Machine learning algorithms for efficient data entry in a cloud-based engineering portal

The most important component in machines and systems, which have become an integral part of our lives with the development of technology, is machine learning algorithms that emerged with the concept of Artificial Intelligence. In this study, machine learning-based studies were carried out to increase the user experience of an engineering portal designed using cloud computing architecture. While making data entries for the parameters in the interface in the system, machine learning was used to evaluate the parameters entered by the user and make a prediction about the missing parameters. For this purpose, models were created using five different regression approaches: linear, support vector, decision trees, random forest and k nearest neighbor, and comparative analyzes were made. The prediction model obtained with the random forest approach selected as a result of these analyzes has been integrated into the system.