Examining the nature of entrepreneurship within the towns and rural areas of Sakhisizwe Local Municipality, Eastern Cape
- Authors: Maliwa, Noluvuyo
- Date: 2022-11
- Subjects: Entrepreneurship , Developing countries--Economic conditions , Small business
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27555 , vital:69315
- Description: Entrepreneurship plays an important role in societies around the world because it supports economic growth and creates job opportunities. This study investigated the nature of entrepreneurship in three distinct parts of Sakhisizwe Local Municipality, including a town within Sakhisizwe’s former homeland area, rural communities within its former homeland area, and a town within an area characterised by large-scale commercial farms. The study sought to establish the share of adults in the different parts of the municipality involved in entrepreneurship, to identify factors that contribute to the decision to become an entrepreneur, and to examine strategies pursued by, and challenges experienced, different types of entrepreneurs. The study involved a random sample of 362 respondents and both quantitative and qualitative data analysis. Among the main findings is that the proportion of adults involved in enterprise in Sakhisizwe is higher than found by other studies for South Africa as a whole, but is especially high for those living in the town in the former homeland area. While those residing in rural communities also engage in enterprise, they are generally compelled to practice their enterprises in town, thus have the disadvantage of needing frequent transport. While residing in the town in the commercial farming area is not disadvantageous in the same way, the challenge is that the town itself is able to support relatively few entrepreneurs due to fewer people coming to town for their shopping. , Thesis (MSci) -- Faculty of Science and Agricultures, 2022
- Full Text:
- Date Issued: 2022-11
- Authors: Maliwa, Noluvuyo
- Date: 2022-11
- Subjects: Entrepreneurship , Developing countries--Economic conditions , Small business
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27555 , vital:69315
- Description: Entrepreneurship plays an important role in societies around the world because it supports economic growth and creates job opportunities. This study investigated the nature of entrepreneurship in three distinct parts of Sakhisizwe Local Municipality, including a town within Sakhisizwe’s former homeland area, rural communities within its former homeland area, and a town within an area characterised by large-scale commercial farms. The study sought to establish the share of adults in the different parts of the municipality involved in entrepreneurship, to identify factors that contribute to the decision to become an entrepreneur, and to examine strategies pursued by, and challenges experienced, different types of entrepreneurs. The study involved a random sample of 362 respondents and both quantitative and qualitative data analysis. Among the main findings is that the proportion of adults involved in enterprise in Sakhisizwe is higher than found by other studies for South Africa as a whole, but is especially high for those living in the town in the former homeland area. While those residing in rural communities also engage in enterprise, they are generally compelled to practice their enterprises in town, thus have the disadvantage of needing frequent transport. While residing in the town in the commercial farming area is not disadvantageous in the same way, the challenge is that the town itself is able to support relatively few entrepreneurs due to fewer people coming to town for their shopping. , Thesis (MSci) -- Faculty of Science and Agricultures, 2022
- Full Text:
- Date Issued: 2022-11
The classification performance of ensemble decision tree classifiers: a case study of detecting fraud in credit card transactions
- Authors: Chogugudza, Mcdonald
- Date: 2022-11
- Subjects: fraud , Commercial fraud , Accounting fraud
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27590 , vital:69317
- Description: In this dissertation, we propose ensemble decision tree classifiers as an ideal classification technique for solving the problem of fraud in the domain of credit card transactions. Ensemble tree classifiers have been applied in many areas like speech recognition, image recognition and medical diagnostics and have shown excellent results. At the centre of fraud, credit card fraud has been a major concern. The rise in credit card fraud is largely attributed to the nature in which it can be done. A fraudster does not need to always be physically present to commit fraud making it the number one target for criminals. Card-Not-Present refers to this type of fraud where an electronic transaction can be conducted without the need for a client to be present. This can be done via telephonic calls or the web. To be able to come up with better classifiers it was important for the researcher to first investigate what causes misclassifications in fraud detection systems. A systematic literature review was done to uncover the factors that have been identified as causes of misclassifications. It was discovered that many factors lead to misclassifications and several authors have proposed techniques to handle these factors. However, there is no universal techniques for addressing factors that lead to misclassifications as different domains have different datasets which require different techniques. This study investigates how parameters involved in modelling fraud detection