Publication

Abstract : The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and Light GBM was found to be the best regressor for price prediction.