A Model for prediction of consumer conduct using machine learning algorithm
Sandeep Kumar Budhani, Rajendra Singh Bisht, Neelima Budhani · 2020
The machine learning algorithm has become important because of their accuracy in forecasting. It is very difficult to predict a customer's performance due to an unexpected customer situation. Many algorithms are designed for the same purpose. In this paper, we have studied and analysed three Bays algorithms such as AODE, Naive Bayes and AODEsr. We implemented these algorithms in the WEKA tool and built a new model that provides better accuracy than the existing one. During development we have tried to reduce the noise and error in the data and we also need to filter the information. This process will allocate Wjweight to the new filtered data. The error can be demarcated as: E (j, k), Where j ∈ J or it's an assumption. k is a function of purpose. Similarly noise can be defined by another function N = E + Wj.