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"}}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">About Dataset</p>","metadata":{}},{"cell_type":"markdown","source":"## Dataset Overview\n- **Training Data**: 1,200,000 rows, 21 columns  \n- **Test Data**: 800,000 rows, 20 columns  \n\n## Missing Values in Training Data (Percentage)\n- **Age**: 1.55875%  \n- **Annual Income**: 3.74575%  \n- **Marital Status**: 1.54408%  \n- **Number of Dependents**: 9.13933%  \n- **Occupation**: 29.83958%  \n- **Health Score**: 6.17300%  \n- **Previous Claims**: 30.33575%  \n- **Vehicle Age**: 0.00050%  \n- **Credit Score**: 11.49017%  \n- **Insurance Duration**: 0.00008%  \n- **Customer Feedback**: 6.48533%  \n\n## Missing Values in Test Data (Percentage)\n- **Age**: 1.56113%  \n- **Annual Income**: 3.73250%  \n- **Marital Status**: 1.54200%  \n- **Number of Dependents**: 9.14125%  \n- **Occupation**: 29.89063%  \n- **Health Score**: 6.18113%  \n- **Previous Claims**: 30.35025%  \n- **Vehicle Age**: 0.00038%  \n- **Credit Score**: 11.43138%  \n- **Insurance Duration**: 0.00025%  \n- **Customer Feedback**: 6.53450%  \n\n## Column Types\n- **Numeric Columns** (10):  \n  - ID, Age, Annual Income, Number of Dependents, Health Score, Previous Claims, Vehicle Age, Credit Score, Insurance Duration, Premium Amount  \n- **Categorical Columns** (11):  \n  - Gender, Marital Status, Education Level, Occupation, Location, Policy Type, Policy Start Date, Customer Feedback, Smoking Status, Exercise Frequency, Property Type  ","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Training & Testing Data Columns</p>","metadata":{}},{"cell_type":"markdown","source":"\n\n## Columns of Training Data\nThe training dataset contains the following columns:\n\n| Column Name              | Description                                 |\n|--------------------------|---------------------------------------------|\n| **id**                   | Unique identifier for each record           |\n| **Age**                  | Age of the customer                         |\n| **Gender**               | Gender of the customer                      |\n| **Annual Income**        | Annual income of the customer               |\n| **Marital Status**       | Marital status of the customer              |\n| **Number of Dependents** | Number of dependents of the customer        |\n| **Education Level**      | Education level of the customer             |\n| **Occupation**           | Occupation of the customer                  |\n| **Health Score**         | Health score of the customer                |\n| **Location**             | Location of the customer                    |\n| **Policy Type**          | Type of insurance policy                    |\n| **Previous Claims**      | Number of previous claims made by the customer |\n| **Vehicle Age**          | Age of the vehicle insured                  |\n| **Credit Score**         | Credit score of the customer                |\n| **Insurance Duration**   | Duration of the insurance policy           |\n| **Policy Start Date**    | Start date of the insurance policy          |\n| **Customer Feedback**    | Customer feedback on the insurance policy  |\n| **Smoking Status**       | Smoking status of the customer              |\n| **Exercise Frequency**   | Frequency of exercise by the customer       |\n| **Property Type**        | Type of property owned by the customer      |\n| **Premium Amount**       | Target variable: The premium amount charged |\n\n## Columns of Testing Data\nThe testing dataset contains the following columns:\n\n| Column Name              | Description                                 |\n|--------------------------|---------------------------------------------|\n| **id**                   | Unique identifier for each record           |\n| **Age**                  | Age of the customer                         |\n| **Gender**               | Gender of the customer                      |\n| **Annual Income**        | Annual income of the customer               |\n| **Marital Status**       | Marital status of the customer              |\n| **Number of Dependents** | Number of dependents of the customer        |\n| **Education Level**      | Education level of the customer             |\n| **Occupation**           | Occupation of the customer                  |\n| **Health Score**         | Health score of the customer                |\n| **Location**             | Location of the customer                    |\n| **Policy Type**          | Type of insurance policy                    |\n| **Previous Claims**      | Number of previous claims made by the customer |\n| **Vehicle Age**          | Age of the vehicle insured                  |\n| **Credit Score**         | Credit score of the customer                |\n| **Insurance Duration**   | Duration of the insurance policy           |\n| **Policy Start Date**    | Start date of the insurance policy          |\n| **Customer Feedback**    | Customer feedback on the insurance policy  |\n| **Smoking Status**       | Smoking status of the customer              |\n| **Exercise Frequency**   | Frequency of exercise by the customer       |\n| **Property Type**        | Type of property owned by the customer      |\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">About Author</p>","metadata":{}},{"cell_type":"markdown","source":"# **Arshman Khalid**  \n<p style=\"font-size: 1.5rem; font-weight: bold;\">Data Scientist | Software Engineer | ex Consultant PwC | ex Senior Data Analyst Fortune 500</p>\n\nWith over 5 years of expertise in data science and software engineering, I am dedicated to transforming complex data into actionable insights. My focus lies in predictive analytics, data strategy, and the implementation of robust machine learning models that drive measurable business outcomes. I have a track record of optimizing operations, reducing costs, and improving decision-making processes across industries. Proficient in Python, Alteryx, Power BI, and cloud platforms.\n\nWhen I am not wrangling datasets, you will find me attempting to code my way to the perfect cup of coffee!\n\n# **Lets Connect**\n\n<div style=\"text-align: left; font-family: Arial, sans-serif; margin-top: 20px;\">\n    <a href=\"https://www.linkedin.com/in/arshmankhalid/\" style=\"text-decoration: none; color: #fff; margin-right: 10px;\">\n        <span style=\"background-color: #0077B5; padding: 8px 20px; border-radius: 5px; font-size: 14px; display: inline-block; width: 120px; text-align: center;\">LinkedIn</span>\n    </a>\n    <a href=\"https://x.com/arshmankhalid\" style=\"text-decoration: none; color: #fff; margin-right: 10px;\">\n        <span style=\"background-color: #000; padding: 8px 20px; border-radius: 5px; font-size: 14px; display: inline-block; width: 120px; text-align: center;\">X</span>\n    </a>\n    <a href=\"https://github.com/arshmankhalid88\" style=\"text-decoration: none; color: #fff; margin-right: 10px;\">\n        <span style=\"background-color: #333; padding: 8px 20px; border-radius: 5px; font-size: 14px; display: inline-block; width: 120px; text-align: center;\">GitHub</span>\n    </a>\n    <a href=\"https://www.kaggle.com/arshmankhalid\" style=\"text-decoration: none; color: #fff; margin-right: 10px;\">\n        <span style=\"background-color: #20BEFF; padding: 8px 20px; border-radius: 5px; font-size: 14px; display: inline-block; width: 120px; text-align: center;\">Kaggle</span>\n    </a>\n</div>","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Import Libraries</p>","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.experimental import enable_iterative_imputer  # noqa\nfrom sklearn.impute import IterativeImputer\nfrom matplotlib.ticker import FuncFormatter\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.metrics import mean_squared_log_error\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:03:44.216929Z","iopub.execute_input":"2025-02-03T18:03:44.21742Z","iopub.status.idle":"2025-02-03T18:03:47.363301Z","shell.execute_reply.started":"2025-02-03T18:03:44.217383Z","shell.execute_reply":"2025-02-03T18:03:47.362237Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Load the datasets\ndf_train = pd.read_csv('/kaggle/input/playground-series-s4e12/train.csv')\ndf_test = pd.read_csv('/kaggle/input/playground-series-s4e12/test.csv')\nsample_sub = pd.read_csv('/kaggle/input/playground-series-s4e12/sample_submission.csv')\ndisplay(\"Display First Few Rows of Training Data\", df_train.head())\ndisplay(\"Display First Few Rows of Testing Data\", df_test.head())\ndisplay(\"Display First Few Rows of Sample Submission\", sample_sub.head())","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:03:47.365006Z","iopub.execute_input":"2025-02-03T18:03:47.365598Z","iopub.status.idle":"2025-02-03T18:03:59.610198Z","shell.execute_reply.started":"2025-02-03T18:03:47.36557Z","shell.execute_reply":"2025-02-03T18:03:59.608167Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Training Data Structure & Shape</p>","metadata":{}},{"cell_type":"code","source":"display(\"Info of the Training Data\", df_train.info())\nprint(\"=====================================================\")\ndisplay(\"Info of Testing Data\", df_test.info())","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:03:59.612303Z","iopub.execute_input":"2025-02-03T18:03:59.612676Z","iopub.status.idle":"2025-02-03T18:04:00.7948Z","shell.execute_reply.started":"2025-02-03T18:03:59.612629Z","shell.execute_reply":"2025-02-03T18:04:00.793589Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Training Data Columns</p>","metadata":{}},{"cell_type":"code","source":"print(\"Columns of Training Data:\\n\", df_train.columns)\nprint(\"==========================================================================\")\nprint(\"Columns of Testing:\\n\", df_test.columns)\nprint(\"==========================================================================\")\nprint(\"Columns of Sample Submisison:\\n\", sample_sub.columns)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:00.797351Z","iopub.execute_input":"2025-02-03T18:04:00.797866Z","iopub.status.idle":"2025-02-03T18:04:00.807531Z","shell.execute_reply.started":"2025-02-03T18:04:00.797824Z","shell.execute_reply":"2025-02-03T18:04:00.806233Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Check the Missing Values</p>","metadata":{}},{"cell_type":"code","source":"print(\"Missing values of Training Data:\\n\", df_train.isnull().sum()/len(df_train)*100)\nprint(\"==========================================================================\")\nprint(\"Missing values of Testing:\\n\", df_test.isnull().sum()/len(df_test)*100)\nprint(\"==========================================================================\")\nprint(\"Missing values of Sample Submisison:\", sample_sub.isnull().sum()/len(sample_sub)*100)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:00.808794Z","iopub.execute_input":"2025-02-03T18:04:00.809228Z","iopub.status.idle":"2025-02-03T18:04:01.969741Z","shell.execute_reply.started":"2025-02-03T18:04:00.809194Z","shell.execute_reply":"2025-02-03T18:04:01.968762Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Descriptive Summary</p>","metadata":{}},{"cell_type":"code","source":"display(\"Descriptive Summary of the Training Data\", df_train.describe())\nprint(\"=====================================================\")\ndisplay(\"Descriptive Summary of Testing Data\", df_test.describe())","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:01.970947Z","iopub.execute_input":"2025-02-03T18:04:01.971354Z","iopub.status.idle":"2025-02-03T18:04:03.250762Z","shell.execute_reply.started":"2025-02-03T18:04:01.971313Z","shell.execute_reply":"2025-02-03T18:04:03.24969Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Observations\n\n### Dataset Overview\n- **Training Data**: 1,200,000 rows, 21 columns  \n- **Test Data**: 800,000 rows, 20 columns  \n\n### Columns in Training Data\n- 'id', 'Age', 'Gender', 'Annual Income', 'Marital Status', 'Number of Dependents', 'Education Level', 'Occupation', 'Health Score',  \n  'Location', 'Policy Type', 'Previous Claims', 'Vehicle Age', 'Credit Score', 'Insurance Duration', 'Policy Start Date',  \n  'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type', 'Premium Amount'  \n\n### Columns in Test Data\n- 'id', 'Age', 'Gender', 'Annual Income', 'Marital Status', 'Number of Dependents', 'Education Level', 'Occupation', 'Health Score',  \n  'Location', 'Policy Type', 'Previous Claims', 'Vehicle Age', 'Credit Score', 'Insurance Duration', 'Policy Start Date',  \n  'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type'  \n\n### Missing Values in Training Data (Percentage)\n- **Age**: 1.55875%  \n- **Annual Income**: 3.74575%  \n- **Marital Status**: 1.54408%  \n- **Number of Dependents**: 9.13933%  \n- **Occupation**: 29.83958%  \n- **Health Score**: 6.17300%  \n- **Previous Claims**: 30.33575%  \n- **Vehicle Age**: 0.00050%  \n- **Credit Score**: 11.49017%  \n- **Insurance Duration**: 0.00008%  \n- **Customer Feedback**: 6.48533%  \n\n### Missing Values in Test Data (Percentage)\n- **Age**: 1.56113%  \n- **Annual Income**: 3.73250%  \n- **Marital Status**: 1.54200%  \n- **Number of Dependents**: 9.14125%  \n- **Occupation**: 29.89063%  \n- **Health Score**: 6.18113%  \n- **Previous Claims**: 30.35025%  \n- **Vehicle Age**: 0.00038%  \n- **Credit Score**: 11.43138%  \n- **Insurance Duration**: 0.00025%  \n- **Customer Feedback**: 6.53450%  \n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Gender & Age Column</p>","metadata":{}},{"cell_type":"code","source":"# Analyze value counts for 'Gender'\ngender_counts = df_train['Gender'].value_counts()\n\nprint(\"Gender Counts:\")\nprint(gender_counts)\n\n# Analyze the range of 'Age'\nage_min = df_train['Age'].min()\nage_max = df_train['Age'].max()\nprint(\"====================================\")\nprint(\"\\nAge Range:\")\nprint(f\"Minimum Age: {age_min}\")\nprint(f\"Maximum Age: {age_max}\")\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:03.251894Z","iopub.execute_input":"2025-02-03T18:04:03.252241Z","iopub.status.idle":"2025-02-03T18:04:03.357233Z","shell.execute_reply.started":"2025-02-03T18:04:03.252213Z","shell.execute_reply":"2025-02-03T18:04:03.356054Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Age & Gender Distribution</p>","metadata":{}},{"cell_type":"code","source":"# Create a figure with two subplots: one for 'Gender' and one for 'Age'\nfig, axes = plt.subplots(1, 2, figsize=(14, 7))\n\n# Refined color palette for Gender (Male: Vivid Amber, Female: Charcoal Blue)\npie_colors = ['#F4A300', '#003B49']  # Vivid Amber and Charcoal Blue colors\n\n# Plot for 'Gender' value counts with refined colors\ngender_counts = df_train['Gender'].value_counts()\nsns.barplot(x=gender_counts.index, y=gender_counts.values, \n            palette=pie_colors, ax=axes[0])  # Applying the custom color palette\n\n# Customize the 'Gender' plot\naxes[0].set_title('Gender Distribution', fontsize=16, fontweight='bold', color='darkblue')\naxes[0].set_xlabel('Gender', fontsize=12, color='black')\naxes[0].set_ylabel('Count', fontsize=12, color='black')\naxes[0].tick_params(axis='x', rotation=0, labelcolor='black')\naxes[0].tick_params(axis='y', labelcolor='black')\n\n# Add count annotations on the bars with white text\nfor p in axes[0].patches:\n    axes[0].annotate(f'{p.get_height():,.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                     ha='center', va='center', fontsize=12, color='black', fontweight='bold')\n\n# Custom colors for the Age plot\nage_color = '#6b0a42'  # Lime Green color for the histogram\nage_annotation_color = 'darkred'  # White text for annotations\n\n# Plot for 'Age' range (min and max)\nsns.histplot(df_train['Age'], bins=15, kde=False, color=age_color, ax=axes[1])\n\n# Customize the 'Age' plot\naxes[1].set_title('Age Range Distribution', fontsize=16, fontweight='bold', color='darkblue')\naxes[1].set_xlabel('Age', fontsize=12, color='black')\naxes[1].set_ylabel('Count', fontsize=12, color='black')\naxes[1].tick_params(axis='x', labelcolor='black')\naxes[1].tick_params(axis='y', labelcolor='black')\n\n# Annotate Age Range with custom text color\nage_min = df_train['Age'].min()\nage_max = df_train['Age'].max()\naxes[1].annotate(f'Min: {age_min}\\nMax: {age_max}', xy=(0.5, 0.9), xycoords='axes fraction', \n                 ha='center', va='center', fontsize=14, fontweight='bold', color=age_annotation_color)\n\n# Adjust layout for better spacing\nplt.tight_layout()\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:03.360724Z","iopub.execute_input":"2025-02-03T18:04:03.361085Z","iopub.status.idle":"2025-02-03T18:04:05.020406Z","shell.execute_reply.started":"2025-02-03T18:04:03.361057Z","shell.execute_reply":"2025-02-03T18:04:05.01901Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Gender Column on the basis of Proportion</p>","metadata":{}},{"cell_type":"code","source":"# Proportion of each gender in the dataset\ngender_proportion = df_train['Gender'].value_counts(normalize=True) * 100\n\nprint(\"Proportion of Each Gender in the Dataset:\")\nprint(gender_proportion)\n# Check if the dataset has a gender imbalance\nmost_common_gender = gender_proportion.idxmax()\nimbalance_percentage = gender_proportion.max() - gender_proportion.min()\n\nprint(f\"The most common gender is '{most_common_gender}' with a {gender_proportion.max():.2f}% share.\")\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:05.02273Z","iopub.execute_input":"2025-02-03T18:04:05.023107Z","iopub.status.idle":"2025-02-03T18:04:05.12258Z","shell.execute_reply.started":"2025-02-03T18:04:05.023079Z","shell.execute_reply":"2025-02-03T18:04:05.121309Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize  Gender on the basis of Proportion</p>","metadata":{}},{"cell_type":"code","source":"# Calculate gender proportions\ngender_proportion = df_train['Gender'].value_counts(normalize=True) * 100\n\n# Create a figure with two subplots: one for Pie chart and one for Bar plot\nfig, axes = plt.subplots(1, 2, figsize=(16, 8))\n\n# Define a refined color palette for both plots\npie_colors = ['#F4A300', '#003B49']  # Vivid Amber and Charcoal Blue colors\n\n# Plot 1: Pie chart for gender proportions with enhanced visuals\nwedges, texts, autotexts = axes[0].pie(gender_proportion, labels=gender_proportion.index, autopct='%1.1f%%', \n                                      colors=pie_colors, startangle=90, \n                                      wedgeprops={'edgecolor': 'white', 'linewidth': 2, 'linestyle': 'solid'}, \n                                      shadow=True, textprops={'color': 'white'})  # Set text inside pie chart to white\n\n# Add legend for the Pie chart\naxes[0].legend(wedges, gender_proportion.index, title=\"Gender\", loc=\"center left\", bbox_to_anchor=(1, 0.5), fontsize=12)\n\n# Customize Pie chart\naxes[0].set_title('Gender Proportion', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[0].axis('equal')  # Equal aspect ratio ensures that pie chart is circular.