Insurance-Price-Prediction

Content

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

Process

• 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.

Source

https://www.kaggle.com/mirichoi0218/insurance

References:

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