Min-max scaling is a technique used to normalize data by transforming features to a common scale, typically between 0 and 1. This is achieved by subtracting the minimum value of the feature and then dividing by the range (the difference between the maximum and minimum values). This method helps ensure that each feature contributes equally to the analysis, especially in algorithms sensitive to the scale of data, such as k-nearest neighbors and neural networks. This scaling method is particularly useful when dealing with datasets that have varying units or ranges. By applying min-max scaling, you can improve the performance of machine learning models and enhance the interpretability of the data. However, it is important to note that min-max scaling can be sensitive to outliers, as they can significantly affect the minimum and maximum values used in the transformation.