The purpose of this project is to develop machine learning models that can forecast Medical Insurance Premium Charges by considering factors such as age, gender, BMI, smoking status, number of children, and geolocation. SCIKIT was used to implement linear regression and put an artificial neural network into action.
Problem Statement A medical insurance company seeks to develop an automated system capable of predicting an individual's medical insurance costs based on personal factors. As a computer science student eager to apply machine learning knowledge to real-world situations, I am exicited to assist them in constructing a machine learning framework. By using the data that has been provided, this system will be able to learn and produce rough estimates of insurance prices. Implementing a machine learning system for predicting insurance costs will help the company to accurately assess risk, optimize pricing, and make informed strategic decisions, thus gaining a competitive edge in the market.
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