Medical insurance premium prediction involves using data analysis and machine learning to estimate the cost individuals will pay for their medical coverage. By considering factors like age, health history, lifestyle, and location, insurers can create models that forecast future premiums. This benefits both insurers, who can price accurately, and policyholders, who gain insights into costs before committing to coverage.
Columns
age: age of primary beneficiary
sex: insurance contractor gender, female, male
bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9
children: Number of children covered by health insurance / Number of dependents
smoker: Smoking
region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charges: Individual medical costs billed by health insurance
The dataset is available on GitHub here.
Can you accurately predict insurance costs?
Ensuring data privacy and security when handling sensitive personal and medical information. Handling potential outliers and noisy data that might impact model performance. Continuous adaptation of the model to changing trends and customer profiles.