An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest
الموضوعات :
Md. Nahid Hasan
1
(Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka-1212)
Tanvir Ahmed
2
(Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka-1212 )
Md. Ashik
3
(Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka-1212 )
Md. Jahid Hasan
4
(Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka-1212 )
Tahaziba Azmin
5
(Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka-1212 )
Jia Uddin
6
(Artifical Intelligence and Big Data Department, Endicott College, Woosong University, Daejeon, South Korea)
الکلمات المفتاحية: Multi-layer perceptron (MLP), Long Short-Term Memory (LSTM), Random Forest, Economic Recession, Machine learning (ML), Covid-19,
ملخص المقالة :
The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively).
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