Neutrosophic prediction of consumer decisions using the RBF neural network method

The utilization of neutrosophic concept to forecast patron purchase conduct has been thoroughly tested in preceding research using various fashions. This study examines the number one elements affecting clients' selections to shop for mobile phones, dividing them into 4 separate ranges consistent with their purchasing behaviours. The tiers, from the first to the fourth layer, characterize exclusive ranges of customer hobby and participation. The main intention is to create an efficient neutrosophic predictive version that examines purchaser conduct thru pertinent traits that signify their opportunity of buying. We utilize the Neutrosophic Radial Basis Function (NRBF) model for neutrosophic class to do that. The results indicate a minimal blunders fee and improved neutrosophic category accuracy, mainly in contrast to the BIC version, which exhibited lower accuracy. NRBF exhibited a sturdy location below the curve (AUC) rating, underscoring the model's efficacy. These findings provide big insights into consumer preferences and decision-making methods, enhancing procedures for market analysis and cantered advertising initiatives.