PREDICTION OF IMPACTS AND OUTBREAK OF COVID-19 USING DISTINCT LEARNING ALGORITHM
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Date
2023-03-13
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Abstract
The COVID-19 pandemic has caused significant negative impacts on daily wage workers
in various professions due to restrictions such as lockdowns and social distancing
measures. To analyze and predict these impacts, we collected data on 1665 daily wage
workers from eight different professions through an offline survey. After preprocessing
the data to remove duplicates, null values, and cleaning, we encoded categorical features
and split the dataset into training and testing sets. We used seven machine learning
algorithms including logistic regression, decision tree, random forest, support vector
machine, AdaBoost, KNN, and XGBoost to develop a predictive model. Our study found
that the random forest algorithm had the highest accuracy of 92.1%, followed by
XGBoost with 91% accuracy. Our research highlights the potential of machine learning
techniques in predicting the impacts of COVID-19 on daily wage workers in various
professions. Our thesis paper serves as a guide for policymakers and government officials
to understand the effects of COVID-19 on daily wage workers and to implement measures
to mitigate these negative effects. Furthermore, the predictive model developed in this
study can help predict the impact of future pandemics or crises on these workers and
support the development of targeted policies and interventions to help them.