Ramana S¹*, Gomathi S², Jayashree S², Pranesh K² and Vignesh S²

¹Assistant Professor, Department of Information Technology, Nandha College of Technology, Perundurai Main Road, India
²UG – Final Year, Department of Information Technology, Nandha College of Technology, Perundurai Main Road, India

*Corresponding Author: Ramana S, Assistant Professor, Department of Information Technology, Nandha College of Technology, Perundurai Main Road, India.

Received: May 09, 2024; Published: May 26, 2025

Abstract

In the present digital era massive amount of data is being continuously generated at exceptional and increasing scales. In supervised learning, missing values usually appear in the training set. The missing values in a dataset may generate bias, affecting the quality of the supervised learning process or the performance of classification algorithms. These imply that a reliable method for dealing with missing values is necessary. In this paper, we analyse the difference between imputation of missing values and imputation in real world applications. This data has become an important and indispensable part of every economy, industry, organization, business and individual. Further handling of these large datasets due to the heterogeneity in their formats is one of the major challenges. There is a need for efficient data processing techniques to handle the heterogeneous data and also to meet the computational requirements to process this huge volume of data. The objective of this project is to review, describe and recreate on heterogeneous data with its complexity in processing, and also the use of machine learning algorithms which plays a major role in data analytics. We experimentally show that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.

Keywords: Heterogeneous Data; Missing Data; Predictive Analytics; Convolutional Neural Network; Long Short-Term Memory; Data Imputation

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Citation

Citation: Ramana S., et al. “Machine Learning Based Integrative Regression Analysis of High – Dimensional Heterogeneous Data".Acta Scientific Computer Sciences 7.3 (2025): 14-20.

Copyright

Copyright: © 2025 Ramana S., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.