Predicting Health Insurance Cost
[Case Study] Examining the cost of health insurance through multiple linear regression, focusing on predictors and their interaction terms.
This project provides a step-by-step guide to conducting a predictive analysis using multiple linear regression models in R. Acting as a healthcare consulting study, the report explores the key factors that influence health insurance pricing based on a dataset of 1,338 observations. The analysis begins with Exploratory Data Analysis (EDA) to understand the distribution, correlation, and variance of predictors such as age, BMI, children, and smoking status. It then demonstrates how to build and interpret multiple linear regression models, incorporating interaction terms to accurately predict insurance costs.
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