CS 412: Introduction to Machine Learning — Final Project (Fall 2024)
University of Illinois Chicago (UIC)
This machine learning project was developed as part of the CS 412 course at UIC, aimed at predicting obesity risk based on lifestyle and demographic factors. Using a dataset of 2,111 entries and 17 features (e.g., dietary habits, physical activity, transportation), we trained and evaluated several classification models to accurately predict a person's obesity level.
We tested and compared the performance of:
- Naive Bayes
- Support Vector Classifier (SVC)
- Voting Ensemble Classifier (combining Random Forest, SVC, and Gradient Boosting)
Our best-performing model — the ensemble — achieved up to 98% accuracy and F1-scores as high as 1.00 across multiple obesity categories.
While this was a collaborative team project, this fork highlights my individual contributions, which include:
- Writing and refining the
dataCleaning.pyscript used for preprocessing the dataset - Training and evaluating machine learning models in Jupyter/Colab
- Analyzing performance using confusion matrices and classification reports
- Synthesizing results for our final report and class presentation
Note: Much of our collaborative work took place in Google Colab, so the original GitHub commit history does not fully reflect my role. This fork serves to showcase the parts of the project I personally contributed to and understand deeply.
- Python, Jupyter Notebook
- scikit-learn, pandas, matplotlib, seaborn
- Ensemble models via
VotingClassifierfromsklearn.ensemble
- 2,111 records with 17 features
- BMI range: 13.0 to 50.8
- Labels: 7 multiclass obesity levels including "Normal Weight", "Obesity Type I", etc.
├── dataCleaning.py # Custom data preprocessing script
├── CS412Project.ipynb # Main notebook (model training + evaluation)
├── ObesityDataSet.csv # Dataset used for training/testing
├── Final Report + Slides # PDF deliverables from class (in /docs or root)
Hi! I’m Srijita Banerjee, a recent Computer Science graduate from UIC passionate about applying ML to meaningful, real-world problems.
This project was an early but important step in my journey into machine learning, and I’m proud of what we built as a team.
📫 Reach out to me or connect via LinkedIn