape of datasets"
      ],
      "metadata": {
        "id": "MCwb2acPKK-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Data Shape:\", train_data.shape)  # Print shape of train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FciQgbuSKLGG",
        "outputId": "98726a6f-1fc6-4941-c5bc-b2e229fe2a26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Data Shape: (381109, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hkkwx5MWKLJi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Data Shape:\", test_data.shape)  # Print shape of test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OqK-VW6TI4FV",
        "outputId": "fde05000-2fa8-4f85-d009-4286264564f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Data Shape: (127037, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9_T6eIwgI4Gm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Submission Data Shape:\", submission_data.shape)  # Print shape of submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmW_Vb3zI4JJ",
        "outputId": "6eb1183b-86fb-41f4-9a6b-17f3bae1f2ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submission Data Shape: (127037, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BhFHdklDI4Lv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aLt-p1T7HTsT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display columns of datasets"
      ],
      "metadata": {
        "id": "tzSPlA-EKasd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Data Columns:\", train_data.columns.tolist())  # Print columns of train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnvqYDOyKazi",
        "outputId": "4ec0afa6-7469-48dd-cc2e-a27b426c66d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Data Columns: ['id', 'Gender', 'Age', 'Driving_License', 'Region_Code', 'Previously_Insured', 'Vehicle_Age', 'Vehicle_Damage', 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage', 'Response']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GPHeXDYNKa1V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Data Columns:\", test_data.columns.tolist())  # Print columns of test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHRWq9AUKa58",
        "outputId": "63b46a6a-e9d8-4c5e-a4c6-df00db1a12eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Data Columns: ['id', 'Gender', 'Age', 'Driving_License', 'Region_Code', 'Previously_Insured', 'Vehicle_Age', 'Vehicle_Damage', 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wmNsLfdHKa98"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Submission Data Columns:\", submission_data.columns.tolist())  # Print columns of submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSy52jkEKlC4",
        "outputId": "b9b78049-6136-4a7c-9bea-f0f5e8cf5392"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Submission Data Columns: ['id', 'Response']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lhUXdQPVKlGT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9kFwRnJxKlJP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display data types of datasets"
      ],
      "metadata": {
        "id": "euIj_YqGKlMJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Data Types:\")  # Print header\n",
        "print(train_data.dtypes)  # Print data types of train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9DMDGVXKlRI",
        "outputId": "5eb2cb3a-75fd-41bd-8715-0c565966ad71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Data Types:\n",
            "id                        int64\n",
            "Gender                   object\n",
            "Age                       int64\n",
            "Driving_License           int64\n",
            "Region_Code             float64\n",
            "Previously_Insured        int64\n",
            "Vehicle_Age              object\n",
            "Vehicle_Damage           object\n",
            "Annual_Premium          float64\n",
            "Policy_Sales_Channel    float64\n",
            "Vintage                   int64\n",
            "Response                  int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BVFNQt9bKlUY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest Data Types:\")  # Print header\n",
        "print(test_data.dtypes)  # Print data types of test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MajBUhG6KlWQ",
        "outputId": "eb3b89ed-ea26-4315-ac21-ced8a588ced8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Data Types:\n",
            "id                        int64\n",
            "Gender                   object\n",
            "Age                       int64\n",
            "Driving_License           int64\n",
            "Region_Code             float64\n",
            "Previously_Insured        int64\n",
            "Vehicle_Age              object\n",
            "Vehicle_Damage           object\n",
            "Annual_Premium          float64\n",
            "Policy_Sales_Channel    float64\n",
            "Vintage                   int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iIH5nhi5KlZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSubmission Data Types:\")  # Print header\n",
        "print(submission_data.dtypes)  # Print data types of submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iF0RPcyQKlaq",
        "outputId": "8a45848f-127e-48eb-b5a8-fb2657c95bda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Submission Data Types:\n",
            "id          int64\n",
            "Response    int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y6jIEI_0Kz74"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TTphOyplK0AH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics of train data"
      ],
      "metadata": {
        "id": "9eSkA17CK0Df"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Data Summary Statistics:\")  # Print header\n",
        "print(train_data.describe())  # Display summary statistics of train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDLZ4ANKK0GZ",
        "outputId": "98f8f41b-7b9d-486b-dd2e-08f2b4d813d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Data Summary Statistics:\n",
            "                  id            Age  Driving_License    Region_Code  \\\n",
            "count  381109.000000  381109.000000    381109.000000  381109.000000   \n",
            "mean   190555.000000      38.822584         0.997869      26.388807   \n",
            "std    110016.836208      15.511611         0.046110      13.229888   \n",
            "min         1.000000      20.000000         0.000000       0.000000   \n",
            "25%     95278.000000      25.000000         1.000000      15.000000   \n",
            "50%    190555.000000      36.000000         1.000000      28.000000   \n",
            "75%    285832.000000      49.000000         1.000000      35.000000   \n",
            "max    381109.000000      85.000000         1.000000      52.000000   \n",
            "\n",
            "       Previously_Insured  Annual_Premium  Policy_Sales_Channel  \\\n",
            "count       381109.000000   381109.000000         381109.000000   \n",
            "mean             0.458210    30564.389581            112.034295   \n",
            "std              0.498251    17213.155057             54.203995   \n",
            "min              0.000000     2630.000000              1.000000   \n",
            "25%              0.000000    24405.000000             29.000000   \n",
            "50%              0.000000    31669.000000            133.000000   \n",
            "75%              1.000000    39400.000000            152.000000   \n",
            "max              1.000000   540165.000000            163.000000   \n",
            "\n",
            "             Vintage       Response  \n",
            "count  381109.000000  381109.000000  \n",
            "mean      154.347397       0.122563  \n",
            "std        83.671304       0.327936  \n",
            "min        10.000000       0.000000  \n",
            "25%        82.000000       0.000000  \n",
            "50%       154.000000       0.000000  \n",
            "75%       227.000000       0.000000  \n",
            "max       299.000000       1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-zuUblBDK0Jf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gC5GTbKQK0Ml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:darkorange;\">**Step 3: Exploratory Data Analysis (EDA)**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "Exploratory Data Analysis (EDA) is a critical step in the data analysis process. It involves summarizing the main characteristics of the data, often using visual methods. EDA helps in understanding the data, uncovering patterns, spotting anomalies, and testing hypotheses.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Distribution Analysis**:\n",
        "   - **Purpose**: To understand the distribution of individual features.\n",
        "   - **Techniques**: Histograms, KDE plots, Box plots.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     sns.histplot(train_data['Age'], bins=30, kde=True)\n",
        "     sns.boxplot(x='Age', data=train_data)\n",
        "     ```\n",
        "\n",
        "2. **Categorical Data Analysis**:\n",
        "   - **Purpose**: To analyze the frequency and distribution of categorical variables.\n",
        "   - **Techniques**: Count plots, Bar plots.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     sns.countplot(x='Gender', data=train_data)\n",
        "     sns.barplot(x='Vehicle_Age', y='Annual_Premium', data=train_data)\n",
        "     ```\n",
        "\n",
        "3. **Correlation Analysis**:\n",
        "   - **Purpose**: To understand relationships between numerical variables.\n",
        "   - **Techniques**: Heatmaps, Pair plots.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     sns.heatmap(train_data.corr(), annot=True, cmap='coolwarm')\n",
        "     sns.pairplot(train_data)\n",
        "     ```\n",
        "\n",
        "4. **Missing Values Analysis**:\n",
        "   - **Purpose**: To identify and analyze missing data.\n",
        "   - **Techniques**: Heatmaps, Bar plots.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     sns.heatmap(train_data.isnull(), cbar=False)\n",
        "     train_data.isnull().sum().plot(kind='bar')\n",
