Machine and deep learning techniques applied to retail telecommunication data

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2019-11-07
Authors/Contributors
Author: Naz, Fariha
Abstract
Telecommunication service providers have franchise dealers to sell their services and products to a wide range of customers. These franchise dealers are small-sized businesses working with a small financial budget and limited human resources for analyzing the performance of the business. There are numerous commercial business intelligence (BI) tools to monitor data and generate business insights. However, most of the retail entrepreneurs still use manual and/or simple techniques, having little time to dedicate to sophisticated BI tools. In this work, we investigate machine and deep learning techniques to analyze some retail telecommunication business datasets. Specifically, we examine how nearest neighbor techniques, feed forward artificial neural networks, Bayesian classifiers, and support vector machines can be used with retail telecommunication data. As indicated by our initial results we have been able to achieve precision, recall, and f-measures of 95%, for the task of classification, demonstrating that we can categorize retail telecommunication data based on the gross profit. We also developed a variant of recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) deep neural network models. Based on our initial results, we are able to acquire the root mean square error of 191 (training) and 281 (testing) from developed univariate models. A feed forward artificial neural network is applied to perform binary classification where we obtain an accuracy of 85% when categorizing the dataset based on the product type.
Document
Identifier
etd20594
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Popowich, Fred
Member of collection
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