Ignore outliers, normalize numerical data by scaling it to a range of 0-1, and standardize categorical data by converting it to numerical values.

Remove all outliers, normalize numerical data using z-score normalization, and standardize categorical data by encoding it.

Handle outliers by capping them at a certain threshold, normalize numerical data using min-max scaling, and standardize categorical data by one-hot encoding.

Delete the entire dataset if outliers are found, avoid normalizing numerical data, and leave categorical data as is.