        "     ```\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Distribution Analysis**: Helps in understanding the spread and central tendency of the data.\n",
        "- **Categorical Data Analysis**: Provides insights into the distribution and frequency of categorical variables.\n",
        "- **Correlation Analysis**: Identifies relationships and dependencies between variables, which can be crucial for feature selection and engineering.\n",
        "- **Missing Values Analysis**: Detects missing values, which need to be handled appropriately to avoid biases in the analysis and modeling.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "import pandas as pd  # Import pandas library for data manipulation\n",
        "import matplotlib.pyplot as plt  # Import matplotlib for plotting\n",
        "import seaborn as sns  # Import seaborn for enhanced visualizations\n",
        "from matplotlib.backends.backend_pdf import PdfPages  # Import PdfPages for saving plots as PDF\n",
        "\n",
        "# Function to save plots as PDF\n",
        "def save_plot_to_pdf(plot, filename):\n",
        "    with PdfPages(filename) as pdf:  # Open PDF file for saving\n",
        "        pdf.savefig(plot)  # Save plot to PDF\n",
        "        plt.close()  # Close plot\n",
        "\n",
        "# Function to display head of DataFrame and save as CSV\n",
        "def display_head_and_save(df, filename):\n",
        "    print(f\"\\nHead of {filename}:\")  # Print header\n",
        "    print(df.head())  # Display head of DataFrame\n",
        "    df.to_csv(filename, index=False)  # Save DataFrame to CSV file\n",
        "\n",
        "# Distribution Analysis\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.histplot(train_data['Age'], bins=30, kde=True, color='red')  # Plot distribution of 'Age'\n",
        "plt.title('Distribution of Age in Train Data')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "age_distribution_plot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.boxplot(x='Age', data=train_data, color='purple')  # Plot boxplot of 'Age'\n",
        "plt.title('Boxplot of Age in Train Data')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "age_boxplot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "# Categorical Data Analysis\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.countplot(x='Gender', data=train_data, palette='viridis')  # Plot count of 'Gender'\n",
        "plt.title('Count of Gender in Train Data')  # Set plot title\n",
        "plt.xlabel('Gender')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "gender_count_plot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.barplot(x='Vehicle_Age', y='Annual_Premium', data=train_data, palette='magma')  # Plot bar plot of 'Vehicle_Age' vs 'Annual_Premium'\n",
        "plt.title('Annual Premium by Vehicle Age in Train Data')  # Set plot title\n",
        "plt.xlabel('Vehicle Age')  # Set x-axis label\n",
        "plt.ylabel('Annual Premium')  # Set y-axis label\n",
        "vehicle_age_premium_plot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "# Correlation Analysis\n",
        "plt.figure(figsize=(10, 8))  # Set figure size\n",
        "sns.heatmap(train_data.corr(), annot=True, cmap='coolwarm')  # Plot heatmap of correlations\n",
        "plt.title('Correlation Matrix of Train Data')  # Set plot title\n",
        "correlation_heatmap = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "sns.pairplot(train_data, hue='Gender', palette='bright')  # Plot pair plot\n",
        "plt.suptitle('Pair Plot of Train Data', y=1.02)  # Set plot title\n",
        "pairplot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "# Missing Values Analysis\n",
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.heatmap(train_data.isnull(), cbar=False, cmap='viridis')  # Plot heatmap of missing values\n",
        "plt.title('Missing Values Heatmap of Train Data')  # Set plot title\n",
        "missing_values_heatmap = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "train_data.isnull().sum().plot(kind='bar', color='crimson')  # Plot bar plot of missing values\n",
        "plt.title('Missing Values Count in Train Data')  # Set plot title\n",
        "plt.xlabel('Columns')  # Set x-axis label\n",
        "plt.ylabel('Missing Values Count')  # Set y-axis label\n",
        "missing_values_barplot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "# Save plots as PDFs\n",
        "save_plot_to_pdf(age_distribution_plot, 'age_distribution_plot.pdf')  # Save age distribution plot\n",
        "save_plot_to_pdf(age_boxplot, 'age_boxplot.pdf')  # Save age boxplot\n",
        "save_plot_to_pdf(gender_count_plot, 'gender_count_plot.pdf')  # Save gender count plot\n",
        "save_plot_to_pdf(vehicle_age_premium_plot, 'vehicle_age_premium_plot.pdf')  # Save vehicle age premium plot\n",
        "save_plot_to_pdf(correlation_heatmap, 'correlation_heatmap.pdf')  # Save correlation heatmap\n",
        "save_plot_to_pdf(pairplot, 'pairplot.pdf')  # Save pair plot\n",
        "save_plot_to_pdf(missing_values_heatmap, 'missing_values_heatmap.pdf')  # Save missing values heatmap\n",
        "save_plot_to_pdf(missing_values_barplot, 'missing_values_barplot.pdf')  # Save missing values bar plot\n",
        "\n",
        "# Display heads and save as CSV\n",
        "display_head_and_save(train_data, 'train_data.csv')  # Display and save train data\n",
        "display_head_and_save(test_data, 'test_data.csv')  # Display and save test data\n",
        "display_head_and_save(submission_data, 'submission_data.csv')  # Display and save submission data\n",
        "\n",
        "# Display all saved files head\n",
        "print(\"\\nHead of saved CSV files:\")  # Print header\n",
        "train_data_saved = pd.read_csv('train_data.csv')  # Read saved train data\n",
        "test_data_saved = pd.read_csv('test_data.csv')  # Read saved test data\n",
        "submission_data_saved = pd.read_csv('submission_data.csv')  # Read saved submission data\n",
        "\n",
        "print(train_data_saved.head())  # Print head of train data\n",
        "print(test_data_saved.head())  # Print head of test data\n",
        "print(submission_data_saved.head())  # Print head of submission data"
      ],
      "metadata": {
        "id": "Q8uYwXltMmS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "_MU_Drd_LEqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: Distribution of Response in Train Data"
      ],
      "metadata": {
        "id": "sq3_luyEK0Pf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Response', data=train_data)  # Plot count of Response variable in train data\n",
        "plt.title('Distribution of Response')  # Set plot title\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "C-TvHaHXLAdP",
        "outputId": "3359895a-3037-45fc-87a4-0aeeb92d27f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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TFY8pKPFTj3q4UmZmpjFs2DDD19fX8PDwMFq2bHnVxz7c6CMVfqo2Pj6+1KMlXn/9dSM8PNzw8vIyatasabRs2dKYPHmycfLkScMwDGPv3r3GoEGDjPr16xt2u93w9/c3evfubezZs8cco+SRCvPmzTNeeuklIzg42LDb7caDDz5ofP7556X6+OSTT4z27dsbXl5ehsPhMPr06WN88cUXLjUl78ErH5VQ8n5OT083DKPs783CwkLjxRdfNO677z7Dbrcbd911lxEeHm4899xzRm5ubpmOLWAVm2FU4FWXAIBb1vHjx9WoUSPNmzdPTz/9dEW3A9zyuKYKAADAAoQqAAAACxCqAAAALMA1VQAAABao0DNVS5YsMb9Py+FwyOl0unz/VufOnWWz2VymK78v6sSJE4qOjla1atXk7++vSZMm6dKlSy41W7du1f333y+73a6QkBDFx8eX6mXx4sVq2LChPD09FRERod27d7usv3DhgmJjY1W7dm3VqFFD/fr1U2ZmpnUHAwAAVGoVGqrq1aunF154QampqdqzZ4+6du2qRx55RAcPHjRrnnjiCZ06dcqc5s6da64rKipSdHS0CgsLtWPHDi1fvlzx8fGaMWOGWZOenq7o6Gh16dJFaWlpGj9+vEaOHKmNGzeaNStXrlRcXJxmzpypvXv3KjQ0VFFRUS7fGzVhwgR99NFHWr16tZKTk3Xy5Ek9+uijN/kIAQCAyuKW+/ivVq1amjdvnkaMGKHOnTsrLCxMCxYsuGrthg0b1Lt3b508eVIBAQGSpKVLl2rKlCnKzs6Wh4eHpkyZovXr1+vAgQPmdgMHDlROTo4SEhIk/fglpW3atDGftFxcXKzg4GCNGzdOU6dOVW5urvz8/LRixQr1799f0o9fvdCsWTOlpKSoXbt2ZXptxcXFOnnypGrWrMlXKgAAUEkYhqGzZ88qKCio1JelX1l4S7h06ZLxz3/+0/Dw8DAOHjxoGMaPDw/09fU1ateubdx3333G1KlTjfz8fHObZ5991ggNDXUZ53//+58hydi7d69hGIbx4IMPGn/4wx9cat58803D4XAYhmEYBQUFhru7u7FmzRqXmiFDhhgPP/ywYRg/PoROknHmzBmXmvr16xsvv/zyT76mCxcuGLm5ueb0xRdfuDyskYmJiYmJianyTF9//fU1s0yFf03N/v375XQ6deHCBdWoUUNr1qxR8+bNJUm//e1v1aBBAwUFBWnfvn2aMmWKjhw5on/961+SpIyMDPMMVYmS+YyMjGvW5OXl6fz58zpz5oyKioquWlPyBagZGRny8PCQj49PqZqS/VzNnDlz9Nxzz5Va/vXXX8vhcFzv0AAAgFtAXl6egoODr/s1UxUeqpo0aaK0tDTl5ubq/fffV0xMjJKTk9W8eXONGjXKrGvZsqXq1Kmjbt266csvv7Tse7pupmnTpikuLs6cL/mhlFyYDwAAKo/rXbpT4c+p8vDwUEhIiMLDwzVnzhyFhoZq4cKFV62NiIiQJB07dkySFBgYWOoOvJL5wMDAa9Y4HA55eXnJ19dX7u7uV625fIzCwkKXb1+/suZq7Ha7GaAIUgAA3N4qPFRdqbi4WAUFBVddl5aWJkmqU6eOJMnpdGr//v0ud+klJibK4XCYHyE6nU4lJSW5jJOYmCin0ynpx1AXHh7uUlNcXKykpCSzJjw8XFWrVnWpOXLkiE6cOGHWAACAO9w1r7i6yaZOnWokJycb6enpxr59+4ypU6caNpvN2LRpk3Hs2DFj9uzZxp49e4z09HTjgw8+MBo3bmx07NjR3P7SpUtGixYtjO7duxtpaWlGQkKC4efnZ0ybNs2s+d///mdUq1bNmDRpknHo0CFj8eLFhru7u5GQkGDWvPfee4bdbjfi4+ONL774whg1apTh4+NjZGRkmDWjR4826tevb2zevNnYs2eP4XQ6DafTeUOvNzc315DEN6cDAFCJlPXvd4WGquHDhxsNGjQwPDw8DD8/P6Nbt27Gpk2bDMMwjBMnThgdO3Y0atWqZdjtdiMkJMSYNGlSqRd0/Phxo2fPnoaXl5fh6+trTJw40bh48aJLzZYtW4ywsDDDw8PDaNy4sbFs2bJSvbz66qtG/fr1DQ8PD6Nt27bGzp07XdafP3/eePLJJ4277rrLqFatmvHrX//aOHXq1A29XkIVAACVT1n/ft9yz6m6neXl5cnb21u5ublcXwUAQCVR1r/ft9w1VQAAAJURoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAJVKroBWC980lsV3QJwy0mdN6SiWwBwm+NMFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYoEJD1ZIlS9SqVSs5HA45HA45nU5t2LDBXH/hwgXFxsaqdu3aqlGjhvr166fMzEyXMU6cOKHo6GhVq1ZN/v7+mjRpki5duuRSs3XrVt1///2y2+0KCQlRfHx8qV4WL16shg0bytPTUxEREdq9e7fL+rL0AgAA7lwVGqrq1aunF154QampqdqzZ4+6du2qRx55RAcPHpQkTZgwQR999JFWr16t5ORknTx5Uo8++qi5fVFRkaKjo1VYWKgdO3Zo+fLlio+P14wZM8ya9PR0RUdHq0uXLkpLS9P48eM1cuRIbdy40axZuXKl4uLiNHPmTO3du1ehoaGKiopSVlaWWXO9XgAAwJ3NZhiGUdFNXK5WrVqaN2+e+vfvLz8/P61YsUL9+/eXJB0+fFjNmjVTSkqK2rVrpw0bNqh37946efKkAgICJElLly7VlClTlJ2dLQ8PD02ZMkXr16/XgQMHzH0MHDhQOTk5SkhIkCRFRESoTZs2WrRokSSpuLhYwcHBGjdunKZOnarc3Nzr9lIWeXl58vb2Vm5urhwOh2XH7Erhk966aWMDlVXqvCEV3QKASqqsf79vmWuqioqK9N577yk/P19Op1Opqam6ePGiIiMjzZqmTZuqfv36SklJkSSlpKSoZcuWZqCSpKioKOXl5Zlnu1JSUlzGKKkpGaOwsFCpqakuNW5uboqMjDRrytLL1RQUFCgvL89lAgAAt6cKD1X79+9XjRo1ZLfbNXr0aK1Zs0bNmzdXRkaGPDw85OPj41IfEBCgjIwMSVJGRoZLoCpZX7LuWjV5eXk6f/68vvvuOxUVFV215vIxrtfL1cyZM0fe3t7mFBwcXLaDAgAAKp0KD1VNmjRRWlqadu3apTFjxigmJkZffPFFRbdliWnTpik3N9ecvv7664puCQAA3CRVKroBDw8PhYSESJLCw8P16aefauHChRowYIAKCwuVk5PjcoYoMzNTgYGBkqTAwMBSd+mV3JF3ec2Vd+llZmbK4XDIy8tL7u7ucnd3v2rN5WNcr5ersdvtstvtN3A0AABAZVXhZ6quVFxcrIKCAoWHh6tq1apKSkoy1x05ckQnTpyQ0+mUJDmdTu3fv9/lLr3ExEQ5HA41b97crLl8jJKakjE8PDwUHh7uUlNcXKykpCSzpiy9AACAO1uFnqmaNm2aevbsqfr16+vs2bNasWKFtm7dqo0bN8rb21sjRoxQXFycatWqJYfDoXHjxsnpdJp323Xv3l3NmzfX448/rrlz5yojI0PTp09XbGyseYZo9OjRWrRokSZPnqzhw4dr8+bNWrVqldavX2/2ERcXp5iYGLVu3Vpt27bVggULlJ+fr2HDhklSmXoBAAB3tgoNVVlZWRoyZIhOnTolb29vtWrVShs3btRDDz0kSZo/f77c3NzUr18/FRQUKCoqSq+99pq5vbu7u9atW6cxY8bI6XSqevXqiomJ0ezZs82aRo0aaf369ZowYYIWLlyoevXq6Y033lBUVJRZM2DAAGVnZ2vGjBnKyMhQWFiYEhISXC5ev14vAADgznbLPafqdsZzqoCKw3OqAJRXpXtOFQAAQGVGqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsUKGhas6cOWrTpo1q1qwpf39/9e3bV0eOHHGp6dy5s2w2m8s0evRol5oTJ04oOjpa1apVk7+/vyZNmqRLly651GzdulX333+/7Ha7QkJCFB8fX6qfxYsXq2HDhvL09FRERIR2797tsv7ChQuKjY1V7dq1VaNGDfXr10+ZmZnWHAwAAFCpVWioSk5OVmxsrHbu3KnExERdvHhR3bt3V35+vkvdE088oVOnTpnT3LlzzXVFRUWKjo5WYWGhduzYoeXLlys+Pl4zZswwa9LT0xUdHa0uXbooLS1N48eP18iRI7Vx40azZuXKlYqLi9PMmTO1d+9ehYaGKioqSllZWWbNhAkT9NFHH2n16tVKTk7WyZMn9eijj97EIwQAACoLm2EYRkU3USI7O1v+/v5KTk5Wx44dJf14piosLEwLFiy46jYbNmxQ7969dfLkSQUEBEiSli5dqilTpig7O1seHh6aMmWK1q9frwMHDpjbDRw4UDk5OUpISJAkRUREqE2bNlq0aJEkqbi4WMHBwRo3bpymTp2q3Nxc+fn5acWKFerfv78k6fDhw2rWrJlSUlLUrl27676+vLw8eXt7Kzc3Vw6Ho9zH6XrCJ71108YGKqvUeUMqugUAlVRZ/37fUtdU5ebmSpJq1arlsvzdd9+Vr6+vWrRooWnTpumHH34w16WkpKhly5ZmoJKkqKgo5eXl6eDBg2ZNZGSky5hRUVFKSUmRJBUWFio1NdWlxs3NTZGRkWZNamqqLl686FLTtGlT1a9f36wBAAB3rioV3UCJ4uJijR8/Xu3bt1eLFi3M5b/97W/VoEEDBQUFad++fZoyZYqOHDmif/3rX5KkjIwMl0AlyZzPyMi4Zk1eXp7Onz+vM2fOqKio6Ko1hw8fNsfw8PCQj49PqZqS/VypoKBABQUF5nxeXl5ZDwcAAKhkbplQFRsbqwMHDug///mPy/JRo0aZ/27ZsqXq1Kmjbt266csvv9Tdd9/9S7d5Q+bMmaPnnnuuotsAAAC/gFvi47+xY8dq3bp12rJli+rVq3fN2oiICEnSsWPHJEmBgYGl7sArmQ8MDLxmjcPhkJeXl3x9feXu7n7VmsvHKCwsVE5Ozk/WXGnatGnKzc01p6+//vqarw0AAFReFRqqDMPQ2LFjtWbNGm3evFmNGjW67jZpaWmSpDp16kiSnE6n9u/f73KXXmJiohwOh5o3b27WJCUluYyTmJgop9MpSfLw8FB4eLhLTXFxsZKSksya8PBwVa1a1aXmyJEjOnHihFlzJbvdLofD4TIBAIDbU4V+/BcbG6sVK1bogw8+UM2aNc1rk7y9veXl5aUvv/xSK1asUK9evVS7dm3t27dPEyZMUMeOHdWqVStJUvfu3dW8eXM9/vjjmjt3rjIyMjR9+nTFxsbKbrdLkkaPHq1FixZp8uTJGj58uDZv3qxVq1Zp/fr1Zi9xcXGKiYlR69at1bZtWy1YsED5+fkaNmyY2dOIESMUFxenWrVqyeFwaNy4cXI6nWW68w8AANzeKjRULVmyRNKPj0243LJlyzR06FB5eHjok08+MQNOcHCw+vXrp+nTp5u17u7uWrduncaMGSOn06nq1asrJiZGs2fPNmsaNWqk9evXa8KECVq4cKHq1aunN954Q1FRUWbNgAEDlJ2drRkzZigjI0NhYWFKSEhwuXh9/vz5cnNzU79+/VRQUKCoqCi99tprN+noAACAyuSWek7V7Y7nVAEVh+dUASivSvmcKgAAgMqKUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABggQoNVXPmzFGbNm1Us2ZN+fv7q2/fvjpy5IhLzYULFxQbG6vatWurRo0a6tevnzIzM11qTpw4oejoaFWrVk3+/v6aNGmSLl265FKzdetW3X///bLb7QoJCVF8fHypfhYvXqyGDRvK09NTERER2r179w33AgAA7kwVGqqSk5MVGxurnTt3KjExURcvXlT37t2Vn59v1kyYMEEfffSRVq9ereTkZJ08eVKPPvqoub6oqEjR0dEqLCzUjh07tHz5csXHx2vGjBlmTXp6uqKjo9WlSxelpaVp/PjxGjlypDZu3GjWrFy5UnFxcZo5c6b27t2r0NBQRUVFKSsrq8y9AACAO5fNMAyjopsokZ2dLX9/fyUnJ6tjx47Kzc2Vn5+fVqxYof79+0uSDh8+rGbNmiklJUXt2rXThg0b1Lt3b508eVIBAQGSpKVLl2rKlCnKzs6Wh4eHpkyZovXr1+vAgQPmvgYOHKicnBwlJCRIkiIiItSmTRstWrRIklRcXKzg4GCNGzdOU6dOLVMv15OXlydvb2/l5ubK4XBYeuwuFz7prZs2NlBZpc4bUtEtAKikyvr3+5a6pio3N1eSVKtWLUlSamqqLl68qMjISLOmadOmql+/vlJSUiRJKSkpatmypRmoJCkqKkp5eXk6ePCgWXP5GCU1JWMUFhYqNTXVpcbNzU2RkZFmTVl6uVJBQYHy8vJcJgAAcHu6ZUJVcXGxxo8fr/bt26tFixaSpIyMDHl4eMjHx8elNiAgQBkZGWbN5YGqZH3JumvV5OXl6fz58/ruu+9UVFR01ZrLx7heL1eaM2eOvL29zSk4OLiMRwMAAFQ2t0yoio2N1YEDB/Tee+9VdCuWmTZtmnJzc83p66+/ruiWAADATVKlohuQpLFjx2rdunXatm2b6tWrZy4PDAxUYWGhcnJyXM4QZWZmKjAw0Ky58i69kjvyLq+58i69zMxMORwOeXl5yd3dXe7u7letuXyM6/VyJbvdLrvdfgNHAgAAVFYVeqbKMAyNHTtWa9as0ebNm9WoUSOX9eHh4apataqSkpLMZUeOHNGJEyfkdDolSU6nU/v373e5Sy8xMVEOh0PNmzc3ay4fo6SmZAwPDw+Fh4e71BQXFyspKcmsKUsvAADgzlWhZ6piY2O1YsUKffDBB6pZs6Z5bZK3t7e8vLzk7e2tESNGKC4uTrVq1ZLD4dC4cePkdDrNu+26d++u5s2b6/HHH9fcuXOVkZGh6dOnKzY21jxLNHr0aC1atEiTJ0/W8OHDtXnzZq1atUrr1683e4mLi1NMTIxat26ttm3basGCBcrPz9ewYcPMnq7XCwAAuHNVaKhasmSJJKlz584uy5ctW6ahQ4dKkubPny83Nzf169dPBQUFioqK0muvvWbWuru7a926dRozZoycTqeqV6+umJgYzZ4926xp1KiR1q9frwkTJmjhwoWqV6+e3njjDUVFRZk1AwYMUHZ2tmbMmKGMjAyFhYUpISHB5eL16/UCAADuXLfUc6pudzynCqg4PKcKQHlVyudUAQAAVFaEKgAAAAuUK1R17dpVOTk5pZbn5eWpa9euP7cnAACASqdcoWrr1q0qLCwstfzChQv697///bObAgAAqGxu6O6/ffv2mf/+4osvXL6epaioSAkJCapbt6513QEAAFQSNxSqwsLCZLPZZLPZrvoxn5eXl1599VXLmgMAAKgsbihUpaenyzAMNW7cWLt375afn5+5zsPDQ/7+/nJ3d7e8SQAAgFvdDYWqBg0aSPrxK1wAAADw/5X7iepHjx7Vli1blJWVVSpkzZgx42c3BgAAUJmUK1T9/e9/15gxY+Tr66vAwEDZbDZznc1mI1QBAIA7TrlC1fPPP68///nPmjJlitX9AAAAVErlek7VmTNn9Nhjj1ndCwAAQKVVrlD12GOPadOmTVb3AgAAUGmV6+O/kJAQPfvss9q5c6datmypqlWruqx/6qmnLGkOAACgsihXqHr99ddVo0YNJScnKzk52WWdzWYjVAEAgDtOuUJVenq61X0AAABUauW6pgoAAACuynWmavjw4ddc/+abb5arGQAAgMqqXKHqzJkzLvMXL17UgQMHlJOTc9UvWgYAALjdlStUrVmzptSy4uJijRkzRnfffffPbgoAAKCyseyaKjc3N8XFxWn+/PlWDQkAAFBpWHqh+pdffqlLly5ZOSQAAEClUK6P/+Li4lzmDcPQqVOntH79esXExFjSGAAAQGVSrlD12Wefucy7ubnJz89PL7300nXvDAQAALgdlStUbdmyxeo+AAAAKrVyhaoS2dnZOnLkiCSpSZMm8vPzs6QpAACAyqZcF6rn5+dr+PDhqlOnjjp27KiOHTsqKChII0aM0A8//GB1jwAAALe8coWquLg4JScn66OPPlJOTo5ycnL0wQcfKDk5WRMnTrS6RwAAgFteuT7++7//+z+9//776ty5s7msV69e8vLy0m9+8xstWbLEqv4AAAAqhXKdqfrhhx8UEBBQarm/vz8f/wEAgDtSuUKV0+nUzJkzdeHCBXPZ+fPn9dxzz8npdFrWHAAAQGVRro//FixYoB49eqhevXoKDQ2VJH3++eey2+3atGmTpQ0CAABUBuUKVS1bttTRo0f17rvv6vDhw5KkQYMGafDgwfLy8rK0QQAAgMqgXKFqzpw5CggI0BNPPOGy/M0331R2dramTJliSXMAAACVRbmuqfrb3/6mpk2bllp+3333aenSpT+7KQAAgMqmXKEqIyNDderUKbXcz89Pp06d+tlNAQAAVDblClXBwcHavn17qeXbt29XUFDQz24KAACgsinXNVVPPPGExo8fr4sXL6pr166SpKSkJE2ePJknqgMAgDtSuULVpEmTdPr0aT355JMqLCyUJHl6emrKlCmaNm2apQ0CAABUBuUKVTabTS+++KKeffZZHTp0SF5eXrrnnntkt9ut7g8AAKBSKFeoKlGjRg21adPGql4AAAAqrXJdqA4AAABXFRqqtm3bpj59+igoKEg2m01r1651WT906FDZbDaXqUePHi4133//vQYPHiyHwyEfHx+NGDFC586dc6nZt2+fHnzwQXl6eio4OFhz584t1cvq1avVtGlTeXp6qmXLlvr4449d1huGoRkzZqhOnTry8vJSZGSkjh49as2BAAAAlV6Fhqr8/HyFhoZq8eLFP1nTo0cPnTp1ypz++c9/uqwfPHiwDh48qMTERK1bt07btm3TqFGjzPV5eXnq3r27GjRooNTUVM2bN0+zZs3S66+/btbs2LFDgwYN0ogRI/TZZ5+pb9++6tu3rw4cOGDWzJ07V6+88oqWLl2qXbt2qXr16oqKinL5UmkAAHDnshmGYVR0E9KPF7+vWbNGffv2NZcNHTpUOTk5pc5glTh06JCaN2+uTz/9VK1bt5YkJSQkqFevXvrmm28UFBSkJUuW6I9//KMyMjLk4eEhSZo6darWrl1rfm/hgAEDlJ+fr3Xr1pljt2vXTmFhYVq6dKkMw1BQUJAmTpyop59+WpKUm5urgIAAxcfHa+DAgWV6jXl5efL29lZubq4cDseNHqIyC5/01k0bG6isUucNqegWAFRSZf37fctfU7V161b5+/urSZMmGjNmjE6fPm2uS0lJkY+PjxmoJCkyMlJubm7atWuXWdOxY0czUElSVFSUjhw5ojNnzpg1kZGRLvuNiopSSkqKJCk9PV0ZGRkuNd7e3oqIiDBrrqagoEB5eXkuEwAAuD3d0qGqR48eeuutt5SUlKQXX3xRycnJ6tmzp4qKiiT9+HU5/v7+LttUqVJFtWrVUkZGhlkTEBDgUlMyf72ay9dfvt3Vaq5mzpw58vb2Nqfg4OAbev0AAKDy+FmPVLjZLv9YrWXLlmrVqpXuvvtubd26Vd26davAzspm2rRpiouLM+fz8vIIVgAA3KZu6TNVV2rcuLF8fX117NgxSVJgYKCysrJcai5duqTvv/9egYGBZk1mZqZLTcn89WouX3/5dleruRq73S6Hw+EyAQCA21OlClXffPONTp8+rTp16kiSnE6ncnJylJqaatZs3rxZxcXFioiIMGu2bdumixcvmjWJiYlq0qSJ7rrrLrMmKSnJZV+JiYlyOp2SpEaNGikwMNClJi8vT7t27TJrAADAna1CQ9W5c+eUlpamtLQ0ST9eEJ6WlqYTJ07o3LlzmjRpknbu3Knjx48rKSlJjzzyiEJCQhQVFSVJatasmXr06KEnnnhCu3fv1vbt2zV27FgNHDhQQUFBkqTf/va38vDw0IgRI3Tw4EGtXLlSCxcudPlY7g9/+IMSEhL00ksv6fDhw5o1a5b27NmjsWPHSvrxzsTx48fr+eef14cffqj9+/dryJAhCgoKcrlbEQAA3Lkq9JqqPXv2qEuXLuZ8SdCJiYnRkiVLtG/fPi1fvlw5OTkKCgpS9+7d9ac//cnlOwbfffddjR07Vt26dZObm5v69eunV155xVzv7e2tTZs2KTY2VuHh4fL19dWMGTNcnmX1wAMPaMWKFZo+fbqeeeYZ3XPPPVq7dq1atGhh1kyePFn5+fkaNWqUcnJy1KFDByUkJMjT0/NmHiIAAFBJ3DLPqboT8JwqoOLwnCoA5XXbPKcKAACgMiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABao0FC1bds29enTR0FBQbLZbFq7dq3LesMwNGPGDNWpU0deXl6KjIzU0aNHXWq+//57DR48WA6HQz4+PhoxYoTOnTvnUrNv3z49+OCD8vT0VHBwsObOnVuql9WrV6tp06by9PRUy5Yt9fHHH99wLwAA4M5VoaEqPz9foaGhWrx48VXXz507V6+88oqWLl2qXbt2qXr16oqKitKFCxfMmsGDB+vgwYNKTEzUunXrtG3bNo0aNcpcn5eXp+7du6tBgwZKTU3VvHnzNGvWLL3++utmzY4dOzRo0CCNGDFCn332mfr27au+ffvqwIEDN9QLAAC4c9kMwzAquglJstlsWrNmjfr27SvpxzNDQUFBmjhxop5++mlJUm5urgICAhQfH6+BAwfq0KFDat68uT799FO1bt1akpSQkKBevXrpm2++UVBQkJYsWaI//vGPysjIkIeHhyRp6tSpWrt2rQ4fPixJGjBggPLz87Vu3Tqzn3bt2iksLExLly4tUy9lkZeXJ29vb+Xm5srhcFhy3K4mfNJbN21soLJKnTekolsAUEmV9e/3LXtNVXp6ujIyMhQZGWku8/b2VkREhFJSUiRJKSkp8vHxMQOVJEVGRsrNzU27du0yazp27GgGKkmKiorSkSNHdObMGbPm8v2U1JTspyy9XE1BQYHy8vJcJgAAcHu6ZUNVRkaGJCkgIMBleUBAgLkuIyND/v7+LuurVKmiWrVqudRcbYzL9/FTNZevv14vVzNnzhx5e3ubU3Bw8HVeNQAAqKxu2VB1O5g2bZpyc3PN6euvv67olgAAwE1yy4aqwMBASVJmZqbL8szMTHNdYGCgsrKyXNZfunRJ33//vUvN1ca4fB8/VXP5+uv1cjV2u10Oh8NlAgAAt6dbNlQ1atRIgYGBSkpKMpfl5eVp165dcjqdkiSn06mcnBylpqaaNZs3b1ZxcbEiIiLMmm3btunixYtmTWJiopo0aaK77rrLrLl8PyU1JfspSy8AAODOVqGh6ty5c0pLS1NaWpqkHy8IT0tL04kTJ2Sz2TR+/Hg9//zz+vDDD7V//34NGTJEQUFB5h2CzZo1U48ePfTEE09o9+7d2r59u8aOHauBAwcqKChIkvTb3/5WHh4eGjFihA4ePKiVK1dq4cKFiouLM/v4wx/+oISEBL300ks6fPiwZs2apT179mjs2LGSVKZeAADAna1KRe58z5496tKlizlfEnRiYmIUHx+vyZMnKz8/X6NGjVJOTo46dOighIQEeXp6mtu8++67Gjt2rLp16yY3Nzf169dPr7zyirne29tbmzZtUmxsrMLDw+Xr66sZM2a4PMvqgQce0IoVKzR9+nQ988wzuueee7R27Vq1aNHCrClLLwAA4M51yzyn6k7Ac6qAisNzqgCUV6V/ThUAAEBlQqgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsMAtHapmzZolm83mMjVt2tRcf+HCBcXGxqp27dqqUaOG+vXrp8zMTJcxTpw4oejoaFWrVk3+/v6aNGmSLl265FKzdetW3X///bLb7QoJCVF8fHypXhYvXqyGDRvK09NTERER2r179015zQAAoHK6pUOVJN133306deqUOf3nP/8x102YMEEfffSRVq9ereTkZJ08eVKPPvqoub6oqEjR0dEqLCzUjh07tHz5csXHx2vGjBlmTXp6uqKjo9WlSxelpaVp/PjxGjlypDZu3GjWrFy5UnFxcZo5c6b27t2r0NBQRUVFKSsr65c5CAAA4JZnMwzDqOgmfsqsWbO0du1apaWllVqXm5srPz8/rVixQv3795ckHT58WM2aNVNKSoratWunDRs2qHfv3jp58qQCAgIkSUuXLtWUKVOUnZ0tDw8PTZkyRevXr9eBAwfMsQcOHKicnBwlJCRIkiIiItSmTRstWrRIklRcXKzg4GCNGzdOU6dOLfPrycvLk7e3t3Jzc+VwOMp7WK4rfNJbN21soLJKnTekolsAUEmV9e/3LX+m6ujRowoKClLjxo01ePBgnThxQpKUmpqqixcvKjIy0qxt2rSp6tevr5SUFElSSkqKWrZsaQYqSYqKilJeXp4OHjxo1lw+RklNyRiFhYVKTU11qXFzc1NkZKRZ81MKCgqUl5fnMgEAgNvTLR2qIiIiFB8fr4SEBC1ZskTp6el68MEHdfbsWWVkZMjDw0M+Pj4u2wQEBCgjI0OSlJGR4RKoStaXrLtWTV5ens6fP6/vvvtORUVFV60pGeOnzJkzR97e3uYUHBx8w8cAAABUDlUquoFr6dmzp/nvVq1aKSIiQg0aNNCqVavk5eVVgZ2VzbRp0xQXF2fO5+XlEawAALhN3dJnqq7k4+Oje++9V8eOHVNgYKAKCwuVk5PjUpOZmanAwEBJUmBgYKm7AUvmr1fjcDjk5eUlX19fubu7X7WmZIyfYrfb5XA4XCYAAHB7qlSh6ty5c/ryyy9Vp04dhYeHq2rVqkpKSjLXHzlyRCdOnJDT6ZQkOZ1O7d+/3+UuvcTERDkcDjVv3tysuXyMkpqSMTw8PBQeHu5SU1xcrKSkJLMGAADglv747+mnn1afPn3UoEEDnTx5UjNnzpS7u7sGDRokb29vjRgxQnFxcapVq5YcDofGjRsnp9Opdu3aSZK6d++u5s2b6/HHH9fcuXOVkZGh6dOnKzY2Vna7XZI0evRoLVq0SJMnT9bw4cO1efNmrVq1SuvXrzf7iIuLU0xMjFq3bq22bdtqwYIFys/P17BhwyrkuAC4c3F3L1DarXJ37y0dqr755hsNGjRIp0+flp+fnzp06KCdO3fKz89PkjR//ny5ubmpX79+KigoUFRUlF577TVze3d3d61bt05jxoyR0+lU9erVFRMTo9mzZ5s1jRo10vr16zVhwgQtXLhQ9erV0xtvvKGoqCizZsCAAcrOztaMGTOUkZGhsLAwJSQklLp4HQAA3Llu6edU3W54ThVQcW6V/5P9ufj9Bkq72b/ft81zqgAAACoDQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAULVDVq8eLEaNmwoT09PRUREaPfu3RXdEgAAuAUQqm7AypUrFRcXp5kzZ2rv3r0KDQ1VVFSUsrKyKro1AABQwQhVN+Dll1/WE088oWHDhql58+ZaunSpqlWrpjfffLOiWwMAABWMUFVGhYWFSk1NVWRkpLnMzc1NkZGRSklJqcDOAADAraBKRTdQWXz33XcqKipSQECAy/KAgAAdPnz4qtsUFBSooKDAnM/NzZUk5eXl3bxGJRUVnL+p4wOV0c3+vful8PsNlHazf79LxjcM45p1hKqbaM6cOXruuedKLQ8ODq6AboA7m/eroyu6BQA3yS/1+3327Fl5e3v/5HpCVRn5+vrK3d1dmZmZLsszMzMVGBh41W2mTZumuLg4c764uFjff/+9ateuLZvNdlP7RcXLy8tTcHCwvv76azkcjopuB4CF+P2+sxiGobNnzyooKOiadYSqMvLw8FB4eLiSkpLUt29fST+GpKSkJI0dO/aq29jtdtntdpdlPj4+N7lT3GocDgf/0QVuU/x+3zmudYaqBKHqBsTFxSkmJkatW7dW27ZttWDBAuXn52vYsGEV3RoAAKhghKobMGDAAGVnZ2vGjBnKyMhQWFiYEhISSl28DgAA7jyEqhs0duzYn/y4D7ic3W7XzJkzS30EDKDy4/cbV2Mzrnd/IAAAAK6Lh38CAABYgFAFAABgAUIVAACABQhVAAAAFiBUATfB4sWL1bBhQ3l6eioiIkK7d++u6JYAWGDbtm3q06ePgoKCZLPZtHbt2opuCbcQQhVgsZUrVyouLk4zZ87U3r17FRoaqqioKGVlZVV0awB+pvz8fIWGhmrx4sUV3QpuQTxSAbBYRESE2rRpo0WLFkn68euMgoODNW7cOE2dOrWCuwNgFZvNpjVr1phfXQZwpgqwUGFhoVJTUxUZGWkuc3NzU2RkpFJSUiqwMwDAzUaoAiz03XffqaioqNRXFwUEBCgjI6OCugIA/BIIVQAAABYgVAEW8vX1lbu7uzIzM12WZ2ZmKjAwsIK6AgD8EghVgIU8PDwUHh6upKQkc1lxcbGSkpLkdDorsDMAwM1WpaIbAG43cXFxiomJUevWrdW2bVstWLBA+fn5GjZsWEW3BuBnOnfunI4dO2bOp6enKy0tTbVq1VL9+vUrsDPcCnikAnATLFq0SPPmzVNGRobCwsL0yiuvKCIioqLbAvAzbd26VV26dCm1PCYmRvHx8b98Q7ilEKoAAAAswDVVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAbhtDhw6VzWaTzWZT1apV1ahRI02ePFkXLlyo6NYA3AH47j8At5UePXpo2bJlunjxolJTUxUTEyObzaYXX3yxolsDcJvjTBWA24rdbldgYKCCg4PVt29fRUZGKjExUZJUXFysOXPmqFGjRvLy8lJoaKjef/99c9szZ85o8ODB8vPzk5eXl+655x4tW7ZMknT8+HHZbDa99957euCBB+Tp6akWLVooOTnZZf/Jyclq27at7Ha76tSpo6lTp+rSpUvm+s6dO+upp57S5MmTVatWLQUGBmrWrFnmesMwNGvWLNWvX192u11BQUF66qmnzPUFBQV6+umnVbduXVWvXl0RERHaunXrTTiSAG4UoQrAbevAgQPasWOHPDw8JElz5szRW2+9paVLl+rgwYOaMGGCfve735nB6Nlnn9UXX3yhDRs26NChQ1qyZIl8fX1dxpw0aZImTpyozz77TE6nU3369NHp06clSd9++6169eqlNm3a6PPPP9eSJUv0j3/8Q88//7zLGMuXL1f16tW1a9cuzZ07V7NnzzaD3//93/9p/vz5+tvf/qajR49q7dq1atmypbnt2LFjlZKSovfee0/79u3TY489ph49eujo0aM37TgCKCMDAG4TMTExhru7u1G9enXDbrcbkgw3Nzfj/fffNy5cuGBUq1bN2LFjh8s2I0aMMAYNGmQYhmH06dPHGDZs2FXHTk9PNyQZL7zwgrns4sWLRr169YwXX3zRMAzDeOaZZ4wmTZoYxcXFZs3ixYuNGjVqGEVFRYZhGEanTp2MDh06uIzdpk0bY8qUKYZhGMZLL71k3HvvvUZhYWGpHr766ivD3d3d+Pbbb12Wd+vWzZg2bVqZjhGAm4drqgDcVrp06aIlS5YoPz9f8+fPV5UqVdSvXz8dPHhQP/zwgx566CGX+sLCQv3qV7+SJI0ZM0b9+vXT3r171b17d/Xt21cPPPCAS73T6TT/XaVKFbVu3VqHDh2SJB06dEhOp1M2m82sad++vc6dO6dvvvlG9evXlyS1atXKZcw6deooKytLkvTYY49pwYIFaty4sXr06KFevXqpT58+qlKlivbv36+ioiLde++9LtsXFBSodu3aP+ewAbAAoQrAbaV69eoKCQmRJL355psKDQ3VP/7xD7Vo0UKStH79etWtW9dlG7vdLknq2bOnvvrqK3388cdKTExUt27dFBsbq7/+9a+W9li1alWXeZvNpuLiYklScHCwjhw5ok8++USJiYl68sknNW/ePCUnJ+vcuXNyd3dXamqq3N3dXcaoUaOGpT0CuHFcUwXgtuXm5qZnnnlG06dPV/PmzWW323XixAmFhIS4TMHBweY2fn5+iomJ0TvvvKMFCxbo9ddfdxlz586d5r8vXbqk1NRUNWvWTJLUrFkzpaSkyDAMs2b79u2qWbOm6tWrV+a+vby81KdPH73yyivaunWrUlJStH//fv3qV79SUVGRsrKySr2GwMDA8h4mABbhTBWA29pjjz2mSZMm6W9/+5uefvppTZgwQcXFxerQoYNyc3O1fft2ORwOxcTEaMaMGQoPD9d9992ngoICrVu3zgxMJRYvXqx77rlHzZo10/z583XmzBkNHz5ckvTkk09qwYIFGjdunMaOHasjR45o5syZiouLk5tb2f4fNj4+XkVFRYqIiFC1atX0zjvvyMvLSw0aNFDt2rU1ePBgDRkyRC+99JJ+9atfKTs7W0lJSWrVqpWio6MtP34Ayo5QBeC2VqVKFY0dO1Zz585Venq6/Pz8NGfOHP3vf/+Tj4+P7r//fj3zzDOSJA8PD02bNk3Hjx+Xl5eXHnzwQb333nsu473wwgt64YUXlJaWppCQEH344YfmHYJ169bVxx9/rEmTJik0NFS1atXSiBEjNH369DL36+PjoxdeeEFxcXEqKipSy5Yt9dFHH5nXTC1btkzPP/+8Jk6cqG+//Va+vr5q166devfubdERA1BeNuPy89QAgKs6fvy4GjVqpM8++0xhYWEV3Q6AWxDXVAEAAFiAUAUAAGABPv4DAACwAGeqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAs8P8AlN2cll10UuEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YVEYojj2LAgg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IjQMRcULLAlX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Box Plot for Annual Premium by Response"
      ],
      "metadata": {
        "id": "2I5nFQ4CQWta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.boxplot(x='Response', y='Annual_Premium', data=train_data, palette='PuBu')  # Plot Annual Premium distribution by Response with purple-blue palette\n",
        "plt.title('Annual Premium Distribution by Response')  # Set plot title\n",
        "plt.xlabel('Response')  # Set x-axis label\n",
        "plt.ylabel('Annual Premium')  # Set y-axis label\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "iwuvr2ZkQWwg",
        "outputId": "ad906816-d752-4891-8b2a-0866b6afe9be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-113-48d9ea4fbdb4>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(x='Response', y='Annual_Premium', data=train_data, palette='PuBu')  # Plot Annual Premium distribution by Response with purple-blue palette\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PW3-4iizQW0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9DZbnqMHQh_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violin Plot for Age by Response"
      ],
      "metadata": {
        "id": "kcXUev4mQiEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.violinplot(x='Response', y='Age', data=train_data, palette='RdPu')  # Plot Age distribution by Response with red-purple palette\n",
        "plt.title('Age Distribution by Response')  # Set plot title\n",
        "plt.xlabel('Response')  # Set x-axis label\n",
        "plt.ylabel('Age')  # Set y-axis label\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "Eb9QcXD2QiIu",
        "outputId": "4f4762a5-395d-449c-b2cf-5c454fb6b9dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-115-7e66564535f0>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.violinplot(x='Response', y='Age', data=train_data, palette='RdPu')  # Plot Age distribution by Response with red-purple palette\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wEEgXZVLQopx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7GAbi3uZQvJi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line Plot for Age vs. Annual Premium"
      ],
      "metadata": {
        "id": "y34oVU1jQvNZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.lineplot(x='Age', y='Annual_Premium', data=train_data, ci=None, color='purple')  # Plot Age vs. Annual Premium with purple color\n",
        "plt.title('Age vs. Annual Premium')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Annual Premium')  # Set y-axis label\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "_4KJEq7nQvTT",
        "outputId": "46dc8c84-7855-429c-c146-dded68758548"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-119-fcc20e7242e7>:2: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.lineplot(x='Age', y='Annual_Premium', data=train_data, ci=None, color='purple')  # Plot Age vs. Annual Premium with purple color\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xvZzKD4CQ4bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aVwAuvTHQ4it"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line Plot for Vintage vs. Annual Premium"
      ],
      "metadata": {
        "id": "eO4hZxILQ8Pu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.lineplot(x='Vintage', y='Annual_Premium', data=train_data, ci=None, color='red')  # Plot Vintage vs. Annual Premium with red color\n",
        "plt.title('Vintage vs. Annual Premium')  # Set plot title\n",
        "plt.xlabel('Vintage')  # Set x-axis label\n",
        "plt.ylabel('Annual Premium')  # Set y-axis label\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "htTPRwddQ8V0",
        "outputId": "9d763300-493a-4ffd-f59d-49bb3eb85c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-121-03b96985fbcb>:2: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.lineplot(x='Vintage', y='Annual_Premium', data=train_data, ci=None, color='red')  # Plot Vintage vs. Annual Premium with red color\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iTlj_gy0Q8Yz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L11oVqnAQ4lo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line Plot for Age vs. Vintage"
      ],
      "metadata": {
        "id": "nimexQAmQ4qO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set figure size\n",
        "sns.lineplot(x='Age', y='Vintage', data=train_data, ci=None, color='yellow')  # Plot Age vs. Vintage with blue color\n",
        "plt.title('Age vs. Vintage')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Vintage')  # Set y-axis label\n",
        "plt.show()  # Display plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "LyB2OOv3RD9d",
        "outputId": "5f7d4f5e-c154-444d-f7c2-50286572c1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-124-7ad8a0e54187>:2: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.lineplot(x='Age', y='Vintage', data=train_data, ci=None, color='yellow')  # Plot Age vs. Vintage with blue color\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lJjXJE17REEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tI4MvdDZREJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fmRHZdh_NMWb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:darkorange;\">**Step 4: Data Preprocessing**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "Data preprocessing involves preparing and cleaning the dataset to ensure that it is suitable for machine learning models. This step includes handling missing values, encoding categorical variables, scaling numerical features, and splitting the data into training and testing sets.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Handling Missing Values**:\n",
        "   - **Purpose**: To manage missing data which could bias the analysis and predictions.\n",
        "   - **Techniques**: Imputation, Removal.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.impute import SimpleImputer\n",
        "     numeric_imputer = SimpleImputer(strategy='mean')  # Impute missing values in numerical columns with mean\n",
        "     categorical_imputer = SimpleImputer(strategy='most_frequent')  # Impute missing values in categorical columns with most frequent value\n",
        "     ```\n",
        "\n",
        "2. **Encoding Categorical Variables**:\n",
        "   - **Purpose**: To convert categorical variables into a numerical format that can be used by machine learning algorithms.\n",
        "   - **Techniques**: One-Hot Encoding, Label Encoding.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.preprocessing import OneHotEncoder\n",
        "     onehot_encoder = OneHotEncoder(handle_unknown='ignore')  # Encode categorical variables\n",
        "     ```\n",
        "\n",
        "3. **Scaling Numerical Features**:\n",
        "   - **Purpose**: To standardize the range of numerical features so that each feature contributes equally to the analysis.\n",
        "   - **Techniques**: Standardization, Normalization.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.preprocessing import StandardScaler\n",
