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Medical Insurance Cost Prediction Dataset
Healthcare Insurance & Costs
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About
This dataset focuses on medical insurance price prediction, offering key insights into factors that influence healthcare expenses. It is designed to facilitate the development of machine learning models capable of forecasting medical expenses for new customers. The dataset’s primary purpose is to reveal significant contributors to higher insurance costs, thereby assisting insurance companies in making more informed decisions regarding pricing strategies and risk assessments. It addresses important questions concerning the most impactful factors on medical expenses, the effectiveness of machine learning in prediction, and how these models can enhance the efficiency and profitability of health insurance operations.
Columns
- Age: Represents the age of the individual. Values range from 18 to 64, with a mean age of 39.1.
- Sex: Indicates the gender of the individual, split almost equally between male (51%) and female (49%).
- BMI (Body Mass Index): Details the individual’s Body Mass Index, with values spanning from 15.96 to 53.13 and a mean of 30.7.
- Children: Shows the number of children an individual has, ranging from 0 to 5, with a mean of 1.1.
- Smoker: A boolean field indicating whether the individual is a smoker (20%) or a non-smoker (80%).
- Region: Categorises the individual's residential region. Common regions include southeast (28%) and southwest (25%), with other regions making up 48%.
- Charges: Represents the medical insurance price. Charges vary widely from £1,121.87 to £63,770.43, with an average charge of £13,300.
Distribution
The dataset is provided in CSV format (
Medical_insurance.csv) and is approximately 115.14 kB in size. It comprises 2,700 rows and 7 distinct columns, providing a structured collection of data points.Usage
This dataset is ideal for several applications:
- Training machine learning models to accurately predict medical expenses for individuals.
- Identifying critical factors that significantly influence higher insurance costs.
- Informing strategic decisions related to insurance pricing and risk assessment within health insurance companies.
- Improving the operational efficiency and profitability of health insurance providers through data-driven insights.
Coverage
The dataset's coverage includes demographic information such as age (18-64 years), gender (male/female), and number of children (0-5). It also incorporates a smoker status. Geographic coverage is indicated by regions, including southeast and southwest, suggesting a focus on specific regional demographics. A specific time range for the data collection is not provided.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly valuable for:
- Data scientists and machine learning engineers looking to build and evaluate predictive models for healthcare costs.
- Actuaries and risk assessment professionals in the insurance sector seeking to understand and quantify risk factors.
- Healthcare policy analysts interested in the socio-economic determinants of medical expenses.
- Academics and researchers exploring the economics of health insurance and healthcare costs.
Dataset Name Suggestions
- Medical Insurance Cost Prediction Dataset
- Health Insurance Expense Factors
- Insurance Premium Prediction Data
- Medical Charges Forecasting Data
- Healthcare Insurance Determinants
Attributes
Original Data Source: Medical Insurance Cost Prediction Dataset
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