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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

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

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.

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! 🚀📊👩‍⚕️👨‍⚕️