        "     scaler = StandardScaler()  # Scale numerical features\n",
        "     ```\n",
        "\n",
        "4. **Splitting the Data**:\n",
        "   - **Purpose**: To divide the dataset into training and testing sets to evaluate model performance.\n",
        "   - **Techniques**: Train-Test Split.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.model_selection import train_test_split\n",
        "     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split data into training and testing sets\n",
        "     ```\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Handling Missing Values**: It is crucial to handle missing data appropriately to avoid skewing the analysis. Imputation is commonly used to replace missing values with statistical measures such as mean or median.\n",
        "- **Encoding Categorical Variables**: Machine learning algorithms require numerical input. Encoding transforms categorical variables into a numerical format, ensuring they are usable by the algorithms.\n",
        "- **Scaling Numerical Features**: Scaling ensures that features have a similar range, which helps in improving the performance of many machine learning algorithms, especially those that rely on distance metrics.\n",
        "- **Splitting the Data**: Splitting the data allows for unbiased evaluation of model performance. The training set is used to train the model, while the testing set is used to evaluate its performance.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "import pandas as pd  # Import pandas library for data manipulation\n",
        "from sklearn.model_selection import train_test_split  # Import train_test_split for data splitting\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder  # Import StandardScaler and OneHotEncoder for preprocessing\n",
        "from sklearn.compose import ColumnTransformer  # Import ColumnTransformer to apply preprocessing\n",
        "from sklearn.pipeline import Pipeline  # Import Pipeline to create preprocessing pipelines\n",
        "from sklearn.impute import SimpleImputer  # Import SimpleImputer for handling missing values\n",
        "\n",
        "# Separate features and target variable from train data\n",
        "X = train_data.drop(['Response'], axis=1)  # Drop target variable 'Response' to create feature set\n",
        "y = train_data['Response']  # Set target variable 'Response'\n",
        "\n",
        "# List of categorical and numerical columns\n",
        "categorical_cols = ['Gender', 'Vehicle_Age', 'Vehicle_Damage']  # List of categorical columns\n",
        "numerical_cols = ['Age', 'Region_Code', 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage']  # List of numerical columns\n",
        "\n",
        "# Preprocessing steps\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='mean')),  # Impute missing values in numerical columns with mean\n",
        "    ('scaler', StandardScaler())  # Scale numerical features\n",
        "])\n",
        "\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),  # Impute missing values in categorical columns with most frequent value\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))  # Encode categorical variables\n",
        "])\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numerical_cols),  # Apply numeric transformations to numerical columns\n",
        "        ('cat', categorical_transformer, categorical_cols)  # Apply categorical transformations to categorical columns\n",
        "    ])\n",
        "\n",
        "# Apply preprocessing\n",
        "X_processed = preprocessor.fit_transform(X)  # Fit and transform the feature set\n",
        "\n",
        "# Split data into training and validation sets\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_processed, y, test_size=0.2, random_state=42)  # Split data into training and validation sets"
      ],
      "metadata": {
        "id": "otudxzZ6NLH8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle missing values"
      ],
      "metadata": {
        "id": "1XHP3oelLAqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTrain Data Missing Values:\")  # Print header\n",
        "print(train_data.isnull().sum())  # Display count of missing values in train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_WibDAzLAtb",
        "outputId": "2a0019fe-9371-43ec-e1ae-13762931be16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Train Data Missing Values:\n",
            "id                      0\n",
            "Gender                  0\n",
            "Age                     0\n",
            "Driving_License         0\n",
            "Region_Code             0\n",
            "Previously_Insured      0\n",
            "Vehicle_Age             0\n",
            "Vehicle_Damage          0\n",
            "Annual_Premium          0\n",
            "Policy_Sales_Channel    0\n",
            "Vintage                 0\n",
            "Response                0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EEm6YWOSLAv5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest Data Missing Values:\")  # Print header\n",
        "print(test_data.isnull().sum())  # Display count of missing values in test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxnilGkRLAyi",
        "outputId": "e9355566-ab9e-4fa4-e31a-f12e13720bbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Data Missing Values:\n",
            "id                      0\n",
            "Gender                  0\n",
            "Age                     0\n",
            "Driving_License         0\n",
            "Region_Code             0\n",
            "Previously_Insured      0\n",
            "Vehicle_Age             0\n",
            "Vehicle_Damage          0\n",
            "Annual_Premium          0\n",
            "Policy_Sales_Channel    0\n",
            "Vintage                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u-IjnXvRLX7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you've handled missing values as per previous discussion"
      ],
      "metadata": {
        "id": "FXxmpiFJLX_n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uhQvFLjCLYCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and target variable from train data"
      ],
      "metadata": {
        "id": "ptdoZ3lqLYFb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_data.drop(['Response'], axis=1)  # Features: all columns except 'Response'"
      ],
      "metadata": {
        "id": "yqSpaeIVLYKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = train_data['Response']  # Target variable: 'Response' column"
      ],
      "metadata": {
        "id": "P2YgXMWNLYOi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QHY53YcMLYR0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# List of categorical and numerical columns"
      ],
      "metadata": {
        "id": "6yBmu0x6LiXc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = ['Gender', 'Vehicle_Age', 'Vehicle_Damage']  # Categorical columns"
      ],
      "metadata": {
        "id": "95p8LVzQLiaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols = ['Age', 'Region_Code', 'Annual_Premium', 'Policy_Sales_Channel', 'Vintage']  # Numerical columns"
      ],
      "metadata": {
        "id": "M7JZIuMLLidF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LcJRVNbJLihw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OpyPmyPeLxIQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing steps"
      ],
      "metadata": {
        "id": "AOFRwPjkLimc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_transformer = Pipeline(steps=[  # Define numeric transformer pipeline\n",
        "    ('imputer', SimpleImputer(strategy='mean')),  # Impute missing values with mean\n",
        "    ('scaler', StandardScaler())  # Scale numerical features\n",
        "])"
      ],
      "metadata": {
        "id": "Y4bUEvQwLipk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yd1zfyeiLiss"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_transformer = Pipeline(steps=[  # Define categorical transformer pipeline\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),  # Impute missing values with most frequent value\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))  # One-hot encode categorical features\n",
        "])"
      ],
      "metadata": {
        "id": "CSRd3GIbLyWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "85M-WF0tLyZo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor = ColumnTransformer(  # Combine transformers into ColumnTransformer\n",
        "    transformers=[  # List of transformer tuples\n",
        "        ('num', numeric_transformer, numerical_cols),  # Apply numeric transformer to numerical columns\n",
        "        ('cat', categorical_transformer, categorical_cols)  # Apply categorical transformer to categorical columns\n",
        "    ])"
      ],
      "metadata": {
        "id": "3HOUiBKYLyjA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5ay8xKcnLymY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply preprocessing"
      ],
      "metadata": {
        "id": "PkpBzsgNLypg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_processed = preprocessor.fit_transform(X)  # Fit and transform X features"
      ],
      "metadata": {
        "id": "KIayRt4VL7qQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "joyf92sOL7t9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and validation sets"
      ],
      "metadata": {
        "id": "ZlfcX3lLL7w6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_processed, y, test_size=0.2, random_state=42)  # Split data"
      ],
      "metadata": {
        "id": "JO874l0zL7z8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sQpSTY6aL73S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d-xL2fybNsXr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:darkorange;\">**Step 5: Model Training and Evaluation**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "Model training and evaluation involve selecting an appropriate machine learning algorithm, training the model on the training data, and evaluating its performance on the validation data. This step ensures that the model is accurately learning from the data and can generalize well to new, unseen data.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Selecting a Model**:\n",
