A STUDY TO ANALYSE MODELLING LARGE-SCALE CROSS EFFECT IN PURCHASE INCIDENCE: COMPARING ARTIFICIAL NEURAL NETWORKING CADENCE: COMPARING ARTIFICIAL NEURAL NETWORK TECHNIQUES AND MULTIVARIATE PROBIT MODELLING.

Abstract

In this study, large-scale cross effects on sales incidence are investigated by comparing and contrasting the effectiveness of Artificial Neural Network (ANN) approaches with Multivariate Probit (MVP) modelling. With the intention of comparing and contrasting the two state-of-the-art techniques in terms of their capacity to comprehend intricate relationships and anticipate the behaviours of consumers in enormous datasets, the goal of this research is to compare and contrast the two methodologies. Through the application of ANN and MVP techniques to purchase incidence data, the purpose of this study is to assess whether or not the methodology provides more accurate and practical insights into consumer behaviour. If you are doing research on consumers or marketing, this comparative study may assist you in selecting the appropriate modelling techniques by concentrating on the accuracy of the models, the efficiency of the computation, and the interpretability of the results.