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Healthcare Insurance Predictor Data
Healthcare Insurance & Costs
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About
This dataset focuses on healthcare insurance in a developing global context. It contains details regarding the relationship between various personal attributes, geographic factors, and their direct influence on medical insurance charges. The dataset's main purpose is to facilitate the study of how these features impact insurance costs and to enable the creation of predictive models for estimating healthcare expenses.
Columns
- Age: Represents the insured person's age. Values range from 18 to 64, with a mean of 39.2.
- Sex: Indicates the gender of the insured, categorised as male or female. The dataset shows a near even split, with 51% males and 49% females.
- BMI (Body Mass Index): A calculated measure of body fat based on height and weight. Values span from 15.96 to 53.13, with an average of 30.7.
- Children: Denotes the number of dependents covered by the insurance. This ranges from 0 to 5, with a mean of 1.09.
- Smoker: A boolean field indicating whether the insured person is a smoker (true) or not (false). Approximately 20% of the individuals are smokers.
- Region: Specifies the geographic area of coverage. The dataset includes four unique regions, with 'southeast' being the most common at 27% and 'southwest' at 24%.
- Charges: The core value representing the medical insurance costs incurred by the insured individual. Charges vary widely, from £1,121.87 to £63,770.43, with a mean of £13,300.
Distribution
The dataset is typically structured as a data file, often in CSV format. A sample file can be updated separately onto a platform. It comprises 7 columns and contains 1338 records, with no missing values identified across any of the fields. The total size of the dataset is 55.63 kB.
Usage
This dataset is ideal for:
- Studying the impact of demographic and lifestyle factors on healthcare insurance costs.
- Developing and testing machine learning models for predicting individual healthcare expenses.
- Performing exploratory data analysis to uncover patterns and correlations within healthcare insurance data.
- Creating data visualisations to present insights into insurance charges.
- Applications in health conditions analysis and financial modelling related to insurance.
Coverage
The dataset's scope is centred on healthcare insurance in the global world, particularly in developing contexts. Geographically, it includes specific regions such as the southeast and southwest. Demographically, it covers individuals across various age groups (18-64), both genders, with differing BMIs, family sizes (0-5 children), and smoking statuses. There is no explicit time range mentioned for the data collection.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Scientists and Analysts: For building predictive models for insurance costs, performing regression analysis, and identifying key cost drivers.
- Healthcare Researchers: To understand how personal attributes and lifestyle choices influence medical expenses and insurance premiums.
- Insurance Providers and Actuaries: For risk assessment, premium calculation, and policy development.
- Students and Educators: As a practical resource for learning about data analysis, machine learning, and statistical modelling in a real-world context.
- Policy Makers: To inform decisions related to healthcare accessibility and affordability.
Dataset Name Suggestions
- Healthcare Insurance Predictor Data
- Medical Charges and Demographics Dataset
- Insurance Cost Prediction Dataset
- Personal Health Insurance Data
Attributes
Original Data Source: Healthcare Insurance Predictor Data
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