Health Insurance Premium & Risk Factors Dataset

This dataset contains individual-level health insurance records including demographic attributes such as age, gender, region, number of dependents, and lifestyle indicators such as smoking status and body mass index (BMI). It also includes insurance premium charges as the target variable. The dataset has undergone a structured exploratory data analysis process including duplicate removal, missing value assessment, categorical standardization, and statistical validation of numeric ranges. Outlier detection was applied to premium charges and BMI values to ensure realistic insurance modeling scenarios. The final dataset is exported in a clean, structured format optimized for regression modeling, classification experiments, and actuarial analytics.

Data Quality

The dataset is organized in a consistent tabular format with standardized column naming and validated data types. Duplicate records have been removed and numeric variables such as age, BMI, and charges have undergone range validation to eliminate unrealistic entries. Categorical attributes including region and smoking status have been normalized to prevent label inconsistencies. Statistical distribution checks were performed to ensure analytical stability. The dataset is fully compatible with machine learning pipelines and statistical modeling workflows.

Use Cases

This dataset is suitable for insurance premium prediction modeling, risk scoring development, and actuarial analytics. It can be used to build regression models that estimate premium charges based on demographic and lifestyle variables. Insurance companies can leverage it for underwriting simulation research and pricing strategy experimentation. Researchers can analyze the impact of smoking, BMI, and age on healthcare costs. It also supports feature importance analysis, predictive modeling demonstrations, and academic coursework in data science and actuarial studies.

Limitations

The dataset is community-sourced and may not represent official insurance underwriting records. It reflects a structured sample rather than real-world regulatory insurance databases. It does not include detailed medical histories, claim frequency data, or long-term policy performance metrics. The dataset is intended for analytical and educational purposes and should not be used for actual underwriting decisions without regulatory validation.

Methodology

The dataset was prepared using a structured data validation framework that included schema normalization, duplicate removal, missing value assessment, and categorical standardization. Numeric attributes such as age, BMI, and premium charges were validated using statistical range checks and percentile-based outlier detection. Logical consistency checks were performed across dependent variables such as dependents count and premium charges. The final dataset was exported in UTF-8 encoded CSV format, ensuring compatibility with machine learning libraries, statistical modeling tools, and business intelligence platforms.