In this blog, I’m going to create a few ML models using Scikit-learn library and we’ll compare the accuracy for each of them.

For complete source code and dataset, you can visit my repository. (Link mentioned at the end of this blog).

About Dataset

This dataset contains 7 features as shown below:

age: age of the policyholder

sex: gender of policyholder (female=0, male=1)

BMI: Body mass index, providing an understanding of the 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 25

steps: average walking steps per day of the policyholder

children: number of children/dependents of the policyholder

smoker: smoking state of policyholder (non-smoke=0;smoker=1)

region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)

charges: individual medical costs billed by health insurance.

#machine-learning #insurance #predictive-analytics