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A Machine Learning Based Approach to Multiclass Classification of Customer Loyalty Using Deep Nets

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Identification of customer’s loyalty is one of the most captivating area of today’s growing business scenario. For any organization, retaining customer is more important than exploring new customers. In this paper, Deep Belief Network (DBN) based approach is implemented for classifying customer loyalties. Training a Deep Belief Network (DBN) is a tedious task but once it trains, the accuracy of classification improves immensely. It also learns from its environment and does not need to be reprogrammed for new situations completely. After training, classifier relies on weight matrices to classify examples. The proposed approach is tested over real as well as sample datasets. The results so acquired are compared with Deep Neural Networks and Support Vector Machine based approaches, which shows Deep Belief Network (DBN) gives accuracy up to 99%.
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Classification; Customer Loyalty; Deep Belief Network; Deep learning; Deep Neural Network; Machine Learning; Probabilistic Graphical Model; Restricted Boltzmann Machine

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