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
- Import libraries and load the data
- Exploratory data analysis
- Create dummy variables
- Regression model comparison: Linear, Lasso, Ridge, and Elastic Net. Linear regressor gave the best result.
https://www.kaggle.com/mirichoi0218/insurance
https://www.kaggle.com/kadirkaya28/medical-cost-personal-dataset-predict
https://www.kaggle.com/adrynh/predictmedicalcost-4-regression-models
https://www.kaggle.com/jnikhilsai/cross-validation-with-linear-regression