Medical expenses are a significant recurring expense in human life, and the rising cost of healthcare is a major concern for many individuals and families. In this project, we aim to study the correlation between personal medical expenses and various factors such as age, BMI, smoking, and others, to predict medical costs using linear regression models.
The dataset used in this project is publicly available on GitHub and Kaggle. The dataset contains 1338 observations on 7 variables, including age, BMI, smoking status, and medical expenses. Although we don't have data on the diagnosis of patients, we have other information that can help us make conclusions about the health of patients and perform regression analysis.
We will use linear regression models to identify the prominent attributes that predict medical costs and compare them using ANOVA. Our findings will provide insights into the factors driving medical costs and inform strategies for managing healthcare expenses.
Through this project, we aim to contribute to the understanding of medical expenses and improve the management of healthcare services. By identifying the significant factors affecting medical costs, we can help individuals and families make informed decisions about their healthcare expenses.