A Hybrid Machine Learning Method for Estimating Software Project Cost
Beatrice Obianiberi Akumba, Emmanuel Ogalla, Iorshase Agaji, Nachamada Vachaku Blamah · Zenodo (CERN European Organization for Nuclear Research) · 2023
This paper introduced the concept of a hybrid machine learning method for estimating software project cost. The literature review of some of the models commonly used in the software project cost estimation was carried out. A hybrid method of algorithms comprising Random Forest (RF), Kalman Filter (KF) and Support Vector Machine (SVM) algorithms respectively were proposed to predict the software project cost and its completion time for software projects. The proposed architecture of the model was presented as well as the proposed the model.