• Mr. K. Sudhakar
• Goli Harshini
• Ganapathi Dharma Teja
• Chirla Siva Shankar Reddy
• Chandamala Rakesh

Software effort estimation is a vital component of project management, playing a critical role in determining the success of software development projects. Accurate predictions can significantly impact project outcomes, enabling project managers to plan and track the development of software systems more effectively. By providing reliable estimates of the time and resources required to complete a project, software effort estimation helps project managers make informed decisions, allocate resources efficiently, and mitigate potential risks. This study proposes a machine learning-based approach for software effort estimation, leveraging the strengths of multiple algorithms and datasets, including ISBSG, NASA 93, and Desharnais, to improve prediction accuracy. Our approach employs an ensemble of machine learning algorithms, including Support Vector Machine (SVM), Linear Regression, Random Forest, and Decision Tree. These algorithms work in conjunction to model complex relationships between features and effort, handle non-linear relationships and feature interactions, and extract latent features, ultimately improving model interpretability. Our results demonstrate significant improvements in estimation accuracy compared to traditional methods, highlighting the potential of machine learning-based approaches in software effort estimation.

Software effort estimation, Machine learning, Project management, ISBSG, NASA 93, Desharnais, Support Vector Machine (SVM), Linear Regression, Random Forest, Decision Tree.

"Software Effort Estimation Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d845-d855, March-2025, Available :http://www.jetir.org/papers/JETIR2503394.pdf

"Software Effort Estimation Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd845-d855, March-2025, Available at : http://www.jetir.org/papers/JETIR2503394.pdf