ML-Project is an end-to-end machine learning project for regression tasks. It includes modules for data ingestion, preprocessing, exploratory data analysis (EDA), feature transformation, model training, and prediction pipelines. This project is structured to be modular, maintainable, and scalable for real-world ML applications.
Industry specific framework of Feature Engineering in Machine Learning
Modular ML pipeline for student performance prediction — powered by FastAPI, Optuna, MLflow, PostgreSQL, and S3. Production-ready with YAML configs, Celery, DVC, and Docker support.
Add a description, image, and links to the regression-pipeline topic page so that developers can more easily learn about it.
To associate your repository with the regression-pipeline topic, visit your repo's landing page and select "manage topics."