Welcome to GlucoTrack: Predicting Diabetes Likelihood Using Clinical Data 🎉
This project is a collaborative initiative brought to you by SuperDataScience, a thriving community dedicated to advancing the fields of data science, machine learning, and AI. We’re excited to have you join us on this journey of exploration, modeling, and deployment.
To contribute, please follow the guidelines in our CONTRIBUTING.md file.
GlucoTrack is an end-to-end data science project built on demographic and clinical health data. Participants will analyze patient attributes such as glucose levels, BMI, blood pressure, and age to predict the likelihood of diabetes.
The project is structured into two learning tracks so members can join at their preferred skill level:
- 🟢 Beginner Track – Feature-based ML pipeline with Streamlit deployment
- 🔴 Advanced Track – Deep learning classification with explainability and interpretability
Dataset: Diabetes Health Dataset
The Beginner Track emphasizes:
- End-to-end ML workflow using scikit-learn
- Data preprocessing (handling missing values, normalization, encoding)
- Training models such as Logistic Regression, Random Forest, and XGBoost
- Tracking experiments with MLflow
- Deploying a Streamlit app for interactive predictions
📌 Get started: ➡️ Beginner Track Scope of Works ➡️ Beginner Report Template ➡️ Submit your work
The Advanced Track emphasizes:
- Building deep learning pipelines with PyTorch/TensorFlow
- Using embeddings, dropout, batch normalization, and regularization
- Model explainability with SHAP or Integrated Gradients
- Residual/error analysis for clinical interpretability
- Deploying a Streamlit app with model predictions and interpretability visuals
📌 Get started: ➡️ Advanced Track Scope of Works ➡️ Advanced Report Template ➡️ Submit your work
| Phase | Core Tasks | Duration |
|---|---|---|
| 1 · Setup + EDA | Set up repo, clean dataset, explore health indicators, answer key EDA Qs | Week 1 |
| 2 · Model Development | Train and tune ML/DL models, track experiments with MLflow | Weeks 2–4 |
| 3 · Deployment | Build Streamlit app and deploy to Streamlit Cloud | Week 5 |