A4.2.1 Describe the significance of data cleaning.
Before a machine learning model can learn useful patterns, the data it trains on must be accurate, consistent, and relevant. Raw, real-world data is almost never in that condition. Data cleaning is the process of detecting and correcting problems in a dataset before training begins. This page covers why data quality matters, the six categories of data problems and the techniques for addressing each, and the...