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What is Handling Real-World Data in ML about?

This document provides a comprehensive guide on handling real-world data in machine learning, highlighting challenges such as missing, noisy, and imbalanced data. It outlines essential steps for data preprocessing, including data collection, cleaning, feature engineering, and dealing with concept drift. Best practices emphasize the importance of diverse data collection, automation in cleaning, and continuous monitoring of model performance.

Author

Saravanan R

Language

EN