Data binning, also known as bucketing, is a data preprocessing technique used to group continuous data into discrete intervals or “bins.” This process simplifies the data by replacing individual values within a small range with a single representative value, which can enhance the accuracy of predictive models. Binning is particularly useful in machine learning for discretizing continuous variables, making it easier to analyze trends and patterns. It can also aid in reducing noise in the data, thereby improving the overall quality of the analysis. Techniques such as binning by distance and binning by frequency are commonly employed in this process.