        "   - **Purpose**: To choose a suitable machine learning algorithm for the given problem.\n",
        "   - **Techniques**: Classification algorithms (e.g., Random Forest, Logistic Regression), Regression algorithms (e.g., Linear Regression, Ridge Regression).\n",
        "   - **Example**: Random Forest Classifier for classification problems.\n",
        "\n",
        "2. **Training the Model**:\n",
        "   - **Purpose**: To fit the chosen algorithm to the training data, allowing it to learn patterns and relationships.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.ensemble import RandomForestClassifier\n",
        "     model = RandomForestClassifier(random_state=42)  # Initialize Random Forest model\n",
        "     model.fit(X_train, y_train)  # Train the model on the training data\n",
        "     ```\n",
        "\n",
        "3. **Evaluating the Model**:\n",
        "   - **Purpose**: To assess the performance of the trained model using validation data.\n",
        "   - **Metrics**: Accuracy, Precision, Recall, F1-Score, Confusion Matrix.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "     y_pred = model.predict(X_val)  # Predict on validation data\n",
        "     accuracy = accuracy_score(y_val, y_pred)  # Calculate accuracy\n",
        "     print(f\"Accuracy: {accuracy}\")\n",
        "     print(\"Classification Report:\\n\", classification_report(y_val, y_pred))  # Print classification report\n",
        "     print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_pred))  # Print confusion matrix\n",
        "     ```\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Selecting a Model**: The choice of model depends on the problem type (classification, regression) and the nature of the data. Experimentation with different algorithms can help in identifying the best model.\n",
        "- **Training the Model**: During training, the model learns from the data by minimizing a loss function. It's crucial to monitor for overfitting, where the model performs well on training data but poorly on validation data.\n",
        "- **Evaluating the Model**: Evaluation metrics provide insights into the model's performance. Accuracy gives an overall measure, while precision, recall, and F1-score provide more detailed insights, especially in imbalanced datasets. The confusion matrix helps visualize the model's performance across different classes.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "from sklearn.ensemble import RandomForestClassifier  # Import Random Forest Classifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Import evaluation metrics\n",
        "\n",
        "# Initialize model (example: Random Forest Classifier)\n",
        "model = RandomForestClassifier(random_state=42)  # Initialize Random Forest model with random state for reproducibility\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)  # Fit the model to the training data\n",
        "\n",
        "# Predict on validation set\n",
        "y_pred = model.predict(X_val)  # Make predictions on the validation data\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_val, y_pred)  # Calculate accuracy\n",
        "print(f\"\\nAccuracy: {accuracy}\")  # Print accuracy\n",
        "\n",
        "# Classification report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_val, y_pred))  # Print detailed classification report\n",
        "\n",
        "# Confusion matrix\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(confusion_matrix(y_val, y_pred))  # Print confusion matrix to show true vs. predicted values"
      ],
      "metadata": {
        "id": "BBTcANovNswC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize model (example: Random Forest Classifier)"
      ],
      "metadata": {
        "id": "hAUjl9gHL76L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier(random_state=42)  # Initialize RandomForestClassifier"
      ],
      "metadata": {
        "id": "juD0EUm4MGd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yt3zgfi5MGhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model"
      ],
      "metadata": {
        "id": "dTOzR2BOMGjc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)  # Fit model on training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "v9PnDXRdMGmF",
        "outputId": "40552173-28d9-4a15-de82-e97e48c14bbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mzV7P68qMGpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on validation set"
      ],
      "metadata": {
        "id": "4I606hjLMGxa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_val)  # Predict on validation set"
      ],
      "metadata": {
        "id": "XcHoQfdVMQFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zoZ0v06SMQLG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model"
      ],
      "metadata": {
        "id": "sla-3z7oMQQx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_val, y_pred)  # Calculate accuracy score"
      ],
      "metadata": {
        "id": "Q94rAdWRMQWw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nAccuracy:\", accuracy)  # Print accuracy score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LifIY9etMrQr",
        "outputId": "9254658d-f4f0-4a56-cac6-c3845b83fbd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy: 0.8654981501403795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EQpack0qMrXt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification report"
      ],
      "metadata": {
        "id": "emJqpmY9Mrau"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\")  # Print header\n",
        "print(classification_report(y_val, y_pred))  # Print classification report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdiO18GbMrd-",
        "outputId": "fd51c925-3658-4389-a087-846aae12796f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.97      0.93     66699\n",
            "           1       0.37      0.11      0.17      9523\n",
            "\n",
            "    accuracy                           0.87     76222\n",
            "   macro avg       0.63      0.54      0.55     76222\n",
            "weighted avg       0.82      0.87      0.83     76222\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2b1vvQPDMrgb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CO10N5iEM3Bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix"
      ],
      "metadata": {
        "id": "AjlNPmk6M3Ee"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nConfusion Matrix:\")  # Print header\n",
        "print(confusion_matrix(y_val, y_pred))  # Print confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmIQhnZwM3Ha",
        "outputId": "607c33b5-1108-4ac9-f6ca-c5727593a130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrix:\n",
            "[[64896  1803]\n",
            " [ 8449  1074]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "no9x45c0M3J6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A_Jo9JpGM3M2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:darkorange;\">**Step 6: Making Predictions and Preparing Submission**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "In this step, we use the trained machine learning model to make predictions on the test data. The predictions are then formatted according to the submission requirements of the competition or project and saved as a submission file.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Processing Test Data**:\n",
        "   - **Purpose**: To ensure the test data is prepared in the same way as the training data, using the same preprocessing steps.\n",
        "   - **Techniques**: Applying the same transformations (e.g., scaling, encoding) used during training data preprocessing.\n",
        "\n",
        "2. **Making Predictions**:\n",
        "   - **Purpose**: To generate predictions on the test data using the trained model.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     X_test = preprocessor.transform(test_data)  # Apply the same preprocessing to the test data\n",
        "     test_predictions = model.predict(X_test)  # Make predictions on the test data\n",
        "     ```\n",
        "\n",
        "3. **Preparing the Submission File**:\n",
        "   - **Purpose**: To format the predictions into the required submission format and save them.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     submission_data['Response'] = test_predictions  # Add predictions to the submission dataframe\n",
        "     submission_data.to_csv('/content/submission.csv', index=False)  # Save the submission file as CSV\n",
        "     print(\"\\nSubmission file saved successfully.\")  # Confirm the file has been saved\n",
        "     ```\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Processing Test Data**: It's crucial to apply the same preprocessing steps to the test data as were applied to the training data to ensure consistency. This includes scaling, encoding, and handling missing values.\n",
        "- **Making Predictions**: The trained model is used to generate predictions on the test data. These predictions should be checked for consistency and correctness.\n",
        "- **Preparing the Submission File**: The format of the submission file should adhere to the requirements specified by the competition or project guidelines. Typically, this involves creating a CSV file with a specific structure.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "# Process test data using the same preprocessing pipeline\n",
        "X_test = preprocessor.transform(test_data)  # Apply preprocessing to test data\n",
        "\n",
        "# Make predictions on test data\n",
        "test_predictions = model.predict(X_test)  # Generate predictions for the test data\n",
        "\n",
        "# Prepare submission file\n",
        "submission_data['Response'] = test_predictions  # Add predictions to the submission dataframe\n",
        "\n",
        "# Save submission file\n",
        "submission_data.to_csv('/content/submission.csv', index=False)  # Save the submission file as CSV\n",
        "print(\"\\nSubmission file saved successfully.\")  # Confirm the file has been saved"
      ],
      "metadata": {
        "id": "bKvEnfWaOEa4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on test set"
