Predictive Analytics in Data Engineering |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-8 |
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| Year of Publication : 2024 | ||
| Authors : Manohar Sai Jasti | ||
| DOI : 10.14445/22312803/IJCTT-V72I8P104 | ||
How to Cite?
Manohar Sai Jasti, "Predictive Analytics in Data Engineering," International Journal of Computer Trends and Technology, vol. 72, no. 8, pp.19-25, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I8P104
Abstract
With the advent of exponential growth of the digital landscape Artificial Intelligence has become part of mainstream applications. One of the key techniques of AI which every business intends to leverage is the approach of Predictive Analytics. In the recent past, predictive analytics has emerged as a transformative capability, and platforms are looking to integrate these techniques to forecast future trends, customer behaviours, and probable outcomes. In the field of data engineering, predictive analytics plays a significant role in enhancing decision-making processes, optimizing operational efficiencies and calibrating supply chain processes. This paper explores the integration of predictive analytics techniques in the field of data engineering and its frameworks and examines existing methodologies, technologies and algorithms. Businesses, while implementing predictive analytics techniques, should also address key considerations, including data quality, scalability, and the key area of privacy, emphasizing the importance of ethical and regulatory compliance.
Keywords
Predictive analytics, Data engineering, Machine learning, Data integration, Predictive modelling, Data-driven decision-making, Real-time analytics, Data warehousing, Data mining, Algorithm development.
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