Min-max normalization is a technique used to scale data to a specific range, typically between 0 and 1. This method transforms each data point by subtracting the minimum value of the dataset and then dividing by the range (the difference between the maximum and minimum values). This ensures that all values are proportionally adjusted, making it easier to compare different datasets. This normalization is particularly useful in machine learning and data analysis, as it helps algorithms perform better by ensuring that features contribute equally to the results. By using min-max normalization, datasets become more uniform, which can improve the accuracy of models and reduce bias in predictions.