Intelligent Assessment of Secondhand Mobile Phone Prices by Machine Learning Techniques

Abstract

In many areas of price prediction such as house prediction, stock prediction, different classification algorithms can be used. Classification techniques that are based on Machine Learning help to solve the problem that is related to decision making. This research paper aims at the comparing the accuracy of two different classification algorithms which are used in supervised machine learning. Mobile price prediction is the case study of this research work. Dataset is collected from kaggle. Two different classifiers named as SVM and Rigid classifier are used to achieve best possible accuracy. Results are compared on the basis of outcome accuracy score that is achieved from the research experiment. This approach can be used to a variety of industries, like marketing and business, to locate the best goods (with minimal costs and maximum features). Future research is expected to expand on this work, resulting in more sophisticated answers to specific situations and more precise tools for price prediction.