Definition
Data preparation for machine learning is the process of cleaning, transforming, and structuring raw data into a format suitable for training machine learning models. This involves tasks like handling missing values, removing duplicates, standardizing formats, and feature engineering to create meaningful inputs. Effective data preparation is crucial because the quality of the data directly impacts the performance, accuracy, and reliability of the resulting ML model. For instance, preparing customer transaction data might involve aggregating purchases by customer and standardizing product names before feeding it into a recommendation engine. This differs from simple data cleaning, as it often involves creating new features or significantly restructuring data to optimize for model learning. Understanding data preparation is essential for roles covered in cloud certifications like AWS Certified Solutions Architect and data science certifications.