Project Showcase
Highlight of my featured works
Cirrhosis Stages Prediction
A machine learning project for predicting cirrhosis disease stages. Compared Random Forest and XGBoost models, with tuning via GridSearch, RandomizedSearch, and Hyperopt.
🎯 What I Learned
- Applied multiple scaling and encoding techniques
- Trained Random Forest and XGBoost classifiers
- Used GridSearch, RandomizedSearch, and Hyperopt for tuning
🛠 Tech Stack
- Python, Pandas, NumPy
- Matplotlib, Seaborn, Plotly
- Statsmodels
- Scikit-learn
- XGBoost, Hyperopt
📌 Evaluation / Next Step
Random Forest provided a reliable baseline, but XGBoost with Hyperopt tuning achieved superior recall and overall performance. Data imbalance and limited size remain challenges. Next steps include oversampling, larger datasets, and testing deep learning approaches for medical prediction.