Predictive Power- Leveraging Data Analytics and Mining for Future Trends Forecasting

Authors

  • Niyati Agarwal M. Tech Scholar, Department of Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India
  • Dipti Ranjan Tiwari Head & Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India

Keywords:

Predictive analytics, Data mining, Future trends forecasting, Machine learning, Decision-making

Abstract

In an era of vast data availability, the ability to predict future trends accurately has become a critical advantage for businesses, policymakers, and researchers alike. This paper examines the predictive power inherent in data analytics and mining techniques and their applications in forecasting future trends across various domains. Through a comprehensive review of methodologies, case studies, and real-world applications, we explore how data analytics and mining enable the extraction of valuable insights from large datasets to anticipate trends in finance, healthcare, marketing, and beyond. We delve into the tools, and best practices employed in predictive modeling, emphasizing their role in enhancing decision-making processes and strategic planning.

References

P. Domingos and M. Pazzani, "On the optimality of the simple Bayesian classifier under zero-one loss," Machine Learning, vol. 29, no. 2-3, pp. 103-130, 1997.

T. Hastie, R. Tibshirani, and J. Friedman, "The elements of statistical learning: data mining, inference, and prediction," Springer Science & Business Media, 2009.

C. D. Manning, P. Raghavan, and H. Schütze, "Introduction to information retrieval," Cambridge University Press, 2008.

D. Jurafsky and J. H. Martin, "Speech and language processing" (3rd ed.), Pearson, 2019.

B. Liu, "Sentiment analysis and opinion mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, 2012.

B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.

B. D. Mittelstadt et al., "The ethics of algorithms: Mapping the debate," Big Data & Society, vol. 3, no. 2, 2053951716679679, 2016.

L. Floridi et al., "AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations," Minds and Machines, vol. 28, no. 4, pp. 689-707, 2018.

B. Babcock et al., "Models and issues in data stream systems," in Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 1-16, 2002.

C. C. Aggarwal et al., "A framework for clustering evolving data streams," in Proceedings of the 29th international conference on Very large data bases - Volume 29, pp. 81-92, 2007.

A. Sharma and V. Kumar, "Machine Learning Prospects: Insights for Social Media Data Mining and Analytics," International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 11, no. 3, pp. 12-19, 2023. [Online]. Available: doi:10.55524/ijircst.2023.11.3.3

Y. Liu, Y. Wang, J. Huang, and W. Pedrycz, "Deep Learning for Time Series Forecasting: A Survey," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3328-3358, 2021.

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Published

2024-04-09

How to Cite

Agarwal, N., & Tiwari, D. R. (2024). Predictive Power- Leveraging Data Analytics and Mining for Future Trends Forecasting. International Journal of Innovative Research in Engineering and Management, 11(2), 74–78. Retrieved from http://ijirem.irpublications.org/index.php/ijirem/article/view/21

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