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MRI India Journals Vol. 9 No. 8 (2025): Volume 9 Issue 8 2025

Machine Learning Models for Predictive Analytics in Business Decision- Making

Authors

  • R. P. Ambilwade Associate Professor, Department of Computer Science, National Defence Academy, Pune
  • Dr. Sujatha Jayaraj Assistant Professor, Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh
  • Prof. Dr. Ajim Shaikh Professor, Yashwantrao Mohite Institute of Management, Bharati Vidyapeeth (Deemed to be University) Pune Maharashtra
  • Dr. Mahesh Manohar Bhanushali Dr. V. N. Bedekar Institute of Management Studies, University of Mumbai

DOI:

https://doi.org/10.65521/ijasret.v9i8.1522

Keywords:

Machine Learning Business Decision-Making Predictive Analytics Support Vector Machine (SVM) Naïve Bayes (NB) KNearest Neighbors (KNN)

Abstract

Business activities have been greatly influenced by the quick development of technology, which has led to the use of machine learning—a branch of artificial intelligence—to help with decision-making processes. To glean insights from vast, diverse records, machine learning relies on complex data structures and algorithms. There is, however, little study on ML in many commercial decisionmaking domains. The purpose of this qualitative study is to investigate how using machine learning may strengthen its standing and facilitate the decision-making process Information was collected from several resources such as academic papers, interviews, and the perspectives of businesses that produce AI. According to the study, machine learning analyzes all aspects of databases to provide unique and tailored information. The report emphasizes the difficulties in using machine learning while highlighting its market position. In generating significant economic expansion.

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Published

2025-08-21

How to Cite

Ambilwade, R. P., Jayaraj, D. S., Shaikh, P. D. A., & Bhanushali, D. M. M. (2025). Machine Learning Models for Predictive Analytics in Business Decision- Making. International Journal of Advanced Scientific Research and Engineering Trends, 9(8), 56–65. https://doi.org/10.65521/ijasret.v9i8.1522

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