👉 Variables engineering, also known as feature engineering or feature construction, is the process of creating new input variables (features) from existing ones to improve the performance and efficiency of machine learning models. This involves transforming raw data into a format that better represents the underlying problem to the predictive models, enhancing their ability to learn and generalize. Techniques include scaling, encoding categorical variables, extracting new features from existing ones, and creating interaction terms between variables. The goal is to improve model accuracy, reduce overfitting, and make models more interpretable by highlighting important patterns in the data.
