Stat 615 Final Project: Estimating Medical Costs

Introduction

This project aims to estimate medical costs based on various factors using regression analysis. We explore a dataset related to health insurance and investigate the impact of age, gender, and body mass index (BMI) on medical expenses.

Acknowledgments

• "I am not what happened to me, I am what I choose to become." – Christopher Gardner, The Pursuit of Happiness
• Special thanks to Professor James C. Dickens for guiding us during the Regression program.
• Gratitude to our family, friends, and American University for their support and encouragement.

Table of Contents

1. About the Dataset
2. Data Description
3. Libraries Used

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About the Dataset

• Data Source: Kaggle Insurance Dataset
• The dataset contains information on health insurance beneficiaries.
• Key variables include:
  • Age: Age of the primary beneficiary
  • Sex: Gender of the insurance contractor (female or male)
  • BMI: Body mass index, providing insights into relative weight compared to height

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Data Description

• Age: Represents the age of the insured individual.
• Sex: Indicates the gender of the insurance contractor (female or male).
• BMI: Body mass index, which helps understand weight relative to height.

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Libraries Used

We utilized the following R libraries for our analysis:

• olsrr
• tidyverse
• dbplyr
• dplyr
• Matrix
• MASS
• ggplot2
• tibble
• data.table
• ggmosaic
• ggforce
• ggmap
• ggthemes
• purrr
• keep
• readr
• gridExtra
• randomForest
• corrplot
• PerformanceAnalytics

Feel free to explore the code and adapt it to your specific needs. Good luck with your project! 🚀📊👩‍⚕️👨‍⚕️