Project Overview: The project is focused on developing a predictive model to estimate medical expenses based on demographic and health-related features. It utilizes a dataset containing attributes such as age, sex, BMI, number of children, smoker status, region, and charges.
Key Features:
- Analysis of medical charges by smoker status, showcasing a significant cost disparity between smokers and non-smokers.
- Examination of distributions for age, BMI, and children across smoker and non-smoker categories.
- Insights into the regional variation of medical charges, with a particular emphasis on higher costs in the Southeast.
- Correlation matrix creation to investigate relationships between numerical attributes.
Tools and Technologies:
- R Programming Language: For data manipulation, analysis, and visualization.
- Caret Package: To streamline the modeling process.
- ggplot2 and corrplot Packages: For advanced graphical representations.
Project Structure:
- Data loading and cleaning to prepare the dataset for analysis.
- Descriptive statistics and data exploration to gain initial insights.
- Data visualization to uncover patterns and relationships.
- Regression and decision tree modeling to predict medical charges.
- Evaluation of model performance using RMSE (Root Mean Square Error).
- Integration of categorical data using factor conversion to enhance model accuracy.
The project's predictive modeling approach emphasizes the importance of smoker status and demonstrates how age and BMI can impact medical expenses. It also explores the application of machine learning techniques to improve predictions and guide decision-making in healthcare cost management.