Open AccessRecommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network
Author(s) -
Shawni Dutta,
Samir Kumar Bandyopadhyay
Publication year - 2021
Publication title -
south asian journal of social studies and economics
Language(s) - English
Resource type - Journals
ISSN - 2581-821X
DOI - 10.9734/sajsse/2021/v11i330286
Subject(s) - computer science , artificial neural network , decision tree , term (time) , classifier (uml) , artificial intelligence , cross validation , perceptron , data mining , machine learning , benchmark (surveying) , multilayer perceptron , predictive modelling , physics , geodesy , quantum mechanics , geography
For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.