Handling missing values are the process of addressing missing data in large datasets. Techniques involve imputing missing values using statistical methods or leveraging imputation algorithms to fill gaps, ensuring that analysis is not compromised by incomplete information.
Published in Chapter:
Big Data Preprocessing, Techniques, Integration, Transformation, Normalisation, Cleaning, Discretization, and Binning
Pranali Dhawas (G.H. Raisoni College of Engineering, Nagpur, India),
Abhishek Dhore (MIT School of Computing, MIT ADT University, Pune, India),
Dhananjay Bhagat (G.H. Raisoni College of Engineering, Nagpur, India),
Ritu Dorlikar Pawar (G.H. Raisoni College of Engineering, Nagpur, India),
Ashwini Kukade (G.H. Raisoni College of Engineering, Nagpur, India), and
Kamlesh Kalbande (G.H. Raisoni College of Engineering, Nagpur, India)
Copyright: © 2024
|Pages: 24
DOI: 10.4018/979-8-3693-0413-6.ch006
Abstract
“Unleashing the Power of Big Data: Innovative Approaches to Preprocessing for Enhanced Analytics” is a groundbreaking chapter that explores the pivotal role of preprocessing in big data analytics. It introduces diverse techniques to transform raw, unstructured data into a clean, analyzable format, addressing the challenges posed by data volume, velocity, and variety. The chapter emphasizes the significance of preprocessing for accurate outcomes, covers advanced data cleaning, integration, and transformation techniques, and discusses real-time data preprocessing, emerging technologies, and future directions. This chapter is a comprehensive resource for researchers and practitioners, enabling them to enhance data analytics and derive valuable insights from big data.