Data Preprocessing Methods - Strategies and Best Practices: Investigating strategies and best practices for preprocessing data, including cleaning, transformation, and feature engineering

Abstract

Data preprocessing is a crucial step in the data mining and machine learning pipeline, involving the transformation of raw data into a format suitable for analysis. This paper provides a comprehensive review of strategies and best practices for data preprocessing, focusing on cleaning, transformation, and feature engineering techniques. We begin by discussing the importance of data preprocessing and its impact on the quality of machine learning models. Next, we delve into various data cleaning techniques, including handling missing values, dealing with outliers, and addressing inconsistencies in the data. We then explore different data transformation methods, such as normalization, standardization, and encoding categorical variables. Finally, we examine feature engineering approaches to create new features from existing ones, including techniques like binning, one-hot encoding, and feature scaling. Throughout the paper, we highlight the importance of each preprocessing step and provide practical recommendations for implementing these techniques effectively.