The project provides a Regression on the Insurance Prediction Data which shows the features of individuals, tuned using Ridge & Lasso.
Following is the attribute related information:
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, yes or no
region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charges: Individual medical costs billed by health insurance
To predict the approximate insurance cost based upon the rest of the features provided for each individual.
It predicts the approximate insurance cost based upon the features provided for each individual. It is a Regression problem. The algorithm used in this project is Linear Regression. Executed the steps like Data preprocessing, Feature selection, Model Building, Evaluation & Tuning.
Performed Base Linear Regression and Tuned using Ridge & Lasso Techniques. Out of Both, Ridge is giving the lowest RMSE (0.48) and R-Squared (0.709), Adj-R Squared (0.709) are Closed to each other.