Abstract:
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.
