The Patient Profiles & Insurance Charges Dataset (Synthetic) is designed for educational and research purposes to analyze the factors influencing insurance costs. It provides anonymized, synthetic data on individuals' demographics, health conditions, and insurance coverage details.

Dataset Features

• Age: Age of the individual.
• Gender: Gender of the individual (Male/Female).
• BMI: Body Mass Index (BMI), a measure of body fat based on height and weight.
• Children: Number of dependent children.
• Smoker: Smoking status (Yes/No).
• Region: Geographical region where the individual resides.
• Medical History: Individual's medical conditions (e.g., Diabetes, Heart disease, High blood pressure).
• Family Medical History: Presence of illnesses in the family (e.g., Diabetes, Heart disease).
• Exercise Frequency: Frequency of physical exercise (Never, Rarely, Occasionally, Frequently).
• Occupation: Type of employment (e.g., White collar, Blue collar, Student, Unemployed).
• Coverage Level: Type of insurance coverage (Basic, Standard, Premium).
• Charges: Insurance cost associated with the individual.

Distribution

Usage

This dataset can be used for the following applications:

• Health Risk Assessment: Analyze how medical history, lifestyle choices, and demographics impact insurance costs.
• Predictive Modeling: Develop machine learning models to predict insurance charges based on personal attributes.
• Public Health Research: Explore trends in health conditions across different demographics.
• Policy Optimization: Aid insurance companies in refining pricing strategies based on risk factors.
• Educational Purposes: Provide a dataset for students and researchers in data science, healthcare, and actuarial studies.

Coverage

This synthetic dataset is fully anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles to facilitate comprehensive analysis.

License

CC0 (Public Domain)

Who Can Use It

• Data Scientists and Machine Learning Practitioners: For predictive modeling of insurance costs.
• Healthcare Researchers: To analyze correlations between health, lifestyle, and insurance expenses.
• Insurance Analysts: To refine premium pricing strategies based on risk factors.
• Educators and Students: As a resource for actuarial science, economics, and healthcare analytics projects.