\n\n# Plot 2: Bar plot for gender proportions with improved style\nsns.barplot(x=gender_proportion.index, y=gender_proportion.values, \n            palette=pie_colors, ax=axes[1])\n\n# Customize Bar plot\naxes[1].set_title('Gender Proportion Bar Plot', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[1].set_xlabel('Gender', fontsize=14, color='darkblue')\naxes[1].set_ylabel('Proportion (%)', fontsize=14, color='darkblue')\naxes[1].tick_params(axis='x', rotation=0, labelcolor='black', labelsize=12)\naxes[1].tick_params(axis='y', labelcolor='black', labelsize=12)\n\n# Add percentage annotations on the bars with improved styling\nfor p in axes[1].patches:\n    height = p.get_height()\n    axes[1].annotate(f'{height:.1f}%', \n                     (p.get_x() + p.get_width() / 2., height),\n                     ha='center', va='center', fontsize=14, color='black', fontweight='bold')\n\n# Add gridlines to the bar plot for better readability\naxes[1].grid(axis='y', linestyle='--', alpha=0.7)\n\n# Adjust layout for better spacing and make the plot more cohesive\nplt.tight_layout(pad=5)\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:05.124082Z","iopub.execute_input":"2025-02-03T18:04:05.124458Z","iopub.status.idle":"2025-02-03T18:04:05.635638Z","shell.execute_reply.started":"2025-02-03T18:04:05.124429Z","shell.execute_reply":"2025-02-03T18:04:05.634195Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Age on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Calculate mean and median age by gender\nmean_age_by_gender = df_train.groupby('Gender')['Age'].mean()\nmedian_age_by_gender = df_train.groupby('Gender')['Age'].median()\n\nprint(\"Mean Age by Gender:\")\nprint(mean_age_by_gender)\nprint(\"\\nMedian Age by Gender:\")\nprint(median_age_by_gender)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:05.637022Z","iopub.execute_input":"2025-02-03T18:04:05.637447Z","iopub.status.idle":"2025-02-03T18:04:05.878489Z","shell.execute_reply.started":"2025-02-03T18:04:05.637405Z","shell.execute_reply":"2025-02-03T18:04:05.877178Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Age on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n\n# Assuming df_train is your DataFrame\n\n# Calculate mean and median age by gender\nmean_age_by_gender = df_train.groupby('Gender')['Age'].mean()\nmedian_age_by_gender = df_train.groupby('Gender')['Age'].median()\n\n# Create a figure with two rows: the first row for bar plots and the second row for pie charts\nfig, axes = plt.subplots(2, 2, figsize=(16, 14))\n\n# Define a refined color palette for both plots\nbar_colors = ['#F4A300', '#003B49']  # Vivid Amber and Charcoal Blue colors for bar plots\npie_colors = ['#F4A300', '#003B49']  # Vivid Amber and Charcoal Blue colors for pie charts\n\n# Plot 1: Bar plot for mean age by gender\nsns.barplot(x=mean_age_by_gender.index, y=mean_age_by_gender.values, \n            palette=bar_colors, ax=axes[0, 0])\n\n# Customize Mean Age plot\naxes[0, 0].set_title('Mean Age by Gender', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[0, 0].set_xlabel('Gender', fontsize=14, color='darkblue')\naxes[0, 0].set_ylabel('Mean Age', fontsize=14, color='darkblue')\naxes[0, 0].tick_params(axis='x', labelcolor='black', labelsize=12)\naxes[0, 0].tick_params(axis='y', labelcolor='black', labelsize=12)\n\n# Add value annotations on the bars (three digits after the decimal point)\nfor p in axes[0, 0].patches:\n    height = p.get_height()\n    axes[0, 0].annotate(f'{height:.3f}',  # Format to show 3 digits after the decimal point\n                        (p.get_x() + p.get_width() / 2., height),\n                        ha='center', va='center', fontsize=14, color='black', fontweight='bold')\n\n# Plot 2: Bar plot for median age by gender\nsns.barplot(x=median_age_by_gender.index, y=median_age_by_gender.values, \n            palette=bar_colors, ax=axes[0, 1])\n\n# Customize Median Age plot\naxes[0, 1].set_title('Median Age by Gender', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[0, 1].set_xlabel('Gender', fontsize=14, color='darkblue')\naxes[0, 1].set_ylabel('Median Age', fontsize=14, color='darkblue')\naxes[0, 1].tick_params(axis='x', labelcolor='black', labelsize=12)\naxes[0, 1].tick_params(axis='y', labelcolor='black', labelsize=12)\n\n# Add value annotations on the bars (three digits after the decimal point)\nfor p in axes[0, 1].patches:\n    height = p.get_height()\n    axes[0, 1].annotate(f'{height:.3f}',  # Format to show 3 digits after the decimal point\n                        (p.get_x() + p.get_width() / 2., height),\n                        ha='center', va='center', fontsize=14, color='black', fontweight='bold')\n\n# Plot 3: Pie chart for mean age by gender\naxes[1, 0].pie(mean_age_by_gender, labels=mean_age_by_gender.index, autopct='%1.3f%%', \n               colors=pie_colors, startangle=90, \n               wedgeprops={'edgecolor': 'white', 'linewidth': 2, 'linestyle': 'solid'}, \n               shadow=True, textprops={'color': 'white'})  # Set text inside pie chart to white\n\n# Customize Pie chart for Mean Age\naxes[1, 0].set_title('Mean Age by Gender (Pie)', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[1, 0].axis('equal')  # Equal aspect ratio ensures that pie chart is circular.\naxes[1, 0].legend(mean_age_by_gender.index, title=\"Gender\", loc='upper right', fontsize=12)\n\n# Plot 4: Pie chart for median age by gender\naxes[1, 1].pie(median_age_by_gender, labels=median_age_by_gender.index, autopct='%1.3f%%', \n               colors=pie_colors, startangle=90, \n               wedgeprops={'edgecolor': 'white', 'linewidth': 2, 'linestyle': 'solid'}, \n               shadow=True, textprops={'color': 'white'})  # Set text inside pie chart to white\n\n# Customize Pie chart for Median Age\naxes[1, 1].set_title('Median Age by Gender (Pie)', fontsize=18, fontweight='bold', color='darkblue', pad=20)\naxes[1, 1].axis('equal')  # Equal aspect ratio ensures that pie chart is circular.\naxes[1, 1].legend(median_age_by_gender.index, title=\"Gender\", loc='upper right', fontsize=12)\n\n# Adjust layout for better spacing and make the plot more cohesive\nplt.tight_layout(pad=5)\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:05.880037Z","iopub.execute_input":"2025-02-03T18:04:05.88047Z","iopub.status.idle":"2025-02-03T18:04:06.81848Z","shell.execute_reply.started":"2025-02-03T18:04:05.880429Z","shell.execute_reply":"2025-02-03T18:04:06.817192Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Age Range Distribution on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Calculate the range of age for each gender\nage_range_by_gender = df_train.groupby('Gender').agg({'Age': lambda x: x.max() - x.min()})\n\nprint(\"Age Range by Gender:\")\nprint(age_range_by_gender)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:06.820543Z","iopub.execute_input":"2025-02-03T18:04:06.820959Z","iopub.status.idle":"2025-02-03T18:04:06.950786Z","shell.execute_reply.started":"2025-02-03T18:04:06.820928Z","shell.execute_reply":"2025-02-03T18:04:06.94965Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define age bins\nbins = [18, 25, 35, 45, 55, 64]\nlabels = ['18-25', '26-35', '36-45', '46-55', '56-64']\n\n# Create an Age Range column\ndf_train['Age Range'] = pd.cut(df_train['Age'], bins=bins, labels=labels, right=True)\n\n# Distribution of Gender within Age Ranges\nage_range_gender_dist = df_train.groupby('Age Range')['Gender'].value_counts(normalize=True)\n\n# Output the distribution\nprint(\"Gender distribution within each Age Range:\\n\", age_range_gender_dist)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:06.951732Z","iopub.execute_input":"2025-02-03T18:04:06.952006Z","iopub.status.idle":"2025-02-03T18:04:07.122911Z","shell.execute_reply.started":"2025-02-03T18:04:06.951983Z","shell.execute_reply":"2025-02-03T18:04:07.121292Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Age Range Distribution on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Define updated age bins and labels\nbins = [18, 22, 25, 30, 35, 40, 45, 50, 55, 60, 64]\nlabels = ['18-22', '23-25', '26-30', '31-35', '36-40', '41-45', '46-50', '51-55', '56-60', '61-64']\n\n# Create an Age Range column\ndf_train['Age Range'] = pd.cut(df_train['Age'], bins=bins, labels=labels, right=True)\n\n# Define the color palette for gender (darker shades)\ngender_palette = ['#B77A00', '#001F2D']  # Darker shades of Vivid Amber and Charcoal Blue\n\n# Create the figure with 2 subplots (2 rows, 1 column)\nfig, axes = plt.subplots(2, 1, figsize=(10, 14))\n\n# Histogram plot: Gender distribution within age ranges\nsns.histplot(data=df_train, x='Age Range', hue='Gender', stat='probability', common_norm=False, multiple=\"stack\", palette=gender_palette, ax=axes[0])\n\n# Add the proportion values inside the histogram bars for each gender\nfor p in axes[0].patches:\n    height = p.get_height()\n    x = p.get_x() + p.get_width() / 2  # x position of the bar\n    y = p.get_y() + height / 2  # y position of the bar\n    axes[0].annotate(f\"{height:.5f}\", (x, y), textcoords=\"offset points\", xytext=(0, 5), ha='center', fontsize=8, color='black')\n\naxes[0].set_title('Proportion of Gender Distribution within Age Ranges', fontsize=14, fontweight='bold', color='darkblue', pad=20)\naxes[0].set_xlabel('Age Range', fontsize=10, color='darkblue')\naxes[0].set_ylabel('Proportion', fontsize=10, color='darkblue')\naxes[0].tick_params(axis='x', rotation=45, labelsize=8)  # Decreased tick label size for x-axis\naxes[0].tick_params(axis='y', labelsize=8)  # Decreased tick label size for y-axis\naxes[0].legend(title='Gender', labels=['Female', 'Male'], loc='upper right', fontsize=12)\n\n# Line plot: Gender proportion trend across age ranges\n# Calculate the proportion of males and females for each age range\ngender_counts = df_train.groupby(['Age Range', 'Gender']).size().unstack(fill_value=0)\ngender_proportions = gender_counts.div(gender_counts.sum(axis=1), axis=0)\n\n# Plot the gender proportions as lines\ngender_proportions.plot(ax=axes[1], color=gender_palette, marker='o', linewidth=2)\naxes[1].set_title('Gender Proportions Across Age Ranges', fontsize=14, fontweight='bold', color='darkblue', pad=20)\naxes[1].set_xlabel('Age Range', fontsize=10, color='darkblue')\naxes[1].set_ylabel('Proportion', fontsize=10, color='darkblue')\naxes[1].tick_params(axis='x', rotation=45, labelsize=8)  # Decreased tick label size for x-axis\naxes[1].tick_params(axis='y', labelsize=8)  # Decreased tick label size for y-axis\naxes[1].legend(title='Gender', labels=['Female', 'Male'], loc='upper right', fontsize=12)\n\n# Add the proportion values above the lines in black\nfor i, age_range in enumerate(gender_proportions.index):\n    for j, gender in enumerate(gender_proportions.columns):\n        axes[1].annotate(f\"{gender_proportions.loc[age_range, gender]:.5f}\",\n                         (i, gender_proportions.loc[age_range, gender]),\n                         textcoords=\"offset points\",\n                         xytext=(0, 10),  # 10 points vertical offset\n                         ha='center', fontsize=7, color=gender_palette[j])\n\n# Adjust layout to avoid overlap\nplt.tight_layout(pad=5)\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:07.124563Z","iopub.execute_input":"2025-02-03T18:04:07.125118Z","iopub.status.idle":"2025-02-03T18:04:10.214309Z","shell.execute_reply.started":"2025-02-03T18:04:07.125051Z","shell.execute_reply":"2025-02-03T18:04:10.213091Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore most common Gender present in Data on the Basis of Age</p>","metadata":{}},{"cell_type":"code","source":"# Mode of Age and Gender\nage_mode = df_train['Age'].mode()[0]\ngender_mode = df_train['Gender'].mode()[0]\n\n# Unique pair combinations of Age and Gender\nunique_age_gender_pairs = df_train[['Age', 'Gender']]\n\n# Output the results\nprint(\"Mode of 'Age':\", age_mode)\ndisplay(\"Unique combinations of 'Age' and 'Gender':\", unique_age_gender_pairs.head(20))\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:10.215751Z","iopub.execute_input":"2025-02-03T18:04:10.216183Z","iopub.status.idle":"2025-02-03T18:04:10.351882Z","shell.execute_reply.started":"2025-02-03T18:04:10.21613Z","shell.execute_reply":"2025-02-03T18:04:10.350717Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize most common Gender present in Data on the Basis of Age</p>","metadata":{}},{"cell_type":"code","source":"# Mode of Age and Gender\nage_mode = df_train['Age'].mode()[0]\ngender_mode = df_train['Gender'].mode()[0]\n\n# Unique pair combinations of Age and Gender\nunique_age_gender_pairs = df_train[['Age', 'Gender']]\n\n# Filter top 20 'Age' values by frequency\ntop_20_ages = df_train['Age'].value_counts().head(20).index\n# Group by Age and Gender, then get the counts\nage_gender_counts = df_train[df_train['Age'].isin(top_20_ages)].groupby(['Age', 'Gender']).size().reset_index(name='Count')\n\n# Display the dataframe with top 20 Age and Gender combinations along with their counts\nage_gender_counts_sorted = age_gender_counts.sort_values(by='Count', ascending=False)\nage_gender_counts_sorted\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:10.352995Z","iopub.execute_input":"2025-02-03T18:04:10.353342Z","iopub.status.idle":"2025-02-03T18:04:10.717054Z","shell.execute_reply.started":"2025-02-03T18:04:10.353298Z","shell.execute_reply":"2025-02-03T18:04:10.716018Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Mode of Age and Gender\nage_mode = df_train['Age'].mode()[0]\ngender_mode = df_train['Gender'].mode()[0]\n\n# Unique pair combinations of Age and Gender\nunique_age_gender_pairs = df_train[['Age', 'Gender']]\n\n# Filter top 20 'Age' values by frequency\ntop_20_ages = df_train['Age'].value_counts().head(20).index\n\n# Create a barplot and pie chart subplot with increased figure size\nfig, axes = plt.subplots(1, 2, figsize=(25, 10))  # Increased figure size\n\n# Barplot: Mode of Age by Gender (only top 20 ages)\nsns.countplot(x='Age', hue='Gender', data=df_train[df_train['Age'].isin(top_20_ages)], ax=axes[0], palette=['#F4A300', '#003B49'])\naxes[0].set_title('Count of Mode Age by Gender', fontsize=20, fontweight='bold')  # Make title bold and larger\naxes[0].set_xlabel('Age', fontsize=16, fontweight='bold')  # Larger and bold x-label\naxes[0].set_ylabel('Count', fontsize=16, fontweight='bold')  # Larger and bold y-label\n\n# Increase font size and bold tick labels\naxes[0].tick_params(axis='x', labelsize=18, labelrotation=45)  # Larger x-tick labels\naxes[0].tick_params(axis='y', labelsize=18)  # Larger y-tick labels\n\n# Move the legend outside the plot\naxes[0].legend(title='Gender', loc='upper left', bbox_to_anchor=(1, 1), labels=['Female', 'Male'], fontsize=20)\n\n# Pie chart: Mode values for Gender (based on the mode of 'Gender' in df_train)\ngender_counts = df_train['Gender'].value_counts()\n\n# Increase font size for the pie chart labels and percentage values, and set color to white\naxes[1].pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=90, colors=['#F4A300', '#003B49'], \n            textprops={'fontsize': 18, 'fontweight': 'bold', 'color': 'white'})  # Set text color to white\n\naxes[1].set_title('Gender Distribution (Mode Values)', fontsize=25, fontweight='bold')  # Make title bold and larger\n\n# Display the results\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:10.718251Z","iopub.execute_input":"2025-02-03T18:04:10.718677Z","iopub.status.idle":"2025-02-03T18:04:12.031996Z","shell.execute_reply.started":"2025-02-03T18:04:10.718638Z","shell.execute_reply":"2025-02-03T18:04:12.030693Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Observations\n\n- **Gender Distribution**:  \n  - **Male**: 602,571 (49.8%)  \n  - **Female**: 597,429 (50.2%)  \n\n- **Age Statistics**:  \n  - **Minimum Age**: 18  \n  - **Maximum Age**: 64  \n  - **Mean Age**: 41.1  \n  - **Median Age**: 41.0  \n  - **Age Range**: 46  \n\n- **Age Group Distribution (Proportion of Males & Females)**:  \n  - **18-25**: Male (50.14%), Female (49.86%)  \n  - **26-35**: Male (50.23%), Female (49.77%)  \n  - **36-45**: Male (50.26%), Female (49.74%)  \n  - **46-55**: Male (50.21%), Female (49.79%)  \n  - **56-64**: Male (50.20%), Female (49.80%)  \n\n- **Highest Age Frequency**:  \n  - **Male**: Age 53, Count: 13,315  \n  - **Female**: Age 63, Count: 13,123  \n\n- **Conclusion**:  \n  - The dataset has a slightly higher proportion of females (50.2%) compared to males (49.8%).  \n  - The **36-45** age group has the highest male proportion (50.26%).  \n  - Males and females are fairly balanced across all age groups.  \n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Marital Status on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Get the frequency counts of each category in Gender and Marital Status\ngender_counts = df_train['Gender'].value_counts().reset_index()\ngender_counts.columns = ['Gender', 'Gender Count']\n\nmarital_status_counts = df_train['Marital Status'].value_counts().reset_index()\nmarital_status_counts.columns = ['Marital Status', 'Marital Status Count']\n\n# Merge the two dataframes into one\ncombined_analysis = pd.merge(gender_counts, marital_status_counts, how='cross')\n\n# Display the combined analysis\ndisplay(combined_analysis)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:12.033136Z","iopub.execute_input":"2025-02-03T18:04:12.033457Z","iopub.status.idle":"2025-02-03T18:04:12.233967Z","shell.execute_reply.started":"2025-02-03T18:04:12.033424Z","shell.execute_reply":"2025-02-03T18:04:12.232756Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Annual Income on the basis of Age</p>","metadata":{}},{"cell_type":"code","source":"# Get summary statistics for Age and Annual Income\nage_summary = df_train['Age'].describe()\nincome_summary = df_train['Annual Income'].describe()\n\n# Display the summaries\nprint(\"Age Summary:\\n\", age_summary)\nprint(\"\\nAnnual Income Summary:\\n\", income_summary)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:12.235451Z","iopub.execute_input":"2025-02-03T18:04:12.23591Z","iopub.status.idle":"2025-02-03T18:04:12.399674Z","shell.execute_reply.started":"2025-02-03T18:04:12.235877Z","shell.execute_reply":"2025-02-03T18:04:12.398331Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Calculate the statistics for Age and Annual Income\nage_mean = df_train['Age'].mean()\nage_max = df_train['Age'].max()\nage_min = df_train['Age'].min()\n\nincome_mean = df_train['Annual Income'].mean()\nincome_max = df_train['Annual Income'].max()\nincome_min = df_train['Annual Income'].min()\n\n\n# Calculate the statistics for Age and Annual Income grouped by Gender\ngender_stats = df_train.groupby('Gender').agg({\n    'Age': ['mean', 'max', 'min'],\n    'Annual Income': ['mean', 'max', 'min']\n})\n\n# Reset the index and flatten the multi-level columns\ngender_stats_reset = gender_stats.reset_index()\ngender_stats_reset.columns = ['Gender', 'Age_mean', 'Age_max', 'Age_min', 'Annual_Income_mean', 'Annual_Income_max', 'Annual_Income_min']\n\n# Display the summary\ndisplay(gender_stats_reset)\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:12.400994Z","iopub.execute_input":"2025-02-03T18:04:12.4013Z","iopub.status.idle":"2025-02-03T18:04:12.567874Z","shell.execute_reply.started":"2025-02-03T18:04:12.401276Z","shell.execute_reply":"2025-02-03T18:04:12.566724Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Annual Income & Age on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Define the color palette for gender (same colors for both bar plots and box plots)\ngender_palette = ['#B77A00', '#001F2D']  # Darker shades of Vivid Amber and Charcoal Blue\n\n# Create a figure with subplots (2 rows, 2 columns)\nfig, axes = plt.subplots(2, 2, figsize=(16, 12))\n\n# Bar plot for Age statistics by Gender (Mean)\nsns.barplot(\n    x='Gender', \n    y='Age_mean', \n    data=gender_stats_reset, \n    ax=axes[0, 0], \n    palette=gender_palette  # Use the same palette for the bar plot\n)\naxes[0, 0].set_title('Age Statistics by Gender (Mean)', fontsize=18, fontweight='bold')\naxes[0, 0].set_ylabel('Age', fontsize=14, fontweight='bold')\naxes[0, 0].set_xlabel('Gender', fontsize=14, fontweight='bold')\n\n# Add values above bars\nfor container in axes[0, 0].containers:\n    axes[0, 0].bar_label(container, fmt='%.3f', fontsize=12, fontweight='bold', label_type='edge', padding=3)\n\n# Bar plot for Annual Income statistics by Gender (Mean)\nsns.barplot(\n    x='Gender', \n    y='Annual_Income_mean', \n    data=gender_stats_reset, \n    ax=axes[0, 1], \n    palette=gender_palette  # Use the same palette for the bar plot\n)\naxes[0, 1].set_title('Annual Income Statistics by Gender (Mean)', fontsize=18, fontweight='bold')\naxes[0, 1].set_ylabel('Annual Income', fontsize=14, fontweight='bold')\naxes[0, 1].set_xlabel('Gender', fontsize=14, fontweight='bold')\n\n# Add values above bars\nfor container in axes[0, 1].containers:\n    axes[0, 1].bar_label(container, fmt='%.1f', fontsize=12, fontweight='bold', label_type='edge', padding=3)\n\n# Box plot for Age by Gender (Distribution)\nsns.boxplot(\n    x='Gender', \n    y='Age', \n    data=df_train, \n    ax=axes[1, 0], \n    palette=gender_palette  # Use the same palette for the box plot\n)\naxes[1, 0].set_title('Age Distribution by Gender', fontsize=18, fontweight='bold')\naxes[1, 0].set_ylabel('Age', fontsize=14, fontweight='bold')\naxes[1, 0].set_xlabel('Gender', fontsize=14, fontweight='bold')\n\n# Box plot for Annual Income by Gender (Distribution)\nsns.boxplot(\n    x='Gender', \n    y='Annual Income', \n    data=df_train, \n    ax=axes[1, 1], \n    palette=gender_palette  # Use the same palette for the box plot\n)\naxes[1, 1].set_title('Annual Income Distribution by Gender', fontsize=18, fontweight='bold')\naxes[1, 1].set_ylabel('Annual Income', fontsize=14, fontweight='bold')\naxes[1, 1].set_xlabel('Gender', fontsize=14, fontweight='bold')\n\n# Add spacing between subplots\nplt.tight_layout()\n\n# Display the visualization\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:12.569086Z","iopub.execute_input":"2025-02-03T18:04:12.56949Z","iopub.status.idle":"2025-02-03T18:04:14.803378Z","shell.execute_reply.started":"2025-02-03T18:04:12.569452Z","shell.execute_reply":"2025-02-03T18:04:14.801762Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Annual Income & Age on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Binning and combining age and income for df_train only\nage_bins = [18, 22, 25, 30, 35, 40, 45, 50, 55, 60, 64]\nage_labels = ['18-22', '23-25', '26-30', '31-35', '36-40', '41-45', '46-50', '51-55', '56-60', '61-64']\nincome_bins = [0, 30000, 50000, 70000, 100000, 150000]\nincome_labels = ['Low', 'Medium', 'High', 'Very High', 'Top']\n\n# Assuming df_train is your training dataset\ndf_train['Age Range'] = pd.cut(df_train['Age'], bins=age_bins, labels=age_labels)\ndf_train['Income Group'] = pd.cut(df_train['Annual Income'], bins=income_bins, labels=income_labels)\n\n# Count the occurrences of each combination of Age Range and Income Group\ndf_train['Count'] = df_train.groupby(['Age Range', 'Income Group'])['Age Range'].transform('count')\n\n# Find the maximum values for Age and Income within each Age Range and Income Group\ndf_train['Max Age'] = df_train.groupby(['Age Range', 'Income Group'])['Age'].transform('max')\ndf_train['Max Income'] = df_train.groupby(['Age Range', 'Income Group'])['Annual Income'].transform('max')\n\n# Display only the new columns (Age Range, Income Group, Count, Max Age, Max Income)\ndf_train[['Age Range', 'Income Group', 'Count', 'Max Age', 'Max Income']].head()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:14.810457Z","iopub.execute_input":"2025-02-03T18:04:14.810906Z","iopub.status.idle":"2025-02-03T18:04:15.107228Z","shell.execute_reply.started":"2025-02-03T18:04:14.810875Z","shell.execute_reply":"2025-02-03T18:04:15.105891Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Age Range, Income Group Visualization on the basis of Gender</p>","metadata":{}},{"cell_type":"code","source":"# Define custom color palettes for each plot\nage_range_palette = ['#7f278f', '#6B5B95', '#8f2727', '#F7B7A3', '#36b0d1', '#32a852', '#a83281', '#7ba832', '#ebcf52', '#27598f']  # Soft and warm colors\nincome_group_palette = ['#A6D608', '#A4B1B4', '#F4D03F', '#F39C12', '#2f278f']  # Earthy tones with pops of yellow\ngender_palette = ['#D54F39', '#34A853']  # Bright red and green for gender\nage_income_palette = ['#888f27', '#278f8f', '#8f273a', '#278f3d', '#8f2a27']  # Bright pastels for the count plot\n\n# Set up the figure and axes for multiple subplots\nfig, axes = plt.subplots(2, 2, figsize=(16, 12))\n\n# Plot 1: Barplot for Age Range vs Count with custom colors\nsns.barplot(x='Age Range', y='Count', data=df_train, ax=axes[0, 0], palette=age_range_palette)\naxes[0, 0].set_title('Age Range vs Count', fontsize=16, fontweight='bold')\naxes[0, 0].set_xlabel('Age Range', fontsize=12)\naxes[0, 0].set_ylabel('Count', fontsize=12)\n\n# Add values above bars in first plot\nfor p in axes[0, 0].patches:\n    axes[0, 0].annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                        ha='center', va='center', fontsize=7, color='black', xytext=(0, 10),\n                        textcoords='offset points')\n\n# Plot 2: Barplot for Income Group vs Count with custom colors\nsns.barplot(x='Income Group', y='Count', data=df_train, ax=axes[0, 1], palette=income_group_palette)\naxes[0, 1].set_title('Income Group vs Count', fontsize=16, fontweight='bold')\naxes[0, 1].set_xlabel('Income Group', fontsize=12)\naxes[0, 1].set_ylabel('Count', fontsize=12)\n\n# Add values above bars in second plot\nfor p in axes[0, 1].patches:\n    axes[0, 1].annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                        ha='center', va='center', fontsize=7, color='black', xytext=(0, 10),\n                        textcoords='offset points')\n\n# Plot 3: Scatterplot for Max Age vs Max Income based on Gender with custom colors\nsns.scatterplot(x='Max Age', y='Max Income', hue='Gender', data=df_train, ax=axes[1, 0], palette=gender_palette, style='Gender', markers=[\"o\", \"X\"], edgecolor='darkred')\naxes[1, 0].set_title('Max Age vs Max Income (Gender)', fontsize=16, fontweight='bold')\naxes[1, 0].set_xlabel('Max Age', fontsize=12)\naxes[1, 0].set_ylabel('Max Income', fontsize=12)\n\n# Place legend outside the scatter plot\naxes[1, 0].legend(title='Gender', bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Plot 4: Countplot for combinations of Age Range and Income Group with custom colors\nsns.countplot(x='Age Range', hue='Income Group', data=df_train, ax=axes[1, 1], palette=age_income_palette)\naxes[1, 1].set_title('Age Range and Income Group visualization', fontsize=16, fontweight='bold')\naxes[1, 1].set_xlabel('Age Range', fontsize=12)\naxes[1, 1].set_ylabel('Count', fontsize=12)\n\n# Place legend outside the countplot\naxes[1, 1].legend(title='Income Group', bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Adjust layout for better spacing\nplt.tight_layout()\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:04:15.1099Z","iopub.execute_input":"2025-02-03T18:04:15.110277Z","iopub.status.idle":"2025-02-03T18:05:25.047728Z","shell.execute_reply.started":"2025-02-03T18:04:15.110246Z","shell.execute_reply":"2025-02-03T18:05:25.046336Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Observations:**\n\n- **Gender & Marital Status Distribution:**\n  - The Gender column contains two unique values: **'Male'** and **'Female'**.\n  - The Marital Status column contains three unique values: **'Single'**, **'Married'**, and **'Divorced'**.\n  - **Male Distribution:** Single (395,391), Married (394,316), Divorced (391,764).\n  - **Female Distribution:** Single (395,391), Married (394,316), Divorced (391,764). *(Seems like a duplication, please verify.)*\n\n- **Age Statistics:**\n  - **Minimum Age:** 1.8  \n  - **Maximum Age:** 64  \n  - **Mean Age:** 41.14  \n  - **Highest Age Group Count:** **56-60** (50,908 people).  \n  - **Lowest Age Group Count:** **23-25** (29,149 people).  \n  - **Maximum Age of Females:** 54-55  \n  - **Maximum Age of Males:** 53-54  \n\n- **Annual Income Distribution:**\n  - **Minimum Income:** 1  \n  - **Maximum Income:** 149,997  \n  - **Mean Income:** 32,745.22  \n  - **Mean Income by Gender:**  \n    - **Male:** 32,714.7  \n    - **Female:** 32,776.0  \n\n- **Income Group Distribution:**\n  - **Low Income Group:** 68,872 (Highest Count)  \n  - **Medium Income Group:** 21,442  \n  - **High Income Group:** 8,590  \n  - **Very High Income Group:** 8,467  \n  - **Lowest Income Group Count:** 6,563  \n\n- **Income Trends by Age:**\n  - **Highest Income Group:** **Ages 56-60** (mostly in the Low Income category).  \n  - **Lowest Income Group:** **Ages 23-25**.  \n  - **Minimum Income Holders:**\n    - **Males:** Ages **22, 30-35, 50**.  \n    - **Females:** Ages **25, 40, 45, 55, 60, 65**.  \n  - **Highest Income Holders:**\n    - **Males:** Age **22** (149,993.0).  \n    - **Females:** Ages **30-40 & 50-64**.  \n  - **Top Income Groups:**  \n    - **Ages 56-60** have the highest income.  \n    - **Ages 31-35, 36-40, 46-50, and 51-55** mostly belong to the **Medium Income Group**.  \n\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Educational Level & Number of Dependents Column</p>","metadata":{}},{"cell_type":"code","source":"# Count the occurrences of each Number of Dependents for each Education Level\ndependents_distribution = df_train.groupby('Education Level')['Number of Dependents'].value_counts(normalize=True).unstack()\n\n# Rename columns for clarity\ndependents_distribution.columns.name = \"Number of Dependents\"\ndependents_distribution.fillna(0, inplace=True)\n\n# Output the distribution\ndisplay(\"Distribution of Number of Dependents by Education Level:\\n\", dependents_distribution)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:25.048984Z","iopub.execute_input":"2025-02-03T18:05:25.049327Z","iopub.status.idle":"2025-02-03T18:05:25.265276Z","shell.execute_reply.started":"2025-02-03T18:05:25.049298Z","shell.execute_reply":"2025-02-03T18:05:25.264058Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Educational Level & Number of Dependents Distribution</p>","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set up a figure with 2 rows and 2 columns of subplots, with same figsize for clarity\nfig, axes = plt.subplots(2, 2, figsize=(35, 19))  # Increased figure size for better prominence\n\n# Plot 1: Grouped bar plot for Number of Dependents by Education Level\ndependents_distribution.plot(kind='bar', ax=axes[0, 0], color=sns.color_palette('Set2', len(dependents_distribution.columns)))\naxes[0, 0].set_title('Number of Dependents by Education Level', fontsize=28, fontweight='bold', color='darkblue')\naxes[0, 0].set_xlabel('Education Level', fontsize=22, fontweight='bold', color='darkblue')\naxes[0, 0].set_ylabel('Proportion', fontsize=22, fontweight='bold', color='darkblue')\naxes[0, 0].legend(title='Number of Dependents', fontsize=18, title_fontsize=20, loc='upper left', bbox_to_anchor=(1.05, 1),\n                  frameon=True, shadow=True)\naxes[0, 0].tick_params(axis='x', labelsize=16, labelrotation=45, labelcolor='black')\naxes[0, 0].tick_params(axis='y', labelsize=16, labelcolor='black')\n\n# Annotate values above the bars\nfor p in axes[0, 0].patches:\n    height = p.get_height()\n    axes[0, 0].annotate(f'{height:.2f}',  # Value above the bar\n                        (p.get_x() + p.get_width() / 2., height),  # Position above the bar\n                        ha='center', va='center', fontsize=10, color='black', fontweight='bold', \n                        xytext=(0, 5), textcoords='offset points')\n\n# Plot 2: Pie chart for proportions of Education Levels\neducation_level_proportions = dependents_distribution.sum(axis=1) / dependents_distribution.sum().sum()  # Calculate the proportions\naxes[0, 1].pie(education_level_proportions, labels=education_level_proportions.index, \n               autopct='%1.1f%%', startangle=90, colors=sns.color_palette('tab20', len(education_level_proportions)),\n               wedgeprops={'edgecolor': 'black', 'linewidth': 1, 'linestyle': 'solid'}, radius=0.8, labeldistance=1.05)\naxes[0, 1].set_title('Proportion of Education Levels', fontsize=28, fontweight='bold', color='darkred')\naxes[0, 1].legend(title='Education Level', fontsize=18, title_fontsize=20, loc='upper left', bbox_to_anchor=(1.05, 1),\n                  frameon=True, shadow=True)\n\n# Plot 3: Violin plot to visualize the distribution of proportions\nsns.violinplot(data=dependents_distribution, ax=axes[1, 0], palette='coolwarm', linewidth=2)\naxes[1, 0].set_title('Distribution of Dependents Proportions', fontsize=28, fontweight='bold', color='green')\naxes[1, 0].set_xlabel('Number of Dependents', fontsize=22, fontweight='bold', color='green')\naxes[1, 0].set_ylabel('Proportion', fontsize=22, fontweight='bold', color='green')\naxes[1, 0].set_xticks(range(len(dependents_distribution.columns)))\naxes[1, 0].set_xticklabels(dependents_distribution.columns, fontsize=18, rotation=45, fontweight='bold', color='black')\naxes[1, 0].tick_params(axis='y', labelsize=18, labelcolor='black')\naxes[1, 0].grid(True, linestyle='--', alpha=0.7)\n\n# Plot 4: Scatter plot for proportions with Education Levels as categories\nfor level in dependents_distribution.index:\n    sns.scatterplot(x=dependents_distribution.columns, y=dependents_distribution.loc[level],\n                    label=level, ax=axes[1, 1], s=200, alpha=0.8, marker='o', edgecolor='black', linewidth=2)\naxes[1, 1].set_title('Scatter Plot of Dependents Distribution', fontsize=28, fontweight='bold', color='purple')\naxes[1, 1].set_xlabel('Number of Dependents', fontsize=22, fontweight='bold', color='purple')\naxes[1, 1].set_ylabel('Proportion', fontsize=22, fontweight='bold', color='purple')\naxes[1, 1].legend(title='Education Level', fontsize=18, title_fontsize=20, loc='upper left', bbox_to_anchor=(1.05, 1),\n                  frameon=True, shadow=True)\naxes[1, 1].tick_params(axis='x', labelsize=18, labelrotation=45, labelcolor='black')\naxes[1, 1].tick_params(axis='y', labelsize=18, labelcolor='black')\n\n# Add borders to the plots for emphasis\nfor ax in axes.flat:\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.spines['left'].set_color('black')\n    ax.spines['bottom'].set_color('black')\n    ax.spines['left'].set_linewidth(2)\n    ax.spines['bottom'].set_linewidth(2)\n\n# Adjust layout for better spacing\nplt.tight_layout(pad=4.0)  # Increased padding to avoid overlap\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:25.266494Z","iopub.execute_input":"2025-02-03T18:05:25.266999Z","iopub.status.idle":"2025-02-03T18:05:26.931545Z","shell.execute_reply.started":"2025-02-03T18:05:25.2669Z","shell.execute_reply":"2025-02-03T18:05:26.930088Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Average Distribution of Educational Level & Number of Dependents</p>","metadata":{}},{"cell_type":"code","source":"# Calculate the average and most common number of dependents for each education level\ndependents_summary = df_train.groupby('Education Level')['Number of Dependents'].agg(\n    Average='mean',\n    Most_Common=lambda x: x.value_counts().idxmax()\n).reset_index()\n\n# Display the summarized DataFrame\ndisplay(\"Summary of Dependents by Education Level:\", dependents_summary)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:26.932795Z","iopub.execute_input":"2025-02-03T18:05:26.933138Z","iopub.status.idle":"2025-02-03T18:05:27.102116Z","shell.execute_reply.started":"2025-02-03T18:05:26.933111Z","shell.execute_reply":"2025-02-03T18:05:27.101002Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Average Distribution of Educational Level & Number of Dependents</p>","metadata":{}},{"cell_type":"code","source":"# Define the color palette for gender (same colors for both bar plots and pie charts)\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']  # Darker shades of Vivid Amber and Charcoal Blue\n\n# Create a figure with 1 