systems impact the classification performance of ensemble decision tree classifiers. The factors that were investigated include sample size, sampling technique, learning method and choice of split criterion and how they affect classification performance. A series of experiments were conducted to investigate how the aforementioned factors contributed to better classifiers. Ecommerce data from Vesta corporation made available on Kaggle was used in the experiments. The data was split into two sets, one for training the models and the other for testing the performance of the models. Accuracy, confusion matrix, precision and recall were used as performance measures. Our results showed that a larger sample size resulted in better classifiers. This is attributed to models having more instances to learn from which covers most patterns of fraudulent transactions. The sampling technique was shown to be pivotal in classification performance as under sampling showed a great reduction in performance as it achieved a maximum accuracy of 89.6223 while oversampling produced increased performance with maximum accuracy of 99.9531. Furthermore, our results showed that the choice of split criterion impacts the performance of ensemble tree classifiers. The use of entropy as the choice of split criterion resulted in better classifiers compared to the use of the Gini index. However, the downside is that entropy requires more time to execute compared to the Gini index. Lastly, the learning method proved to impact the performance of ensemble classifiers. Models that used supervised learning had better performance compared to those that use unsupervised learning in detecting credit card fraud. The conclusions from this research are insightful when designing fraud detection systems that use ensemble decision tree classifiers as base learners. , Thesis (Msci) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-11
- Authors: Chogugudza, Mcdonald
- Date: 2022-11
- Subjects: fraud , Commercial fraud , Accounting fraud
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/27590 , vital:69317
- Description: In this dissertation, we propose ensemble decision tree classifiers as an ideal classification technique for solving the problem of fraud in the domain of credit card transactions. Ensemble tree classifiers have been applied in many areas like speech recognition, image recognition and medical diagnostics and have shown excellent results. At the centre of fraud, credit card fraud has been a major concern. The rise in credit card fraud is largely attributed to the nature in which it can be done. A fraudster does not need to always be physically present to commit fraud making it the number one target for criminals. Card-Not-Present refers to this type of fraud where an electronic transaction can be conducted without the need for a client to be present. This can be done via telephonic calls or the web. To be able to come up with better classifiers it was important for the researcher to first investigate what causes misclassifications in fraud detection systems. A systematic literature review was done to uncover the factors that have been identified as causes of misclassifications. It was discovered that many factors lead to misclassifications and several authors have proposed techniques to handle these factors. However, there is no universal techniques for addressing factors that lead to misclassifications as different domains have different datasets which require different techniques. This study investigates how parameters involved in modelling fraud detection systems impact the classification performance of ensemble decision tree classifiers. The factors that were investigated include sample size, sampling technique, learning method and choice of split criterion and how they affect classification performance. A series of experiments were conducted to investigate how the aforementioned factors contributed to better classifiers. Ecommerce data from Vesta corporation made available on Kaggle was used in the experiments. The data was split into two sets, one for training the models and the other for testing the performance of the models. Accuracy, confusion matrix, precision and recall were used as performance measures. Our results showed that a larger sample size resulted in better classifiers. This is attributed to models having more instances to learn from which covers most patterns of fraudulent transactions. The sampling technique was shown to be pivotal in classification performance as under sampling showed a great reduction in performance as it achieved a maximum accuracy of 89.6223 while oversampling produced increased performance with maximum accuracy of 99.9531. Furthermore, our results showed that the choice of split criterion impacts the performance of ensemble tree classifiers. The use of entropy as the choice of split criterion resulted in better classifiers compared to the use of the Gini index. However, the downside is that entropy requires more time to execute compared to the Gini index. Lastly, the learning method proved to impact the performance of ensemble classifiers. Models that used supervised learning had better performance compared to those that use unsupervised learning in detecting credit card fraud. The conclusions from this research are insightful when designing fraud detection systems that use ensemble decision tree classifiers as base learners. , Thesis (Msci) -- Faculty of Science and Agriculture, 2022
- Full Text:
- Date Issued: 2022-11
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