Transformations of Input or Output

This lecture covers the handling of missing data in machine learning, including dropping data points or imputing missing values with mean or median. It also introduces feature engineering techniques like data cleaning, dealing with missing data, and transformations of the output variable. The instructor explains the implications of transforming the response variable and demonstrates methods like imputation and removing predictors. Additionally, the lecture explores standardization techniques and the application of categorical predictors in feature engineering.