Data preparation involves cleaning and organizing the data to make it suitable for modeling. This includes:
- Data Cleaning: Removing duplicates, correcting errors, and dealing with missing values. - Normalization: Scaling data to a common range to ensure fair comparison. - Feature Engineering: Creating new features that might be more predictive, such as the time since last purchase or the average time between purchases.