A Model for Prediction of Consumer Conduct Using Machine Learning Algorithm - Mr. B. Narasimha Rao, Mr. Y. M. S. D. Sastry, Kamma Vanyasri, Kanchustambham Venkayya Swamy, Kavala Sarath Siva Chaitanya, Gandrothu Nivas Ram (Mathematical Statistician and Engineering Applications, 2022)
Because machine learning algorithms are so good at making predictions, they have become very important. Due to unanticipated customer situations, it's hard to know how a customer will do. There are many algorithms that all do the same thing. In this paper, we looked at and analysed three Bays algorithms: AODE, Naive Bayes, and AODEsr. We put these algorithms into the WEKA tool and made a new model that is more accurate than the one we were using before. During development, we've tried to cut down on noise and mistakes in the data, and we also need to filter the information. This process will give the new data that has been filtered a weight of Wj. The error can be described as E(j,k), where j is an assumption and k is a function of purpose. In the same way, the function N = E + Wj can be used to describe noise.