Data Gush – Trend Analysis & Capacity Forecasting Web Traffic via Web UI Interface Using Machine Learning Platform |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Authors : Akash Shah | ||
| DOI : 10.14445/22312803/IJCTT-V73I3P105 | ||
How to Cite?
Akash Shah, "Data Gush – Trend Analysis & Capacity Forecasting Web Traffic via Web UI Interface Using Machine Learning Platform," International Journal of Computer Trends and Technology, vol. 73, no. 3, pp. 42-48, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I3P105
Abstract
This research investigates how Machine Learning (ML) estimating strategies can be utilized for capacity planning, particularly utilizing Prophet. The ponder investigates the challenges and openings in applying ML strategies to complex operations. We prepare and test a few AI/ML and conventional measurable models utilizing broad information. Based on this, this approach viably progresses in estimating exactness, underscoring the importance of fitting ML models for determining requests. This investigation presents an AI/ML-driven approach that rises above simple estimating, advertising a down-to-earth pathway to oversee capacity considering imperatives. This paper presents a system planned to precisely estimate future exchange activity in the budgetary segment and classify the item portfolio based on the anticipated level of determining unwavering quality. The system, useful for any company in the budgetary industry, leverages Facebook's Prophet calculation. Real-world determining benchmark information obtained tentatively in a generation environment at one of the biggest money-related companies was utilized to assess the system and highlight its adequacy in a real-world scenario.
Keywords
Capacity Planning & Forecasting, Machine Learning Platform, Stream lit, Automation, Prophit.
Reference
[1] Emir Zunic et al., “Application of Facebook's Prophet Algorithm for Successful Sales Forecasting Based on Real-world Data,” International Journal of Computer Science and Information Technology, vol. 12, no. 2, pp. 23-36, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ilya Jackson et al., “Revolutionize Cold Chain: An AI/ML Driven Approach to Overcome Capacity Shortages,” International Journal of Production Research, vol. 63, no. 6, pp. 2190-2212, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Rejwan Bin Sulaiman et al., “Time-Series Forecasting of Web Traffic Using Prophet Machine Learning Model,” FMDB Transactions on Sustainable Computer Letters, vol. 1, no. 3, pp. 161-177, 2023.
[Google Scholar] [Publisher Link]
[4] Emily Barnes, and James Hutson, “Developing Empathetic AI: Exploring the Potential of Artificial Intelligence to Understand and Simulate Family Dynamics and Cultural Identity,” DS Journal of Artificial Intelligence and Robotics, vol. 2, no. 3, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] P. Rajadurai, “Machine Learning-based Secure Cloud-IoT Monitoring System for Wireless Communications,” DS Journal of Artificial Intelligence and Robotics, vol. 1, no. 1, pp. 37-43, 2023.
[CrossRef] [Google Scholar] [Publisher Link]