Comparative Study of Machine Learning Algorithms and Software Platforms for Cost Estimation in the Steel Industry

Cost estimation is crucial for competitiveness in capital-intensive industries. This study compares Multiple Linear Regression and Artificial Neural Networks for forecasting crude steel costs using Turkish industry data from 2008–2019 across SPSS, Python, Minitab, and EViews. Results show that MLR achieves superior predictive accuracy (R2 > 0.99), particularly in SPSS and Python, while liquid steel cost is the most influential driver of total production expenses. Although ANN captures nonlinear dynamics, its error rates remain higher than regression models. Overall, findings highlight the robustness of MLR in structured datasets and the potential of ANN for nonlinear or incomplete data, offering practical insights for cost minimization, resource allocation, and competitiveness in the steel sector.