Enhancing Machine Learning Projects: Strategies for Effective Data Handling and Model Performance
Machine learning has revolutionized numerous industries, from finance to healthcare, by enabling the development of intelligent systems capable of making predictions and decisions based on data. However, the success of machine learning projects relies heavily on the proper handling of data and the ability to build models that can adapt to real-world scenarios. In this essay, we will explore key aspects of data handling in machine learning, including data partitioning, bias mitigation, data leakage prevention, and addressing data drift.
To start with we should have quality data. I suggest you to read the following:
