Prediction of Mobile Phone Prices using Machine Learning

Abstract

This research investigates upon the prediction of mobile phone prices based on various factors through the applications of multiple machine learning algorithms. Leveraging linear regression, decision tree regressor, random forest regressor, gradient boosting regressor, voting regressor and support vector regressor. This work explores the effectiveness in capturing the intricate relationships between the pricing of mobile phones in the market and the various factors affecting it which may be based on the hardware, software, the brand value, etc. The experimental results reveal distinct strengths and limitations of each algorithm, with the ensemble-based voting regressor demonstrating superior predictive performance with a training accuracy of 93.21% and testing accuracy of 88.98%. Gradient boosting regressor overfits the model with a training accuracy of 100% and testing accuracy of 97.91% and the linear regression model is observed to be the least accurate with a training and testing accuracy of 7.77% and 7.12% respectively. This research lays the groundwork for informed algorithm selection and implementation in the development of advanced mobile price prediction systems.