Feature Scaling

Feature scaling is a process in data analysis that adjusts the range of different variables to be comparable. When data has features with varying units or scales—like age in years and income in thousands—it can bias algorithms that rely on distances. Scaling transforms these features so they contribute equally, improving the model's accuracy and efficiency. Common methods include normalization (rescaling to between 0 and 1) and standardization (adjusting to have a mean of 0 and a standard deviation of 1). This ensures that all features are on a similar scale, allowing the algorithms to learn more effectively from the data.