A Machine Learning Regression Analysis Application: Smart Device Price

Humans are trying to take new measures every day for the natural resources that have been depleted in recent years. This is an inevitable situation for sustainability. One of the most waste-generated areas in the world is the waste created by technological equipment. The loss of many precious materials such as iron, copper, semiconductors, etc. is a major threat to the national assets of countries. Recycling waste is important in every field, but electronic waste (e-waste) is more valuable in terms of both production difficulties and cost. In this study, regression analysis was carried out with machine learning methods to contribute to the optimum determination of the new prices of used smart devices, which is one of the customers' preference criteria, to prevent the generation of electronic waste to some extent. traditional and hybrid methods were applied. In the results obtained, it was seen that the most successful results were achieved with the mean square error value of 0.0458 by using the proposed regression method.