Predicting healthcare costs for individuals using accurate prediction models is important for various stakeholders beyond health insurers, and for various purposes.

The purpose of my study is to investigate different features, observe their relationship, and to come up with the best prediction model based on several features of individual such as age, physical/family condition and location against their existing medical expense to be used for predicting future medical expenses of individuals that help medical insurance to make decision on charging the premium. With the study, I have been able to highlight importance of different input features in predicting insurance charges.

Key stakeholders in these efforts to manage healthcare costs include health insurers, employers, society, and healthcare delivery organizations. Although many researchers have highlighted the importance of predicting people’s health costs to improve healthcare budget management, most of them do not address the frequent need to know the reasons behind this prediction, i.e., knowing the factors that influence this prediction. The objective of this research is to accurately predict insurance costs based on people's data, including age, body mass index, smoking or not, etc.