ABSTRACT
The primary objective of this paper is to forecast optimal prices for top-tier smartphones, while considering their available features. Our approach involves the development of a machine learning (ML)-based price range prediction model, which harnesses various algorithmic techniques applied to an extensive dataset. This model generates comprehensive data visualization, aiding decision-making processes. Additionally, our proposed model facilitates market analysis within the sector by comparing its accuracy against other models. For instance, numerous companies engaged in the purchase of pre-owned mobile phones employ their proprietary models. Users can cross-reference these models with ours to pinpoint the most suitable price for their mobile devices. The accuracy level can be gauged against alternative models to obtain the most reliable results. Our research encompasses a wide array of features and events to predict mobile phone prices, thereby addressing buyer concerns and simplifying their quest for smartphones within their budgetary constraints.
