There is no need to perform any data preparation steps before using AutoML, as the algorithm automatically handles all aspects of the machine learning workflow.

Data cleaning and feature engineering are not necessary before using AutoML, but data splitting is required to create training and testing sets.

Data cleaning, feature engineering, and data splitting are all important steps to prepare the dataset for AutoML. Data cleaning involves handling missing values, outliers, and errors in the dataset. Feature engineering involves creating new features or transforming existing features to improve the model's performance. Data splitting involves dividing the dataset into training and testing sets to evaluate the model's performance.

Data cleaning and data splitting are not necessary before using AutoML, but feature engineering is required to create new features for the model.