Feature Engineering: Enhancing Model Performance

Feature extraction is one of the most central steps in the machine-learning process. In this step, raw data is manipulated and ready to assist it to be fit for modeling the problem into analytical models.

Imputation

Handling the missing values is crucial since most machine learning models cannot holder such values in their computations.

Numerical Imputation: Replace missing histories by substituting missing values with the variable’s mean, median, or mode value.