row and 2 columns for the pie chart and bar plot\nfig, axes = plt.subplots(1, 2, figsize=(16, 7))\n\n# Plot 1: Pie chart showing the average number of dependents by Education Level\ndependents_summary.set_index('Education Level')['Average'].plot(kind='pie', autopct='%1.1f%%', ax=axes[0], \n                                                              colors=gender_palette[:len(dependents_summary)],\n                                                              legend=False, textprops={'color': 'white'})  # Set text color to white\naxes[0].set_title('Average Number of Dependents by Education Level', fontsize=16, fontweight='bold')\naxes[0].set_ylabel('')  # Remove y-axis label for pie chart\n\n# Plot 2: Bar plot showing the most common number of dependents by Education Level\nbar_plot = sns.barplot(x='Education Level', y='Most_Common', data=dependents_summary, ax=axes[1], \n                       palette=gender_palette[:len(dependents_summary)])\n\n# Add values above bars with black text\nfor p in bar_plot.patches:\n    bar_plot.annotate(f'{p.get_height():.0f}', \n                      (p.get_x() + p.get_width() / 2., p.get_height()), \n                      ha='center', va='center', fontsize=12, color='black', \n                      xytext=(0, 10), textcoords='offset points')\n\naxes[1].set_title('Most Common Number of Dependents by Education Level', fontsize=16, fontweight='bold')\naxes[1].set_xlabel('Education Level', fontsize=12)\naxes[1].set_ylabel('Most Common Number of Dependents', fontsize=12)\n\n# Create a custom legend to match the color palette\ncustom_legend_handles = [plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=10, label=name)\n                         for color, name in zip(gender_palette[:len(dependents_summary)], dependents_summary['Education Level'])]\nfig.legend(handles=custom_legend_handles, title=\"Education Level\", fontsize=12, title_fontsize=14, loc='lower center', ncol=4)\n\n# Adjust layout for better spacing\nplt.tight_layout(rect=[0, 0.1, 1, 1])  # Leave space for legend at the bottom\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:27.103537Z","iopub.execute_input":"2025-02-03T18:05:27.103997Z","iopub.status.idle":"2025-02-03T18:05:27.605243Z","shell.execute_reply.started":"2025-02-03T18:05:27.103954Z","shell.execute_reply":"2025-02-03T18:05:27.60395Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Income Analysis on the basis of Education Level and Number of Dependents</p>","metadata":{}},{"cell_type":"code","source":"# Calculate mean and median income for each combination of Number of Dependents and Education Level\nincome_analysis = df_train.groupby(['Education Level', 'Number of Dependents'])['Annual Income'].agg(['mean', 'max']).reset_index()\n\n# Output income analysis\nprint(\"Income Analysis by Education Level and Number of Dependents:\\n\", income_analysis)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:27.606354Z","iopub.execute_input":"2025-02-03T18:05:27.606682Z","iopub.status.idle":"2025-02-03T18:05:27.783357Z","shell.execute_reply.started":"2025-02-03T18:05:27.606651Z","shell.execute_reply":"2025-02-03T18:05:27.782116Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Annual Income Visualization on the basis of Education Level and Number of Dependents</p>","metadata":{}},{"cell_type":"code","source":"# Calculate mean and max income for each combination of Number of Dependents and Education Level\nincome_analysis = df_train.groupby(['Education Level', 'Number of Dependents'])['Annual Income'].agg(['mean', 'max']).reset_index()\n\n# Define the color palette for gender (same colors for both bar plots and pie charts)\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']  # Darker shades of Vivid Amber and Charcoal Blue\n\n# Create a figure with 2 rows and 1 column for the bar plot and pie chart (row-wise arrangement)\nfig, axes = plt.subplots(2, 1, figsize=(20, 18))  # Increased figsize for better prominence\n\n# Plot 1: Bar plot showing mean and max income for each combination of Education Level and Number of Dependents\nsns.barplot(x='Number of Dependents', y='mean', hue='Education Level', data=income_analysis, ax=axes[0], palette=gender_palette)\naxes[0].set_title('Mean Annual Income by Education Level and Number of Dependents', fontsize=22, fontweight='bold')\naxes[0].set_xlabel('Number of Dependents', fontsize=18)\naxes[0].set_ylabel('Mean Annual Income', fontsize=18)\n\n# Add values above the bars with black color and increase font size\nfor p in axes[0].patches:\n    axes[0].annotate(f'{p.get_height():,.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                     ha='center', va='center', fontsize=9, color='black', fontweight='bold', xytext=(0, 5),\n                     textcoords='offset points')\n\n# Move the legend outside of the bar plot with larger font size\naxes[0].legend(title='Education Level', fontsize=14, title_fontsize=16, bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Plot 2: Pie chart showing the distribution of mean income by Education Level\neducation_income = income_analysis.groupby('Education Level')['mean'].mean()\neducation_income.plot(kind='pie', autopct='%1.1f%%', ax=axes[1], colors=gender_palette,\n                      legend=False, textprops={'color': 'white', 'fontsize': 14}, radius=1.2)  # Increased radius for larger pie chart\naxes[1].set_title('Mean Annual Income Distribution by Education Level', fontsize=22, fontweight='bold')\naxes[1].set_ylabel('')  # Remove y-axis label for pie chart\n\n# Adjust layout for better spacing\nplt.tight_layout(pad=5.0)  # Increased padding for better spacing between plots\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:27.784781Z","iopub.execute_input":"2025-02-03T18:05:27.785281Z","iopub.status.idle":"2025-02-03T18:05:28.619529Z","shell.execute_reply.started":"2025-02-03T18:05:27.785235Z","shell.execute_reply":"2025-02-03T18:05:28.61826Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Proportion of High-Dependency Individuals by Education Level</p>","metadata":{}},{"cell_type":"code","source":"# Define a threshold for high-dependency (e.g., 3 or more dependents)\nhigh_dependency_threshold = 3\n\n# Calculate the proportion of individuals with high dependency for each Education Level\nhigh_dependency_profile = df_train[df_train['Number of Dependents'] >= high_dependency_threshold].groupby('Education Level').size() / df_train.groupby('Education Level').size()\n\n# Output high-dependency profile\nprint(\"Proportion of High-Dependency Individuals by Education Level:\\n\", high_dependency_profile)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:28.621014Z","iopub.execute_input":"2025-02-03T18:05:28.621459Z","iopub.status.idle":"2025-02-03T18:05:28.874557Z","shell.execute_reply.started":"2025-02-03T18:05:28.621427Z","shell.execute_reply":"2025-02-03T18:05:28.873202Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Proportion of High-Dependency Individuals by Education Level</p>","metadata":{}},{"cell_type":"code","source":"# Define a threshold for high dependency\nhigh_dependency_threshold = 3\n\n# Calculate the proportion of individuals with high dependency for each Education Level\nhigh_dependency_profile = df_train[df_train['Number of Dependents'] >= high_dependency_threshold].groupby('Education Level').size() / df_train.groupby('Education Level').size()\n\n# Define the color palette for gender (same colors for both bar plots and pie charts)\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']  # Darker shades of Vivid Amber and Charcoal Blue\n\n# Create a figure with 2 subplots (bar plot and pie chart)\nfig, axes = plt.subplots(1, 2, figsize=(30, 15))  # Increased width for side-by-side arrangement\n\n# Plot 1: Bar plot showing the proportion of high-dependency individuals by Education Level\nsns.barplot(x=high_dependency_profile.index, y=high_dependency_profile.values, ax=axes[0], palette=gender_palette)\naxes[0].set_title('Proportion of High-Dependency Individuals by Education Level', fontsize=24, fontweight='bold')\naxes[0].set_xlabel('Education Level', fontsize=20)\naxes[0].set_ylabel('Proportion of High-Dependency', fontsize=20)\n\n# Make ticks more prominent\naxes[0].tick_params(axis='x', labelsize=18, labelrotation=45, width=3, colors='black')  # x-axis ticks\naxes[0].tick_params(axis='y', labelsize=18, width=3, colors='black')  # y-axis ticks\n\n# Add values above the bars with black color and increase font size\nfor p in axes[0].patches:\n    axes[0].annotate(f'{p.get_height():.4f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                     ha='center', va='center', fontsize=20, color='black', fontweight='bold', xytext=(0, 5),\n                     textcoords='offset points')\n\n# Plot 2: Pie chart showing the proportion of high-dependency individuals by Education Level\nhigh_dependency_profile.plot(kind='pie', autopct='%1.1f%%', ax=axes[1], colors=gender_palette,\n                             legend=False, textprops={'color': 'white', 'fontsize': 18}, radius=1.2)  # Increased radius for larger pie chart\naxes[1].set_title('Proportion of High-Dependency Individuals by Education Level', fontsize=24, fontweight='bold')\naxes[1].set_ylabel('')  # Remove y-axis label for pie chart\n\n# Create a custom legend to match the color palette\ncustom_legend_handles = [\n    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=15, label=name)\n    for color, name in zip(gender_palette, high_dependency_profile.index)\n]\nfig.legend(handles=custom_legend_handles, title=\"Education Level\", fontsize=18, title_fontsize=20, loc='lower center', ncol=4)\n\n# Adjust layout for better spacing\nplt.tight_layout(rect=[0, 0.1, 1, 1])  # Leave space for legend at the bottom\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:28.876213Z","iopub.execute_input":"2025-02-03T18:05:28.876572Z","iopub.status.idle":"2025-02-03T18:05:30.132047Z","shell.execute_reply.started":"2025-02-03T18:05:28.876542Z","shell.execute_reply":"2025-02-03T18:05:30.130667Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Observations**\n\n### **Education Level and Number of Dependents**\n- The highest frequency count (0.203571) is observed for individuals with a **High School** education level and **3 dependents**.\n- The lowest frequency count (0.196724) is found among individuals with a **High School** education level and **1 dependent**.\n- People with a **Master's** education level have a frequency count of **0.198223**, which is neither the highest nor the lowest.\n- Individuals with a **Bachelor’s** education level have the highest proportion based on **3 dependents**, with a frequency count of **≥0.2**.\n- The highest proportion of individuals with **Bachelor’s and PhD** education levels is observed for **3 & 4 dependents**, with a frequency count between **0.2-0.4**.\n- Similarly, **High School & Master’s** education levels show the highest proportion for **3 dependents**, with a frequency count between **0.2-0.4**.\n\n### **Educational Proportions**\n- All education levels have approximately equal proportions:\n  - **Bachelor’s**: **0.368247**\n  - **High School**: **0.368313**\n  - **Master’s**: **0.367447**\n  - **PhD**: **0.368716**\n- The highest proportion of people falls under the **Master’s** education level.\n\n### **Number of Dependents**\n- **3 dependents** have the highest proportion (**0.3-0.5 range**).\n- **1 dependent** has the lowest proportion (**0.1451**).\n- **0 dependents** have a proportion between **0.197-0.208**.\n- **2 dependents** have a proportion between **0.195-0.199**.\n\n### **Mean and Most Common Number of Dependents by Education Level**\n| Education Level | Mean Dependents | Most Common Dependents |\n|----------------|---------------|----------------------|\n| **Bachelor’s**  | 2.011277       | 4                    |\n| **High School** | 2.007098       | 3                    |\n| **Master’s**    | 2.009063       | 3                    |\n| **PhD**        | 2.012166       | 3                    |\n\n### **Income Statistics by Education Level and Dependents**\n- **Highest mean income**:  \n  - **High School** graduates with **4 dependents** have an average income of **$33,014.09**.\n- **Lowest mean income**:  \n  - **PhD** holders with **0 dependents** have an average income of **$32,439.47**.\n- **Maximum income**:  \n  - **High School** graduates with **4 dependents** have the highest recorded income of **$149,997**.\n- **Minimum income**:  \n  - Individuals with **Bachelor’s & High School** education and **4 dependents** have the lowest recorded income of **$149,992**.\n- **Maximum mean income**:  \n  - People with a **High School** education and **3 dependents** have the highest mean income of **$32,930**.\n- **Minimum mean income**:  \n  - People with a **Master’s** education and **2 dependents** have the lowest mean income of **$32,519**.\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Health Score on the Basis of Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Calculate the mean, max, min, and count of Health Score for each Occupation\nhealth_score_by_occupation = df_train.groupby('Occupation')['Health Score'].agg(['mean', 'max', 'min', 'count'])\n\n# Output the selected statistics (mean, max, min, count) of Health Score by Occupation\nprint(\"Health Score by Occupation:\\n\", health_score_by_occupation)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:30.13334Z","iopub.execute_input":"2025-02-03T18:05:30.133696Z","iopub.status.idle":"2025-02-03T18:05:30.283499Z","shell.execute_reply.started":"2025-02-03T18:05:30.133666Z","shell.execute_reply":"2025-02-03T18:05:30.282367Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Health Score on the Basis of Occupation</p>","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.lines import Line2D\nfrom sklearn.impute import SimpleImputer\n\n# Impute missing values in 'Occupation' with the mode (most frequent value)\noccupation_imputer = SimpleImputer(strategy='most_frequent')\n\n# Ensure the input to fit_transform is a 2D array by selecting the column as DataFrame\ndf_train['Occupation'] = occupation_imputer.fit_transform(df_train[['Occupation']]).flatten()\ndf_test['Occupation'] = occupation_imputer.transform(df_test[['Occupation']]).flatten()\n\n# Define the custom color palette\ngender_palette = ['#B77A00', '#001F2D', '#29002d']\n\n# Ensure the palette has enough colors for the number of unique occupations by cycling the colors\noccupation_palette = gender_palette * (len(df_train['Occupation'].unique()) // len(gender_palette)) + gender_palette[:len(df_train['Occupation'].unique()) % len(gender_palette)]\n\n# Create a dictionary that maps occupations to the colors in the palette\noccupation_color_map = dict(zip(df_train['Occupation'].unique(), occupation_palette))\n\n# Assign navy blue to \"Unemployed\"\noccupation_color_map['Unemployed'] = '#001F2D'  # Navy blue color\n\n# Calculate the mean, max, min, and count of Health Score for each Occupation\nhealth_score_by_occupation = df_train.groupby('Occupation')['Health Score'].agg(['mean', 'max', 'min', 'count'])\n\n# Set up the figure and axes for subplots with a larger size\nfig, axes = plt.subplots(2, 2, figsize=(22, 18))  # Increased figsize for better prominence\n\n# Plot 1: Bar plot of mean Health Score by Occupation with the consistent color palette\nbarplot = sns.barplot(x=health_score_by_occupation.index, \n                      y=health_score_by_occupation['mean'], \n                      ax=axes[0, 0], \n                      palette=occupation_color_map)\naxes[0, 0].set_title('Mean Health Score by Occupation', fontsize=24, fontweight='bold')\naxes[0, 0].set_xlabel('Occupation', fontsize=18)\naxes[0, 0].set_ylabel('Mean Health Score', fontsize=18)\naxes[0, 0].tick_params(axis='x', rotation=45, labelsize=16)\n\n# Add values above bars\nfor container in barplot.containers:\n    barplot.bar_label(container, fmt='%.2f', fontsize=16, fontweight='bold', label_type='edge', padding=6)\n\n# Create custom legend for bar plot\nlegend_labels = [Line2D([0], [0], marker='o', color='w', markerfacecolor=color, markersize=12) \n                 for color in occupation_color_map.values()]\naxes[0, 0].legend(legend_labels, occupation_color_map.keys(), title='Occupation', loc='upper left', bbox_to_anchor=(1, 1), fontsize=16)\n\n# Plot 2: Box plot for Health Score distribution by Occupation\nsns.boxplot(x='Occupation', y='Health Score', data=df_train, ax=axes[0, 1], palette=occupation_color_map)\naxes[0, 1].set_title('Health Score Distribution by Occupation', fontsize=24, fontweight='bold')\naxes[0, 1].set_xlabel('Occupation', fontsize=18)\naxes[0, 1].set_ylabel('Health Score', fontsize=18)\naxes[0, 1].tick_params(axis='x', rotation=45, labelsize=16)\n\n# Add custom legend for the box plot\naxes[0, 1].legend(legend_labels, occupation_color_map.keys(), title='Occupation', loc='upper left', bbox_to_anchor=(1, 1), fontsize=16)\n\n# Plot 3: Count plot of Occupation (to show how many records per occupation)\ncountplot = sns.countplot(x='Occupation', data=df_train, ax=axes[1, 0], palette=occupation_color_map)\naxes[1, 0].set_title('Count of Records by Occupation', fontsize=24, fontweight='bold')\naxes[1, 0].set_xlabel('Occupation', fontsize=18)\naxes[1, 0].set_ylabel('Count', fontsize=18)\naxes[1, 0].tick_params(axis='x', rotation=45, labelsize=16)\n\n# Add custom legend for the count plot\naxes[1, 0].legend(legend_labels, occupation_color_map.keys(), title='Occupation', loc='upper left', bbox_to_anchor=(1, 1), fontsize=16)\n\n# Add values above the bars in the count plot\nfor p in countplot.patches:\n    height = p.get_height()\n    countplot.text(p.get_x() + p.get_width() / 2., height + 2, str(int(height)), ha=\"center\", fontsize=16, fontweight='bold')\n\n# Plot 4: Grouped Histogram for Health Score distribution by Occupation\nsns.histplot(data=df_train, x='Health Score', hue='Occupation', multiple='stack', kde=True, ax=axes[1, 1], palette=occupation_color_map, bins=20)\naxes[1, 1].set_title('Grouped Health Score Distribution by Occupation (Histogram)', fontsize=24, fontweight='bold')\naxes[1, 1].set_xlabel('Health Score', fontsize=18)\naxes[1, 1].set_ylabel('Frequency', fontsize=18)\n\n# Add custom legend for the histogram\naxes[1, 1].legend(legend_labels, occupation_color_map.keys(), title='Occupation', loc='upper left', bbox_to_anchor=(1, 1), fontsize=16)\n\n# Adjust