Capstone project

Decision Trees and Random Forest Analysis

Overview

This repository contains the capstone project for the Master’s Program at the University of West Florida (UWF).
The project explores the application of the Random Forest algorithm, a widely used ensemble learning method, to solve a data-driven research problem.

Objectives

• Investigate the strengths and limitations of Random Forest.
• Apply the algorithm to a real-world dataset.
• Evaluate performance using appropriate metrics.
• Provide insights into parameter tuning and model interpretation.

Repository Structure

• data/ → Raw and processed datasets.
• notebooks/ → Jupyter notebooks (Python) and R scripts for analysis.
• src/ → Source code in Python and R for data preprocessing, model training, and evaluation.
• results/ → Output files, figures, and tables generated during the analysis.
• README.md → Project documentation (this file).

Software

You can run this project using either Python or R.

Python

• Python 3.x
• scikit-learn
• pandas
• numpy
• matplotlib / seaborn

R

• 4.0 or higher recommended
• randomForest → Random Forest implementation
• caret → model training and evaluation
• ggplot2 → visualization
• dplyr → data manipulation