The Role of Machine Learning in Enhancing Cost Estimation Accuracy: A Study Using Historical Data from Project Control Software
Abstract
In this paper, historical data from a project control software is utilised for analysis of the possibility that machine learning models can improve the precision of cost estimation. Which individual and ensemble techniques are appropriate for predicting project cost as well as effort in software development is determined through this research, comparing individual and ensemble strategies. Case studies and extensive reviews in the literature provide preliminary results showing that ensemble strategies significantly raise the precision of prediction. This advance provides more better resource allocation, planning of finance, and overall success for the project. This research study underlines that one needs to integrate advanced models of machine learning into project management procedures in order to improve on decisions and reduce risk.
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