Version 1
: Received: 29 June 2020 / Approved: 30 June 2020 / Online: 30 June 2020 (08:22:58 CEST)
How to cite:
Dutta, S.; Bandyopadhyay, S.K. Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network. Preprints2020, 2020060360. https://doi.org/10.20944/preprints202006.0360.v1.
Dutta, S.; Bandyopadhyay, S.K. Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network. Preprints 2020, 2020060360. https://doi.org/10.20944/preprints202006.0360.v1.
Cite as:
Dutta, S.; Bandyopadhyay, S.K. Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network. Preprints2020, 2020060360. https://doi.org/10.20944/preprints202006.0360.v1.
Dutta, S.; Bandyopadhyay, S.K. Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network. Preprints 2020, 2020060360. https://doi.org/10.20944/preprints202006.0360.v1.
Abstract
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(NN) 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 Mean Squared Error (MSE) of 0.1168.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