layout for better spacing\nplt.tight_layout(pad=6.0)  # Increased padding for better spacing\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:30.284518Z","iopub.execute_input":"2025-02-03T18:05:30.284834Z","iopub.status.idle":"2025-02-03T18:05:39.617661Z","shell.execute_reply.started":"2025-02-03T18:05:30.284807Z","shell.execute_reply":"2025-02-03T18:05:39.61626Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Location on the basis of Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Find the most frequent Occupation for each Location\nmost_frequent_occupation_by_location = df_train.groupby('Location')['Occupation'].agg(lambda x: x.mode()[0])\n\n# Output the most frequent Occupation for each Location\nprint(\"Most Frequent Occupation for each Location:\\n\", most_frequent_occupation_by_location)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:39.618762Z","iopub.execute_input":"2025-02-03T18:05:39.619055Z","iopub.status.idle":"2025-02-03T18:05:39.817885Z","shell.execute_reply.started":"2025-02-03T18:05:39.619031Z","shell.execute_reply":"2025-02-03T18:05:39.816643Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Health Score on the basis of Location & Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Calculate the distribution of Occupation for each Location\noccupation_by_location = df_train.groupby('Location')['Occupation'].value_counts(normalize=True).unstack().fillna(0)\n\n# Calculate the mean, max, and count of Health Score for each Location and Occupation\nhealth_score_by_location_occupation = df_train.groupby(['Location', 'Occupation'])['Health Score'].agg(['count', 'mean', 'max'])\n\n# Output the results\ndisplay(\"Occupation Distribution within each Location:\", occupation_by_location)\ndisplay(\"Health Score by Location and Occupation:\", health_score_by_location_occupation)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:39.81901Z","iopub.execute_input":"2025-02-03T18:05:39.819301Z","iopub.status.idle":"2025-02-03T18:05:40.220101Z","shell.execute_reply.started":"2025-02-03T18:05:39.819278Z","shell.execute_reply":"2025-02-03T18:05:40.218893Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Health Score on the basis of Location & Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Impute missing values in 'Occupation' with the mode (most frequent value)\noccupation_imputer = SimpleImputer(strategy='most_frequent')\n\n# Ensure the input to fit_transform is a 2D array by selecting the column as DataFrame\ndf_train['Occupation'] = occupation_imputer.fit_transform(df_train[['Occupation']]).flatten()\ndf_test['Occupation'] = occupation_imputer.transform(df_test[['Occupation']]).flatten()\n\n# Define the custom color palette for each plot\noccupation_palette = ['#B77A00', '#001F2D', '#29002d']\n\n# Set up the figure and axes for subplots with much larger figsize\nfig, axes = plt.subplots(2, 2, figsize=(40, 32))  # Much larger figsize\n\n# Plot 1: Stacked bar plot of Occupation distribution within each Location\noccupation_by_location.plot(kind='bar', stacked=True, ax=axes[0, 0], color=occupation_palette)\naxes[0, 0].set_title('Occupation Distribution within Each Location', fontsize=30, fontweight='bold')\naxes[0, 0].set_xlabel('Location', fontsize=24)\naxes[0, 0].set_ylabel('Proportion', fontsize=24)\naxes[0, 0].tick_params(axis='x', rotation=45, labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[0, 0].tick_params(axis='y', labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[0, 0].legend(title='Occupation', fontsize=20, bbox_to_anchor=(1.05, 1), loc='upper left')  # Legend outside\n\n# Add bold values on bars for Plot 1\nfor p in axes[0, 0].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x, y = p.get_xy()  # Get the x and y coordinates of the rectangle\n    axes[0, 0].text(x + width / 2, y + height / 2, f'{height:.2f}', ha='center', va='center', fontsize=18, color='white', fontweight='bold')\n\n# Plot 2: Boxplot of Health Score by Location and Occupation\nsns.boxplot(x='Location', y='Health Score', hue='Occupation', data=df_train, ax=axes[0, 1], palette=occupation_palette)\naxes[0, 1].set_title('Health Score Distribution by Location and Occupation (Boxplot)', fontsize=30, fontweight='bold')\naxes[0, 1].set_xlabel('Location', fontsize=24)\naxes[0, 1].set_ylabel('Health Score', fontsize=24)\naxes[0, 1].tick_params(axis='x', rotation=45, labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[0, 1].tick_params(axis='y', labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[0, 1].legend(title='Occupation', fontsize=20, bbox_to_anchor=(1.05, 1), loc='upper left')  # Legend outside\n\n# Plot 3: Clustered bar plot of Health Score count by Location and Occupation\nhealth_score_by_location_occupation['count'].unstack().plot(kind='bar', ax=axes[1, 0], color=occupation_palette, width=0.8)\naxes[1, 0].set_title('Health Score Count by Location and Occupation', fontsize=30, fontweight='bold')\naxes[1, 0].set_xlabel('Location', fontsize=24)\naxes[1, 0].set_ylabel('Count', fontsize=24)\naxes[1, 0].tick_params(axis='x', rotation=45, labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[1, 0].tick_params(axis='y', labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[1, 0].legend(title='Occupation', fontsize=20, bbox_to_anchor=(1.05, 1), loc='upper left')  # Legend outside\n\n# Add bold values on bars for Plot 3\nfor p in axes[1, 0].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x, y = p.get_xy()  # Get the x and y coordinates of the rectangle\n    axes[1, 0].text(x + width / 2, y + height / 2, f'{height:.0f}', ha='center', va='center', fontsize=18, color='white', fontweight='bold')\n\n# Plot 4: Clustered bar plot of mean Health Score by Location and Occupation\nhealth_score_by_location_occupation['mean'].unstack().plot(kind='bar', ax=axes[1, 1], color=occupation_palette, width=0.8)\naxes[1, 1].set_title('Mean Health Score by Location and Occupation', fontsize=30, fontweight='bold')\naxes[1, 1].set_xlabel('Location', fontsize=24)\naxes[1, 1].set_ylabel('Mean Health Score', fontsize=24)\naxes[1, 1].tick_params(axis='x', rotation=45, labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[1, 1].tick_params(axis='y', labelsize=24, width=2, length=10, colors='black', grid_color='gray', grid_alpha=0.5)\naxes[1, 1].legend(title='Occupation', fontsize=20, bbox_to_anchor=(1.05, 1), loc='upper left')  # Legend outside\n\n# Add bold values on bars for Plot 4\nfor p in axes[1, 1].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x, y = p.get_xy()  # Get the x and y coordinates of the rectangle\n    axes[1, 1].text(x + width / 2, y + height / 2, f'{height:.2f}', ha='center', va='center', fontsize=18, color='white', fontweight='bold')\n\n# Adjust layout for better spacing\nplt.tight_layout(pad=8.0)  # Increased padding for better spacing\n\n# Show the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:40.221133Z","iopub.execute_input":"2025-02-03T18:05:40.221425Z","iopub.status.idle":"2025-02-03T18:05:43.409442Z","shell.execute_reply.started":"2025-02-03T18:05:40.221402Z","shell.execute_reply":"2025-02-03T18:05:43.408121Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Low Vs High Health Score Proportions by Location and Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Define a threshold for low Health Score (e.g., Health Score < 20)\nlow_health_score_threshold = 20\n# Define a threshold for high Health Score (e.g., Health Score >= 50)\nhigh_health_score_threshold = 50\n# Calculate the proportion of low-health individuals (Health Score < threshold) by Location and Occupation\nlow_health_score_proportion = df_train[df_train['Health Score'] < low_health_score_threshold] \\\n    .groupby(['Location', 'Occupation']).size() / df_train.groupby(['Location', 'Occupation']).size()\n\n# Calculate the proportion of high-health individuals (Health Score >= threshold) by Location and Occupation\nhigh_health_score_proportion = df_train[df_train['Health Score'] >= high_health_score_threshold] \\\n    .groupby(['Location', 'Occupation']).size() / df_train.groupby(['Location', 'Occupation']).size()\n# Combine both low and high health score proportions into a single DataFrame for easy comparison\nhealth_score_comparison = pd.DataFrame({\n    'Low Health Score Proportion': low_health_score_proportion,\n    'High Health Score Proportion': high_health_score_proportion\n}).fillna(0)  # Fill missing values with 0 if there are any categories without low or high health individuals\n\n# Output the comparative analysis\ndisplay(\"Comparative Analysis of Low and High Health Score Proportions by Location and Occupation:\", health_score_comparison)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:43.410833Z","iopub.execute_input":"2025-02-03T18:05:43.411282Z","iopub.status.idle":"2025-02-03T18:05:44.032334Z","shell.execute_reply.started":"2025-02-03T18:05:43.411235Z","shell.execute_reply":"2025-02-03T18:05:44.031135Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Low Vs High Health Score Proportions by Location and Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Reshape the data for the bar plot\nhealth_score_comparison_reset = health_score_comparison.reset_index()\n\n# Aggregate proportions for the pie chart\ntotal_low = health_score_comparison['Low Health Score Proportion'].sum()\ntotal_high = health_score_comparison['High Health Score Proportion'].sum()\n\n# Data for the pie chart\npie_data = [total_low, total_high]\npie_labels = ['Low Health Score', 'High Health Score']\noccupation_palette = ['#B77A00', '#001F2D']\n\n# Create the subplots\nfig, axes = plt.subplots(1, 2, figsize=(25, 10))\n\n# Bar plot\nsns.barplot(\n    data=health_score_comparison_reset.melt(\n        id_vars=['Location', 'Occupation'],\n        value_vars=['Low Health Score Proportion', 'High Health Score Proportion']\n    ),\n    x='Location',\n    y='value',\n    hue='variable',\n    ci=None,\n    palette=occupation_palette,\n    ax=axes[0]\n)\naxes[0].set_title('Comparative Proportions of Low and High Health Scores by Location and Occupation', fontsize=18, fontweight='bold')\naxes[0].set_xlabel('Location', fontsize=16, fontweight='bold')\naxes[0].set_ylabel('Proportion', fontsize=16, fontweight='bold')\naxes[0].tick_params(axis='x', rotation=45, labelsize=14, labelcolor='black')\naxes[0].tick_params(axis='y', labelsize=14, labelcolor='black')\n\n# Move legend outside the plot\naxes[0].legend(\n    title='Health Score Category', \n    fontsize=14, \n    loc='upper left', \n    bbox_to_anchor=(1, 1),\n    title_fontsize=14\n)\n\n# Add values above bars\nfor p in axes[0].patches:\n    height = p.get_height()\n    if not pd.isna(height):\n        axes[0].text(\n            p.get_x() + p.get_width() / 2., \n            height + 0.01, \n            f'{height:.4f}', \n            ha='center', fontsize=12, color='black', fontweight='bold'\n        )\n\n# Pie chart\nwedges, texts, autotexts = axes[1].pie(\n    pie_data,\n    labels=pie_labels,\n    autopct='%1.3f%%',\n    startangle=90,\n    colors=occupation_palette,\n    textprops={'fontsize': 14, 'fontweight': 'bold'},\n    wedgeprops={'edgecolor': 'black', 'linewidth': 1.2}\n)\n\n# Format pie chart values\nfor autotext in autotexts:\n    autotext.set_color('white')\n    autotext.set_fontweight('bold')\n\naxes[1].set_title('Overall Proportions of Low and High Health Scores', fontsize=18, fontweight='bold')\n\n# Adjust layout\nplt.tight_layout()\nplt.subplots_adjust(right=0.8)  # Adjust to make space for the legend\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:44.033337Z","iopub.execute_input":"2025-02-03T18:05:44.033733Z","iopub.status.idle":"2025-02-03T18:05:44.574446Z","shell.execute_reply.started":"2025-02-03T18:05:44.033703Z","shell.execute_reply":"2025-02-03T18:05:44.572704Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Health Score, Annual Income & Age of Gender on the basis of Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Calculate the mean values of Age, Health Score, and Annual Income for each Occupation\noccupation_health_income_age_mean = df_train.groupby('Occupation')[['Age', 'Health Score', 'Annual Income']].mean()\n\n# Output the mean values for Age, Health Score, and Annual Income for each Occupation\nprint(\"Mean values of Age, Health Score, and Annual Income for each Occupation:\\n\", occupation_health_income_age_mean)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:44.575881Z","iopub.execute_input":"2025-02-03T18:05:44.576337Z","iopub.status.idle":"2025-02-03T18:05:44.701389Z","shell.execute_reply.started":"2025-02-03T18:05:44.576299Z","shell.execute_reply":"2025-02-03T18:05:44.700081Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Health Score Annual Income & Age of Gender on the basis of Occupation</p>","metadata":{}},{"cell_type":"code","source":"# Define the custom color palette for each plot\noccupation_palette = ['#B77A00', '#001F2D', '#29002d']\n\n# Set a style for the plots\nsns.set_style(\"whitegrid\")\n\n# Create subplots\nfig, axes = plt.subplots(nrows=2, ncols=3, figsize=(24, 16))  # 2 rows and 3 columns\n\n# --- First Row: Bar Plots ---\n# Vertical Bar Plot for Age\nsns.barplot(\n    x=occupation_health_income_age_mean.index,  # 'Occupation' on the y-axis\n    y=occupation_health_income_age_mean['Age'],  # 'Mean Age' on the x-axis\n    color=occupation_palette[0],\n    ax=axes[0, 0]\n)\naxes[0, 0].set_title('Mean Age by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[0])\naxes[0, 0].set_ylabel('Occupation', fontsize=16, fontweight='bold', color=occupation_palette[0])\naxes[0, 0].set_xlabel('Mean Age', fontsize=16, fontweight='bold', color=occupation_palette[0])\naxes[0, 0].tick_params(axis='x', labelsize=14, rotation=45, length=6)\naxes[0, 0].tick_params(axis='y', labelsize=14, length=6)\n\n# Annotating bars with values\nfor p in axes[0, 0].patches:\n    axes[0, 0].annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 4., p.get_height()),\n                        ha='center', va='center', fontsize=14, fontweight='bold', color='black', xytext=(0, 10), textcoords='offset points')\n\n# Vertical Bar Plot for Health Score\nsns.barplot(\n    x=occupation_health_income_age_mean.index,  # 'Occupation' on the y-axis\n    y=occupation_health_income_age_mean['Health Score'],  # 'Mean Health Score' on the x-axis\n    color=occupation_palette[1],\n    ax=axes[0, 1]\n)\naxes[0, 1].set_title('Mean Health Score by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[1])\naxes[0, 1].set_ylabel('Occupation', fontsize=16, fontweight='bold', color=occupation_palette[1])\naxes[0, 1].set_xlabel('Mean Health Score', fontsize=16, fontweight='bold', color=occupation_palette[1])\naxes[0, 1].tick_params(axis='x', labelsize=14, rotation=45, length=6)\naxes[0, 1].tick_params(axis='y', labelsize=14, length=6)\n\n# Annotating bars with values\nfor p in axes[0, 1].patches:\n    axes[0, 1].annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 4., p.get_height()),\n                        ha='center', va='center', fontsize=14, fontweight='bold', color='black', xytext=(0, 10), textcoords='offset points')\n\n# Vertical Bar Plot for Annual Income\nsns.barplot(\n    x=occupation_health_income_age_mean.index,  # 'Occupation' on the y-axis\n    y=occupation_health_income_age_mean['Annual Income'],  # 'Mean Annual Income' on the x-axis\n    color=occupation_palette[2],\n    ax=axes[0, 2]\n)\naxes[0, 2].set_title('Mean Annual Income by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[2])\naxes[0, 2].set_ylabel('Occupation', fontsize=16, fontweight='bold', color=occupation_palette[2])\naxes[0, 2].set_xlabel('Mean Annual Income', fontsize=16, fontweight='bold', color=occupation_palette[2])\naxes[0, 2].tick_params(axis='x', labelsize=14, rotation=45, length=6)\naxes[0, 2].tick_params(axis='y', labelsize=14, length=6)\n\n# Annotating bars with values\nfor p in axes[0, 2].patches:\n    axes[0, 2].annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 2., p.get_height()),\n                        ha='center', va='center', fontsize=14, fontweight='bold', color='black', xytext=(0, 10), textcoords='offset points')\n\n# --- Second Row: Pie Charts ---\n# Pie Chart for Mean Age\nage_labels = occupation_health_income_age_mean.index\nage_sizes = occupation_health_income_age_mean['Age']\npie1 = axes[1, 0].pie(age_sizes, labels=age_labels, autopct='%1.1f%%', colors=occupation_palette, startangle=90, \n                      textprops={'fontsize': 14, 'fontweight': 'bold', 'color': 'white'}, wedgeprops={'edgecolor': 'black'})\naxes[1, 0].set_title('Mean Age Distribution by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[0])\n\n# Add legend for Age\naxes[1, 0].legend(age_labels, loc='upper right', bbox_to_anchor=(1.3, 1), fontsize=14, title=\"Occupations\")\n\n# Pie Chart for Mean Health Score\nhealth_score_labels = occupation_health_income_age_mean.index\nhealth_score_sizes = occupation_health_income_age_mean['Health Score']\npie2 = axes[1, 1].pie(health_score_sizes, labels=health_score_labels, autopct='%1.1f%%', colors=occupation_palette, startangle=90, \n                      textprops={'fontsize': 14, 'fontweight': 'bold', 'color': 'white'}, wedgeprops={'edgecolor': 'black'})\naxes[1, 1].set_title('Mean Health Score Distribution by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[1])\n\n# Add legend for Health Score\naxes[1, 1].legend(health_score_labels, loc='upper right', bbox_to_anchor=(1.3, 1), fontsize=14, title=\"Occupations\")\n\n# Pie Chart for Mean Annual Income\nincome_labels = occupation_health_income_age_mean.index\nincome_sizes = occupation_health_income_age_mean['Annual Income']\npie3 = axes[1, 2].pie(income_sizes, labels=income_labels, autopct='%1.1f%%', colors=occupation_palette, startangle=90, \n                      textprops={'fontsize': 14, 'fontweight': 'bold', 'color': 'white'}, wedgeprops={'edgecolor': 'black'})\naxes[1, 2].set_title('Mean Annual Income Distribution by Occupation', fontsize=18, fontweight='bold', color=occupation_palette[2])\n\n# Add legend for Annual Income\naxes[1, 2].legend(income_labels, loc='upper right', bbox_to_anchor=(1.3, 1), fontsize=14, title=\"Occupations\")\n\n# Adjust layout\nplt.tight_layout()\nplt.subplots_adjust(hspace=0.4, wspace=0.6)\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:44.702937Z","iopub.execute_input":"2025-02-03T18:05:44.703466Z","iopub.status.idle":"2025-02-03T18:05:46.420746Z","shell.execute_reply.started":"2025-02-03T18:05:44.703419Z","shell.execute_reply":"2025-02-03T18:05:46.419368Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Observations:**\n\n- **Health