MARKET TREND PREDICTION IN DIGITAL BUSINESS THROUGH MACHINE LEARNING INTEGRATION
DOI:
https://doi.org/10.1234/aira.v3i1.66Keywords:
Machine Learning, Market Trend, Digital BusinessAbstract
In the rapidly evolving digital economy, businesses are increasingly relying on data-driven strategies to remain competitive. This essay explores the integration of machine learning (ML) techniques to predict market trends in digital business environments. The key issue addressed is the challenge of identifying and responding to dynamic market shifts in real-time, which traditional forecasting methods often fail to handle effectively. The objective of this study is to examine how ML models can process large-scale, complex datasets to generate accurate and timely predictions. The method involves a qualitative review of various ML algorithms—such as decision trees, random forests, and neural networks—and their application in market trend analysis. The discussion highlights the strengths and limitations of each approach, emphasizing the importance of data quality and contextual relevance. The results indicate that ML offers significant advantages in forecasting market behavior, enabling businesses to enhance decision-making, optimize resource allocation, and gain a strategic edge. This integration not only improves operational efficiency but also supports proactive responses to consumer demand and market volatility. The findings suggest that with continued development, ML will become a core component of future business intelligence systems.
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