rising-stars-by-sunshine / stats201_zhe_niu_ps2

This repository presents a project focused on predicting obesity levels using machine learning models based on dietary habits, physical activity, and genetic factors. It includes data querying scripts, preprocessing guidelines, and detailed analysis notebooks to explore and model obesity risk factors effectively.

Stats201_Zhe_Niu_PS2 Obesity Prediction Analysis

Description

This GitHub repository houses a comprehensive project aimed at predicting obesity levels based on various factors, including dietary habits, physical activities, and genetic predispositions. Utilizing machine learning models such as RandomForestClassifier, XGBoost, and LightGBM, the project encompasses data querying from Kaggle, preprocessing, feature engineering, and model evaluation to explore and predict obesity risk factors effectively.

Table of Contents

• Code
• Data
• Method

Code

Contains scripts and Jupyter notebooks for data retrieval, preprocessing, analysis, and visualization. This includes detailed code for setting up environment variables, using Kaggle API for data download, cleaning data, performing exploratory data analysis, and training machine learning models.

Data

Holds the raw and processed datasets along with a detailed explanation of the data cleaning, preprocessing steps, and feature engineering techniques applied. A README within this folder provides insights into the data query process and the structure of the dataset.

Method

Documents the methodologies adopted for data analysis, including the rationale behind choosing specific machine learning models, model training, evaluation strategies, and interpretation of results. This section aims to provide a clear understanding of the analytical approach and statistical techniques employed in the project.

About the Author

Zhe Niu

Zhe Niu is a Bachelor of Science in Data Science student at Duke Kunshan University, expected to graduate in June 2024. He has a strong background in finance and AI, with experience in research and practical applications of data science in the financial industry.