Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 13 September 2024 (04:25:28 CEST)
How to cite:
Smith, M. Machine Learning for Profitability Prediction in Agribusiness Construction: A Novel Approach Using Vector Space Model and Kernel Ridge Regression. Preprints2024, 2024091043. https://doi.org/10.20944/preprints202409.1043.v1
Smith, M. Machine Learning for Profitability Prediction in Agribusiness Construction: A Novel Approach Using Vector Space Model and Kernel Ridge Regression. Preprints 2024, 2024091043. https://doi.org/10.20944/preprints202409.1043.v1
Smith, M. Machine Learning for Profitability Prediction in Agribusiness Construction: A Novel Approach Using Vector Space Model and Kernel Ridge Regression. Preprints2024, 2024091043. https://doi.org/10.20944/preprints202409.1043.v1
APA Style
Smith, M. (2024). Machine Learning for Profitability Prediction in Agribusiness Construction: A Novel Approach Using Vector Space Model and Kernel Ridge Regression. Preprints. https://doi.org/10.20944/preprints202409.1043.v1
Chicago/Turabian Style
Smith, M. 2024 "Machine Learning for Profitability Prediction in Agribusiness Construction: A Novel Approach Using Vector Space Model and Kernel Ridge Regression" Preprints. https://doi.org/10.20944/preprints202409.1043.v1
Abstract
In the dynamic realm of agribusiness construction, firms are striving to bolster their operational efficiency and profitability amidst the global shift from traditional farming to commercial agriculture. This transition has intensified the demand for sophisticated infrastructure development, presenting new challenges for commercial managers. A critical hurdle is the accurate estimation of profitability for prospective contracts, a task often reliant on intuition rather than data-driven methods. To address this, we propose the development of a mathematical model utilising machine learning techniques to predict contract profitability and identify influential factors. This model aims to aid in bid decision-making, financial forecasting, and enhancing competitiveness in the marketplace. Furthermore, it would provide valuable insights into how altering specific contract attributes might affect predicted profitability. The implementation of such a system necessitates close collaboration between IT professionals and construction executives. This study delineates the development of this predictive model, outlining the data analysis process and the application of machine learning algorithms to tackle this complex commercial challenge. The ultimate goal is to bridge the gap between intuitive estimation and data-driven prediction, thereby enhancing the financial performance and strategic decision-making capabilities of firms in the agribusiness construction sector.
Keywords
Agribusiness Construction; Contract Profitability Prediction; Machine Learning; Vector Space Model; Kernel Ridge Regression (KRR); Driven Decision Making
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
