Project Showcase
Highlight of my featured works
Obesity Prediction Deployment
A machine learning pipeline deployed as a Flask web app to predict obesity categories. Users can input lifestyle and health data to receive predictions, probabilities, and feature importance.
🎯 What I Learned
- Built preprocessing + training ML pipeline
- Deployed scikit-learn model with Flask
- Visualized predictions with feature importance
🛠 Tech Stack
- Python, Pandas, NumPy
- Scikit-learn, Matplotlib, Seaborn
- Flask, Joblib
- Streamlit
📌 Evaluation / Next Step
The deployed model correctly predicts obesity categories and allows real-time interaction. Feature importance adds transparency, making the model explainable to users. Limitations remain in dataset diversity; expanding and retraining could improve generalization. Future steps include adding authentication, logging predictions, and upgrading to deep learning models.