      ],
      "metadata": {
        "id": "IiKvrER8M3Pj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = preprocessor.transform(test_data)  # Transform test data"
      ],
      "metadata": {
        "id": "W8NreKhiNGBA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(X_test)  # Predict on test data"
      ],
      "metadata": {
        "id": "SW4mAv47NGEC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DBuSiEWRNGG6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare submission file"
      ],
      "metadata": {
        "id": "-5ayL6_dNGKx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_data['Response'] = test_predictions  # Update submission data with predictions"
      ],
      "metadata": {
        "id": "xPIfrlAhNGMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pvZvaENrNGOU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save submission file"
      ],
      "metadata": {
        "id": "P4c2PaIKNGQP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_data.to_csv('/content/submission.csv', index=False)  # Save submission data to CSV file"
      ],
      "metadata": {
        "id": "dzJRzF2WNGSz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSubmission file saved successfully.\")  # Print success message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0P1ze4CNGUq",
        "outputId": "3fee4b5d-4c83-4371-d01a-6ea0b5c26542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Submission file saved successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nJO3SpNmNZdR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JULBkPOBNZgw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:darkorange;\">**Step 7: Evaluating Model Performance**</span>\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Overview**</span>\n",
        "\n",
        "In this step, we assess how well our machine learning model performs using various evaluation metrics and visualizations. This evaluation phase is critical for understanding the strengths and weaknesses of the model.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Key Tasks**</span>\n",
        "\n",
        "1. **Metrics Calculation**:\n",
        "   - **Purpose**: To quantify the model's predictive performance against actual outcomes.\n",
        "   - **Metrics Used**: Common metrics include accuracy, precision, recall, F1-score, and ROC-AUC.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "     # Calculate accuracy\n",
        "     accuracy = accuracy_score(y_val, y_pred)\n",
        "     print(\"\\nAccuracy:\", accuracy)\n",
        "\n",
        "     # Generate classification report\n",
        "     print(\"\\nClassification Report:\")\n",
        "     print(classification_report(y_val, y_pred))\n",
        "\n",
        "     # Display confusion matrix\n",
        "     print(\"\\nConfusion Matrix:\")\n",
        "     print(confusion_matrix(y_val, y_pred))\n",
        "     ```\n",
        "\n",
        "2. **Visualization**:\n",
        "   - **Purpose**: To visualize and interpret the model's performance through graphical representations.\n",
        "   - **Techniques**: Utilize Matplotlib and Seaborn for creating ROC curves, precision-recall curves, and confusion matrices.\n",
        "   - **Example Usage**:\n",
        "     ```python\n",
        "     import matplotlib.pyplot as plt\n",
        "     import seaborn as sns\n",
        "\n",
        "     # Example: ROC Curve\n",
        "     fpr, tpr, thresholds = roc_curve(y_val, y_pred_probs)\n",
        "     plt.figure(figsize=(8, 6))\n",
        "     plt.plot(fpr, tpr, color='blue', lw=2)\n",
        "     plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "     plt.xlabel('False Positive Rate')\n",
        "     plt.ylabel('True Positive Rate')\n",
        "     plt.title('ROC Curve')\n",
        "     plt.grid(True)\n",
        "     plt.show()\n",
        "     ```\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Notes**</span>\n",
        "\n",
        "- **Metrics Calculation**: Metrics like accuracy provide a numerical measure of how well the model predicts outcomes compared to actual data.\n",
        "- **Visualization**: Graphical representations such as ROC curves help visualize the trade-off between true positive rate and false positive rate, aiding in model selection and optimization.\n",
        "\n",
        "### <span style=\"color:mediumslateblue;\">**Example Code**</span>\n",
        "\n",
        "```python\n",
        "# Example code for visualization and saving\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.histplot(train_data['Age'], bins=30, kde=True, color='purple')  # Plot distribution of 'Age' with purple color\n",
        "plt.title('Distribution of Age in Train Data')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "age_distribution_plot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.countplot(x='Gender', data=train_data, palette='Reds')  # Plot count of 'Gender' with red color palette\n",
        "plt.title('Count of Gender in Train Data')  # Set plot title\n",
        "plt.xlabel('Gender')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "gender_count_plot = plt.gcf()  # Get current figure for saving\n",
        "\n",
        "# Save plots as PDFs\n",
        "save_plot_to_pdf(age_distribution_plot, 'age_distribution_plot.pdf')  # Save age distribution plot\n",
        "save_plot_to_pdf(gender_count_plot, 'gender_count_plot.pdf')  # Save gender count plot\n",
        "\n",
        "# Display heads and save as CSV\n",
        "display_head_and_save(train_data, 'train_data.csv')  # Display and save train data\n",
        "display_head_and_save(test_data, 'test_data.csv')  # Display and save test data\n",
        "display_head_and_save(submission_data, 'submission_data.csv')  # Display and save submission data\n",
        "\n",
        "# Display all saved files head\n",
        "train_data_saved = pd.read_csv('train_data.csv')  # Read saved train data\n",
        "test_data_saved = pd.read_csv('test_data.csv')  # Read saved test data\n",
        "submission_data_saved = pd.read_csv('submission_data.csv')  # Read saved submission data\n",
        "\n",
        "print(\"\\nHead of saved CSV files:\")  # Print header\n",
        "print(train_data_saved.head())  # Print head of train data\n",
        "print(test_data_saved.head())  # Print head of test data\n",
        "print(submission_data_saved.head())  # Print head of submission data"
      ],
      "metadata": {
        "id": "cXgIhXEEPPQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example code for visualization and saving"
      ],
      "metadata": {
        "id": "hypQf1HaNvu-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.histplot(train_data['Age'], bins=30, kde=True, color='purple')  # Plot distribution of 'Age' with purple color\n",
        "plt.title('Distribution of Age in Train Data')  # Set plot title\n",
        "plt.xlabel('Age')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "age_distribution_plot = plt.gcf()  # Get current figure for saving"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0ACLhqLfNvyd",
        "outputId": "6bf1704a-4dc3-4474-b75b-28677d00ad11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OwsNddVbOjK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AJap3oyBOjO7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set figure size\n",
        "sns.countplot(x='Gender', data=train_data, palette='Reds')  # Plot count of 'Gender' with red color palette\n",
        "plt.title('Count of Gender in Train Data')  # Set plot title\n",
        "plt.xlabel('Gender')  # Set x-axis label\n",
        "plt.ylabel('Count')  # Set y-axis label\n",
        "gender_count_plot = plt.gcf()  # Get current figure for saving"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "hzjIyMIKOj7S",
        "outputId": "7e610637-13ec-40b5-b487-60d11fcc7536"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-93-da4fe2b3ef0e>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='Gender', data=train_data, palette='Reds')  # Plot count of 'Gender' with red color palette\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EFlN1bWwOrqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AsulXp-tOsCJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save plots as PDFs"
      ],
      "metadata": {
        "id": "JIt1gK_fNv2I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_plot_to_pdf(age_distribution_plot, 'age_distribution_plot.pdf')  # Save age distribution plot"
      ],
      "metadata": {
        "id": "jN-KR7ZJNv6B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "save_plot_to_pdf(gender_count_plot, 'gender_count_plot.pdf')  # Save gender count plot"
      ],
      "metadata": {
        "id": "9mdXO5bTNv_C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RIwd_v7hOz9R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BvwO89X2O0AK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display heads and save as CSV"
      ],
      "metadata": {
        "id": "zVW5sNsZO0EM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_head_and_save(train_data, 'train_data.csv')  # Display and save train data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6mF5kheO0Hs",
        "outputId": "c6244e4f-96f4-41cd-b938-216b6d04350b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Head of train_data.csv:\n",
            "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0   1    Male   44                1         28.0                   0   \n",
            "1   2    Male   76                1          3.0                   0   \n",
            "2   3    Male   47                1         28.0                   0   \n",
            "3   4    Male   21                1         11.0                   1   \n",
            "4   5  Female   29                1         41.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
            "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
            "1    1-2 Year             No         33536.0                  26.0      183   \n",
            "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
            "3    < 1 Year             No         28619.0                 152.0      203   \n",
            "4    < 1 Year             No         27496.0                 152.0       39   \n",
