Predicting an Economic Recession Using Machine Learning Techniques

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.

Author dc:creator

• Molepo, Mashaka Ruth

Advisors dc:contributor.advisor

• Chagwiza, Wilbert
• Kubjana, Tlou

• Recession
• Machine learning
• Artificial neural network
• Random forest
• Imbalance data
• Prediction model

Statement dc:rights

• University of Venda

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11602/2381

OAI identifier oai:identifier

oai:univendspace.univen.ac.za:11602/2381