CMPS 460 Machine Learning Project - G2 TCGA-CHOL

This project contains a completed Jupyter Notebook for binary cancer classification using the assigned TCGA-CHOL gene expression dataset.

Files

ML_Project/
├─ CMPS460_G2_CHOL_Final_Project.ipynb
├─ CMPS460-Project.pdf
├─ requirements.txt
├─ README.md
├─ outputs/
│  ├─ figures/
│  └─ tables/
└─ data/
   ├─ G2-TCGA-CHOL_EX.csv
   └─ G2-TCGA-CHOL_Label.csv

Setup on Windows

cd "PATH\ML_Project"
python -m venv .venv
.\.venv\Scripts\activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
jupyter notebook

Open CMPS460_G2_CHOL_Final_Project.ipynb and run all cells from top to bottom.

Important data note

The uploaded CSV files do not have header rows. The final notebook checks this before loading and treats the files as headerless numeric CSVs.

What the final notebook includes

• Dataset explanation based on the project PDF
• Exploratory data analysis with saved figures and tables
• Leakage-safe preprocessing with stratified split, scaling, and SelectKBest
• Logistic Regression, Random Forest, and neural-network models
• Baseline and optimized model comparison
• Feature ranking and final discussion preparation

TensorFlow is optional. If TensorFlow/Keras is unavailable, the notebook uses sklearn.neural_network.MLPClassifier as the fallback deep learning model.