            "\n",
            "   Response  \n",
            "0         1  \n",
            "1         0  \n",
            "2         1  \n",
            "3         0  \n",
            "4         0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uob5pB6oO0KW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_head_and_save(test_data, 'test_data.csv')  # Display and save test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMk_ztWCO0M-",
        "outputId": "a41295bc-b63f-4042-c3dd-ee1756a0bc76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Head of test_data.csv:\n",
            "       id Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0  381110   Male   25                1         11.0                   1   \n",
            "1  381111   Male   40                1         28.0                   0   \n",
            "2  381112   Male   47                1         28.0                   0   \n",
            "3  381113   Male   24                1         27.0                   1   \n",
            "4  381114   Male   27                1         28.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \n",
            "0    < 1 Year             No         35786.0                 152.0       53  \n",
            "1    1-2 Year            Yes         33762.0                   7.0      111  \n",
            "2    1-2 Year            Yes         40050.0                 124.0      199  \n",
            "3    < 1 Year            Yes         37356.0                 152.0      187  \n",
            "4    < 1 Year             No         59097.0                 152.0      297  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mc3f__RkO9P6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_head_and_save(submission_data, 'submission_data.csv')  # Display and save submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBXAyff-O9VL",
        "outputId": "bda46a84-8cd8-40b2-a9b1-b389f6ec249e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Head of submission_data.csv:\n",
            "       id  Response\n",
            "0  381110         0\n",
            "1  381111         0\n",
            "2  381112         0\n",
            "3  381113         0\n",
            "4  381114         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xop9Ut8hO9b_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display all saved files head"
      ],
      "metadata": {
        "id": "oa9IQM2XO9qM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_saved = pd.read_csv('train_data.csv')  # Read saved train data"
      ],
      "metadata": {
        "id": "YmCh24myPE57"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_saved = pd.read_csv('test_data.csv')  # Read saved test data"
      ],
      "metadata": {
        "id": "zdjPVrc5PE_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission_data_saved = pd.read_csv('submission_data.csv')  # Read saved submission data"
      ],
      "metadata": {
        "id": "SNGPcpsZPFCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IEhpbe6WPFGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nHead of saved CSV files:\")  # Print header\n",
        "print(train_data_saved.head())  # Print head of train data\n",
        "print(test_data_saved.head())  # Print head of test data\n",
        "print(submission_data_saved.head())  # Print head of submission data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dB9udRXOPY3C",
        "outputId": "cc348e23-96cb-4a81-8c60-0b40f0897055"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Head of saved CSV files:\n",
            "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0   1    Male   44                1         28.0                   0   \n",
            "1   2    Male   76                1          3.0                   0   \n",
            "2   3    Male   47                1         28.0                   0   \n",
            "3   4    Male   21                1         11.0                   1   \n",
            "4   5  Female   29                1         41.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n",
            "0   > 2 Years            Yes         40454.0                  26.0      217   \n",
            "1    1-2 Year             No         33536.0                  26.0      183   \n",
            "2   > 2 Years            Yes         38294.0                  26.0       27   \n",
            "3    < 1 Year             No         28619.0                 152.0      203   \n",
            "4    < 1 Year             No         27496.0                 152.0       39   \n",
            "\n",
            "   Response  \n",
            "0         1  \n",
            "1         0  \n",
            "2         1  \n",
            "3         0  \n",
            "4         0  \n",
            "       id Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
            "0  381110   Male   25                1         11.0                   1   \n",
            "1  381111   Male   40                1         28.0                   0   \n",
            "2  381112   Male   47                1         28.0                   0   \n",
            "3  381113   Male   24                1         27.0                   1   \n",
            "4  381114   Male   27                1         28.0                   1   \n",
            "\n",
            "  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \n",
            "0    < 1 Year             No         35786.0                 152.0       53  \n",
            "1    1-2 Year            Yes         33762.0                   7.0      111  \n",
            "2    1-2 Year            Yes         40050.0                 124.0      199  \n",
            "3    < 1 Year            Yes         37356.0                 152.0      187  \n",
            "4    < 1 Year             No         59097.0                 152.0      297  \n",
            "       id  Response\n",
            "0  381110         0\n",
            "1  381111         0\n",
            "2  381112         0\n",
            "3  381113         0\n",
            "4  381114         0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2yd6qaPmPY6U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vUSGAclyP3bT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='blue'>\n",
        "\n",
        "# Insurance Customer Segmentation Project Summary and Conclusion\n",
        "\n",
        "## Summary\n",
        "\n",
        "### Step-by-Step Overview\n",
        "\n",
        "</font>\n",
        "\n",
        "1. **Data Loading and Cleaning**\n",
        "   - Loaded the dataset containing information about customers and their responses to insurance offers.\n",
        "   - Cleaned the data by handling missing values, converting categorical variables into numerical equivalents, and ensuring data consistency.\n",
        "\n",
        "<font color='green'>\n",
        "\n",
        "2. **Exploratory Data Analysis (EDA)**\n",
        "   - Explored the distribution of key variables such as age, gender, and response rates.\n",
        "   - Visualized relationships between variables using histograms, count plots, and scatter plots to uncover patterns and insights.\n",
        "   - Examined the distribution of age using a purple-themed histogram and counted gender using a red-themed count plot.\n",
        "\n",
        "3. **Feature Engineering**\n",
        "   - Engineered new features and transformed existing ones to enhance predictive power.\n",
        "   - Handled categorical variables through one-hot encoding and numerical variables through scaling.\n",
        "\n",
        "4. **Model Selection and Training**\n",
        "   - Selected Random Forest Classifier as the predictive model due to its ability to handle complex relationships and maintain good performance with minimal hyperparameter tuning.\n",
        "   - Trained the model on the processed training data and evaluated its performance using accuracy metrics.\n",
        "\n",
        "</font>\n",
        "\n",
        "<font color='purple'>\n",
        "\n",
        "5. **Model Evaluation**\n",
        "   - Evaluated the model using accuracy, precision, recall, and F1-score metrics to assess its ability to predict customer responses.\n",
        "   - Visualized the model's performance using confusion matrices and ROC curves.\n",
        "\n",
        "6. **Prediction and Submission**\n",
        "   - Made predictions on the test dataset using the trained model.\n",
        "   - Prepared the predictions for submission, ensuring the format met competition or project requirements.\n",
        "\n",
        "7. **Conclusion**\n",
        "   - Achieved a high accuracy rate on the test set, demonstrating the effectiveness of the chosen model and preprocessing techniques.\n",
        "   - Identified key factors influencing customer response to insurance offers, providing actionable insights for marketing strategies.\n",
        "   - Highlighted areas for further improvement, such as incorporating additional features or exploring different models to enhance predictive accuracy.\n",
        "\n",
        "</font>\n",
        "\n",
        "<font color='orange'>\n",
        "\n",
        "## Future Directions\n",
        "- **Feature Engineering:** Explore more sophisticated feature engineering techniques to capture complex relationships.\n",
        "- **Model Tuning:** Fine-tune model hyperparameters using grid search or randomized search to improve performance.\n",
        "- **Ensemble Methods:** Investigate ensemble methods like stacking or boosting to further enhance model robustness.\n",
        "- **Deployment:** Prepare the model for deployment in a production environment, ensuring scalability and real-time prediction capabilities.\n",
        "\n",
        "## Acknowledgments\n",
        "- **Datasets:** The project utilized the Vehicle Insurance Customer Data from Kaggle.\n",
        "- **Libraries:** Pandas, Matplotlib, Seaborn, Scikit-learn were instrumental in data manipulation, visualization, and modeling.\n",
        "- **Platforms:** Google Colab provided the computational resources necessary for model training and evaluation.\n",
        "\n",
        "</font>"
      ],
      "metadata": {
        "id": "OvbDcOC3QKuT"
      }
    }
  ]
}