Mobile Phone Sales Forecast Based on Support Vector Machine

Abstract

In this paper, we select price, wear resistance, resistance to fall, charging interval, battery life, communication stability, photo effect, appearance design, memory size and whether to buy again as input variables, take different mobile phone sales foreground grade as output variables based on the survey data of all kinds of mobile phone users in the current Chinese market, using support vector machine regression algorithm (SVMR), BP Neural Network Algorithms and K-Nearest Neighbor Algorithms to establish models and predict the sales prospects of various kinds of mobile phones in China. The prediction results show that the predicted value of the mobile phone sales prediction model constructed by SVMR is basically consistent with the actual sales of all kinds of mobile phones in the market, which can provide some guidance for the manufacture and sale of various of mobile phones.