Smartphone Price Prediction in Retail Industry Using Machine Learning Techniques

Abstract

The goal of any organization is to make their product to get succeed and compete with other products in the market where pricing of their products plays a vital role. To sell any product in market, the most important aspect is to determine the price. There are many traditional and new methods for estimating before pricing their products, and a method is chosen which gives more appropriate result. In this study, support vector regression analysis is used as a machine learning technique in order to predict the market price of smartphones based on their features. Many variants of features are utilized for data preprocessing or input technique for SVR model. If required factors are derived and used accordingly, it can provide a good prediction result. Different features of the smartphone are considered in this experiment in order to get more reliable outputs. Support vector regression gives more promising predictions for making better decisions in price prediction of smartphones compared to other models.