Data binning, also known as bucketing or discretization, is a data preprocessing technique used to group continuous data into discrete intervals called “bins” or “buckets.” This method helps in simplifying the data analysis process by reducing the number of distinct values, which can enhance the performance of machine learning models and improve interpretability.

There are two primary approaches to data binning: converting numeric data into categorical data and reducing the number of samples through data sampling. The first approach involves replacing values within a small interval with a representative value, often the central value of that interval. The second approach, sampling, groups similar or contiguous values to reduce the overall number of samples. Binning can also help in data smoothing, which removes noise from the data, thereby improving the accuracy of predictive models ¹⁴⁵.

If you’re interested in implementing data binning, libraries like Pandas in Python provide functions such as cut() and qcut() to facilitate this process.