Score & Occupation:**\n  - **Max Mean Health Score:** Unemployed (25.68)\n  - **Max Health Score:** Employed (58.89)\n\n- **Occupation Count & Frequency:**\n  - **Highest Count:** Self-Employed (265,143)\n  - **Highest Location Frequency:** Employed in Rural areas (0.534)\n  - **Lowest Location Frequency:** Unemployed in Rural areas (0.229)\n  - **Mid-Frequency:** Self-Employed in Urban areas (0.236)\n\n- **Mean Frequency Count:**\n  - **Highest:** Unemployed in Rural areas (25.73)\n  - **Lowest:** Self-Employed in Urban areas (25.49)\n\n- **Occupation Proportions:**\n  - **Highest:** Employed (53%)\n  - **Lowest Health Score:** Unemployed in Urban (85,953), Suburban (86,861), Rural (86,400)\n  - **Highest Health Score:** Employed in Urban (198,176), Suburban (201,817), Rural (201,037)\n  - **Highest Mean Score:** Unemployed in Rural (25.94)\n  - **Lowest Mean Score:** Employed in Urban (25.49)\n\n- **Health Score Proportions:**\n  - **Highest:** Employed in Suburban (0.0259)\n  - **Lowest:** Unemployed in Rural (0.342)\n  - **Overall Lowest Proportion:** 93.2%\n  - **Overall Highest Proportion:** 6.8%\n\n- **Demographics by Occupation:**\n  - **Employed:** Mean Age (41.12), Mean Health Score (25.59), Mean Income ($32,621)\n  - **Self-Employed:** Mean Age (41.18), Mean Health Score (25.58), Mean Income ($32,909)\n  - **Unemployed:** Mean Age (41.15), Mean Health Score (25.68), Mean Income ($32,864)\n\n- **Key Insights:**\n  - **Highest Mean Age Distribution:** Self-Employed (33.4%)\n  - **Highest Health Score Proportion:** Unemployed (33.4%)\n  - **Maximum Annual Income:** Self-Employed\n\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Policy Type on the basis of Location</p>","metadata":{}},{"cell_type":"code","source":"# Analyze the frequency of different Policy Types in each Location\npolicy_type_by_location = df_train.groupby('Policy Type')['Location'].value_counts().unstack().fillna(0)\n\n# Output the distribution of Policy Type for each Location\nprint(\"Distribution of Policy Type by Location:\\n\", policy_type_by_location)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:46.422155Z","iopub.execute_input":"2025-02-03T18:05:46.422696Z","iopub.status.idle":"2025-02-03T18:05:46.613631Z","shell.execute_reply.started":"2025-02-03T18:05:46.422645Z","shell.execute_reply":"2025-02-03T18:05:46.612432Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Policy Type on the basis of Location</p>","metadata":{}},{"cell_type":"code","source":"# Set a style for the plot\nsns.set(style=\"whitegrid\")\n\n# Grouping the data by Policy Type and Location\npolicy_type_by_location = df_train.groupby('Policy Type')['Location'].value_counts().unstack().fillna(0)\n\n# Define the custom color palette for each Policy Type (use colors from gender_palette)\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Create a bar plot for the distribution of Policy Type by Location\nax = policy_type_by_location.plot(kind='bar', stacked=True, figsize=(18, 8), color=gender_palette)\n\n# Adding titles and labels\nax.set_title('Distribution of Policy Type by Location', fontsize=16, fontweight='bold')\nax.set_xlabel('Policy Type', fontsize=14)\nax.set_ylabel('Frequency', fontsize=14)\nax.tick_params(axis='x', rotation=45, labelsize=12)\nax.tick_params(axis='y', labelsize=12)\n\n# Display the value counts inside the bars with bold text\nfor p in ax.patches:\n    height = p.get_height()\n    width = p.get_width()\n    x = p.get_x() + width / 2\n    y = p.get_y() + height / 2  # Positioning the text inside the bar\n    ax.annotate(f'{height:.0f}', (x, y), ha='center', va='center', fontsize=10, color='white', fontweight='bold')\n\n# Show the plot\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:46.614766Z","iopub.execute_input":"2025-02-03T18:05:46.615066Z","iopub.status.idle":"2025-02-03T18:05:47.203568Z","shell.execute_reply.started":"2025-02-03T18:05:46.615035Z","shell.execute_reply":"2025-02-03T18:05:47.202298Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Vehicle Type, previous Claim, Credit Svore on the basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Group by 'Policy Type' and calculate the mean values of 'Previous Claims', 'Credit Score', and 'Vehicle Age',\n# as well as the count of these columns\nclaims_by_policy_type = df_train.groupby('Policy Type').agg(\n    Previous_Claims_Mean=('Previous Claims', 'mean'),\n    Credit_Score_Mean=('Credit Score', 'mean'),\n    Vehicle_Age_Mean=('Vehicle Age', 'mean'),\n    Policy_Type_Count=('Policy Type', 'size'),\n    Previous_Claims_Count=('Previous Claims', 'count'),\n    Credit_Score_Count=('Credit Score', 'count'),\n    Vehicle_Age_Count=('Vehicle Age', 'count')\n)\n\n# Reset the index to make 'Policy Type' a column, and put all column names in one row\nclaims_by_policy_type_reset = claims_by_policy_type.reset_index()\n\n# Output the detailed summary by Policy Type\ndisplay(claims_by_policy_type_reset)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:47.204826Z","iopub.execute_input":"2025-02-03T18:05:47.205236Z","iopub.status.idle":"2025-02-03T18:05:47.391367Z","shell.execute_reply.started":"2025-02-03T18:05:47.205202Z","shell.execute_reply":"2025-02-03T18:05:47.390082Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Vehicle Type, previous Claim, Credit Svore on the basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Set a style for the plot\nsns.set(style=\"whitegrid\")\n\n# Define the custom color palette for each metric (mean and count)\nmean_colors = ['#1f77b4', '#ff7f0e', '#2ca02c']  # Default mean colors\ncount_colors = ['#d62728', '#9467bd', '#8c564b']  # Default count colors\n\n# Custom color palette for 'Credit Score' (used for both mean and count)\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Create a figure with two subplots (one for mean and one for count)\nfig, axes = plt.subplots(1, 2, figsize=(18, 8))\n\n# Bar width and position adjustment\nbar_width = 0.25\nindex = np.arange(len(claims_by_policy_type_reset))\n\n# Plotting Mean Values in the first subplot (not stacked)\nmean_bars_1 = axes[0].bar(index, claims_by_policy_type_reset['Previous_Claims_Mean'], bar_width, color=mean_colors[0], label='Mean Previous Claims')\nmean_bars_2 = axes[0].bar(index + bar_width, claims_by_policy_type_reset['Credit_Score_Mean'], bar_width, color=gender_palette[0], label='Mean Credit Score')  # Apply custom color for Credit Score\nmean_bars_3 = axes[0].bar(index + 2 * bar_width, claims_by_policy_type_reset['Vehicle_Age_Mean'], bar_width, color=mean_colors[2], label='Mean Vehicle Age')\n\n# Adding values above the bars for Mean Values\nfor bar in mean_bars_1:\n    yval = bar.get_height()\n    axes[0].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.2f}', ha='center', va='bottom', fontsize=10)\nfor bar in mean_bars_2:\n    yval = bar.get_height()\n    axes[0].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.2f}', ha='center', va='bottom', fontsize=10)\nfor bar in mean_bars_3:\n    yval = bar.get_height()\n    axes[0].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.2f}', ha='center', va='bottom', fontsize=10)\n\n# Adding titles, labels, and legend for the first subplot\naxes[0].set_title('Mean Values by Policy Type', fontsize=16, fontweight='bold')\naxes[0].set_xlabel('Policy Type', fontsize=14)\naxes[0].set_ylabel('Mean Values', fontsize=14)\naxes[0].tick_params(axis='x', rotation=45, labelsize=12)\naxes[0].tick_params(axis='y', labelsize=12)\naxes[0].set_xticks(index + bar_width)\naxes[0].set_xticklabels(claims_by_policy_type_reset['Policy Type'], fontsize=12)\naxes[0].legend(title='Mean Metrics', loc='upper left', bbox_to_anchor=(1, 1))\n\n# Plotting Count Values in the second subplot (not stacked)\ncount_bars_1 = axes[1].bar(index, claims_by_policy_type_reset['Previous_Claims_Count'], bar_width, color=count_colors[0], label='Count Previous Claims')\ncount_bars_2 = axes[1].bar(index + bar_width, claims_by_policy_type_reset['Credit_Score_Count'], bar_width, color=gender_palette[1], label='Count Credit Score')  # Apply custom color for Credit Score\ncount_bars_3 = axes[1].bar(index + 2 * bar_width, claims_by_policy_type_reset['Vehicle_Age_Count'], bar_width, color=count_colors[2], label='Count Vehicle Age')\n\n# Adding values above the bars for Count Values\nfor bar in count_bars_1:\n    yval = bar.get_height()\n    axes[1].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.0f}', ha='center', va='bottom', fontsize=10)\nfor bar in count_bars_2:\n    yval = bar.get_height()\n    axes[1].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.0f}', ha='center', va='bottom', fontsize=10)\nfor bar in count_bars_3:\n    yval = bar.get_height()\n    axes[1].text(bar.get_x() + bar.get_width() / 2, yval + 0.05, f'{yval:.0f}', ha='center', va='bottom', fontsize=10)\n\n# Adding titles, labels, and legend for the second subplot\naxes[1].set_title('Count Values by Policy Type', fontsize=16, fontweight='bold')\naxes[1].set_xlabel('Policy Type', fontsize=14)\naxes[1].set_ylabel('Count Values', fontsize=14)\naxes[1].tick_params(axis='x', rotation=45, labelsize=12)\naxes[1].tick_params(axis='y', labelsize=12)\naxes[1].set_xticks(index + bar_width)\naxes[1].set_xticklabels(claims_by_policy_type_reset['Policy Type'], fontsize=12)\naxes[1].legend(title='Count Metrics', loc='upper left', bbox_to_anchor=(1, 1))\n\n# Adjust the layout to avoid overlapping\nplt.tight_layout()\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:47.392635Z","iopub.execute_input":"2025-02-03T18:05:47.393069Z","iopub.status.idle":"2025-02-03T18:05:48.082315Z","shell.execute_reply.started":"2025-02-03T18:05:47.393023Z","shell.execute_reply":"2025-02-03T18:05:48.080766Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Vehicle Age Range on the basis of Credit Score</p>","metadata":{}},{"cell_type":"code","source":"# Create bins for Vehicle Age\nvehicle_age_bins = [0, 2, 5, 10, 20]\nvehicle_age_labels = ['0-2', '2-5', '5-10', '10-20']\n\n# Categorize Vehicle Age into bins\ndf_train['Vehicle Age Range'] = pd.cut(df_train['Vehicle Age'], bins=vehicle_age_bins, labels=vehicle_age_labels)\n\n# Analyze Previous Claims by Vehicle Age and Credit Score Range\nclaims_by_vehicle_age_credit = df_train.groupby(['Credit Score', 'Vehicle Age Range'])['Previous Claims'].mean().unstack()\n\n# Output the relationship between Vehicle Age, Credit Score, and Previous Claims\ndisplay(\"Previous Claims by Vehicle Age and Credit Score:\", claims_by_vehicle_age_credit.head(20))\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:48.08366Z","iopub.execute_input":"2025-02-03T18:05:48.084064Z","iopub.status.idle":"2025-02-03T18:05:48.226938Z","shell.execute_reply.started":"2025-02-03T18:05:48.084031Z","shell.execute_reply":"2025-02-03T18:05:48.225873Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Vehicle Age Range on the basis of Credit Score</p>","metadata":{}},{"cell_type":"code","source":"\n# Create bins for Vehicle Age\nvehicle_age_bins = [0, 2, 5, 10, 20]\nvehicle_age_labels = ['0-2', '2-5', '5-10', '10-20']\n\n# Categorize Vehicle Age into bins\ndf_train['Vehicle Age Range'] = pd.cut(df_train['Vehicle Age'], bins=vehicle_age_bins, labels=vehicle_age_labels)\n\n# Analyze Previous Claims by Vehicle Age and Credit Score Range\nclaims_by_vehicle_age_credit = df_train.groupby(['Credit Score', 'Vehicle Age Range'])['Previous Claims'].mean().unstack()\n\n# Output the relationship between Vehicle Age, Credit Score, and Previous Claims\nclaims_top_20 = claims_by_vehicle_age_credit.head(20)\n\n# Reset the index so 'Credit Score' and 'Vehicle Age Range' become columns\nclaims_top_20 = claims_top_20.reset_index()\n\n# Create a custom color palette for 'Credit Score'\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Create a scatter plot for 'Previous Claims' vs 'Vehicle Age Range', colored by 'Credit Score'\nplt.figure(figsize=(12, 6))\nsns.scatterplot(data=claims_top_20.melt(id_vars=['Credit Score'], value_vars=claims_top_20.columns[1:]),\n                x='Vehicle Age Range', y='value', hue='Credit Score', palette=gender_palette, s=100, marker='o')\n\n# Customize the plot\nplt.title('Scatter Plot of Previous Claims by Vehicle Age Range and Credit Score', fontsize=16, fontweight='bold')\nplt.xlabel('Vehicle Age Range', fontsize=14)\nplt.ylabel('Average Previous Claims', fontsize=14)\nplt.xticks(rotation=45, fontsize=12)\nplt.yticks(fontsize=12)\n\n# Adjust legend position to be outside the plot\nplt.legend(title='Credit Score', fontsize=12, bbox_to_anchor=(1.05, 1), loc='upper left')\n\n# Show the plot\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:48.227888Z","iopub.execute_input":"2025-02-03T18:05:48.228187Z","iopub.status.idle":"2025-02-03T18:05:49.177638Z","shell.execute_reply.started":"2025-02-03T18:05:48.228159Z","shell.execute_reply":"2025-02-03T18:05:49.176225Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Observations:**\n\n- **Policy Type & Location Counts:**\n  - **Max Count:** Comprehensive Policy in Rural (133,781)\n  - **Min Count:** Basic Policy in Rural (131,974)\n\n- **Credit Score & Policy Type:**\n  - **Highest Mean Credit Score:** Comprehensive Policy (593.13)  \n    - Mean Previous Claim: 1.0046  \n    - Mean Vehicle Age: 9.57  \n    - Previous Claim Count: 278,142  \n  - **Lowest Mean Credit Score:** Premium Policy (592.62)  \n    - Mean Previous Claim: 0.9987  \n    - Mean Vehicle Age: 9.58  \n    - Previous Claim Count: 279,634  \n\n- **Mean Values by Policy Type:**\n  - **Mean Previous Claim:** Basic, Comprehensive, Premium (1.0)  \n  - **Highest Mean Vehicle Age:** Premium Policy (9.58)  \n  - **Lowest Mean Vehicle Age:** Basic Policy (9.56)  \n\n- **Credit Score & Vehicle Age Counts:**\n  - **Highest Credit Score Count:** Premium Policy (355,628)  \n  - **Lowest Credit Score Count:** Basic Policy (352,965)  \n  - **Highest Vehicle Age Count:** Premium Policy (401,845)  \n  - **Lowest Vehicle Age Count:** Basic Policy (398,552)  \n\n- **Previous Claim Counts:**\n  - **Highest:** Premium Policy (279,634)  \n  - **Lowest:** Basic Policy (278,142)  \n\n- **Frequent Data Analysis:**\n  - **Highest Credit Score:** Vehicles aged **0-2 years** with **1.25 previous claims**  \n  - **Lowest Credit Score:** Vehicles aged **5-10 years** with **0.75 previous claims**  \n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Insurance Duration, Customer Feedback, Smooking Status on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Convert 'Customer Feedback' to numeric, coercing errors to NaN\ndf_train['Customer Feedback'] = pd.to_numeric(df_train['Customer Feedback'], errors='coerce')\n\n# Calculate the average Insurance Duration by Policy Type\ninsurance_duration_by_policy = df_train.groupby('Policy Type')['Insurance Duration'].mean()\n\n# Calculate the total number of policies by Policy Type and Insurance Duration\npolicy_count_by_type_duration = df_train.groupby(['Policy Type', 'Insurance Duration']).size().unstack(fill_value=0)\n\n# Calculate the average Customer Feedback by Smoking Status\nfeedback_by_smoking_status = df_train.groupby('Smoking Status')['Customer Feedback'].mean()\n\n# Calculate the frequency of Smoking Status by Policy Type\nsmoking_status_by_policy = df_train.groupby('Policy Type')['Smoking Status'].value_counts().unstack(fill_value=0)\n\n# Merge all the results into a single DataFrame\npivot_table = pd.DataFrame({\n    'Average Insurance Duration': insurance_duration_by_policy\n})\n\n# Merge the total number of policies by Policy Type and Insurance Duration\npivot_table = pivot_table.join(policy_count_by_type_duration)\n\n# Merge the frequency of Smoking Status by Policy Type\npivot_table = pivot_table.join(smoking_status_by_policy)\n\n# Reset the index to make 'Policy Type' a column and put all column names in one row\npivot_table_reset = pivot_table.reset_index()\n\n# Output the combined pivot table\ndisplay(\"Combined Pivot Table:\", pivot_table_reset)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:49.179161Z","iopub.execute_input":"2025-02-03T18:05:49.179578Z","iopub.status.idle":"2025-02-03T18:05:50.40923Z","shell.execute_reply.started":"2025-02-03T18:05:49.179537Z","shell.execute_reply":"2025-02-03T18:05:50.408162Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Previous Claim, Credit Score, Vehicle Age on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Group by 'Policy Type' and calculate the mean values of 'Previous Claims', 'Credit Score', and 'Vehicle Age',\n# as well as the count of these columns\nclaims_by_policy_type = df_train.groupby('Policy Type').agg(\n    Previous_Claims_Mean=('Previous Claims', 'mean'),\n    Credit_Score_Mean=('Credit Score', 'mean'),\n    Vehicle_Age_Mean=('Vehicle Age', 'mean'),\n    Policy_Type_Count=('Policy Type', 'size'),\n    Previous_Claims_Count=('Previous Claims', 'count'),\n    Credit_Score_Count=('Credit Score', 'count'),\n    Vehicle_Age_Count=('Vehicle Age', 'count')\n)\n\n# Reset the index to make 'Policy Type' a column, and put all column names in one row\nclaims_by_policy_type_reset = claims_by_policy_type.reset_index()\n\n# Output the detailed summary by Policy Type\ndisplay(claims_by_policy_type_reset)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:50.410328Z","iopub.execute_input":"2025-02-03T18:05:50.41067Z","iopub.status.idle":"2025-02-03T18:05:50.583197Z","shell.execute_reply.started":"2025-02-03T18:05:50.410635Z","shell.execute_reply":"2025-02-03T18:05:50.581584Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Previous Claim, Credit Score, Vehicle Age on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Set the style for the plot\nsns.set(style=\"whitegrid\")\n\n# Define a custom color palette based on the colors you provided\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Create the plot with multiple subplots (2 rows, 2 columns)\nfig, axes = plt.subplots(2, 2, figsize=(20, 14), sharex=False)\n\n# Define the policy types to use for the legend\npolicy_types = claims_by_policy_type_reset['Policy Type'].unique()\n\n# Bar plot for the mean values of 'Previous Claims'\nsns.barplot(x='Policy Type', y='Previous_Claims_Mean', data=claims_by_policy_type_reset, \n            hue='Policy Type', palette=gender_palette, ax=axes[0, 