Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

<Previous Article In Volume
Next Article In Volume>

Data Driven Predictive Analytics for Strategic Decision-Making and Innovation

Authors
Anushree A. Aserkar1, Navaneetha Krishnan Rajagopal2, P. Jayatharani3, K. Arulini4, Vinod Waikar5, M. Baritha Begum6, *
1Department of Mathematics & Humanities, Yeshwantrao Chavan College of Engineering, Nagpur, India, 441110
2Assistant Professor, College of Economics and Business Administration College, University of Technology and Applied Sciences, Salalah, Oman
3MBA Final Year, Management Studies, Nandha Engineering College, Erode, India
4Assistant Professor, Management Studies, Nandha Engineering College, Erode, India
5Datta Meghe Institute of Management Studies, Nagpur, India, 440022
6Associate Professor, Saranathan College of Engineering, Trichy, Tamil Nadu, India
*Corresponding author. Email: barithab@gamil.com
Corresponding Author
M. Baritha Begum
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_29How to use a DOI?
Keywords
Random Forest; data-driven; organizational efficiency; innovation; predictive modeling; decision-making
Abstract

This study presents a data-driven system utilizing Random Forest to enhance organizational efficiency and foster innovation. The approach begins with data collection from diverse sources, followed by cleaning and preprocessing to ensure data quality. Feature engineering is applied to create relevant variables that strengthen predictive modeling. Random Forest, acknowledged for its robustness and capability to treat complex, non-linear data, is employed to develop accurate predictive models. By analyzing historical and real-time data, the system supports process optimization, resource management, and market trend analysis. The insights gained enable data-driven decision-making, driving strategic innovation and operational efficiency. Continuous monitoring of model performance ensures adaptability to evolving data patterns, minimizing prediction errors and maximizing accuracy. The proposed system promotes a data-centric culture, empowering organizations to yield informed decisions, optimize sources, and innovate effectively. This approach offers a scalable, reliable, and impactful resolution for businesses seeking to leverage data analytics for competitive advantage.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_29How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Anushree A. Aserkar
AU  - Navaneetha Krishnan Rajagopal
AU  - P. Jayatharani
AU  - K. Arulini
AU  - Vinod Waikar
AU  - M. Baritha Begum
PY  - 2025
DA  - 2025/11/04
TI  - Data Driven Predictive Analytics for Strategic Decision-Making and Innovation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 326
EP  - 339
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_29
DO  - 10.2991/978-94-6463-858-5_29
ID  - Aserkar2025
ER  -