Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Predictive Analytics in Business Intelligence: Analyzing predictive analytics techniques in business intelligence applications for forecasting sales, customer behavior, etc

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  • Dr. Sun-Young Park
Dr. Sun-Young Park
Professor of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)
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Published 20-09-2023

Keywords

  • Predictive Analytics,
  • Forecasting
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Sun-Young Park, “Predictive Analytics in Business Intelligence: Analyzing predictive analytics techniques in business intelligence applications for forecasting sales, customer behavior, etc”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 179–187, Sep. 2023, Accessed: Nov. 25, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/121

Abstract

Predictive analytics is a crucial component of business intelligence, enabling organizations to forecast future trends and make data-driven decisions. This paper provides an overview of predictive analytics techniques in business intelligence applications, focusing on their use in forecasting sales, customer behavior, and other key business metrics. The paper discusses various predictive modeling approaches, including machine learning algorithms, time series analysis, and data mining techniques. It also explores the challenges and best practices associated with implementing predictive analytics in business intelligence systems. The insights provided in this paper can help organizations leverage predictive analytics to gain a competitive advantage and improve decision-making processes.

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