Predicting Health Insurance Charges

The first major project I was a part of in this program was in Intro to Data Science (IST 687). We were tasked with garnering actionable insights from a health insurance dataset provided by our professor. The goal was to find the biggest factors what drives the cost of healthcare insurance for individuals using certain metrics such as age, body mass index, children, gender, and smoking. The analysis was done in R and involved both predictive modeling and visualization. Overall, individuals should be incentivized to exercise more and to smoke less if they’re to decrease their healthcare costs. Smoking was the leading factor in driving up costs, while exercising was the greatest mitigator to those costs increasing. Other factors such as age, BMI, hypertension, and children are also major contributors, but more difficult to address in the short term. Additionally, exercising more should lead individuals to a healthier BMI and overall lifestyle. The learning outcome best shown in this project was