Toward an Effective Analysis of COVID-19 Moroccan Business Survey Data using Machine Learning Techniques

Abstract

COVID-19 pandemic has gravely affected our societies and economies with severe consequences. To contain the spread of the disease, most governments around the world authorized unprecedented measures, including Morocco, which has closed the borders and adopted full lockdown between March and June 2020. However, these measures have resulted in economic loss and have led to dramatic changes in how businesses act and consumers behave. The main focus of this study was to examine the impact of the full lockdown on Moroccan enterprises based on the COVID-19 Moroccan business survey carried out by the High Commission for Planning (HCP). A three-stage analysis method was employed. First, multiple correspondence analysis (MCA) was used to reduce the dimensionality of the categorical variables, and k-means clustering algorithm was used to cluster the data, then decision tree algorithm was performed in order to interpret each cluster and the maximum accuracy achieved is 84.45%. Compared with the decision tree algorithm, an artificial neural network (ANN) with stratified 10-fold cross-validation was applied to the dataset and has reached an accuracy of 83.4%. The simulation results confirm the effectiveness of the proposed techniques for analyzing survey data.