End-to-End Data Science Project

This project predicts the quality of medical tests based on input numerical values.

ML Pipeline Workflow

1. Data Ingestion
2. Data Validation
3. Data Transformation - Feature Engineering, Data Preprocessing
4. Model Trainer
5. Model Evaluation - Using MLflow & DagsHub for tracking

Project Workflows

1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src/config
6. Update the components
7. Update the pipeline
8. Update main.py

Experiment Tracking

You can view the experiment tracking on DagsHub: 🔗 DagsHub Repository

Deployment

- A web application is included to make real-time predictions.
- The project is dockerized to ensure smooth and scalable deployment.

Technologies Used

- Python 🐍
- MLflow for experiment tracking 📊
- DagsHub for model monitoring 📡
- Docker for containerization 🐳