This self-initiated project improves my submitted project for the CB4247 Statistics & Computational Inference to Big Data module at NTU. After my initial project was graded, I sought my lecturer's feedback and incorporated it into this version of this project. This repository contains my project's code and report (To be uploaded soon) and showcases my machine learning, data analysis, and pre-processing, Python, and report writing skills.
This self-initiated project aims to:
- Apply data analysis and visualization techniques to analyze a real-world dataset.
- Train machine learning algorithms on the chosen dataset, including Ordinary Least Squares Regression, Random Forest Regressor, and XGBoost Regressor.
- Conduct ANOVA and residual analysis to test the validity of Ordinary Least Squares Regression assumptions.
- Evaluate each algorithm's performance via metrics, including mean absolute error, root mean squared error, and R2.
- Identify critical variables via feature importance.
- Develop the most accurate model for the chosen dataset by combining high-performing algorithms.
This file serves as a short summary of my report. My report will soon be uploaded into this repository, detailing my project's aim, background, exploratory analysis, regression, modeling, discussions, and conclusion. Thank you very much for your patience!