0])\naxes[0, 0].set_title('Mean of Previous Claims by Policy Type', fontsize=18, fontweight='bold')\naxes[0, 0].set_ylabel('Mean Previous Claims', fontsize=14)\naxes[0, 0].tick_params(axis='x', rotation=45, labelsize=12)\naxes[0, 0].tick_params(axis='y', labelsize=12)\naxes[0, 0].tick_params(axis='x', direction='in', length=6)  # Add x-axis ticks\n\n# Add the values above the bars\nfor p in axes[0, 0].patches:\n    axes[0, 0].annotate(f'{p.get_height():.2f}', \n                        (p.get_x() + p.get_width() / 4., p.get_height()), \n                        ha='center', va='center', fontsize=12, \n                        xytext=(0, 8), textcoords='offset points')\n\n# Bar plot for the mean values of 'Credit Score'\nsns.barplot(x='Policy Type', y='Credit_Score_Mean', data=claims_by_policy_type_reset, \n            hue='Policy Type', palette=gender_palette, ax=axes[0, 1])\naxes[0, 1].set_title('Mean of Credit Score by Policy Type', fontsize=18, fontweight='bold')\naxes[0, 1].set_ylabel('Mean Credit Score', fontsize=14)\naxes[0, 1].tick_params(axis='x', rotation=45, labelsize=12)\naxes[0, 1].tick_params(axis='y', labelsize=12)\naxes[0, 1].tick_params(axis='x', direction='in', length=6)  # Add x-axis ticks\n\n# Add the values above the bars\nfor p in axes[0, 1].patches:\n    axes[0, 1].annotate(f'{p.get_height():.2f}', \n                        (p.get_x() + p.get_width() / 4., p.get_height()), \n                        ha='center', va='center', fontsize=12, \n                        xytext=(0, 8), textcoords='offset points')\n\n# Bar plot for the mean values of 'Vehicle Age'\nsns.barplot(x='Policy Type', y='Vehicle_Age_Mean', data=claims_by_policy_type_reset, \n            hue='Policy Type', palette=gender_palette, ax=axes[1, 0])\naxes[1, 0].set_title('Mean of Vehicle Age by Policy Type', fontsize=18, fontweight='bold')\naxes[1, 0].set_ylabel('Mean Vehicle Age', fontsize=14)\naxes[1, 0].tick_params(axis='x', rotation=45, labelsize=12)\naxes[1, 0].tick_params(axis='y', labelsize=12)\naxes[1, 0].tick_params(axis='x', direction='in', length=6)  # Add x-axis ticks\n\n# Add the values above the bars\nfor p in axes[1, 0].patches:\n    axes[1, 0].annotate(f'{p.get_height():.2f}', \n                        (p.get_x() + p.get_width() / 4., p.get_height()), \n                        ha='center', va='center', fontsize=12, \n                        xytext=(0, 8), textcoords='offset points')\n\n# Bar plot for the counts of 'Previous Claims'\nsns.barplot(x='Policy Type', y='Previous_Claims_Count', data=claims_by_policy_type_reset, \n            hue='Policy Type', palette=gender_palette, ax=axes[1, 1])\naxes[1, 1].set_title('Count of Previous Claims by Policy Type', fontsize=18, fontweight='bold')\naxes[1, 1].set_ylabel('Count of Previous Claims', fontsize=14)\naxes[1, 1].tick_params(axis='x', rotation=45, labelsize=12)\naxes[1, 1].tick_params(axis='y', labelsize=12)\naxes[1, 1].tick_params(axis='x', direction='in', length=6)  # Add x-axis ticks\n\n# Add the values above the bars\nfor p in axes[1, 1].patches:\n    axes[1, 1].annotate(f'{p.get_height():.0f}', \n                        (p.get_x() + p.get_width() / 2., p.get_height()), \n                        ha='center', va='center', fontsize=12, \n                        xytext=(0, 8), textcoords='offset points')\n\n# Set common x-axis label for all subplots\nfig.text(0.5, 0.04, 'Policy Type', ha='center', fontsize=16, fontweight='bold')\n\n# Move the legend outside the plot to the right\nfor ax in axes.flat:\n    ax.legend(title='Policy Type', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# Adjust layout for better spacing\nplt.tight_layout(pad=4.0)\n\n# Display all the plots\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:50.584429Z","iopub.execute_input":"2025-02-03T18:05:50.584808Z","iopub.status.idle":"2025-02-03T18:05:51.915082Z","shell.execute_reply.started":"2025-02-03T18:05:50.584777Z","shell.execute_reply":"2025-02-03T18:05:51.913649Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Avearge Insurance Duration on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Group by 'Policy Type' and calculate the average 'Insurance Duration'\ninsurance_duration_by_policy = df_train.groupby('Policy Type')['Insurance Duration'].mean()\n\n# Output the average 'Insurance Duration' by Policy Type\ndisplay(\"Average Insurance Duration by Policy Type:\", insurance_duration_by_policy)\n\n# Group by 'Smoking Status' to calculate the average 'Customer Feedback'\nfeedback_by_smoking_status = df_train.groupby('Smoking Status')['Customer Feedback'].mean()\nprint(\"=============================================================================\")\n# Calculate the count of 'Smoking Status' for each 'Policy Type'\nsmoking_status_by_policy = df_train.groupby('Policy Type')['Smoking Status'].value_counts().unstack(fill_value=0)\n\n# Output the frequency of Smoking Status by 'Policy Type'\ndisplay(\"Frequency of Smoking Status by Policy Type:\", smoking_status_by_policy)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:51.916539Z","iopub.execute_input":"2025-02-03T18:05:51.916992Z","iopub.status.idle":"2025-02-03T18:05:52.301534Z","shell.execute_reply.started":"2025-02-03T18:05:51.91696Z","shell.execute_reply":"2025-02-03T18:05:52.300324Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Avearge Insurance Duration on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Define custom color palette\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Group by 'Policy Type' and calculate the average 'Insurance Duration'\ninsurance_duration_by_policy = df_train.groupby('Policy Type')['Insurance Duration'].mean()\n\n# Group by 'Smoking Status' to calculate the average 'Customer Feedback'\nfeedback_by_smoking_status = df_train.groupby('Smoking Status')['Customer Feedback'].mean()\n\n# Group by 'Policy Type' to calculate the count of 'Smoking Status'\nsmoking_status_by_policy = df_train.groupby('Policy Type')['Smoking Status'].value_counts().unstack(fill_value=0)\n\n# Create a subplot with 1 row and 2 columns\nfig, axes = plt.subplots(1, 2, figsize=(15, 7))\n\n# Pie Chart for the distribution of 'Insurance Duration' by 'Policy Type'\nwedges, texts, autotexts = axes[0].pie(\n    insurance_duration_by_policy, \n    labels=insurance_duration_by_policy.index, \n    autopct='%1.1f%%', \n    startangle=90, \n    colors=gender_palette[:len(insurance_duration_by_policy)],\n    textprops={'color': 'white', 'fontweight': 'bold'},  # Text color and bold inside pie chart\n    wedgeprops={'edgecolor': 'black'},\n    labeldistance=1.15  # Move labels outside\n)\n\n# Make the pie chart labels bold\nfor autotext in autotexts:\n    autotext.set_fontweight('bold')\n\n# Change label color to black\nfor text in texts:\n    text.set_color('black')\n\n# Set pie chart title and add labels\naxes[0].set_title('Average Insurance Duration by Policy Type', fontsize=14, color='darkblue')\n\n# Add legend to pie chart\naxes[0].legend(wedges, insurance_duration_by_policy.index, title=\"Policy Type\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=12)\n\n# Bar Chart for 'Smoking Status' frequency by 'Policy Type'\nsmoking_status_by_policy.plot(kind='bar', stacked=True, ax=axes[1], color=gender_palette, width=0.8)\n\n# Add the values inside the stacked bars\nfor p in axes[1].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x, y = p.get_xy()  # Get the x and y position of the rectangle\n    axes[1].text(x + width/2, y + height/2, f'{int(height)}', ha='center', va='center', color='white', fontsize=12, fontweight='bold')\n\n# Add title, labels, and other enhancements to the bar chart\naxes[1].set_title('Frequency of Smoking Status by Policy Type', fontsize=14, color='darkgreen')\naxes[1].set_ylabel('Count of Smoking Status')\naxes[1].set_xlabel('Policy Type')\n\n# Improve layout and display\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:52.302849Z","iopub.execute_input":"2025-02-03T18:05:52.303291Z","iopub.status.idle":"2025-02-03T18:05:53.148935Z","shell.execute_reply.started":"2025-02-03T18:05:52.303237Z","shell.execute_reply":"2025-02-03T18:05:53.147541Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Frequency Distribution Insurance Duration on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Group by 'Policy Type' and 'Insurance Duration' to calculate the total number of policies\npolicy_count_by_type_duration = df_train.groupby(['Policy Type', 'Insurance Duration']).size().unstack(fill_value=0)\n\n# Output the number of policies by 'Policy Type' and 'Insurance Duration'\ndisplay(\"Number of Policies by Policy Type and Insurance Duration:\", policy_count_by_type_duration)","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:53.150276Z","iopub.execute_input":"2025-02-03T18:05:53.150692Z","iopub.status.idle":"2025-02-03T18:05:53.302313Z","shell.execute_reply.started":"2025-02-03T18:05:53.150656Z","shell.execute_reply":"2025-02-03T18:05:53.300918Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualze Frequency Distribution Insurance Duration on the Basis of Policy Type</p>","metadata":{}},{"cell_type":"code","source":"# Define a custom color palette\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712', '#34b1eb', '#eb3434', '#61eb34', '#776f7a', '#ff7d03']\n\n# Group by 'Policy Type' and 'Insurance Duration' to calculate the total number of policies\npolicy_count_by_type_duration = df_train.groupby(['Policy Type', 'Insurance Duration']).size().unstack(fill_value=0)\n\n# Create a subplot with 1 row and 1 column for the bar chart\nfig, ax = plt.subplots(figsize=(10, 6))\n\n# Stacked Bar Chart for Policy Count by Policy Type and Insurance Duration\n# Apply the custom color palette to the stacked bars\nbars = policy_count_by_type_duration.plot(kind='bar', stacked=True, ax=ax, width=0.8, color=gender_palette[:len(policy_count_by_type_duration.columns)])\n\n# Set title and labels for the chart\nax.set_title('Stacked Bar Chart: Policies by Type and Duration', fontsize=14, color='darkgreen')\nax.set_ylabel('Number of Policies', fontsize=12)\nax.set_xlabel('Policy Type', fontsize=12)\n\n# Add custom labels for Policy Type (ensure the index of the grouped data has correct Policy Types)\nax.set_xticklabels(policy_count_by_type_duration.index, rotation=45, ha='right', fontsize=10)\n\n# Annotate values inside each bar\nfor container in ax.containers:\n    for bar in container:\n        # Get bar position and height\n        height = bar.get_height()\n        if height > 0:  # Only annotate non-zero bars\n            x = bar.get_x() + bar.get_width() / 2  # Center of the bar\n            y = bar.get_y() + height / 2  # Middle of the bar segment\n            value = int(height)  # Get the value (as an integer)\n            ax.text(x, y, str(value), ha='center', va='center', fontsize=10, color='white', fontweight='bold')\n\n# Add legend outside of the plot\nax.legend(title='Insurance Duration', loc='center left', bbox_to_anchor=(1, 0.5), fontsize=10)\n\n# Improve layout and display\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:53.303535Z","iopub.execute_input":"2025-02-03T18:05:53.303923Z","iopub.status.idle":"2025-02-03T18:05:53.893207Z","shell.execute_reply.started":"2025-02-03T18:05:53.303892Z","shell.execute_reply":"2025-02-03T18:05:53.891586Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Highest Vs Lowest Average Insurance Duration by Policy Start Date</p>","metadata":{}},{"cell_type":"code","source":"# Convert 'Policy Start Date' to datetime format (if not already in datetime)\ndf_train['Policy Start Date'] = pd.to_datetime(df_train['Policy Start Date'], errors='coerce')\n\n# Extract the year and month from the Policy Start Date for analysis\ndf_train['Policy Start Year'] = df_train['Policy Start Date'].dt.year\ndf_train['Policy Start Month'] = df_train['Policy Start Date'].dt.month\n\n# Group by Policy Start Year and Month to calculate the average Insurance Duration\ninsurance_duration_by_start_date = df_train.groupby(['Policy Start Year', 'Policy Start Month'])['Insurance Duration'].mean()\n\n# Get the top 20 highest and lowest values\ntop_20_insurance_duration = insurance_duration_by_start_date.nlargest(20)\nbottom_20_insurance_duration = insurance_duration_by_start_date.nsmallest(20)\n\n# Output the top 20 highest and bottom 20 lowest average Insurance Duration by Policy Start Date\nprint(\"Highest Average Insurance Duration by Policy Start Date:\\n\", top_20_insurance_duration)\nprint(\"Lowest Average Insurance Duration by Policy Start Date:\\n\", bottom_20_insurance_duration)\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:53.894392Z","iopub.execute_input":"2025-02-03T18:05:53.894764Z","iopub.status.idle":"2025-02-03T18:05:54.542018Z","shell.execute_reply.started":"2025-02-03T18:05:53.894732Z","shell.execute_reply":"2025-02-03T18:05:54.540761Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Highest Vs Lowest Average Insurance Duration by Policy Start Date</p>","metadata":{}},{"cell_type":"code","source":"# Set the style for the plot\nsns.set(style=\"whitegrid\")\n\n# Combine the top 20 and bottom 20 values into a single DataFrame for easier plotting\ncombined_insurance_duration = pd.concat([top_20_insurance_duration, bottom_20_insurance_duration])\n\n# Reset index for easier plotting and manipulation\ncombined_insurance_duration = combined_insurance_duration.reset_index()\n\n# Define colors for the top and bottom groups\ncolor_palette = gender_palette[:2]  # Use the first two colors from the palette for the top and bottom\ntop_color = color_palette[0]  # Color for top 20 highest\nbottom_color = color_palette[1]  # Color for bottom 20 lowest\n\n# Create a new column to categorize the data as 'Top 20' or 'Bottom 20'\ncombined_insurance_duration['Category'] = ['Top 20'] * len(top_20_insurance_duration) + ['Bottom 20'] * len(bottom_20_insurance_duration)\n\n# Create the barplot\nplt.figure(figsize=(14, 7))\n\n# Plot the bars for the top 20 and bottom 20\nax = sns.barplot(x='Policy Start Year', y='Insurance Duration', hue='Category', data=combined_insurance_duration, dodge=True, palette=[top_color, bottom_color])\n\n# Adding titles, labels, and customizing the plot\nplt.title('Comparative Analysis of Insurance Duration by Policy Start Date', fontsize=16, fontweight='bold')\nplt.xlabel('Policy Start Year and Month', fontsize=14)\nplt.ylabel('Average Insurance Duration', fontsize=14)\nplt.xticks(rotation=45, fontsize=12)\nplt.yticks(fontsize=12)\n\n# Customizing the legend labels\nhandles, labels = plt.gca().get_legend_handles_labels()\nlabels = ['Highest Average Insurance Duration by Policy Start Date', 'Lowest Average Insurance Duration by Policy Start Date']\nplt.legend(handles=handles, labels=labels, title='Category', loc='upper left', bbox_to_anchor=(1, 1), fontsize=12)\n\n# Add values above the bars (including zero values)\nfor p in ax.patches:\n    height = p.get_height()\n    ax.annotate(f'{height:.4f}', \n                (p.get_x() + p.get_width() / 2., height), \n                ha='center', va='center', fontsize=10, color='black', \n                xytext=(0, 5), textcoords='offset points')\n\n# Display the plot\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:54.54318Z","iopub.execute_input":"2025-02-03T18:05:54.543484Z","iopub.status.idle":"2025-02-03T18:05:55.484163Z","shell.execute_reply.started":"2025-02-03T18:05:54.543458Z","shell.execute_reply":"2025-02-03T18:05:55.482759Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Insurance Duration range on the basis of Smooking Status</p>","metadata":{}},{"cell_type":"code","source":"# Define Insurance Duration bins\ninsurance_duration_bins = [0, 5, 10]\ninsurance_duration_labels = ['0-5', '5-10']\n\n# Categorize Insurance Duration into bins\ndf_train['Insurance Duration Category'] = pd.cut(df_train['Insurance Duration'], bins=insurance_duration_bins, labels=insurance_duration_labels)\n\n# Calculate the frequency of Smoking Status within each Insurance Duration category\nsmoking_status_by_duration_category = df_train.groupby(['Insurance Duration Category', 'Smoking Status']).size().unstack(fill_value=0)\n\n# Output the frequency of Smoking Status by Insurance Duration category\nprint(\"Frequency of Smoking Status by Insurance Duration Category:\\n\", smoking_status_by_duration_category)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:55.485579Z","iopub.execute_input":"2025-02-03T18:05:55.486048Z","iopub.status.idle":"2025-02-03T18:05:55.638146Z","shell.execute_reply.started":"2025-02-03T18:05:55.486005Z","shell.execute_reply":"2025-02-03T18:05:55.636796Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualzie Insurance Duration range on thee basis of Smooking Status</p>","metadata":{}},{"cell_type":"code","source":"# Set the style for the plots\nsns.set(style=\"whitegrid\")\n\n# Define the color palette for Smoking Status (using similar shades as before)\nsmoking_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712']\n\n# Create the plot figure and axes\nfig, axes = plt.subplots(1, 2, figsize=(18, 8), sharey=True)\n\n# Plot for Smoking Status in each Insurance Duration category\nsmoking_status_by_duration_category.plot(kind='bar', stacked=True, ax=axes[0], color=smoking_palette)\n\n# Adding titles, labels, and legends for the first subplot\naxes[0].set_title('Smoking Status by Insurance Duration (0-5 years)', fontsize=16, fontweight='bold')\naxes[0].set_xlabel('Insurance Duration Category', fontsize=14)\naxes[0].set_ylabel('Frequency', fontsize=14)\naxes[0].tick_params(axis='x', rotation=45, labelsize=12)\naxes[0].tick_params(axis='y', labelsize=12)\n\n# Adding values inside the bars (bold text)\nfor p in axes[0].