Comprehensive Data Cleaning for AI and ML

This text provides an in-depth guide on how to prepare tabular data for use in Artificial Intelligence (AI) and Machine Learning (ML) projects, emphasizing the importance of a thorough data cleaning process. The author outlines various steps involved in this process, including standardizing empty values, removing duplicate records, handling missing values, dealing with redundant fields, capping high float precision, removing constant fields, and addressing field-level and record-level outliers. The text also provides code snippets to illustrate these steps using Python's pandas library.