Methods and Algorithms for Evaluating Delays Based on Machine Learning Models

This study addresses the development of methods and algorithms for evaluating delays based on machine learning models. Specifically, intelligent models and algorithms have been developed to assess sales delays in the economic domain. The process began with the preparation of a dataset for analyzing sales delays. Algorithms based on machine learning models such as Logistic Regression and Random Forest were developed to train on the prepared data. Furthermore, these algorithms were optimized using Gradient Descent techniques. Experimental results were obtained using the developed algorithm, and the model's accuracy was thoroughly analyzed. The results demonstrate that the Random Forest-based algorithm outperformed Logistic Regression, achieving higher accuracy and reliability in identifying potential delays. The proposed approach highlights the importance of data preprocessing, feature engineering, and model evaluation metrics such as Accuracy, Recall, Precision, and F1 Score in ensuring the effectiveness of delay detection models.

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