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x = p.get_x() + width / 2\n    y = p.get_y() + height / 2  # Positioning the text inside the bar\n    axes[0].annotate(f'{height:.0f}', (x, y), ha='center', va='center', fontsize=10, color='white', fontweight='bold')\n\n# Create the second subplot with Smoking Status by Insurance Duration (5-10 years)\nsmoking_status_by_duration_category.plot(kind='bar', stacked=True, ax=axes[1], color=smoking_palette)\n\n# Adding titles, labels, and legends for the second subplot\naxes[1].set_title('Smoking Status by Insurance Duration (5-10 years)', fontsize=16, fontweight='bold')\naxes[1].set_xlabel('Insurance Duration Category', fontsize=14)\naxes[1].set_ylabel('Frequency', fontsize=14)\naxes[1].tick_params(axis='x', rotation=45, labelsize=12)\naxes[1].tick_params(axis='y', labelsize=12)\n\n# Adding values inside the bars (bold text)\nfor p in axes[1].patches:\n    height = p.get_height()\n    width = p.get_width()\n    x = p.get_x() + width / 2\n    y = p.get_y() + height / 2  # Positioning the text inside the bar\n    axes[1].annotate(f'{height:.0f}', (x, y), ha='center', va='center', fontsize=10, color='white', fontweight='bold')\n\n# Adjust the layout for better presentation\nplt.tight_layout()\n\n# Show the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:55.63947Z","iopub.execute_input":"2025-02-03T18:05:55.639907Z","iopub.status.idle":"2025-02-03T18:05:56.242243Z","shell.execute_reply.started":"2025-02-03T18:05:55.639861Z","shell.execute_reply":"2025-02-03T18:05:56.241041Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Observations:**\n\n- According to this dataset, the **Highest Average Insurance Duration** is for the **Basic Policy Type**, with an average Insurance Duration of **5.022572**.\n- For the **Basic Policy Type**, the maximum Smoking Status is \"Yes,\" with a count of approximately **200493**.\n- For the **Premium Policy Type**, the vehicles have an average Insurance Duration of **5.016342**. The highest score or mean count for **Customer Feedback** is \"1,\" which means **Good Feedback**, with a count of approximately **45188**.\n- For the **Basic Policy Type**, the vehicles have an average Insurance Duration of **5.022572**, while the lowest score or mean count for **Customer Feedback** is \"3,\" with a count of approximately **43502**.\n- The **Highest Average Credit Score** is for the **Comprehensive Policy Type**, with an average score of **593.13**, while the lowest Average Credit Score is for the **Basic Policy Type**, also with an average score of **593.13**.\n- The **Highest Previous Claim Count** based on the Policy Type is for the **Basic Policy Type**, with a count of approximately **278195**.\n- The **Basic Policy Type** has an Insurance Duration of approximately **5.022572**, while the **Comprehensive Policy Type** has an Insurance Duration of about **5.015766**, and the **Premium Policy Type** has an Insurance Duration of about **5.016342**.\n- The vehicles with a **Premium Policy Type** and a **No Smoking Status** have a count of about **200557**, while the vehicles with a **Basic Policy Type** and a **No Smoking Status** have a count of about **198061**.\n- The **Insurance Duration of 9** for the **Basic Policy Type** has the highest count of about **46099**, while the **Insurance Duration of 3** for the **Basic Policy Type** has the highest count of about **43502**.\n- The **Highest Average Insurance Duration** occurs in the **year 2019**. However, the highest insurance durations by policy are observed in the years **2020, 2021, and 2024**.\n- According to this dataset, the **Frequency of Smoking Status** for Insurance Durations in the range **0-5** shows the maximum Smoking Status as **Yes**, with a count of about **332265**.\n- For the **Insurance Duration range 5-10**, the maximum Smoking Status is **Yes**, with a count of about **269607**.\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Excercise Frequency & Premium Amount Distribution</p>","metadata":{}},{"cell_type":"code","source":"# Analysis of 'Exercise Frequency' column\nexercise_frequency = df_train['Exercise Frequency']\n\n# 1. Frequency Distribution of Exercise Frequency\nexercise_frequency_counts = exercise_frequency.value_counts(normalize=True).sort_index()\n\n# 2. Mean Premium Amount by Exercise Frequency\npremium_by_exercise = df_train.groupby('Exercise Frequency')['Premium Amount'].mean()\n\n# 3. Mean Age by Exercise Frequency\nage_by_exercise = df_train.groupby('Exercise Frequency')['Age'].mean()\n\n# 4. Total Premium Amount by Exercise Frequency\ntotal_premium_by_exercise = df_train.groupby('Exercise Frequency')['Premium Amount'].sum()\n\n# 5. Maximum Age by Exercise Frequency\nmax_age_by_exercise = df_train.groupby('Exercise Frequency')['Age'].max()\n\n# Combine the results into one DataFrame\ncombined_exercise_analysis = pd.DataFrame({\n    'Exercise Frequency Count': exercise_frequency_counts,\n    'Mean Premium Amount': premium_by_exercise,\n    'Mean Age': age_by_exercise,\n    'Total Premium Amount': total_premium_by_exercise,\n    'Max Age': max_age_by_exercise\n})\n\n# Reset the index to have column names as rows\ncombined_exercise_analysis = combined_exercise_analysis.reset_index()\n\n# Display the combined results\ndisplay(\"Exercise Frequency Analysis:\", combined_exercise_analysis)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:56.243531Z","iopub.execute_input":"2025-02-03T18:05:56.24399Z","iopub.status.idle":"2025-02-03T18:05:56.708263Z","shell.execute_reply.started":"2025-02-03T18:05:56.243929Z","shell.execute_reply":"2025-02-03T18:05:56.707231Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Excercise Frequency & Premium Amount Distribution</p>","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.ticker import FuncFormatter\n\n# Define a custom color palette\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712', '#34b1eb', '#eb3434', '#61eb34', '#776f7a', '#ff7d03']\n\n# Assuming df_train is your DataFrame and combined_exercise_analysis is properly defined\nexercise_frequencies = df_train['Exercise Frequency'].unique()\ncolors = gender_palette[:len(exercise_frequencies)]  # Adjust the number of colors if needed\n\n# Create a dictionary to map Exercise Frequency to a specific color\ncolor_dict = dict(zip(exercise_frequencies, colors))\n\n# Create subplots with 3 rows and 2 columns for different plots\nfig, axes = plt.subplots(3, 2, figsize=(18, 16))  # Increased figsize for larger plots\n\n# 1. Bar Chart for Exercise Frequency Count\nfor i, freq in enumerate(exercise_frequencies):\n    count = combined_exercise_analysis[combined_exercise_analysis['Exercise Frequency'] == freq]['Exercise Frequency Count'].values[0]\n    bar = axes[0, 0].bar(freq, count, color=color_dict[freq], label=freq)\n    \n    # Annotate with value above bars, formatted to three digits after the decimal point\n    # Dynamically calculate the position to avoid the text going out of range\n    height = bar[0].get_height()\n    axes[0, 0].text(freq, height + 0.01, f'{height:.3f}', ha='center', fontsize=12, color='black', fontweight='bold')\n\naxes[0, 0].set_title('Exercise Frequency Distribution', fontsize=16, color='darkblue')\naxes[0, 0].set_ylabel('Frequency', fontsize=12)\naxes[0, 0].set_xlabel('Exercise Frequency', fontsize=12)\naxes[0, 0].legend(title='Exercise Frequency', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# Increase the y-axis range to 0.260 for the first subplot\naxes[0, 0].set_ylim(0, 0.280)  # This line will ensure the y-axis range starts from 0 to 0.260\n\n# 2. Box Plot for Premium Amount by Exercise Frequency\nsns.boxplot(x='Exercise Frequency', y='Premium Amount', data=df_train, ax=axes[0, 1], palette=color_dict)\naxes[0, 1].set_title('Premium Amount by Exercise Frequency', fontsize=16, color='darkgreen')\naxes[0, 1].set_ylabel('Premium Amount', fontsize=12)\naxes[0, 1].set_xlabel('Exercise Frequency', fontsize=12)\naxes[0, 1].legend(title='Exercise Frequency', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# 3. Bar Chart for Mean Age by Exercise Frequency\nfor i, freq in enumerate(exercise_frequencies):\n    mean_age = combined_exercise_analysis[combined_exercise_analysis['Exercise Frequency'] == freq]['Mean Age'].values[0]\n    axes[1, 0].bar(freq, mean_age, color=color_dict[freq], label=freq)\n    axes[1, 0].text(freq, mean_age + 0.01, f'{mean_age:.3f}', ha='center', fontsize=12, color='black', fontweight='bold')  # Add value above bars with 3 decimal places\naxes[1, 0].set_title('Mean Age by Exercise Frequency', fontsize=16, color='purple')\naxes[1, 0].set_ylabel('Mean Age', fontsize=12)\naxes[1, 0].set_xlabel('Exercise Frequency', fontsize=12)\naxes[1, 0].legend(title='Exercise Frequency', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# 4. Stacked Bar Chart for Total Premium Amount by Exercise Frequency\nfor i, freq in enumerate(exercise_frequencies):\n    total_premium = combined_exercise_analysis[combined_exercise_analysis['Exercise Frequency'] == freq]['Total Premium Amount'].values[0]\n    axes[1, 1].bar(freq, total_premium, color=color_dict[freq], label=freq)\n    axes[1, 1].text(freq, total_premium + 0.1, f'{total_premium:.1f}', ha='center', fontsize=10, color='black')  # Add value above bars\naxes[1, 1].set_title('Total Premium Amount by Exercise Frequency', fontsize=16, color='red')\naxes[1, 1].set_ylabel('Total Premium Amount', fontsize=12)\naxes[1, 1].set_xlabel('Exercise Frequency', fontsize=12)\naxes[1, 1].legend(title='Exercise Frequency', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# Format y-axis of the 'Total Premium Amount' plot for short form\ndef format_currency(x, pos):\n    \"\"\"Function to format y-axis labels in short form (K, M, etc.)\"\"\"\n    if x >= 1e6:\n        return f'{x*1e-6:.1f}M'  # Millions\n    elif x >= 1e3:\n        return f'{x*1e-3:.1f}K'  # Thousands\n    else:\n        return f'{x:.0f}'\n\naxes[1, 1].yaxis.set_major_formatter(FuncFormatter(format_currency))\n\n# 5. Histogram for Age Distribution by Exercise Frequency (Grouped Mode)\nfor i, freq in enumerate(exercise_frequencies):\n    filtered_data = df_train[df_train['Exercise Frequency'] == freq]['Age']\n    n, bins, patches = axes[2, 0].hist(filtered_data, bins=10, alpha=0.7, color=color_dict[freq], label=freq, histtype='barstacked')\n    # Removed the value above histogram bars\naxes[2, 0].set_title('Age Distribution by Exercise Frequency', fontsize=16, color='orange')\naxes[2, 0].set_ylabel('Count', fontsize=12)\naxes[2, 0].set_xlabel('Age', fontsize=12)\naxes[2, 0].legend(title='Exercise Frequency', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12)\n\n# Hide the empty subplot at axes[2, 1]\naxes[2, 1].axis('off')\n\n# Improve layout and display\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:56.709475Z","iopub.execute_input":"2025-02-03T18:05:56.709819Z","iopub.status.idle":"2025-02-03T18:05:59.83746Z","shell.execute_reply.started":"2025-02-03T18:05:56.709791Z","shell.execute_reply":"2025-02-03T18:05:59.83617Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Premium Amount Distribution on the basis of Age Range</p>","metadata":{}},{"cell_type":"code","source":"# 1. Bin Age into different categories\nage_bins = [18, 22, 25, 30, 35, 40, 45, 50, 55, 60, 64]\nage_labels = ['18-22', '23-25', '26-30', '31-35', '36-40', '41-45', '46-50', '51-55', '56-60', '61-64']\ndf_train['Age Group'] = pd.cut(df_train['Age'], bins=age_bins, labels=age_labels)\n\n# 2. Analyze mean Premium Amount by Age Group\npremium_by_age_group = df_train.groupby('Age Group')['Premium Amount'].mean().reset_index(name=\"Premium Amount\")\nprint(\"\\nMean Premium Amount by Age Group:\")\nprint(premium_by_age_group)\n\n# 3. Analyze the distribution of Premium Amount within each Age Group (only mean, count, max, and min)\npremium_age_group_desc = df_train.groupby('Age Group')['Premium Amount'].agg(['mean', 'count', 'max', 'min'])\nprint(\"\\nPremium Amount Distribution within Age Groups:\")\nprint(premium_age_group_desc)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:05:59.838467Z","iopub.execute_input":"2025-02-03T18:05:59.838789Z","iopub.status.idle":"2025-02-03T18:05:59.948762Z","shell.execute_reply.started":"2025-02-03T18:05:59.838761Z","shell.execute_reply":"2025-02-03T18:05:59.94752Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Explore Premium Amount Distribution on the basis of Age Range & Gender</p>","metadata":{}},{"cell_type":"code","source":"# Group by Gender and Age Group and calculate the mean Premium Amount\npremium_by_age_gender = df_train.groupby(['Gender', 'Age Group'])['Premium Amount'].mean().reset_index(name=\"Premium Amount\")\n\n# Sort the results by 'Premium Amount' for easy comparison\npremium_by_age_gender_desc = premium_by_age_gender.sort_values(by='Premium Amount', ascending=False)\n\ndisplay(\"Premium Amount by Age Group and Gender\", premium_by_age_gender_desc)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:18:34.410632Z","iopub.execute_input":"2025-02-03T18:18:34.411135Z","iopub.status.idle":"2025-02-03T18:18:34.549374Z","shell.execute_reply.started":"2025-02-03T18:18:34.411103Z","shell.execute_reply":"2025-02-03T18:18:34.548239Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Visualize Premium Amount Distribution on the basis of Age Range & Gender</p>","metadata":{}},{"cell_type":"code","source":"# Group by Gender and Age Group and calculate the mean Premium Amount\npremium_by_age_gender = df_train.groupby(['Gender', 'Age Group'])['Premium Amount'].mean().reset_index(name=\"Premium Amount\")\n\n# Sort the results by 'Premium Amount' for easy comparison\npremium_by_age_gender_desc = premium_by_age_gender.sort_values(by='Premium Amount', ascending=False)\n\n# Define a custom color palette for gender\ngender_palette = ['#B77A00', '#001F2D', '#29002d', '#b85712', '#34b1eb', '#eb3434', '#61eb34', '#776f7a', '#ff7d03']\n\n# Create the figure and axes\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))\n\n# --- Bar Plot Enhancements ---\nsns.barplot(x='Premium Amount', y='Age Group', hue='Gender', data=premium_by_age_gender_desc, palette=gender_palette, ax=ax1)\n\n# Add values above the bars in ax1 with styling adjustments\nfor p in ax1.patches:\n    ax1.annotate(f'{p.get_width():.2f}', (p.get_x() + p.get_width() + 0.02, p.get_y() + p.get_height() / 2.),\n                 ha='left', va='center', fontsize=9, color='black', fontweight='bold')\n\n# Add title and labels for the barplot with custom font size and weight\nax1.set_title('Premium Amount by Age Group and Gender', fontsize=20, fontweight='bold', color='#2F4F4F')\nax1.set_xlabel('Mean Premium Amount', fontsize=14)\nax1.set_ylabel('Age Group', fontsize=14)\nax1.legend(title='Gender', loc='upper left', bbox_to_anchor=(1.05, 1), fontsize=12, title_fontsize=14)\n\n# Customize the grid and background for the bar plot\nax1.set_facecolor('#f5f5f5')\nax1.grid(True, axis='x', linestyle='--', alpha=0.7)\n\n# --- Pie Chart Enhancements ---\ngender_counts = df_train['Gender'].value_counts()\n\n# Add shadow and highlight the largest slice in the pie chart\nexplode = (0.1, 0) if len(gender_counts) > 1 else (0, 0)  # Exploding the first slice if more than one\nax2.pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=90, colors=gender_palette, \n        wedgeprops={'edgecolor': 'black', 'linewidth': 2, 'linestyle': 'solid'}, explode=explode)\n\n# Customize the pie chart text to be white, bold, and larger\nfor text in ax2.texts:\n    text.set_fontsize(16)\n    text.set_color('white')\n    text.set_fontweight('bold')\n\n# Add a glowing effect to the pie chart title\nax2.set_title('Gender Distribution', fontsize=20, color='#FF4500', fontweight='bold')\n\n# --- Final Adjustments ---\nplt.tight_layout()\n\n# Display the plot\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-03T18:18:37.589182Z","iopub.execute_input":"2025-02-03T18:18:37.589669Z","iopub.status.idle":"2025-02-03T18:18:38.513766Z","shell.execute_reply.started":"2025-02-03T18:18:37.589622Z","shell.execute_reply":"2025-02-03T18:18:38.512527Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Observations:**\n\n- According to this dataset, the **Highest Average Frequency Count** is for **Weekly Exercise**, with a count of about **0.255149**, indicating that most people prefer weekly exercise. The mean age of the vehicle in this case is **41.157696**.\n- The **Lowest Exercise Frequency Count** is for **Rarely Exercise**, with a count of about **0.249517**, and the mean age of the vehicle in this case is **41.148154**.\n- The **Maximum Mean Premium Count** is for the **Daily Frequency** with a count of about **1103.789093**, and the mean age of the vehicle in this case is **41.147298**.\n- The **Lowest Mean Premium Count** is for the **Weekly Frequency**, with a count of about **1101.234252**, and the mean age of the vehicle in this case is **41.157696**.\n- **Weekly Exercise** has the highest frequency count, with a value of about **0.255**, while **Monthly** and **Rarely Exercise** have the lowest frequency count, both around **0.250**.\n- The **Mean Age of Vehicle** is highest for those with **Weekly Exercise Frequency**, with the vehicle age around **41.2**.\n- The **Total Premium Amount** is highest when the exercise frequency count is about **337174802.0**, while the total premium is lowest when the exercise frequency is based on a **Daily** basis, with a frequency count of about **325144557.0**.\n- The **Premium Amount** is highest for vehicles in the **26-30 age range**, with an average premium amount of about **1109.868955**.\n- Vehicles in the **56-60 age group** have the **lowest Premium Amount**, with a premium amount of about **1098.738754**.\n- The maximum premium amount for vehicles in the **18-22 age range** has an average premium amount of about **1099.176374**, with a count of about **99992**, and the highest premium amount is about **4999.0**.\n- The minimum premium amount for vehicles in the **46-50 age range** has an average premium amount of about **1101.450641**, with a count of about **126349**, and the highest premium amount is about **4992.0**.\n- Vehicles in the **26-30 age range** have the **maximum count** of about **122828**, with a mean premium amount of about **1109.868955**.\n- According to this dataset, the **maximum proportion** is of **Females** (50.2%) compared to **Males** (49.8%).\n- **Females** in the **26-30 age range** have the highest count of about **1110.17**, while **Males** in the **26-30 age range** have a count of about **1109.57**.\n- **Females** in the **61-64 age range** have the lowest count of about **1096.26**, while **Males** in the **18-22 age range** have the lowest count of about **1093.74**.\n","metadata":{}},{"cell_type":"markdown","source":"# <p style=\"font-family: 'Amiri'; font-size: 3rem; color: #B77A00; text-align: center; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); background-color: #001F2D; padding: 20px; border-radius: 20px; border: 7px solid #B77A00; width:95%\">Thanks for watching if you like it then kindly support by upvoting :) </p>","metadata":{}}]}