Predicting The Price Of Used Electronic Devices Using Machine Learning Techniques

Muhammad Hasnain; Abdul Sajid; Amani Ayeb; Arshad Awan

Muhammad Hasnain
Abdul Sajid
Amani Ayeb
Arshad Awan

Keywords: Prediction, Supervised Learning, Machine Learning, Multi-Layer Perceptron, Linear Regression.

Abstract

The trade of used electronics devices plays vital role towards the growth of economy of the nation. In recent years, the commercial field has adopted many technological transformations, except for the used electronics. Moreover, the amount of market data is also increasing at an exponential rate. Hence, in this paper we proposed a predictive model using three Machine Learning (ML) algorithms to forecast the price of used smartphones, which are the most widely used electronic devices. In addition, the data set was collected over the last five years using web scraping techniques for modeling with machine learning. The performance of proposed model is evaluated in terms of Absolute Percentage Difference (APD) and Root Means Square Error (RMSE). Finally, the results show that the Random Forest model predicted the used smartphone price with lower prediction error and better generalization ability compared to Linear Regression and Multi-Layer Perceptron.

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