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Chagwiza, Wilbert Kubjana, Tlou Molepo, Mashaka Ruth 2022 2022-11-24T21:23:30Z 2022-11-24T21:23:30Z 2022-11-10 Molepo, M. R. (2022) Predicting an Economic Recession Using Machine Learning Techniques. University of Venda. South Africa.<http://hdl.handle.net/11602/2381>. http://hdl.handle.net/11602/2381 Molepo MR. Predicting an Economic Recession Using Machine Learning Techniques. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2381 Molepo, M. R. (2022). <i>Predicting an Economic Recession Using Machine Learning Techniques</i>. (). . Retrieved from http://hdl.handle.net/11602/2381 Molepo, Mashaka Ruth. <i>"Predicting an Economic Recession Using Machine Learning Techniques."</i> ., , 2022. http://hdl.handle.net/11602/2381 TY - Dissertation AU - Molepo, Mashaka Ruth AB - few economic downturns were predicted months in advance. This research has the ability to give the best performing models to assist businesses in navigating prior recession periods. The study address the subject of identifying the most important variables to improve the overall performance of the algorithm that would effectively predict recessions. The primary aim of this study was to improve economic recession prediction using machine learning (ML) techniques by developing an inch-perfect and efficient prediction model in order to avoid greater government deficits, growing inequality, significantly decreased income, and higher unemployment. The study objective was to establish the relevant method for addressing imbalance data with suitable features selection strategy to enhance the performance of the machine learning algorithm developed. Furthermore, artificial neural network(ANN) and Random Forest (RF) were used in predicting economic recession using ML techniques. This study would not have been possible without the publicly available data from the online open source Kaggle, which provided ordinal categorical data for the specific data utilized. The major findings of this study were that the ML algorithm RF performed better at recession prediction than its rival ANN. Due to the fact that two ML algorithms in this research were employed , further ML tools can be used to improve the statistical components of the study. DA - 2022-11-10 DB - ResearchSpace DP - Univen KW - Recession KW - Machine learning KW - Artificial neural network KW - Random forest KW - Imbalance data KW - Prediction model LK - https://univendspace.univen.ac.za PY - 2022 T1 - Predicting an Economic Recession Using Machine Learning Techniques TI - Predicting an Economic Recession Using Machine Learning Techniques UR - http://hdl.handle.net/11602/2381 ER - MSc (Applied Mathematics) Department of Mathematical and Computational Sciences few economic downturns were predicted months in advance. This research has the ability to give the best performing models to assist businesses in navigating prior recession periods. The study address the subject of identifying the most important variables to improve the overall performance of the algorithm that would effectively predict recessions. The primary aim of this study was to improve economic recession prediction using machine learning (ML) techniques by developing an inch-perfect and efficient prediction model in order to avoid greater government deficits, growing inequality, significantly decreased income, and higher unemployment. The study objective was to establish the relevant method for addressing imbalance data with suitable features selection strategy to enhance the performance of the machine learning algorithm developed. Furthermore, artificial neural network(ANN) and Random Forest (RF) were used in predicting economic recession using ML techniques. This study would not have been possible without the publicly available data from the online open source Kaggle, which provided ordinal categorical data for the specific data utilized. The major findings of this study were that the ML algorithm RF performed better at recession prediction than its rival ANN. Due to the fact that two ML algorithms in this research were employed , further ML tools can be used to improve the statistical components of the study. NRF 1 online resource (x, 75 leaves) : color illustrations en University of Venda Recession UCTD Machine learning Artificial neural network Random forest Imbalance data Prediction model 338.542 Financial crises Recession Economics Stagnation Predicting an Economic Recession Using Machine Learning Techniques Dissertation open.access