Browsing by Author "Nyirenda, Juwa Chiza"

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    Exploring the application of Word2Vec to basket transaction data in the grocery retail industry
    (2022) De Swardt, Gideon Jacobus; Nyirenda, Juwa Chiza
    In this thesis, we explore the application of Word2vec to basket transaction data provided by a large grocery retailer in South Africa. Word2vec is an algorithm based on representation learning. The objective of the exploration is to establish whether the application of Word2vec to basket transaction data would generate product embeddings that represent a useful relationship between products. Furthermore, we compareWord2vec's outputs and performance to traditional methods for studying product relationships which include Association Rules Mining (ARM) and Recommendation Systems. The results from the experiments showed that indeed product embeddings created by Word2vec on transaction data are meaningful and useful. It was clear that the idea of using transactions in the place of sentences to the neural network, provides analogous results to that of a natural language task. Word2vec clearly demonstrated its ability to cluster products that are homogeneous or fulfill similar needs. Furthermore this sort of product relationship was not provided by any other traditional methods, which was clear when comparing the outputs to that of ARM and Recommendation Systems. We also show that usingWord2vec could potentially provide insight on truly complementary products that ARM perhaps fails to do. Word2vec also proved to be incredibly scalable, taking input data of 20 times the size of what traditional methods could handle on a local computer. We end with a description of a potential application of the ideas learnt during the course of this study, with a real business problem, that we believe could lead to an enhanced customer shopping experience and in turn increase revenue and profits for the retailer.
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    Long short-term memory neural networks for predicting corporate credit ratings
    (2024) Chandoo, Ali Aonali; Nyirenda, Juwa Chiza
    Credit ratings are an important tool when assessing financial instruments and investments. The existing literature shows that long short-term memory (LSTM) neural networks are the best neural network to predict credit ratings, while random forests have been shown to perform better than regular neural networks. As at the beginning of this study, no study had compared the performance of LSTM and random forests despite their reported superior performance. This study compares the performance of random forests and LSTM neural networks in predicting corporate credit ratings in the USA using Standard and Poor's data. The study finds that while LSTM neural networks pose serious competition, random forests have a slight edge over LSTM neural networks, showing that it is still worth using older and simpler techniques in predicting credit ratings.
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    Open Access
    Predicting residential demand: applying random forest to predict housing demand in Cape Town
    (2018) Dyer, Ross; McGaffin, Robert; Nyirenda, Juwa Chiza
    The literature shows that Random Forest is a suitable technique to predict a target variable for a household with completely unseen characteristics. The models produced in this paper show that the characteristics of a household can be used to predict the Type of Dwelling, the Tenure and the Number of Bedrooms to varying degrees of accuracy. While none of the sets of models produced indicate a high degree of predictive accuracy relative to hurdle rates, the paper does demonstrate the value that the Random Forest technique offers in moving closer to an understanding of the complex nature of housing demand. A key finding is that the Census variables available for the models are not discriminatory enough to enable the high degree of accuracy expected from a predictive model.