Mobile phone price prediction: A comparative study among four models

Abstract

As science and technology is advancing by leaps and bounds, mobile phones have become part and parcel of people's life. Because the different models of mobile phones which have different structural foundations, the prices of mobile phones are constantly fluctuating. Mobile phone prices forecasts are becoming more precise as artificial intelligence develops. This article compares various machine learning approaches, and the importance of the variables is ranked in order to determine the most accurate way to forecast the prices of mobile phones. The machine learning techniques used are linear regression (LR), random forest regressor (RFR), XGB Regressor and Support Vector Machine regressor (SVM). In order to determine which model predicts the most accurate mobile phone prices, R^2 evaluation is used. The XGB Regressor model had the greatest score (R-squared = 0.95) for prediction of mobile phone prices, compared to the other three models. In a word, with XGB Regressor methodology as a priority for future mobile phone price predicting, which can improve the accuracy of price predicting.