This is a Machine learning project on Health insurance cost prediction

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(target – y) In this code has few models

MLR_before_outlier(MLR:MULTIPLE LINEAR REGRESSION,RFR:RANDOM FOREST REGRESSION,PCA:PRINCIPAL COMPONENT ANALYSIS)

MLR_after_outlier

RFR_after_outlier

MLR with PCA

RFR

RFR with_hyper_parameter tunning

RFR with PCA