Medical Insurance Cost Prediction using AWS Sage-Maker

Health insurance costs can vary significantly based on factors such as age, smoking status, pre-existing conditions, sum insured, occupation, geographical location, and lifestyle choices. Accurately predicting insurance claims is paramount for insurance companies as it allows them to effectively manage risk, allocate resources efficiently, and optimize pricing strategies. To achieve this, a machine learning approach using AWS SageMaker Studio is proposed in this report. The aim is to build predictive models that can accurately forecast medical insurance claims by leveraging various algorithms such as the sklearn linear regression algorithm, AWS SageMaker's Linear Learner algorithm, and an Artificial Neural Network. By comparing the performance of these algorithms based on evaluation metrics such as squared error, mean absolute error, and R-squared, it can determine which approach offers the highest accuracy and precision. Ultimately, implementing the most effective approach will enable insurance companies to make more informed decisions, enhance customer satisfaction, and further streamline their operations.