Synthetic Healthcare Insurance Dataset

Dataset Features:

• Age: Age of the individual (in years), an important demographic factor influencing insurance charges.
• Sex: Gender of the individual, categorized as “male” or “female.”
• BMI (Body Mass Index): A numerical value representing the individual’s body mass index, calculated from height and weight. BMI is an indicator of body fat and plays a key role in assessing health risks.
• Children: Number of dependents covered under the insurance plan.
• Smoker: Smoking status of the individual, categorized as “yes” or “no.” Smoking is a significant factor affecting insurance costs.
• Region: Residential region of the individual in the United States, categorized as “northeast,” “northwest,” “southeast,” or “southwest.”
• Charges: Medical insurance charges billed to the individual, expressed in USD. This serves as the target variable for predictive modelling.

Usage:

• Insurance Analytics: To explore trends and patterns in healthcare insurance, such as how demographic and lifestyle factors impact insurance charges.
• Educational Training: To teach data cleaning, transformation, and visualization techniques, with a focus on insurance and healthcare costs.
• Predictive Modeling: To develop models that predict insurance charges based on demographic, lifestyle, and health-related features.
• Healthcare Policy Research: To analyze how factors like smoking and BMI influence healthcare costs, informing policy decisions.

Distribution:

Coverage:

License:

Who Can Use It:

• Researchers and Educators: For studies or teaching purposes in healthcare insurance analytics and data science.
• Data Science Enthusiasts: For learning, practising, and applying data manipulation and analysis techniques in the context of insurance data.
• Healthcare Professionals: For exploring and understanding the impact of demographic and health factors on insurance costs, improving cost prediction models.