Data Preprocessing Questions
Data scaling, also known as feature scaling, is a data preprocessing technique used to standardize or normalize the range of features or variables in a dataset. It involves transforming the values of the features to a specific range or distribution, typically between 0 and 1 or -1 and 1. Data scaling is important because it helps to eliminate the influence of different scales and units of measurement on the analysis and modeling process. It ensures that all features contribute equally to the analysis and prevents certain features from dominating the results due to their larger scales.