Discover how to predict house prices with precision using PySpark's robust data processing capabilities and MLflow for seamless model tracking. This project dives into scalable machine learning, leveraging PySpark for regression analysis and MLflow to log metrics, monitor performance, and streamline experimentation. Perfect for those exploring big data ML pipelines! 🚀
Highlights:
Scalable regression modeling with PySpark
Real-time performance tracking using MLflow
Optimized for large datasets and practical insights
Unleash the power of big data for smarter predictions!
Harness the power of big data with this end-to-end machine learning project! Using PySpark. With MLflow, I streamlined experiment tracking, ensuring reproducibility and performance monitoring. A custom function handled categorical features with one-hot encoding, making preprocessing efficient and scalable.
Highlights:
Managed imbalanced data with resampling techniques
Removed duplicates and engineered features
Built a dynamic one-hot encoding function for categorical handling
Tracked metrics and optimized hyperparameters using MLflow
Perfect for those looking to scale ML models with PySpark while maintaining experiment traceability! 🚀