Abstract

Data cleaning and preprocessing are fundamental steps in the data analysis pipeline. These processes involve transforming raw data into a usable format by identifying and rectifying inconsistencies, errors, and missing values. Given the importance of data quality in achieving accurate and reliable analytical results, understanding the best practices for these stages is crucial. This paper outlines key techniques for data cleaning and preprocessing, including handling missing data, detecting and managing outliers, data normalization, encoding categorical variables, and dealing with noisy data. Additionally, it explores the importance of these practices in ensuring robust and insightful analysis.